trevor david mckee - core · student chapter, and in the establishment of a community service...
TRANSCRIPT
Improving the Delivery and Efficacy of MolecularMedicine via Extracellular Matrix Modulation:
Insights from Intravital Microscopyby
Trevor David McKee
B.S. Department of Chemical Engineering, University at Buffalo, 1999
,Submitted to the Biological Engineering Divisionin Dpartial fulfillment of the reauirements for the degree of
Doctor of Philosophy in Biological Engineering
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
MASSACHUSEITS INSiEOF TECHNOLOY
OCT 27 2005
LIBRARIES
June 2005
© Massachusetts Institute of Technology 2005. All rights reserved.
/ A.
Author .......- - -Biological Engineering Division
May 3, 2005
Certified bv · · ...... · · Rakesh. · · · ·· ·. · · · · · . · .. · . · Ja· · ·.....
Rakesh . JainAndrew Werk Cook Professor, Harvard Medical School
- >--- ..Thesis Supervisor
Certified by.Peter T. C. So
Professor of Mechanical and Biological Engineering- Thesis Supervisor
Accepted by ..........- .......................7ha i r, Alan J. Grodzinsky
Chair, Biological Engineering Graduate Program Committee
ARCHIVES
This Doctoral Thesis has been examined by the following Thesis Committee:
Rakesh K. Jain, Ph.D.Thesis SupervisorAndrew Werk Cook Professor of Tumor BiologyHarvard Medical SchoolMassachusetts General HospitalBoston, Massachsetts
.. , ; ... · o~ o. · · · · .
Peter T. C. So, Ph.D.Thesis SupervisorProfessor of Mechanical and Biological EngineeringMassachusetts Institute of TechnologyCambridge, Massachusetts
William M. I)een, Ph.D.Thesis Committee ChairCarbon P. Dubbs Professor of Chemical and Biological EngineeringMassachusetts Institute of TechnologyCambridge, Massachusett
an S . . .
Brian Seed, Ph.D.Professor of Genetics and Health Sciences and TechnologyHarvard Medical SchoolMassachusetts General HospitalBoston, Massachusetts
Ioann~/ V. Yannas, Ph.D.Profe or of Polymer Science and EngineeringDepartment ol Mechanical Engineering,Materials Science and Engineering, and Biological EngineeringMassachusetts Institute of TechnologyCambridge, Massachusetts
3
Improving the Delivery and Efficacy of Molecular Medicine
via Extracellular Matrix Modulation: Insights from
Intravital Microscopy
by
Trevor David McKee
Submitted to the Biological Engineering Divisionon May 3, 2005, in partial fulfillment of the
requirements for the degree ofDoctor of Philosophy in Biological Engineering
AbstractThe extracellular matrix of tumors is a major barrier to the delivery of molecularmedicine. We used fluorescence recovery after photobleaching combined with intrav-ital microscopy to quantitate the transport properties of the tumor interstitium. Wefound that the presence of fibrillar collagen correlated with hindered diffusion in vivo,and also in vitro, in collagen gels prepared to mimic tumor extracellular matrix. Mod-ification of the tumor collagen matrix directly with purified bacterial collagenase, orindirectly with relaxin treatment, resulted in increased diffusion coefficients of macro-molecules within tumors in vivo. In order to quantitate the changes in collagen contentand structure induced by relaxin treatment, we adapted and further developed theimaging technique of intravital second harmonic generation microscopy. Using secondharmonic generation imaging in combination with a fluorescently labeled gene thera-peutic vector, we demonstrated that the spread of these viral vectors within tumorsis limited by the fibrillar collagen in the extracellular matrix. Matrix modification viathe introduction of bacterial collagenase along with the initial virus injection resultedin a significant improvement in the range of viral distribution within the tumor. Thisresulted in an extended range of infection of cells within the tumor, and improvedvirus propagation, ultimately leading to enhanced therapeutic outcome. Thus, weshow that fibrillar collagen is an important barrier to the distribution of molecu-lar medicine within tumors, and that matrix modifying treatments can significantlyenhance both vector distribution, as well as ultimately therapeutic response.
Thesis Supervisor: Rakesh K. JainTitle: Andrew Werk Cook Professor, Harvard Medical School
Thesis Supervisor: Peter T. C. SoTitle: Professor of Mechanical and Biological Engineering
5
AcknowledgementsThis thesis would not have been possible were it not for the support of many peoplewithin the laboratory, and my friends and family.
I would like to dedicate this thesis to my family: To my parents for their unlimitedsupport and encouragement, for sacrificing much, personally and professionally, to moveour family to the United States from South Africa in 1990 in search of betteropportunities for me and my brother, and for teaching me the meaning of persistence bynot giving up despite setbacks after the move. To my brother, for being a great friendand travel buddy, and for allowing me to occasionally take advantage of his gifts withmedical illustration to help me with slides, diagrams and figures. To my grandfather,Allan Trevor Montague McKee, for his continuous moral support, and for being aninspirational role model of someone who overcame tremendous obstacles to achievesuccess through hard work. I can only hope that I will be as adventurous, as healthy, andas eager to learn about new ideas and concepts when I am 88! To my grandmother, MaryJoyce Parfit, who sadly passed away during my tenure at MIT, for being the truepersonification of grace, dignity, and courage, who showed tremendous inner strengthdespite health problems, and for all of her efforts to teach me correct manners, posture,and grammar (with varying degrees of success!). And last but not least, to my girlfriendJenn, who supported and encouraged me in countless ways during the writing of mythesis, for her countless hours of encouragement on the phone, for the large batches offrozen dinners prepared in advance of my thesis crunch, and for being there for mewhenever I needed support.
Thanks also to Ali and Emilie, for being great friends through a tough time, to Paola,Wilson, Ed, Mike, Peigen, Sergey, Leo, Yves, and the whole Steele Lab for being terrificpeople to work with, and for all of the late night companionship. Thanks to CoffeeCentral at Mass General Hospital for feeding my co-addiction to caffeine and cinnamonraisin bagels, and being open until 11. Thanks to everyone in the Grad Student Counciland at Fenway House for giving me many much-welcome distractions from lab work, andproviding opportunities for me to contribute and help out my fellow graduate students.Thanks to Mike Folkert for picking up my slack when I disappeared from the GSCHousing and Community Affairs chairmanship to meet thesis deadlines, and for justbeing a superb advocate for graduate students in general. And thanks to MIT MastersSwim Club for keeping me from being entirely out of shape, and coach Bill for being agreat friend and always willing to chat and listen to my various random complaints. Andfinally, thanks to Bryan Hiles for being a great friend despite huge gaps in time andtremendous distances, and for always welcoming me back during my visits to SA.
7
Biographical Sketch (Curriculum Vitae)Education
Massachusetts Institute of Technology9/99 to 6/05 Ph.D. in Bioengineering, June 2005- 5 years experience in small animal (mouse) surgery; in the design, setup and operationof multiphoton laser scanning microscopes; and in the measurement of transport in vivo.Active in the graduate student council, in teaching and grading assistantships, and as anundergraduate research mentor.
University at Buffalo (S.U.N.Y.)9/95 to 6/99 B.S. in Chemical Engineering, with a minor in Biotechnology,
Graduated Magna Cum Laude- Undergraduate research projects included the application of physiological models of thetransport of inert gases within blood vessels to the understanding of decompressionsickness; the modeling of dielectrophoresis to investigate whether it could be used for theseparation of viral particles; and the programming of a cone-plate viscometer to mimicpulsatile blood flow. Active in the formation of a new Biomedical Engineering Societystudent chapter, and in the establishment of a community service organization.
Industrial ExperienceGIBCO/I Life Technologies, Inc. (now Invitrogen) Grand Island, NY
6/98 to 8/98 Summer internship, Quality Engineering Dept.6/99 to 8/99 Research internship, Cell Culture Research and Development Dept.- Trained in FDA cGMP requirements, cell culture techniques, and bioreactor operation.
PublicationsManuscripts under review:
1.McKee TD*, Grandi P*, Mok W*, Alexandrakis G, Boucher Y, Breakefield XO, JainRK. Matrix modification combined with oncolytic herpesvirus gene therapy resultsin improved gene vector distribution and therapeutic efficacy. Submitted, May 2005.* equal contribution
2.Demou ZN, Awad M, McKee TD, Wang X, Munn LL, Jain RK, Boucher Y. Lack oftelopeptides in fibrillar collagen promotes membrane deformability and amoeboidinvasion of breast adenocarcinoma. Cancer Research, in press.
3.Huang P, McKee TD, Fukumura D, Jain RK. A novel GFP expressing tumor modelderived from a spontaneous osteosarcoma in a VEGF-GFP transgenic mouse.Comparitive Medicine, in press.
Manuscripts published:4.Alexandrakis G, Brown EB, Tong RT, McKee TD, Campbell RB, Boucher Y, Jain
RK. Two-photon fluorescence correlation microscopy reveals the two-phase natureof transport in tumors. Nature Medicine 10(2): 203-7, 2004.
5.Znati CA, Rosenstein M, McKee TD, Brown E, Turner D, Bloomer WD, Watkins S,Jain RK, Boucher Y. Irradiation reduces interstitial fluid transport and increases thecollagen content in tumors. Clinical Cancer Research 9(15): 5508-13, 2003.
9
6.McKee T*, Brown E*, diTomaso E, Pluen A, Seed B, Boucher Y, Jain RK. Dynamicimaging of collagen and its modulation in tumors in vivo using second-harmonicgeneration. Nature Medicine 9(6): 796-800, 2003. * published as Brown E, McKeeT et al., with equal contributions from the two first authors.
7.Ramanujan S, Pluen A, McKee TD, Brown EB, Boucher Y, Jain RK. Diffusion andconvection in collagen gels: implications for transport in the tumor interstitium.Biophysical Journal 83(3): 1650-60, 2002.
10.Koike C, McKee TD, Pluen A, Ramanujan S, Burton K, Munn LL, Boucher Y, JainRK. Solid stress facilitates spheroid formation: potential involvement of hyaluronan.British Journal of Cancer 86(6): 947-53, 2002.
11.Pluen A, Boucher Y, Ramanujan S, McKee TD, Gohongi T, di Tomaso E, Brown EB,Izumi Y', Campbell RB, Berk DA, Jain RK. Role of tumor-host interactions ininterstitial diffusion of macromolecules: cranial vs. subcutaneous tumors.Proceedings of the National Academy of Sciences USA 98(8): 4628-33, 2001.
Presentationsl.McKee TI), Grandi P, Mok W, Boucher Y, Jain RK. Relaxin enhances drug delivery
to tumors via cell-mediated modifications to the collagen matrix. Oral presentation,International Conference on Relaxin and Related Peptides 2004, Jackson Hole, WY.
2.McKee TID, Brown EB, diTomaso E, Pluen A, Seed B, Boucher Y, Jain RK. Relaxinenhances drug delivery to tumors by permeabilizing the collagen matrix: Insightfrom second harmonic generation microscopy. Poster presentation, Gordon ResearchConference on Signal Transduction by Engineered Extracellular Matrices, June 27 -July 2, 2004, Bates College, Lewiston, ME.
3.McKee TDI), Brown EB, diTomaso E, Pluen A, Seed B, Boucher Y, Jain RK. Dynamicimaging of collagen and it's modulation in tumors in vivo using second harmonicgeneration. Oral and Poster Presentation, Hot Topics from Selected Abstracts sectionof Gordon Research Conference on Collagen, July 27 -August 1, 2003, Colby-Sawyer College, New London, NH.
4.McKee TD), Brown EB, diTomaso E, Pluen A, Seed B, Boucher Y, Jain RK. Dynamicimaging of collagen and it's modulation in tumors in vivo using second harmonicgeneration. Oral and Poster Presentation at the 9 4 th Annual Meeting of the AmericanAssociation for Cancer Research, July 11-14, 2003, Washington, DC. Proceedingsof the AACR 44(2):R984, 2003.
5.McKee TD, Pluen A, Boucher Y, Ramanujan S, Seed B and Jain RK. Relaxinimproves the transport of large molecules within tumors. Poster presentation at theGordon Research Conference on Lasers in Medicine and Biology, July 12-15, 2002,Kimball Union Academy, Meriden, NH.
6.McKee TD, Pluen A, Boucher Y, Ramanujan S, Seed B and Jain RK. Relaxinincreases the transport of large molecules in high collagen content tumors. Posterpresentation at the 92nd Annual Meeting of the American Association for CancerResearch, March 2001, New Orleans, LA. Proceedings of AACR 42(1):158, 2001.
7.Van Liew HD, McKee TD. Effect of diffusion of inert gas in or out of arterial andvenous blood vessels on washin and washout of tissues. Presentation at Gulf CoastChapter of the Undersea and Hyperbaric Medical Society, Galveston, TX, March 12-15, 1998.
10
ContentsTitle Page 1
Thesis Committee Page 3
Abstract 5
Acknowledgements 7
Biographical Sketch (Curriculum Vitae) 9
Table of Contents 11
List of Figures 15
List of Tables 16
1 Thesis Outline - Original contributions 17
1.1 Chapter 3: Tumor Host Interactions ............................ 18
1.2 Chapter 4: Collagen Gel Transport ............................. 19
1.3 Chapter 5: Second Harmonic Imaging .......................... 21
1.4 Chapter 6: Improving Gene Therapy .......................... 22
1.5 Chapter 7: Conclusions and future directions ................... . 23
2 Motivation 25
2.1 Cancer therapy ......................................... 25
2.2 Systemic therapy ........................................... 26
2.3 Novel targeted therapeutics ................................... 27
2.4 Transport of therapeutics within tumors ......................... 28
2.5 The tumor extracellular matrix ................................. 29
2.6 Matrix modification ......................................... 32
2.7 Transport within the tumor extracellular matrix ................... 35
2.8 References ................................................ 36
3 Tumor Host Interactions 39
3.1 Introduction .................. .............. ............. 393.2 Materials and Methods ....................................... 41
3.2.1 Fluorescent Tracers ................................... 41
11
3.2.2 Animals and tumors ...................................
3.2.3 Diffusion measurements by FRAP .......................
3.2.4 Extracellular space organization .........................
3.2.5 Immunohistochemistry ................................
3.2.6 Electron microscopy ..................................
3.3 Results ..................................................
3.3.1 Interstitial diffusion decreases with increasing molecular size ..
3.3.2 Diffusion is faster in CW tumors than in DC tumors .........
3.3.3 The tumor capsule has a high density of fibroblast-like cells ...
3.3.4 DC tumors have high levels of fibrillar collagen type I .......
3.3.5 Decorin is restricted to the tumor periphery ................
3.3.6 Hyaluronan staining differences in CW and DC tumors ......
3.3.7 The ECM is of host origin .............................
3.4 Discussion ................................................
3.4.1 Importance of diffusion in drug design and selection ........
3.4.2 Contributions of tortuosity to diffusional hindrance .........
3.4.3 Role of ECM composition and organization in transport .....
3.5 Conclusions ...............................................
3.6 References ............ ...............................
4 Collagen Gel Transport
4.1 Introduction ...............................................
4.2 Materials and Methods ......................................
4.2.1 Experimental Techniques ..............................
4.2.2 Theoretical Models ...................................
4.3 Results ...................................................
4.3.1 Collagen gel imaging reveals heterogeneous fibrillar assembly..
4.3.2 Collagen gels significantly hinder molecular diffusion .........
4.3.3 Gel diffusion data closely match measurements in tumors ......
4.3.4 Gelation of collagen solutions does not affect hindrance .......
4.3.5 Tracer diffusion in gels is influenced by method of preparation ...
4.3.6 The effective medium model underpredicts gel permeability ....
12
41
42
43
43
45
45
45
47
48
49
52
53
53
54
54
55
55
58
60
64
64
65
65
70
71
71
73
75
77
79
80
4.3.7 Measured gel permeability does not match tumor permeability .. 80
4.4 Discussion ................................................. 82
4.4.1 Collagen accounts for most of the tumor diffusional hindrance.. 82
4.4.2 Unassembled collagen is implicated in gel diffusive hindrance.. 83
4.4.3 Pure collagen offers more hindrance than pure hyaluronan ..... 85
4.4.4 Collagen gels pose a greater diffusive than hydraulic barrier ... 86
4.5 Conclusions ....... .. ...... ................................. 87
4.6 References .. ............................................. . 88
5 Second Harmonic Imaging 91
5.1 Introduction ... ........................ .................... 91
5.2 Materials and Methods ...................................... 92
5.2.1 Surgery and Imaging .................................. 92
5.2.2 In vitro SHG and autofluorescence signals ................. 92
5.2.3 In vivo SHG signal of different tumor types ............. ... 93
5.2.4 Collagen quantification with immunostaining ............... 93
5.2.5 Diffusion measurements ............. ..... ............. 94
5.2.6 Enzyme dynamics ...................... .............. 94
5.2.7 Statistics . ....................................... 95
5.3 Results ..................... .............................. 96
5.3.1 Validation of collagen imaging by SHG in tumors ........... 96
5.3.2 Relationship of tumor SHG to diffusive transport ............ 101
5.3.3 Dynamic imaging of collagen modification ......... ........ 104
5.3.4 Relaxin enhances transport in tumors ................... .. 107
5.4 Discussion ................... ............. ................ 109
5.5 References .. . ........ ..................................... 112
6 Improving Gene Therapy 114
6.1 Introduction ............................ .................... 114
6.2 Materials and Methods ............. .......................... 115
6.2.1 Cell culture .. . ........ ...................... ......... 115
6.2.2 Viral vectors ................................... ....... 115
13
6.2.3 Dorsal skinfold window preparation ................... ... 116
6.2.4 Injection and imaging of labeled vectors ................... 116
6.2.5 Image analysis ...................................... . 117
6.2.6 Flank tumor growth delay .............................. 117
6.2.7 Immunostaining ...................................... 118
6.2.8 Statistical analysis .................................... 118
6.3 Results . ......... ...... .... ............. .... 1196.3.1 Virus distribution is hindered by collagen rich regions ....... 119
6.3.2 Collagenase improves virus distribution and gene expression .. 122
6.3.3 Collagenase enhances the efficacy of oncolytic viral therapy ... 122
6.3.4 Initial improved viral distribution improves efficacy ......... 127
6.4 Discussion .................. .............................. 128
6.5 References ................................................ 133
7 Conclusions / Future Directions 136
7.1 Introduction ........ .... .............. ... .. 136
7.2 Diffusive transport mechanisms within the tumor ECM ............. 137
7.3 Matrix modifying treatments and cancer therapy .................. 140
7.4 Conclusions .... ....... ............. ............ 1427.5 References ........................................... 145
14
List of Figures
2.1 90 nm fluorescent nanoparticles within a tumor ................. 29
2.2 Schematic of extracellular matrix composition .................. 31
2.3 Biochemical analysis of four tumor types ....................... 32
2.4 Matrix modifying treatments ................................. 34
3.1 Effective diffusion coefficients of tracers in vivo ................. 46
3.2 Extracellular space in vivo ................................... 49
3.3 Quantification of the fractional tissue area stained for collagen I .. 50
3.4 Extracellular matrix composition in vivo ........................ 51
3.5 Electron microscopy of collagen fibril organization in vivo ........ 52
3.6 The effect of hyaluronidase treatment on IgG diffusion in vivo ..... 58
4.1 Confocal reflectance microscopy of collagen gels ................. 72
4.2 Electron microscopy of collagen gels and tumors ......... ........ 73
4.3 Effective diffusion coefficients of tracers in gels .................. 74
4.4 Schematic of tortuosity experienced by molecules in vivo .......... 76
4.5 Comparisons between gel and in vivo diffusion data .............. 78
4.6 Comparison between collagen solution and gel diffusion data ...... 79
4.7 Comparison between experimental and theoretical permeability... 81
4.8 Comparison between gel and in vivo permeability ................ 82
5.1 Second harmonic generation imaging of tumors in vivo ........... 96
5.2 SHG images of fibrillar collagen I in tumors ................... . 98
5.3 Dependence of SHG signal on tumor type ...................... 102
5.4 Effect of collagenase and relaxin on tumor collagen dynamics ..... 105
6.1 Viral vector distribution following intratumoral injection ......... 120
6.2 Effect of collagenase on oncolytic viral therapy .................. 124
6.3 Effect of collagenase on MGH2-induced tumor growth delay ...... 126
6.4 Immunostaining analysis of tumor cell infection ................. 128
6.5 A representative model of oncolytic viral distribution ............. 130
15
List of Tables
4.1 Interstitial matrix composition of tumors ....................... 76
5.1 Comparison of SHG with collagen and diffusion coefficients ....... 103
16
Chapter 1
Thesis Outline - Original Contributions
In this thesis I will document the work I have done in quantitating the transport properties
of the tumor interstitium in order to improve the delivery and efficacy of cancer
therapeutics. The large size of many novel therapeutics impairs their transport through the
tumor extracellular matrix and thus limits their therapeutic effectiveness. We propose that
extracellular matrix composition, structure, and distribution determine the transport
properties in tumors. Thus, the use of matrix modifying agents in combination with the
delivery of therapeutically relevant macromolecules within tumors, should result ultimately
in improved therapeutic efficacy. I will attempt to prove this hypothesis over the course of
the work contained in this thesis. What follows is a brief summary of the work that has
been performed for each chapter in my thesis, where each chapter fits in to the larger picture,
and what I have contributed towards each chapter.
Chapter 2 is a brief introduction to cancer therapy and describes our motivation for
investigating the movement of macromolecules and viral vectors within tumors. The
goal of my work is to devise methods to modify the tumor extracellular matrix in order to
improve the delivery of cancer therapeutics. To do this, we first need to understand the
nature of transport within the tumor interstitium, we then need to devise methods to be
17
able to monitor transport within the tumor interstitium, and finally be able to test our
predictions using a relevant therapeutic agent. Chapters 3 and 4 describe our work at
trying to further the current understanding of the nature of transport within the tumor
interstitium. Chapter 5 describes the application of second harmonic generation imaging
to the tumor interstitium in order to monitor the changes in fibrillar collagen that are
induced by relaxin, a matrix modifying agent. And finally Chapter 6 describes the use of
a gene therapeutic agent, herpes simplex virus, in combination with an alternate matrix
modifying agent, purified bacterial collagenase, to test whether matrix modification can
have an effect in a clinically relevant model system.
1.1 Chapter 3: Tumor Host Interactions
Chapter 3 describes the work done to characterize the role of the host in determining the
extracellular matrix content of tumors. In this chapter, we determined the diffusion
coefficients of a number of different tracer molecules within the extracellular matrix of
tumors grown in two different anatomical locations, the skin and the pial surface of the
brain. We showed that diffusion of macromolecules and large particles in the skin
tumors was significantly hindered in comparison to the brain tumors. We used
immunohistochemistry and quantitative image analysis to show an increase in
extracellular space, the number of stromal cells, and the amount of collagen type I within
skin tumors in comparison to brain tumors. These results build upon a previous study[l ],
which indicated that collagen content was an important determinant of macromolecular
transport in vivo, and that modification with bacterial collagenase could improve
transport. Our study in chapter 3 improved upon this result by finding that the tumor
18
extracellular matrix derived from the presence of host cells within the tumor, and by
quantitating the transport properties within the tumor matrix of tracers spanning several
orders of magnitude in size. We also quantitated the role of tortuosity due to the cellular
components of the tumor in influencing transport.
This chapter was published in the Proceedings of the National Academy of Sciences,
U.S.A. in 2001[2], and was a collaborative study with the help of the following co-
authors: A. Pluen, Y. Boucher, S. Ramanujan, T.D. McKee, T. Gohongi, E. di Tomaso,
E.B. Brown, Y. Izumi, R.B. Campbell, D.A. Berk, and R.K. Jain. My contribution to this
work included the injection of fluorescent tracers within the tumors, and the acquisition
of fluorescence photobleaching recovery (FRAP) data from these tumors for a number of
the tracers used in the study. I also wrote computer programs for the quantification of
immunohistochemical staining data, and the quantification of parameters such as
extracellular space and the presence of host tumor cells within tissue sections. In
summary, in chapter 3, we confirmed that the transport of macromolecules within tumors
is limited by the presence of collagen I within tumors, and that this collagen is produced
by the host cells within the tumor.
1.2 Chapter 4: Collagen Gel Transport
Building upon this result, we then undertook a study to determine the transport properties
of collagen and hyaluronan gels prepared in vitro to match the content of these molecules
present in the tumor extracellular matrix. The results of this work are described in
chapter 4. We prepared these gels in vitro in order to more closely control the properties
of the resulting gel, as the complexity of the in vivo microenvironment prevented a
19
detailed investigation of the transport properties of the tumor extracellular matrix. While
other groups had investigated the transport properties of tracers within gels of
extracellular matrix components[3, 4], we went further by relating the diffusion of tracers
in these in -vitro preparations to measurements made in tumors of similar collagen
content. We discovered that tracer diffusion data from these collagen type I gels
prepared in vitro, when corrected for cell tortuosity, were in good agreement with data in
vivo in tumors of comparable collagen content. Hyaluronan gels prepared at similar
concentrations to those present in vivo did not pose a significant diffusive barrier to tracer
transport. We determined from gel imaging and transport studies that unassembled
collagen was present in the gel void spaces and contributed to diffusive hindrance, which
was validated through the use of alternate preparation techniques. We measured the
permeability of collagen gels, and compared these results to predictions based on a fit of
the diffusion data to an effective medium model. The experimental permeability
measurements matched the model predictions only for the lower range of collagen
content studied, indicating that high collagen concentration poses a greater barrier to
diffusion than convection. This observation agrees with the in vivo permeability
measurements of Netti et al.[ ], and the in vivo diffusion data described in chapter 3.
This chapter was published in the Biophysical Journal in 2002[5], and was a
collaborative study with the help of the following co-authors: S. Ramanujan, A. Pluen,
T.D. McKee, E.B. Brown, Y. Boucher and R.K. Jain. My contribution to this work
included assisting in the preparation of collagen and hyaluronan gels of the appropriate
concentrations, and the acquisition of diffusion and permeability data within these gels. I
prepared hyaluronan gels of greater concentrations, and showed more significant
20
diffusive hindrance, in agreement with a previous study[3]. I proposed and developed an
alternate preparation method to test the role of unassembled collagen present between
collagen fibers, and also performed imaging of the fibrillar nature of these gels. I
assembled a device to test for leaching of collagen during the permeability
measurements, and used it to show negligible leaching. I also quantitated the amount of
extracellular space present in tissue sections. In summary, this study indicated that the
mechanism of diffusive hindrance of macromolecular tracers within high collagen
content tumors can be reproduced in large part by gels containing solely collagen of
similar concentrations.
1.3 Chapter 5: Second Harmonic Imaging
As described. in chapters 3 and 4, I used two photon microscopy for the quantitative
imaging of tissue sections stained using immunohistochemistry, and for the imaging of
collagen gels in vitro, using second harmonic generation. The ability to image collagen
in vivo in tumors using the same technique would be of great benefit to be able to
quantitate changes induced in the collagen matrix by matrix modifying treatments. We
therefore decided to build upon this work by developing the imaging methodology of
second harmonic generation imaging of collagen in vivo. This work is described in
chapter 5. We determined that the second harmonic signal coming from tumors grown in
vivo was proportional to the content of collagen in those tumors. Using collagen gels of
known concentrations, we determined that second harmonic signal scaled linearly with
collagen concentration. We determined that the second harmonic signal arises from
fibrillar collagen type I in these tumors by comparing immunostaining for collagen type I
21
with second harmonic signal in tissue sections. We then used this technique to obtain
quantitative information on the dynamics of collagen modification in vivo. We
administered bacterial collagenase to the surface of tumors, and were able to show an
exponential decay of second harmonic signal with time, which scaled linearily with the
concentration of collagenase used. We treated the tumors with the hormone relaxin, and
quantitated the resulting change in collagen content and structure with time.
Interestingly,, we were able to show using this technique that relaxin upregulated the rate
of degradation of fibrillar collagen in these tumors, but did not change the overall
collagen content. This indicated an increased turnover of collagen. Additionally,
measurements of macromolecular diffusion coefficients within tumors treated with
relaxin indicated increased molecular mobility in comparison to control tumors. We
were thus able to show that collagen structure, as well as content, plays a critical role in
the diffusion of molecules within the tumor interstitium.
This chapter was published in the journal Nature Medicine in 2003[6], and was a
collaborative study with the help of the following co-authors: E.B. Brown, T.D. McKee,
E. di Tomaso, A. Pluen, B. Seed, Y. Boucher and R.K. Jain; with equal contributions
from the first two authors. I was involved in all aspects of the preparation and
implementation of this study.
1.4 Chapter 6: Improving Gene Therapy
While the diffusion of macromolecular tracers within tumors tells us a lot about how
therapeutically relevant molecules might move within tumors, it would be useful to test
our models using a therapeutically active agent. To this effect, we began studies using
22
herpes simplex virus particles, which have been in use clinically for the treatment of
brain tumorsl[7]. These results are described in chapter 6. We obtained GFP labeled viral
particles from our collaborators (Paola Grandi and Xandra Breakefield), and were able to
show that, upon injection into tumors, these particles were only able to penetrate regions
of the tumor that were devoid of fibrillar collagen, which was imaged using second
harmonic generation. We showed that the addition of purified bacterial collagenase to
the viral mixture allowed the virus to penetrate a greater area of tumor. Using oncolytic
viral vectors., which selectively replicate within and destroy tumor cells, we were able to
show an improved therapeutic response with the addition of bacterial collagenase. We
used imaging of GFP expression and viral proteins labeled with immunohistochemistry in
tissue sections to document the mechanism of this improved viral spread. This work has
been submitted for publication to the journal Nature Biotechnology, and was a
collaborative study with the help of the following co-authors: T.D. McKee, P. Grandi,
W. Mok, G. Alexandrakis, Y. Boucher, X.O. Breakefield and R.K. Jain, with equal
contributions from the first three authors. I was involved in all aspects of the planning
and implementation of this study.
1.5 Chapter 7: Conclusions and Future Directions
Finally, in chapter 7 I summarize the conclusions we have made regarding the role of
collagen in the transport of macromolecules within the tumor extracellular matrix. In
conclusion, I have demonstrated that the fibrillar collagen present within tumors plays an
important role in the distribution of macromolecules and therapeutic particles. Using
optical methods such as FRAP I was able to quantitate differences in diffusion between
23
tumors grown in two different organs, and relate difference in diffusion to the structure
and content of fibrillar collagen in the two sites of tumor implantation. We showed that
gels of collagen type I prepared in vitro could match the diffusive properties of tumors,
indicating that fibrillar collagen poses the main barrier to the transport of macromolecules
and therapeutic particles within tumors. We developed the technology of second
harmonic generation imaging of tumors in order to quantitate the changes induced in the
collagen matrix by the hormone relaxin, and discovered that relaxin acts within tumors by
upregulating both the destruction of old matrix as well as the production of new matrix,
resulting in increased collagen turnover within the tumor, which also leads to increased
diffusion of macromolecules. And finally, we show that the presence of fibrillar collagen
within tumors limits the efficacy of a gene therapeutic agent through the use of
fluorescently labeled herpes virus gene therapeutic particles. Modification of the tumor
collagen using bacterial collagenase results in improved viral spread within tumors,
resulting ultimately in improved viral efficacy.
24
Chapter 2
Motivation
2.1 Cancer therapy
Based on estimates, there will be 1.4 million new cases of cancer in the United States in
2005, and an estimated 570,000 deaths attributed to cancer[8]. Cancer is the second
leading cause of death behind heart disease in the United States, with 90% of all cancer
cases arising initially as solid tumors [8]. Solid tumors consist of abnormal cells that
have evaded the normal controls on cell growth and division, and acquired other
characteristics that provide for their continued survival and growth [9]. Carcinogenesis,
or cancer formation, is a multistep process; genetically inherited mutations or exposure to
mitogenic materials can accelerate this process.
Current methods for medical treatment of solid tumors involve generally one or more
combinations of 3 classes of therapy: surgery, radiation and chemotherapy. Surgery and
radiation are local therapies, while chemotherapy acts systemically. Surgery offers the
possibility of a cure generally for tumors that are caught early in the progression of
disease, and for those tumors growing in surgically accessible locations, where the
25
removal of the tumor will not compromise the function of the organ it is located in.
Radiation therapy kills cancer cells by causing lethal damage to DNA, and is focused
using multiple beam paths to treat solid tumors, even those growing in surgically
inaccessible locations. Radiation therapy offers the potent ability to affect logarithmic
reductions in the number of viable cancer cells within tumors, better than any
chemotherapeutic to date, but is generally limited by radiation-induced damage to normal
tissue [10].
2.2 Systemic therapy
During the course of tumor progression, malignant cells often spread to other organs from
the initial site of tumor growth via the blood or lymphatic vasculature, a process termed
metastasis[11]. This metastatic spread necessitates the use of a systemically acting
therapy, differing from the more localized treatments of surgery and radiotherapy.
Historically, chemotherapeutic agents have been small molecules that have been chosen
for their ability to differentially affect cancerous cells while attempting to cause less harm
to healthy cells within the body[12]. While an ideal chemotherapeutic drug would only
affect cancer cells and have no effect on host cells, there is always some level of host cell
toxicity that limits the amount of drug that can be used to treat a patient. In practice,
cancer therapy often involves the combination of surgery, radiation and chemotherapy to
most effectively treat the disease[13].
26
2.3 Novel targeted therapeutics
New classes of therapeutics are exploiting other aspects of tumor progression, for
example the fact that the tumor has to recruit blood vessels to sustain itself, a process
known as angiogenesis, has led to anti-angiogenic therapy [14]. The ability of molecular
biology techniques to be able to identify specific molecules that are upregulated or
pathways that are dysregulated in malignant cells has led to a much greater understanding
of carcinogenesis and cancer progression. Based on these discoveries new therapies have
been developed, often involving the creation of antibodies[ 15]., or even larger therapeutic
particles such as liposomes[16, 17] and gene therapy vectors[7]. These new types of
therapies have often been referred to as the agents of molecular medicine.
These therapies offer great promise, based on the ability to more selectively target
cancer cells while sparing normal tissue, and based on novel mechanisms of tumor cell
destruction, but they also pose new challenges in their distribution and delivery [18, 19].
Traditional chemotherapeutics are generally small molecules, less than a nanometer in
diameter, but molecular medicine includes antibodies (-lOnm in diameter), liposomes
(-50-100nm) and gene therapeutic vectors (hundreds of nm), which are orders of
magnitude larger in size. Compared to small chemotherapeutic agents there is a
significantly greater transport hindrance of large molecules or particles through normal
and tumor tissue.
27
2.4 Transport of therapeutics within tumors
Transport of molecules through tumors differs from transport in other tissues or organs,
due to pathophysiologic characteristics underlying tumor formation and growth[19]. The
process of angiogenesis, or neovascularization of tumor tissue, serves to provide tumor
cells with adequate nutrients to grow beyond their initial size as a precancerous lesion
[20], and is a critical step in the progression of the disease. However, this process is
dysregulated in comparison to the neovascularization of wound sites or healthy
regenerating tissue (such as liver): an excess of angiogenic stimuli is produced both by
tumor cells and host cells within the tumor, resulting in tumor blood vessels that are
chronically hyperpermeable, and unable to maintain their vascular pressure[21]. This
leads to a uniform, elevated interstitial fluid pressure within tumors, resulting in
negligible convection within the majority of the tumor, except for the tumor
periphery[221. Hyperpermeable tumor vessels can be advantageous to the selective
delivery of molecular medicine, as the pore size cutoff of the vasculature within tumors is
much larger than that for normal tissues[23, 24]. The selective extravasation of
liposomes or gene vectors in tumors is sometimes referred to as the EPR effect, named
for enhanced permeability and retention of liposomal and other macromolecular
formulations within tumors [25].
While it is true that tumor vessels are permeable, making tumor targeting possible
in the sense that liposomal formulations or gene therapy vectors will extravasate
preferentially within tumors, as opposed to normal host tissues, there are still a number of
physiological barriers to the effective delivery of these formulations beyond the peri-
vascular space. For one, many blood vessels in central areas of tumors are compressed or
28
collapsed due to cellular proliferation, resulting in insufficient vascular supply, and thus
molecular delivery, to large portions of the necrotic tumor core [26]. Additionally, even
for functional vessels within tumors, in vivo imaging of nanoparticle delivery has shown
that there is limited transport of liposomes beyond the peri-vascular space [27], as seen in
figure 2.1. To be effective, these therapeutics must have the potential to reach all the cells
within the tumor mass. The tumor cells in the hypoxic environments distant from blood
vessels are more resistant to both radiation therapy [28] and chemotherapy[29], and are
thus the ultimate target for cancer therapeutic agents. Therefore, a major barrier to
overcome for molecular medicine is movement through the interstitial space separating
the extravasated particles from the numerous tumor cells lying distant from blood vessels
[30].
fluorescent nanoparticles that have extravasated within the tumor after they have beencirculating for 24 hours in the mouse. From Yuan et al. [24] bar = 100 [lm
2.5 The tumor extracellular matrix
The interstitial space between tumor cells is filled with an extracellular matrix that
comprises on average approximately 20% of the tumor [31]. The tumor extracellular
29
Fimire 7-1 -- (1) i If, llhcWwq 9 nm
matrix derives from a host of factors, including: i) the original matrix of the host organ
within which the tumor is growing, ii) matrix that is produced by the tumor cells
themselves, and iii) matrix produced by host cells present within the tumor [32],
including that produced by inflammatory cells such as macrophages [33]. In fact, many
cancers, particularly breast cancer, pass through a stage termed desmoplasia over the
course of their development, a fibrotic reaction of the host cells in response to the tumor's
presence [34, 35]. This reaction, while initiated by the host cells as a part of the innate
immune system, can often in fact fuel the progression of the disease due to the release of
growth and angiogenic factors [36].
The interstitial matrix of tumors in the most general sense consists of collagens,
elastin, hyaluronan, proteoglycans and their associated glycosaminoglycans (GAGs) and
other glycoproteins. The collagens are a large family of proteins, all sharing in common
a tripeptide repeat motif containing glycine, proline or hydroxyproline, and a third amino
acid, which assemble into a triple helix. In normal and tumor tissues collagen type IV
forms the basement membrane of blood vessels in association with laminin, fibronectin
and other glycoproteins. In several tumor types he space between the blood vessels and
tumor cells is occupied by fibrillar collagen, which is composed primarily of collagen
type I molecules that associate into fibrils, which then associate into larger collagen fibers
[37]. Proteoglycans consist of a protein core to which a number of GAG chains are
attached. GAGs are sugar molecules composed of disaccharide repeats that extend into
either linear chains or branched structures; a large number of growth factors including the
important angiogenic molecules vascular endothelial growth factor (VEGF) and basic
fibroblast growth factor (bFGF) are known to bind to certain GAG motifs[38]. The
30
unsulfated G(AG hyaluronan in particular is a high molecular weight linear chain of the
disaccharide repeat GlcUA and GlcNAc, and has been shown historically to play a role in
tissue transport, mainly associated with fluid flow[39, 40]. And finally glycoprotens,
such as laminin and fibronectin, in general form structural links bridging the individual
units within the extracellular matrix, or provide a substrate upon which cells can attach
and migrate along. A schematic of the extracellular matrix is shown in figure 2.2.
Figure 2.2: Schematic of extracellular matrix composition
Of these components, the basement membrane is generally the first extracellular matrix
encountered in the delivery of systemically acting therapies to tumors, but the high levels
31
Proteoglycan
Hyaluroacid
Collagefibrils
I
I00=irg
of angiogenesis taking place within tumors render the basement membrane highly
permeable to therapeutics, as mentioned earlier [24]. Netti et al. (2000) [1] quantitated
the amounts of collagen, sulfated and unsulfated GAGs within 4 tumor types grown
subcutaneously within mice, their results are shown in figure 2.3. Thus, part of my
research has been to attempt to quantitate the presence and amount of these matrix
molecules within the tumor interstitium, in order to better understand the role of the
content and structure of the tumor extracellular matrix in influencing the transport
properties of cancer therapeutics.
'I
I
aIIt..
- UaVIL*17 1T1* M*HI'
Figure 2.3: Biochemical analysis of four tumor types A) The tissue content of sulfatedGAG (proteoglycan, light shading) and unsulfated GAG (hyaluronan, dark shading),expressed in terms of equivalent mass of hexuronic acid/g wet tissue. B) total collagencontent (hydroxyproline) in the four different tumor types. No significant differenceswere found between the two carcinomas (MCaIV and LS 174T) or between U87 andHSTS 26T. The collagen content of U87 and HSTS 26T is significantly higher than in thetwo carcinomas (P < 0.007; ANOVA). Bars, SE. From Netti et al. [1]
2.6 Matrix modification
The goal of my research is to attempt to alter the matrix in order to improve the transport
of therapeutics through it. A number of matrix modifying therapies exist currently,
which can be classified into three general categories: endogenous matrix modifying32
I lilt �LI
enzymes, directly acting exogenous enzymes, and indirectly acting anti-fibrotics. The
endogenous matrix modifying enzymes mainly fall under the class of molecules called
matrix metalloproteinases (MMPs), a large family of enzymes so named because they
contain a coordinated metal ion in the catalytic site. These enzymes come in both soluble
and membrane-bound forms, and are secreted initially in an inactive proenzyme state,
becoming active after the cleavage of an inhibitory N terminal propeptide[41]. The
MMPs act on a variety of extracellular matrix molecules, generally by hydrolyzing at
specific location on a molecule, or along the triple helix of collagen [42]. Examples of
directly acting exogenous enzymes include bacterial collagenase, which cleaves the
collagen triple helix at multiple sites [43], and hyaluronidase, which hydrolyzes the large
polysaccharide chain into individual disaccharide units [44]. The indirectly acting anti-
fibrotics act via a different mechanism: instead of directly acting on the target molecules
of interest, they instead target the tumor or host cells, causing the upregulation of MMP
activity in these cells, or an alteration in matrix synthesis. An example is the hormone
relaxin, which is naturally secreted during pregnancy and in reproductive organs, and acts
to stimulate uterine growth and cervical dilation through the fibroblasts present in those
organs[45].
Each of these therapies have benefits and risks associated with them. Many
MMPs have been implicated in tumor progression and metastasis, allowing tumor cells to
escape from their primary site of growth and invading adjacent tissues. The exogenous
enzyme bacterial collagenase degrades all types of collagen, including basement
membrane collagen, and as such can compromise the structure of the tumor vasculature
and induce hemorrhage; it also can evoke an immune response, since it is a bacterial
33
protein. And finally not a lot is known about the actions of anti-fibrotics such as relaxin
in tumors, or in fact in many tissues, due to relaxin's complicated physiology, and the
only recent discovery of the relaxin receptors. Nevertheless, these matrix modifying
treatments give us some tools with which to modify the tumor extracellular matrix in
order to determine the resulting change in transport properties. My work on the
development of second harmonic generation imaging of collagen in combination with
intravital microscopy was done in order to be able to quantitate the influence of these
matrix modifying therapies on the fibrillar collagen present within the tumor interstitium,
in order to further the knowledge of relaxin's mechanism of action.
Collagen triple helix - tropocollagen molecule
A Bactenal collagenase
iv.xY
B Matnx metaloprotease
POG-4AGlGW al(i)- <A; > _,<: i,4 l_ 4,_
!C Antifibrotic Mechanism:Promotes connective tissue turnover
ConXn
4 ;~~~~~~~~~010-61W
.0 0
,XsH=;:e (Da Wbr
co"O
I
-IC e/ A_ w A_ CO __-0 A W.-" so d .W.Om-Sr~IYL
Figure 2.4: MIatrix modifying treatments A) mechanism of action of bacterialcollagenase - nonspecific degradation of the triple helix at all repeating glycine residuesalong the triple helix. B) mechanism of action of MMP- 1, interstitial collagenase:specific cleavage at a particular site (PQG - IAGQRGVV on cl . chain), resulting in 3/4& 1/4 fragments of tropocollagen molecule, C) putative mechanism of action of theantifibrotic therapy relaxin, from the website of Connetics Corp.
34
|
I
.0
2.7 Transport within the tumor extracellular matrix
Hyaluronan has most often been associated with transport hindrance within tissues [40],
although this has generally been associated with flow within the interstitium of the
peritoneal cavity. Diffusion has been measured within hyaluronan solutions [3] and
collagen solutions [4]. Chary and Jain (1989)[46] were the first to adapt fluorescence
recovery after photobleaching to measure diffusion within tumors. Berk et al (1993)[47]
improved upon the fluorescence recovery after photobleaching technique via the addition
of spatial Fourier analysis, which allowed the measurement of diffusion within scattering
media, and used it to quantify diffusion within tumors. To investigate the role of
extracellular matrix in tissue transport, Netti et al. (2000)[1] described the transport of
BSA and IgG within 4 different tumor types, and showed high collagen content tumors
exhibited greater diffusional hindrance of IgG in vivo. The addition of bacterial
collagenase resulted in an increase of the diffusion coefficient of IgG by 100% in tumors
with high collagen content. This formed the basis for my thesis, and in chapter 3 I will
describe the steps we took to determine the influence of collagen on the transport of
macromolecules within tumors.
35
2.8 References
1. Netti, P.A., D.A. Berk, M.A. Swartz, A.J. Grodzinsky, and R.K. Jain, 2000. "Roleof extracellular matrix assembly in interstitial transport in solid tumors." CancerRes, 60(9): p. 2497-503.
2. Pluen, A., et al., 2001. "Role of tumor-host interactions in interstitial diffusion ofmacromolecules: cranial vs. subcutaneous tumors." Proc Natl Acad Sci USA,98(8): p. 4628-33.
3. De Smedt, S.C., A. Lauwers, J. Demeester, Y. Engelborghs, G. De Mey, and M.Du, 1994. "Structural information on hyaluronic acid solutions as studied byproble diffusion experiments." Macromolecules, 27: p. 141-146.
4. Shenoy, V. and J. Rosenblatt, 1995. "Diffusion of macromolecules in colagen andhyaluronic acid, rigid-rod - flexible polymer, composite matrices."Macromolecules, 28: p. 8751-58.
5. Ramanujan, S., A. Pluen, T.D. McKee, E.B. Brown, Y. Boucher, and R.K. Jain,2002. "Diffusion and convection in collagen gels: implications for transport in thetumor interstitium. " Biophys J, 83(3): p. 1650-60.
6. Brown, E., T. McKee, E. diTomaso, A. Pluen, B. Seed, Y. Boucher, and R.K.Jain, 2003. "Dynamic imaging of collagen and its modulation in tumors in vivousing second-harmonic generation." Nat Med, 9(6): p. 796-800.
7. Chiocca, E.A., 2002. "Oncolytic viruses." Nat Rev Cancer, 2(12): p. 938-50.8. American Cancer Society, 2005, "American Cancer Society: Cancer Facts and
Figures 2005": Atlanta, GA.9. Hanahan, D. and R.A. Weinberg, 2000. "The hallmarks of cancer." Cell, 100(1):
p. 57-70.10. Bernier, J., E.J. Hall, and A. Giaccia, 2004. "Radiation oncology: a century of
achievements." Nat Rev Cancer, 4(9): p. 737-47.1 1. Fidler, I.J., 2002. "Critical determinants of metastasis. " Semin Cancer Biol, 12(2):
p. 89-96.12. Chabner, B.A. and T.G. Roberts, Jr., 2005. "Timeline: Chemotherapy and the war
on cancer." Nat Rev Cancer, 5(1): p. 65-72.13. Zhu, A.X. and C.G. Willett, 2005. "Combined modality treatment for rectal
cancer." Semin Oncol, 32(1): p. 103-12.14. Carmeliet, P. and R.K. Jain, 2000. "Angiogenesis in cancer and other diseases."
Nature, 407(6801): p. 249-57.15. Carter, P., 2001. "Improving the efficacy of antibody-based cancer therapies." Nat
Rev Cancer, 1(2): p. 118-29.16. Park, J.W., C.C. Benz, and F.J. Martin, 2004. "Future directions of liposome- and
immunoliposome-based cancer therapeutics." Semin Oncol, 31(6 Suppl 13): p.196-205.
17. Torchilin, V.P., 2005. "Recent advances with liposomes as pharmaceuticalcarriers." Nat Rev Drug Discov, 4(2): p. 145-60.
36
1 8. Jain, R.K., 1998. "The next frontier of molecular medicine: delivery oftherapeutics." Nat Med, 4(6): p. 655-7.
19. Jain, R.K., 1994. "Barriers to drug delivery in solid tumors." Sci Am, 271(1): p.58-65.
20. Folkman, J., 1992. "The role of angiogenesis in tumor growth." Semin CancerBiol, 3(2): p. 65-71.
21. Boucher, Y. and R.K. Jain, 1992. "Microvascular pressure is the principal drivingforce for interstitial hypertension in solid tumors: implications for vascularcollapse." Cancer Res, 52(18): p. 5110-4.
22. Boucher, Y., L.T. Baxter, and R.K. Jain, 1990. "Interstitial pressure gradients intissue-isolated and subcutaneous tumors: implications for therapy." Cancer Res,50(15): p. 4478-84.
23. Hobbs, S.K., W.L. Monsky, F. Yuan, W.G. Roberts, L. Griffith, V.P. Torchilin,and R.K. Jain, 1998. "Regulation of transport pathways in tumor vessels: role oftumor type and microenvironment. " Proc Natl Acad Sci U S A, 95(8): p. 4607-12.
24. Yuan, F., M. Dellian, D. Fukumura, M. Leunig, D.A. Berk, V.P. Torchilin, andR.K. .Jain, 1995. "Vascular permeability in a human tumor xenograft: molecularsize dependence and cutoff size." Cancer Res, 55(17): p. 3752-6.
25. Maeda, H., J. Wu, T. Sawa, Y. Matsumura, and K. Hori, 2000. "Tumor vascularpermeability and the EPR effect in macromolecular therapeutics: a review." JControl Release, 65(1-2): p. 271-84.
26. Padera, T.P., B.R. Stoll, J.B. Tooredman, D. Capen, E. di Tomaso, and R.K. Jain,2004. "Pathology: cancer cells compress intratumour vessels." Nature, 427(6976):p. 695.
27. Yuan, F., M. Leunig, S.K. Huang, D.A. Berk, D. Papahadjopoulos, and R.K. Jain,1994. "Microvascular permeability and interstitial penetration of stericallystabilized (stealth) liposomes in a human tumor xenograft." Cancer Res, 54(13):p. 3352-6.
28. Vaupel, P., 2004. "Tumor microenvironmental physiology and its implications forradiation oncology." Semin Radiat Oncol, 14(3): p. 198-206.
29. Yu, J.L., B.L. Coomber, and R.S. Kerbel, 2002. "A paradigm for therapy-inducedmicroenvironmental changes in solid tumors leading to drug resistance."Differentiation, 70(9-10): p. 599-609.
30. Jain, R.K., 2001. "Delivery of molecular medicine to solid tumors: lessons fromin vivo imaging of gene expression and function." J Control Release, 74(1-3): p.7-25.
31. Jain, R.K., 1987. "Transport of molecules in the tumor interstitium: a review."Cancer Res, 47(12): p. 3039-51.
32. Davies Cde, L., D.A. Berk, A. Pluen, and R.K. Jain, 2002. "Comparison of IgGdiffusion and extracellular matrix composition in rhabdomyosarcomas grown inmice versus in vitro as spheroids reveals the role of host stromal cells." Br JCancer, 86(10): p. 1639-44.
33. Ben-Baruch, A., 2003. "Host microenvironment in breast cancer development:inflammatory cells, cytokines and chemokines in breast cancer progression:reciprocal tumor-microenvironment interactions." Breast Cancer Res, 5(1): p. 31-6.
37
34. Desmouliere, A., C. Guyot, and G. Gabbiani, 2004. "The stroma reactionmyofibroblast: a key player in the control of tumor cell behavior." Int J Dev Biol,48(5-6): p. 509-17.
35. Walker, R.A., 2001. "The complexities of breast cancer desmoplasia." BreastCancer Res, 3(3): p. 143-5.
36. Dvorak, H.F., 1986. "Tumors: wounds that do not heal. Similarities betweentumor stroma generation and wound healing." N Engl J Med, 315(26): p. 1650-9.
37. Kadler, K.E., D.F. Holmes, J.A. Trotter, and J.A. Chapman, 1996. "Collagen fibrilformation." Biochem J, 316 ( Pt 1): p. 1-11.
38. lozzo, R.V. and J.D. San Antonio, 2001. "Heparan sulfate proteoglycans: heavyhitters in the angiogenesis arena." J Clin Invest, 108(3): p. 349-55.
39. Fraser, J.R., T.C. Laurent, and U.B. Laurent, 1997. "Hyaluronan: its nature,distribution, functions and turnover." J Intern Med, 242(1): p. 27-33.
40. Flessner, M.F., 2001. "The role of extracellular matrix in transperitoneal transportof water and solutes." Perit Dial Int, 21 Suppl 3: p. S24-9.
41. Birkedal-Hansen, H., 1995. "Proteolytic remodeling of extracellular matrix." CurrOpin Cell Biol, 7(5): p. 728-35.
42. Gross, J. and Y. Nagai, 1965. "Specific degradation of the collagen molecule bytadpole collagenolytic enzyme. " Proc Natl Acad Sci U S A, 54(4): p. 1197-204.
43. Watanabe, K., 2004. "Collagenolytic proteases from bacteria." Appl MicrobiolBiotechnol, 63(5): p. 520-6.
44. Jedrzejas, M.J., 2000. "Structural and functional comparison of polysaccharide-degrading enzymes. " Crit Rev Biochem Mol Biol, 35(3): p. 221-51.
45. Sherwood, O.D., 2004. "Relaxin's physiological roles and other diverse actions."Endocr Rev, 25(2): p. 205-34.
46. Chary, S.R. and R.K. Jain, 1989. "Direct measurement of interstitial convectionand diffusion of albumin in normal and neoplastic tissues by fluorescencephotobleaching." Proc Natl Acad Sci U S A, 86(14): p. 5385-9.
47. Berk, D.A., F. Yuan, M. Leunig, and R.K. Jain, 1993. "Fluorescencephotobleaching with spatial Fourier analysis: Measurement of diffusion in light-scattering media." Biophysical Journal, 62: p. 2428-36.
38
Chapter 3
Tumor Host Interactions
3.1 Introduction
Blood-borne therapeutics must extravasate and penetrate the interstitial matrix to reach
cancer cells in a tumor [1]. We recently have shown that tumor-host interactions regulate
transvascular transport in tumors [2], but how they affect tumor interstitial transport is not
known. Because of uniformly elevated interstitial fluid pressure in solid tumors,
convection in the tumor interstitium is negligible [3], and drug delivery through the
extracellular matrix (ECM) relies on passive diffusive transport [4]. Unfortunately,
passive delivery becomes increasingly inefficient for larger particles. The success of
novel cancer therapies that rely on large agents such as proteins, liposomes,
nanoparticles, or gene vectors will hinge on their ability to penetrate the tumor
interstitium [1, 5-7]. It is thus vital to identify the ECM constituents and characteristics
that restrict diffusion and to determine how these are affected by tumor type and site.
Different ECM components, including collagen, glycosaminoglycans, and
proteoglycans such as decorin, form a complex structured gel [8]. Resistance to
interstitial flow has been strongly linked to glycosaminoglycans and especially
39
hyaluronan (HA) [8-10]. However, a recent in vivo study from our lab found an inverse
correlation between collagen content of tumors and diffusion of IgG [ 11]. Furthermore, in
vitro experiments found that diffusion of albumin is weakly hindered in HA gels [10] but
significantly hindered in collagen gels [12]. Thus, we expect that tumor interstitial
transport properties will depend on the volume, interaction, structure, and distribution of
the matrix molecules and not simply on their overall levels [13]. Furthermore, because
the bulk of the matrix in many tumors is produced by stromal cells [14, 15], we
hypothesize that the diffusion of macromolecules will depend on tumor-host interactions.
Here we present analysis of the combined effect of the ECM composition,
structure, and distribution and the role of tumor-host interaction on diffusion in the tumor
interstitium. Using the fluorescence recovery after photobleaching (FRAP) technique [ 11,
16, 17], we measured the diffusion coefficients of proteins, dextrans, and liposomes in
two different human tumor xenografts implanted either in the dorsal chamber (DC) or
cranial window (CW) in mice. Diffusion coefficients were related to the distribution and
relative levels of collagen type I, decorin, and HA as determined from stained tissue
sections. Collagen organization was characterized by transmission electron microscopy.
We also estimated the effect of cellular geometry (tortuosity) on transport. The results
provide critical data on the delivery of molecular medicine in solid tumors.
40
3.2 Materials and Methods
3.2.1 Fluorescent tracers
FITC-conjugated particles/molecules of various sizes were studied. In order of increasing
size, these included lactalbumin and BSA (Molecular Probes), nonspecific IgG (Jackson
ImmunoResearch), nonspecific IgM (Sigma), FITC-dextran 2,000,000 MW (Sigma), and
liposomes. IgM was purchased unlabeled and then conjugated to FITC by using the Fluo
EX-protein labeling kit (Molecular Probes). All other molecules were purchased in FITC-
labeled form. Liposomes (150 nm in diameter --- determined from the diffusion
coefficients in solution by using Eq. 1) were prepared from dipamitoyl-
phosphatidylcholine with 1 mol % of the fluorescent phospholipid carboxyfluorescein-
dioleoyl phosphatidylethanolamine [18].
3.2.2 Animals and tumors
Human glioblastoma (U87) and melanoma (Mu89) were implanted in two different sites
in severe combined immunodeficient mice as described: (i) on the s.c. tissue of the skin
(DC) [19], and (ii) on the pial surface (CW) [2]. The pial surface approximates an
orthotopic site for U87 tumors whereas skin is orthotopic for Mu89. Tumors can be
visualized directly in these preparations. Animals were used for experiments 3-4 weeks
after tumor implantation.
41
3.2.3 Diffusion measurements by FRAP
Injection of tracer. Small molecules (lactalbumin, BSA, and IgG) were injected i.v. via
the tail vein., To ensure sufficient fluorescence and homogeneous distribution, molecules
larger than IgG were introduced by direct intratumoral injection: 1 il of fluorescent
solution was infused through thin micropipettes (25-30 jtm inner diameter) at constant
pressure using a syringe pump (Harvard Apparatus) for 15-20 min. Diffusion was
measured by FRAP 30 min after the end of micropipette injection. In preliminary studies,
no statistical difference in the diffusion of IgG was found between i.v. or micropipette
injections in the human sarcoma HSTS26T (high collagen content tumors; ref. [11])
implanted in DC (D = (8.85 + 0.8) 10 8 vs. (9.3 + 0.7) 10 -8 cm2s-1 for micropipette and i.v.
injection, respectively).
FRAP measurements. The FRAP technique and method of analysis are described fully
elsewhere [20]. In brief, redistribution of fluorescent molecules in bleached tissue yields
the effective diffusion coefficient, independently of convection [16]. Unlike multiphoton
FRAP [21], FRAP measurements are restricted to less than 100 [tm from the tumor
surface due to light scatter.
Hydrodynamic radius determination. The hydrodynamic radius of the fluorescent
molecules, RH, was determined from the diffusion coefficient, Do, in PBS solution at
T=26°C (299K) using the Stokes-Einstein equation:
Do = kBT/(6nlrRH )
42
in which kB is Boltzmann constant, T is the temperature in Kelvin (K), and · is the viscosity of
water (0.8705 cP at T=299K). Diffusion coefficients in solution were then scaled to T=370 C by
correcting for the effect of temperature on the viscosity.
3.2.4 Extracellular space organization
Extracellular space organization was characterized in tissue sections embedded in the
hydrophilic resin, LR White (Ted Pella, Redding, CA). Tumors were fixed in 2.5%
glutaraldehyde and 2.0% paraformaldehyde in PBS and embedded in the LR White resin
[22]. Toluidine blue stained sections were photographed using a color CCD camera
mounted on a Nikon microscope.
3.2.5 Immunohistochemistry
Rabbit antiserum against type I collagen (LF-67) [23] and against human (LF-136) [24,
25] and mouse (LF-113) [26] decorin were generously provided by Larry Fisher
(National Institute of Dental Research, Bethesda, MD). LF-67, LF-136, and LF-1 13 were
used at dilutions of 1:50, 1:500, and 1:1,000, respectively. Mouse anti-human collagen
type IV (Dako) and rabbit anti-mouse collagen type IV (Chemicon) were used at
dilutions 1:100 and 1:30, respectively. HA was detected with a HA biotinylated
proteoglycan fragment (8 tpg/ml), a generous gift of Charles Underhill (Georgetown
University, Washington, DC).
Tumors were perfusion-fixed through the heart with 4% paraformaldehyde in
PBS. The tissue was infiltrated with sucrose and embedded in OCT. For immunostaining,
sections were blocked with rabbit or goat serum, incubated with the antibody overnight at
43
4°C and then with the appropriate secondary antibodies conjugated to Cy-5 (Jackson
ImmunoResearch Laboratories). For HA staining, the sections were stained for 1 h with
the biotinylated proteoglycan fragment diluted in 10% calf serum and incubated with
Texas red-conjugated streptavidin (Jackson ImmunoResearch Laboratories). The cell
nuclei were stained with the Alexis nuclear stain (Molecular Probes). Sections were
photographed with a Leica TCS-NT4D confocal microscope. For quantification of the
fraction of tissue occupied by collagen type I staining, photographs were taken with a
custom-built two photon microscope based on a MRC 600 platform (Bio-Rad). Using a
constant 10 mW of 720-nm light through a 0.9 numerical aperture water immersion lens,
we generated image stacks of the histological slices, with 10-20 images per stack. A
maximum intensity projection was performed on the image stacks to form a single image
of the section, thereby ensuring that each pixel value represents the best colocalization of
the excitation volume with the slice. Using a series of threshold pixel values we
automatically segmented images into regions corresponding to 1) tissue section versus
not, 2) cell nuclei versus extracellular space, 3) nonspecific staining, and 4) specific
staining for fibrillar collagen. The fraction of the pixels in a 50 x 100 ILm window
(oriented perpendicular to the tumor surface) that were stained for collagen was
quantified using this technique. The average pixel value of the collagen pixels was
calculated as an indicator of collagen type I staining.
3.2.6 Electron microscopy
Organization of collagen bundles and interfibrillar spacing were characterized by electron
microscopy. Tumors were fixed by immersion in 2.5% glutaraldehyde and 2.0%
44
paraformaldehyde in PBS for 4-6 h. Small tumor pieces were washed overnight in PBS,
dehydrated in ethanol, fixed in 1% osmium, and embedded in Polybed 812. Thin sections
were stained with uranyl acetate and lead citrate and examined with a Phillips CM10
transmission electron microscope (Phillips Electronic Instruments, Mahwah, NJ)
operating at 80 kV.
3.3 Results
3.3.1 Interstitial diffusion decreases with increasing molecular size
Figure 3.1.a presents diffusion coefficients obtained in Mu89 and U87 in both
implantation sites. In the two tumors, the diffusion of larger molecules is significantly
slower than that of smaller molecules. The decrease in diffusion with particle size in
tumors is even greater than one would predict from pure solution data, due to the
presence of cellular obstacles and matrix molecules. To examine these contributions, we
introduce the concept of tortuosity.
45
a 1.A-5IV-
'0)
C
10)
Q 10-'
o1,,4oa 109
0.1 1 10 100
Experimental hydrodynamic radius, RH, nm
0.1 1 10 100
Exp. hydrodynamic radius, R , nm
Figure 3.1. Effective diffusion coefficients of tracers in vivo (a) Effective diffusioncoefficients, De,ff, as a function of their experimental hydrodynamic radius, RH. Diffusioncoefficients in PBS solution were measured at T = 260C and scaled to 37C according tothe Stokes-Einstein equation. Diffusion coefficients were measured in DC (filled symbolsand dotted lines) and CW (open symbols and continuous line) tumors. (b) Interstitialdiffusion coefficients in tumors (Dint = g2 Deff) as a function of hydrodynamic radius, RH,using the experimentally obtained value g = 1.19. The diffusion coefficients in solution(Io) are pictured (black-triangle) to illustrate the ECM influence on retardation.
The increase in path length induced by physical obstacles and ECS connectivity is
described by the tortuosity. The effective diffusion measured in tissues [27] is related to
the tortuosity by Dejj= (1/ 2)Do [28]. Geometric effects imposed by the organization of
cells are likely to be the major hindrance to long-range diffusion of small molecules.
Frictional effects assume greater importance as the size of diffusing particles increases to
become comparable to the dimensions of channels through which they move. On this
basis, we separate tortuosity into viscous (v) and geometric (-g) contributions according
to =gv [291 so that:
Dej = (Defj/Dint) (Dint/Do) Do= (I/g 2) (1]/vj) Do
46
b nt-5lU -
II 104
aF 10-7
0._4
W 10-90-lo
1-
laQ
where Dint is the interstitial diffusion coefficient and Do the diffusion coefficient in
solution. The ratio Defj/Dint=l/g 2 measures hindrance due to cellular obstacles. The ratio
Di,t/Do=l/ 2 measures hindrance within the ECM. The geometric tortuosity may be
estimated using a sufficiently small molecule for which viscous hindrance is negligible
(-v=1) so that Defg(1/ g2)Do. From diffusion measurements of fluorescein (RH= 0.4 nm)
in U87 DC, the geometric tortuosity was estimated at g=1. 19. Figure 3.1.b presents the
interstitial diffusion coefficients in tumors (Dint= g2Deff) as a function of the
hydrodynamic radius, illustrating that the reduction of diffusion coefficient with particle
size is greater in the ECM than in solution.
3.3.2 Diffusion of larger particles is faster in CW tumors than DC tumors
No statistical difference was observed in the diffusion coefficients of small molecules
such as lactalbumin and BSA between the two tumor types and sites of implantation.
However, the diffusion coefficients of larger molecules (particles equal in size and larger
than IgG: RH > 5.5 nm) were significantly decreased (p<0.05) in DC as compared to CW
tumors (Figure 3.1). Figure 3.1.a illustrates the distinction between a "fast diffusion
group" (CW tumors) and a "slow diffusion group" (DC tumors). The difference in
diffusion increases with particle size and is striking for molecules such as dextran
2,000,000 MW. Figure 3.1.a also shows diffusion coefficients for liposomes (RH = 75
nm) in CW tumors. Diffusion coefficients of liposomes in DC tumors could not be
assessed by FRAP due to prohibitively slow diffusion and inhomogeneous distribution of
the particles.
47
3.3.3 The capsule of DC tumors has a high density of fibroblast-like cells
Significant differences in cellular content and ECM organization were found between DC
and CW tumors. Typically, DC tumors were separated from the glass cover slip by a
fibrous capsule composed of several layers of fibroblast-like cells, separated by ECM
(Figure 3.2.a & b). The ECM of the capsule was continuous with that of the underlying
tumor cells. In Mu89, cellular nodules were surrounded by a thin layer of ECM and
stromal cells, whereas in U87 single tumor cells or groups of tumor cells were separated
by larger ECM spaces (Figure 3.2.a & b). In contrast to DC tumors, at the outer edge of
CW tumors, only one layer of fibroblast-like cells was observed in contact with the
underlying tumor cells, which were separated from each other by narrow ECM spaces
(Figure 3.2.c & d).
48
Figure 3.2. Extracellular space in vivo Light microscopy (LR White sections) of theperipheral region of DC and CW tumors. The capsule of U87 (a) and Mu89 (b) DCtumors is composed of several layers of fibroblast-like cells separated by ECM. Note thelarge intercellular spaces in U87 and the narrow space that separates two cellular nodulesin Mu89. The connective tissue at the edge of U87 (c) and Mu89 (d) in the CW iscomposed of one fibroblast cell layer; the tumor cells are separated by narrowintercellular spaces. C, capsule; T, tumor; black arrows, ECM; white arrows, fibroblast-like cells. (Bar = 10 jm.)
3.3.4 DC tumors have high levels of collagen type I and fibrillar collagen
To compare the influence of tumor implantation site on the ECM, the distribution and
staining intensity of collagen types I and IV, decorin and HA were characterized.
Collagen type I staining was abundant in DC tumors, approaching levels found in normal
skin. In these tumors, collagen type I fibers were identified between the layers of
fibroblast-like cells (Figure 3.4.a). In central regions of Mu89, tumor cell clusters were
surrounded by thin layers of type I collagen, whereas in U87, tumor cell clusters or single
cells were separated by wider spaces occupied by type I collagen. In comparison to DC
49
.
tumors the staining occupied a smaller area in CW tumors (Figure 3.4.a & b).
Quantitative image analysis within the superficial 100 pm of Mu89 tumors revealed 36 ±
11% tissue area stained for collagen I in the DC, as opposed to 12 i 5% in the CW
(Figure 3.3). The collagen type I staining also occupied a greater proportion of the ECM
in DC than in CW tumors (Fig 3.4.a & b). In CW tumors collagen type I was
predominantly localized at the tumor edge with scattered staining between tumor cells
(Figure 3.4.b). As expected, staining for collagen type IV was associated with tumor
vessels in both sites (data not shown).
50
Collagen quantification within tissue sections
4i 1U U/oC
90%
0 8 0%
E 70%0u 60)%
U 50%
) 40%o 30%C 20%
0E 1%
E o0%
CW 1 CW 2 CW 3 DC 1 DC 2 DC 3
Tissue section
Figure 3.3. Quantification of the fractional tissue area stained specifically forfibrillar collagen I As described in the methods, we used a series of threshold pixel valuesto automatically segment images into regions corresponding to 1) tissue section versus not('holes' in the tissue section), 2) cell nuclei versus extracellular space, 3) nonspecific staining(determined using negative controls), and 4) specific staining for fibrillar collagen. Thefraction of the pixels in a 50 x 100 jim window (oriented perpendicular to the tumor surface)that were stained for collagen was quantified using this technique. Each bar corresponds tothe results for one tumor, from a total of between 9 and 18 images per tumor. Bar = SE.
��_
Mu89 Dorsal Chamber MuS9 Cranial Window
Collagentype I
Mousedecorin
HA
Figure 3.4. Extracellular matrix composition Immunostaining for collagen type I (aand b) and decorin (c and d), and labeling for HA (e and f) in DC (a, c, and e) and CW (b,d, and f) tumors. Collagen type I occupies a greater area of the periphery in DC than inCW tumors. In both DC and CW tumors the decorin staining is restricted to the peripheryof the tumor. HA staining is intense in the center of Mu89 in the DC, whereas in theperiphery the staining is weak. (Bar = 50 pm.)
The collagen organization was characterized by electron microscopy. Fibrillar
collagen was abundant in the capsule of DC tumors. Bundles of aligned and compact
fibrils (interfibrillar spacing 20-42 nm) were found adjacent to bundles that were poorly
51
-
l
Mu89 Dorsal Chamber Mu89 Cranial Window
organized with larger interfibrillar spaces (75-130 nm) (Figure 3.5.a & b). In the center of
U87 especially, fibrillar collagen was less abundant and poorly organized. This finding,
coupled with the extensive collagen type I staining in the center of U87, suggests that the
deposited collagen is poorly assembled. In CW tumors, collagen fibrils had no specific
organization and appeared as isolated fibrils.
Figure 3.5. Electron microscopy of the organization of collagen fibrils in the capsule ofU87 tumors in the DC. (a) The longitudinally oriented fibrils are parallel to one anotherwith an interfibrillar spacing that varies from 20 to 42 nm. (b) The fibrils are poorlyorganized. The interfibrillar spacing varies between 75 and 130 nm. (Bar = 200 nm.)
3.3.5 Decorin is restricted to the tumor periphery
Because decorin participates in the organization of fibrillar collagen, we characterized its
distribution. Decorin was present between fibroblast-like cells in the capsule of DC
tumors. However, in contrast to type I collagen, decorin immunostaining was not
detected in the extracellular space separating tumor cells (Figure 3.4.c). In CW tumors,
decorin staining was almost exclusively restricted to the tumor edge (Figure 3.4.d).
52
3.3.6 Hyaluronan staining is diffuse in CW tumors but associated with
tumor cells in DC tumors
In comparison to the tumor center, HA staining was absent or significantly reduced in the
capsule of DC tumors (Figure 3.4.e). In the center of Mu89 especially, tumor nodules
were separated by intense HA staining. The staining intensity for HA was greater in skin
than in DC tumors. In U87 and Mu89 in the CW, HA staining was distributed diffusely
throughout the tumor. No obvious difference in the relative levels of HA was detected
between tumor implantation sites.
3.3.7 The ECM is of host origin
The origin (tumor vs. host) of ECM components in the human tumor xenografts implanted in
mice was determined by immunostaining. Staining of the ECM by antibodies against human
decorin and collagen type IV was significantly weaker than for corresponding murine antibodies
(data not shown), indicating that the ECM observed was primarily of host origin.
3.4 Discussion
3.4.1 Importance of diffusion in drug design and selection
Our diffusion measurements provide necessary data for prediction of transport properties
of therapeutic molecules over a wide range of molecular weights. Although no significant
difference in diffusion coefficients was observed for small proteins (lactalbumin,
albumin) between implantation sites, diffusion of larger molecules (IgM and dextran
2,000,000 MW) was 5 to 10-fold faster in CW tumors than in DC tumors.
53
Depending on tumor site, tumors fell into slow-diffusing (DC) and fast-diffusing
(CW) groups, characterized by high and low collagen type I levels, respectively. The
hindrance to diffusion of dextran 2,000,000 MW (R = 19 nm) in DC tumors was
comparable to that of liposomes (RH = 75 nm) in CW tumors (Figure 3.1.a). In DC
tumors, diffusion of the same liposomes was prohibitively slow for measurement. A
rough estimate based on extrapolation of the measured diffusion coefficients suggests
that the diffusion of liposomes in DC tumors would be 1-2 orders of magnitude slower
than in CW tumors. Thus, passive delivery of liposomes might be more feasible in low-
collagen brain tumors than in high collagen tumors. Our results emphasize that the
delivery of larger particles will be highly influenced by the tumor site and possibly by
other factors that influence ECM composition/structure
3.4.2 Contributions of geometric (cellular) and viscous (matrix) tortuosity
to diffusional hindrance
We estimated the geometric tortuosity in U87 DC tumors as g=1.19+0.10. Our results
compare well with previous Monte-Carlo simulations, which predicted a tortuosity of 1.4
for 3-dimensional radial diffusion through an array of evenly spaced cells [29, 30]. The
geometric tortuosity could vary with cellular arrangement and ECS connectivity.
Complex cellular arrangements may differentially affect the transport of large vs. small
particles, restricting large particles to wider intercellular paths. The matrix, its
54
distribution and organization further compound the hindrance via the viscous tortuosity
which, as shown in Figure 3.1.b, increases significantly for larger molecules and at higher
collagen type I levels. Although the true tortuosity of long-range motion may indeed
increase with particle size, the most likely explanation for the increased hindrance for
larger particles is the increased viscous drag from solid obstacles (cells, matrix fibers) as
the size of the diffusing particles becomes significant compared to intercellular or
interfibrillar spacing [29, 31].
3.4.3 The role of ECM composition and organization in determining
transport
Role of collagen. Expanding on the results of Netti et al. (2000) [11], we find that
collagen type I and its organization into fibrils have a significant role in limiting the
diffusion of large molecules (e.g. IgG, IgM and dextran 2,000,000 MW). Fibrillar
collagen occupied a greater portion of the ECM in DC than in CW tumors. The narrow
spacing (20 - 40 nm) between collagen fibrils will exclude or hinder (frictional
interaction, steric hindrance) the migration of larger particles. The tortuous paths around
compact collagen bundles or within loose bundles (interfibrillar spacing = 75 - 130 nm)
will also hinder the diffusion of large molecules. Interestingly, the 5 - 10 fold difference
in diffusion between CW and DC tumors was found for molecules with diameters
approaching the interfibrillar spacing.
Role of proteoglycans. The alignment and spacing of collagen fibrils is modulated by
proteoglycans. The protein core of decorin binds to fibrils, and the dermatan/chondroitin
55
sulfate side chains form complexes that bridge the interfibrillar space at intervals of 60-
65 nm [32]. Decorin knockout mice exhibit wider interfibrillar spaces in the skin, and
inhibition of decorin synthesis by B-D xyloside induces large separations in the fibrillar
collagen network of the corneal stroma [33, 34]. Thus, the wider interfibrillar spaces in
the center of U87 in the DC could be due to the reduced expression of decorin. However,
the presence of occasional compact collagen bundles in the center of U87 and the tightly
organized fibrillar collagen in the center of Mu89 where decorin expression is reduced
suggest that other proteoglycans, possibly lumican [35, 36], may participate in the
organization of fibrillar collagen in these tumors. It remains to be established whether the
interaction between proteoglycans and fibrillar collagen limits the diffusion of
macromolecules in tumors.
Role of' hyaluronan. The low levels or absence of HA staining in the capsule of DC
tumors suggest that HA was not a contributor to transport hindrance in these tumors. In
the tumor center, the higher levels of HA could potentially influence interstitial transport.
Several studies have clearly demonstrated that HA impedes fluid flow in tissues [37, 38],
whereas the effect of HA on the diffusion of macromolecules in normal or tumor tissues
is yet to be determined. The degradation of HA in normal tissues with hyaluronidase
either does not modify or even decreases the diffusion of albumin [38, 39]. Indeed, we
have observed similar effects of hyaluronidase administration on the diffusion of IgG in
tumors (Figure 3.6, unpublished data). Based on these results, it is possible that the
swelling potential of intact HA increases the pore size between ECM molecules and thus
actually facilitates diffusion.
56
Role of tumor site and tumor-host interactions. Differences in the levels of collagen type
I and decorin between DC and CW tumors reflect the greater recruitment of stromal cells
(e.g. fibroblasts) in DC tumors. The greater accumulation of collagen type I and decorin
in DC tumors was associated with a higher density of stromal cells. In general, stromal
cells, and not neoplastic cells, synthesize these molecules in carcinomas [15, 40].
Immunostaining also showed that decorin and collagen IV in Mu89 and U87 were
produced by host (murine) cells. In contrast, previous studies have shown that HA is
produced by neoplastic cells as well as by stromal cells [41-43]. In vitro, paracrine
interactions and direct cell-cell contact between tumor cells and fibroblasts can increase
the fibroblast synthesis of collagen type I, HA, and decorin [40, 43, 44].
57
3 1 n-7
3.5 Conclusions
The present study provides critical data on the diffusive transport of particles in tumors
for a wide range of particle sizes. Tumors studied fell into slow vs. fast diffusion groups,
corresponding to high vs. low collagen type I content respectively, supporting a central
role for fibrillar collagen in determining interstitial hindrance. The results demonstrate
that diffusion of large molecules (IgG. IgM, dextran 2M and liposomes) is much faster in
CW than in DC tumors. The greater hindrance to diffusion in DC tumors was associated
58
5. I .f
U)04-
EC 1.5107
e)
0
I-I(5 5107
ol_
I1 2 3 4 5 6 7
Individual tumors
Figure 3.6. The effect of hyaluronidase treatment on the diffusion coefficient of IgGwithin HSTS tumors Effective diffusion coefficients of IgG administered systemically(IgG) measured by FRAP in HSTS tumors, compared to measurements taken within thesame tumor 24 hours after hyaluronidase administration (IgG+HAse). Effective diffusioncoefficients show either no change or a slight decrease following hyaluronidasetreatment. (unpublished data)
with a higher density of host stromal cells, which synthesize and organize collagen type I.
These results also point to the necessity of site-specific drug carriers to improve drug
delivery. Finally, our results underscore that efficient gene therapy will require a better
integration of drug design and in vivo experimentation.
59
3.6 References
1. Jain, R.K., 1998. "The next frontier of molecular medicine: delivery oftherapeutics." Nat Med, 4(6): p. 655-7.
2. Hobbs, S.K., W.L. Monsky, F. Yuan, W.G. Roberts, L. Griffith, V.P. Torchilin,and R.K. Jain, 1998. "Regulation of transport pathways in tumor vessels: role oftumor type and microenvironment." Proc Natl Acad Sci USA, 95(8): p. 4607-12.
3. Boucher, Y., L.T. Baxter, and R.K. Jain, 1990. "Interstitial pressure gradients intissue-isolated and subcutaneous tumors: implications for therapy." Cancer Res,50(15): p. 4478-84.
4. Netti., P.A., et al., 1999. "Enhancement of fluid filtration across tumor vessels:implication for delivery of macromolecules." Proc Natl Acad Sci U S A, 96(6): p.3137--42.
5. Yuan, F., M. Dellian, D. Fukumura, M. Leunig, D.A. Berk, V.P. Torchilin, andR.K. Jain, 1995. "Vascular permeability in a human tumor xenograft: molecularsize dependence and cutoff size." Cancer Res, 55(17): p. 3752-6.
6. Jacobs, A., X.O. Breakefield, and C. Fraefel, 1999. "HSV-1-based vectors forgene therapy of neurological diseases and brain tumors: part II. Vector systemsand applications." Neoplasia, 1(5): p. 402-16.
7. Weyerbrock, A. and E.H. Oldfield, 1999. "Gene transfer technologies formalignant gliomas." Curr Opin Oncol, 11(3): p. 168-73.
8. Gribbon, P.M., A. Maroudas, K.H. Parker, and C.P. Winlove, Water and solutetransport in the extracellular matrix: physical principles and macromoleculardeterminants, in Connective tissue biology . integration and reductionism, R.K.Reed and K. Rubin, Editors. 1998, Portland: London; Miami. p. 95-124.
9. Levick, J.R., 1987. "Flow through interstitium and other fibrous matrices." Q JExp Physiol, 72(4): p. 409-37.
10. Comper, W.D. and T.C. Laurent, 1978. "Physiological function of connectivetissue polysaccharides." Physiol Rev, 58(1): p. 255-315.
11. Netti.. P.A., D.A. Berk, M.A. Swartz, A.J. Grodzinsky, and R.K. Jain, 2000. "Roleof extracellular matrix assembly in interstitial transport in solid tumors." CancerRes, 60(9): p. 2497-503.
12. Shenoy, V. and J. Rosenblatt, 1995. "Diffusion of macromolecules in collagenand hyaluronic acid, rigid-rod-flexible polymer, composite matrices."Macromolecules, 28(26): p. 8751-8758.
13. Ogston, A.G., B.N. Preston, and J.D. Wells, 1973. "On the Transport of CompactParticles Through Solutions of Chain-Polymers." Proc. R. Soc. Lond. A.,333(1594): p. 297-316.
14. Toole, B.P., C. Biswas, and J. Gross, 1979. "Hyaluronate and invasiveness of therabbit V2 carcinoma." Proc Natl Acad Sci USA, 76(12): p. 6299-303.
15. Brown, L.F., et al., 1999. "Vascular stroma formation in carcinoma in situ,invasive carcinoma, and metastatic carcinoma of the breast." Clin Cancer Res,5(5): p. 1041-56.
16. Berk, D.A., F. Yuan, M. Leunig, and R.K. Jain, 1997. "Direct in vivomeasurement of targeted binding in a human tumor xenograft." Proc Natl AcadSci USA, 94(5): p. 1785-90.
60
17. Chary, S.R. and R.K. Jain, 1989. "Direct measurement of interstitial convectionand diffusion of albumin in normal and neoplastic tissues by fluorescencephotobleaching." Proc Natl Acad Sci USA, 86(14): p. 5385-9.
18. Szoka, F., Jr. and D. Papahadjopoulos, 1980. "Comparative properties andmethods of preparation of lipid vesicles (liposomes)." Annu Rev Biophys Bioeng,9: p. 467-508.
19. Leunig, M., A.E. Goetz, M. Dellian, G. Zetterer, F. Gamarra, R.K. Jain, and K.Messmer, 1992. "Interstitial fluid pressure in solid tumors followinghyperthermia: possible correlation with therapeutic response." Cancer Res, 52(2):p. 487-90.
20. Berk., D.A., F. Yuan, M. Leunig, and R.K. Jain, 1993. "Fluorescencephotobleaching with spatial Fourier analysis: measurement of diffusion in light-scattering media." Biophys J, 65(6): p. 2428-36.
21. Brown, E.B., E.S. Wu, W. Zipfel, and W.W. Webb, 1999. "Measurement ofmolecular diffusion in solution by multiphoton fluorescence photobleachingrecovery." Biophys J, 77(5): p. 2837-49.
22. Newman, G.R., 1987. "Use and abuse of LR White." Histochem J, 19(2): p. 118-20.
23. Bernstein, E.F., L.W. Fisher, K. Li, R.G. LeBaron, E.M. Tan, and J. Uitto, 1995."Differential expression of the versican and decorin genes in photoaged and sun-protected skin. Comparison by immunohistochemical and northern analyses." LabInvest, 72(6): p. 662-9.
24. Bianco, P., L.W. Fisher, M.F. Young, J.D. Termine, and P.G. Robey, 1990."Expression and localization of the two small proteoglycans biglycan and decorinin developing human skeletal and non-skeletal tissues." JHistochem Cytochem,38(11): p. 1549-63.
25. Fisher, L.W., J.D. Termine, and M.F. Young, 1989. "Deduced protein sequence ofbone small proteoglycan I (biglycan) shows homology with proteoglycan II(decorin) and several nonconnective tissue proteins in a variety of species." J BiolChem, 264(8): p. 4571-6.
26. Fisher, L.W., J.T. Stubbs, 3rd, and M.F. Young, 1995. "Antisera and cDNAprobes to human and certain animal model bone matrix noncollagenous proteins."Acta Orthop Scand Suppl, 266: p. 61 -5 .
27. el-Kareh, A.W., S.L. Braunstein, and T.W. Secomb, 1993. "Effect of cellarrangement and interstitial volume fraction on the diffusivity of monoclonalantibodies in tissue." Biophys J, 64(5): p. 1638-46.
28. Nicholson, C. and J.M. Phillips, 1981. "Ion diffusion modified by tortuosity andvolume fraction in the extracellular microenvironment of the rat cerebellum." JPhysiol (Lond), 321: p. 225-57.
29. Rusakov, D.A. and D.M. Kullmann, 1998. "Geometric and viscous components ofthe tortuosity of the extracellular space in the brain." Proc Natl Acad Sci U SA,95(15): p. 8975-80.
30. Chen, K.C. and C. Nicholson, 2000. "Changes in brain cell shape create residualextracellular space volume and explain tortuosity behavior during osmoticchallenge." Proc Natl Acad Sci U SA, 97(15): p. 8306-11.
61
31. Phillips, R.J., 2000. "A hydrodynamic model for hindered diffusion of proteinsand micelles in hydrogels." Biophys J, 79(6): p. 3350-3.
32. Scott, J.E., K.M. Dyne, A.M. Thomlinson, M. Ritchie, J. Bateman, G. Cetta, andM. Valli, 1998. "Human cells unable to express decoron produced disorganizedextracellular matrix lacking "shape modules" (interfibrillar proteoglycanbridges)." Exp Cell Res, 243(1): p. 59-66.
33. Hahn, R.A. and D.E. Birk, 1992. "beta-D xyloside alters dermatan sulfateproteoglycan synthesis and the organization of the developing avian cornealstroma." Development, 115(2): p. 383-93.
34. Danielson, K.G., H. Baribault, D.F. Holmes, H. Graham, K.E. Kadler, and R.V.Iozzo, 1997. "Targeted disruption of decorin leads to abnormal collagen fibrilmorphology and skin fragility." J Cell Biol, 136(3): p. 729-43.
35. Chakravarti, S., T. Magnuson, J.H. Lass, K.J. Jepsen, C. LaMantia, and H.Carroll, 1998. "Lumican regulates collagen fibril assembly: skin fragility andcorneal opacity in the absence of lumican." J Cell Biol, 141(5): p. 1277-86.
36. Leygue, E., et al., 2000. "Lumican and decorin are differentially expressed inhuman breast carcinoma." JPathol, 192(3): p. 313-320.
37. Bert, J.L. and R.H. Pearce, The interstitium and microvascular exchange, inHandbook of Physiology: The Cardiovascular System., R.M. Berne and N.Sperelakis, Editors. 1990, American Physiological Society: Bethesda, Md. p. 521-547.
38. Parameswaran, S., L.V. Brown, G.S. Ibbott, and S.J. Lai-Fook, 1999. "Hydraulicconductivity, albumin reflection and diffusion coefficients of pig mediastinalpleura." Microvasc Res, 58(2): p. 114-27.
39. Qiu, X.L., L.V. Brown, S. Parameswaran, V.W. Marek, G.S. Ibbott, and S.J. Lai-Fook, 1999. "Effect of hyaluronidase on albumin diffusion in lung interstitium."Lung, 177(5): p. 273-88.
40. lozzo., R.V. and I. Cohen, 1994. "Altered proteoglycan gene expression and thetumor stroma." Exs, 70: p. 199-214.
41. Kimata, K., M. Takeda, S. Suzuki, J.P. Pennypacker, H.J. Barrach, and K.S.Brown, 1983. "Presence of link protein in cartilage from cmd/cmd (cartilagematrix deficiency) mice." Arch Biochem Biophys, 226(2): p. 506-16.
42. Turley, E.A., C.A. Erickson, and R.P. Tucker, 1985. "The retention andultrastructural appearances of various extracellular matrix molecules incorporatedinto three-dimensional hydrated collagen lattices." Dev Biol, 109(2): p. 347-69.
43. Knudson, W., 1996. "Tumor-associated hyaluronan. Providing an extracellularmatrix that facilitates invasion." Am JPathol, 148(6): p. 1721-6.
44. Noel, A., C. Munaut, B. Nusgens, J.M. Foidart, and C.M. Lapiere, 1992. "Thestimulation of fibroblasts' collagen synthesis by neoplastic cells is modulated bythe extracellular matrix." Matrix, 12(3): p. 213-20.
62
63
Chapter 4
Collagen Gel Transport
4.1 Introduction
Optimal therapy of tumors requires delivery of sufficient amounts of therapeutic agents to the
target cancer cells. Thus, the agent must penetrate the tumor interstitial matrix (IM), a
complex assembly of collagen, glycosaminoglycans, and proteoglycans [1]. Convection
through the tumor IM is poor due to interstitial hypertension, leaving diffusion as the major
mode of drug transport. As anti-cancer therapy focuses increasingly on larger therapeutics
such as liposomes, which are typically at least 90 nm in diameter [2, 3]and gene vectors,
which range in diameter from 20 to 300 nm [4], diffusion within the tumor IM becomes a
greater barrier to delivery [5-7].
Glycosaminoglycans (GAGs), and particularly hyaluronan (HA), are believed to play
a primary but not exclusive role in regulating fluid movement in the IM [8, 9]. However,
diffusion of large molecules in tumors has been correlated to collagen content and
organization, but not to HA content [10, 11]. These in vivo studies correlated matrix
composition to diffusive hindrance, but the biological complexity prohibited detailed analysis
of the mechanisms of transport hindrance within the tumor IM. For example, even within a
64
given tumor, Pluen et al. (2001) [11] found varying degrees of collagen organization and
heterogeneous distribution of different matrix molecules.
To overcome these problems, we measured diffusion and hydraulic conductivity in
pure collagen type I gels and compared these results directly with previously published
results for tumors of comparable collagen concentration. Furthermore, we compared the
structure of the gels with that seen in tumors. To investigate the role of collagen structure, we
compared diffusion in collagen gels and solutions of the same concentrations. The findings
presented here are important to the development of improved drug delivery strategies [12]
and to pharmaceutical applications of collagen matrices, including the design of tissue
substitutes and controlled release devices [13].
4.2 Materials and Methods
4.2.1 Experimental Techniques
Preparation of Collagen Gels. Vitrogen 100 collagen type I solution was purchased from
Collagen Corp. (Cohesion Technologies, Palo Alto, CA) at a concentration of approximately
3 mg/ml. The pH and ionic strength were adjusted by addition of NaOH (pH 7.4) and 10X
phosphate buffered saline (PBS). To concentrate the solution, the collagen was
ultracentrifuged (Beckman LC-300) at 10°C for 26-48 hours for preparation of 10-45 mg/ml
gels. Supernatant was extracted and pellets were maintained at 40 C. Collagen concentration
in the pellet was determined from the difference between pre-centrifugation and supernatant
collagen content as determined by UV spectrophotometery. Pellet concentration was
adjusted by dilution with PBS. The polymerization of highly concentrated collagen solutions
leads to the formation of fibers and filaments. To obtain a collagen gel formed predominantly
65
of fibers, 30 ml of neutralized collagen type I (0.4 mg/ml) was polymerized at 320 C for 48 h.
The collagen was centrifuged at 11,000 or 25,000 RPM for 12 or 30 min, respectively. The
collagen gel was collected on a plastic coverslip that was attached to the bottom of the
centrifuge tube. To determine the organization of the fibers and the dimensions of the gel,
second harmonic images of the collagen were obtained with a multiphoton microscope [14].
The collagen concentration estimates were based on the unpolymerized collagen volume and
the final gel volume after centrifugation.
For fluorescence recovery after photobleaching (FRAP) experiments at low collagen
concentration (2.4 mg/ml), capillary tubes were partially filled with unconcentrated collagen
solution and kept for 2 hours in a 37°C incubator. After gelation, an aqueous solution of
tracer molecules (2 mg/ml) was added to the capillary, which was then sealed and maintained
overnight at 37C to allow tracer penetration of the gel. For FRAP experiments with more
concentrated gels, the appropriate tracer molecule solution was added during adjustment of
the pellet concentration. The samples were then prepared on concave microscope slides
under coverslips, and sealed with silicone grease. Samples for permeability and visualization
experiments were prepared in Transwell (24 mm diameter; for 0.24% gels) or Snapwell (12
mm diameter, for [I1% gels) membrane-bottomed cell culture chambers (Corning Costar
Corp., Cambridge, MA) and maintained in a 37°C incubator for at least 1 hour to allow
gelation. PBS was then added to chambers to maintain hydration.
Preparation of Hyaluronan Solutions. Hyaluronic acid sodium salt isolated from rooster
comb (Sigma Chemical Co., St. Louis, MO) was dissolved by slow addition of 1X PBS (pH
7.4) for a final concentration of 4 mg/ml. Fluorescent markers at a concentration of 2 mg/ml
66
were added to the solution. The solution was stirred at 40 C for 10 hours and subsequently
stored at 40 C overnight. Samples were prepared and sealed in capillary tubes as described
above for low concentration collagen gels.
Measurement of Diffusion Coefficients. Diffusion coefficients were measured using the
FRAP with spatial Fourier analysis technique described previously [ 15, 16]. Briefly, samples
permeated with FITC-conjugated tracer molecules were placed on a microscope stage. Each
sample was subjected to brief localized 488 nm irradiation from a krypton-argon laser,
resulting in bleaching of fluorescence in the irradiated spot (radius -20 tm). Images were
recorded by CCD camera as the bleached spot recovered fluorescence. The diffusion
coefficient was extracted from the exponential time decay of the spatial Fourier transform of
fluorescence intensity. The diffusion coefficient for a given sample represents the average of
5 - 10 FRAP measurements in the sample. When not specified otherwise, three samples were
used to determine the diffusion coefficient of each molecule-gel combination. Tracer
molecules including lactalbumin (LA), bovine serum albumin (BSA), and dextrans of
molecular weights 4,400 - 2,000,000 were purchased in FITC-labeled form (Sigma).
Nonspecific IgG was purchased unlabeled (Sigma) and subsequently conjugated to FITC
using the Fluo-EX labeling kit (Molecular Probes, Portland OR).
Collagen gel samples were prepared as described above and maintained at 37°C
throughout diffusion measurements. For measurements in unassembled collagen solutions,
samples were maintained at temperatures between 12 and 17°C by supporting the sample on
a metal plate in contact with an ice pack. Gelation did not occur at these temperatures, as
detected by a lack of OD450 absorbance, indicating no turbidity or light-scattering in these
67
samples. For both gels and solutions, temperature was continuously monitored using a
thermocouple and maintained within ±1°C for all measurements on a given sample.
Measurements were also made in solutions of HA (Sigma) at pH 7.4 and 37°C.
Measurement of Darcy Permeability. Permeability was measured by monitoring flow rate
through collagen gels under hydrostatic pressure in an apparatus described previously [17].
Briefly, Transwell or Snapwell cell-culture chambers containing gel samples supported on a
highly porous membrane were fit snugly into a sample holder and maintained at 37°C. By
adjusting the height of the downstream reservoir, a constant hydrostatic pressure was applied
to force flow through the gels. The flow was directed through a thin capillary into which one
small air-bubble had been injected. Air-bubble motion was visually undetectable due to the low
flow rates through the samples. Thus, the linear velocity of the air-bubble was monitored by a
photodiode attached to a servo-null motor, which tracked the bubble for 30 min - 1 hour and
was used to determine volumetric flow rates. Hydrostatic pressures (,uP) of 5-15 cm H20
(depending on sample concentration) were imposed to create flow that resulted in the lowest
measurable bubble velocity. Low concentration (0.24%) gels were not tested as they were
not sufficiently viscous/solid. Gels at 1% were cast in Transwell chambers that fit directly
into the apparatus sample holder. Higher concentration gels (i1%) were cast in Snapwell
inserts, and a silicone ring was used to seal the space between the insert and the outer
Transwell support. All junctions between plastic and gels (collagen or silicone) were sealed
with Krazy Glue to prevent leakage. Leaky samples were quickly detected due to immediate,
rapid movement of the air-bubble and were discarded. The surface area (A) and thickness (L)
of each sample were measured. The Darcy permeability (K) of the sample was then
68
determined from the time-averaged volumetric flow rate (Q) and viscosity () using Darcy's
Law:
Q KA APQ=K--.L
Measurement of gel permeability by this method was validated using agarose gels
prepared and sealed in identical holders. Results at uP=10 cm H20 matched the values
obtained by extrapolating agarose permeability data of Johnson and Deen (1996)[ 18] to
zero pressure drop (data not shown). To determine whether the hydrostatic pressure used
in these experiments actually compacted the gels and hence produced erroneous results,
permeability was measured at two different pressures (10 cm and 5 cm H2 0). The ratio of
the two flow rates was 2.37+0.86 (N = 12), approximately equal to the expected value of 2,
suggesting that compaction was not significant.
Visualization by laser scanning microscopy using either confocal reflectance or second
harmonic generation
Samples were prepared as described in Transwell inserts and sealed under a coverslip. Confocal
reflectance microscopy was performed using a modified Bio-Rad MRC600 (Bio-Rad
Laboratories, Hercules, CA), an Olympus 1OOX 1.4 NA objective (Olympus America Inc.,
Melville, NY), and 488 nm light from a Kr-Ar laser (American Laser Corp., Salt Lake City, UT).
Reflected light from the back surfaces of the objective was attenuated using a quarter wave plate
and an analyzer at the detector [19-211. Gels were also imaged using second harmonic
generation 1141; 810 nm laser light from a mode-locked Ti:Sapphire was scanned through a
sample using a modified Bio-Rad MRC600, and second harmonic light was collected using a
405DF33 bandpass filter and an HC125-02 photomultiplier tube (Hammamatsu, Bridgewater,
NJ).
69
4.2.2 Theoretical Models
Effective Medium Model. To account for hydrodynamic interactions and relate the permeability
of a matrix to its diffusive hindrance, Phillips et al. (1989)[22] proposed the Brinkman (or
effective medium) model for a stationary sphere in imposed flow. This model was later
modified slightly to account for hindered diffusion in a medium of interest [23, 24]:
D jDo j+ Rh X + I Rh
The model relates D and K in an immobile, rigid, and homogeneous medium under the
assumption that the ratio of a molecule's diffusion coefficient in the medium and solution
(D/Do) is related to its partition coefficient between the phases. The factor alpha is a constant
of proportionality introduced to improve the quality of curve fits to this equation. The effective
medium model, when used in combination with the Carman-Kozeny model [25] below, was
found by Pluen et al. (1999)[26] to give the best correlation with pore size in agarose gel
experiments.
Carman-Kozeny Model. We estimated pore size in gels using the Carman-Kozeny model to
relate permeability, K, and pore size, a, for a gel of porosity P:
a 2
K=4k
This model treats the gel as an array of cylinders characterized by a geometric factor, k. If
the cylinders are assumed to be randomly oriented in three dimensions, the geometric factor
is given by:
k = (2k+ + k )
70
where:
2/3
(1 /)21n 1 1+4(1 /)I /
(1 /)In 1 1I1
The porosity of the gel is related to the volume fraction, r, of collagen by the equation
4 = 1 , where r is the product of the collagen concentration and the effective specific
volume of collagen (protein + bound water), previously reported as 1.89 ml/g [9].
4.3 Results
4.3.1 Visualization of collagen gels revealed varying degrees of three-
dimensional fibrillar assembly
The organization of gels was visualized using a laser-scanning microscope (in confocal
reflectance or 2HG mode). Confocal reflectance microscopy and second harmonic generation
are both performed in unfixed, hydrated samples, and are useful techniques for the
visualization of the collagen network with a spatial resolution of -0.5 m, including
distribution and bulk organization of fibers [14, 20]. No structure was detected in collagen
solutions at 12-17°C (data not shown). Figure 4.1 shows the isotropic, three-dimensional
nature of collagen gels of concentrations 0.24% and 4.5%. After gelation, low-concentration
gels (0.24%, Figure 4.1.a) show a highly fibrillar organization as seen previously in gels of
comparable concentration [20, 21]. Unlike the long fibers oriented primarily in two
dimensions seen by Friedl et al. (1997)[20], our gels show more 3-dimensionally oriented
71
fibers. At higher collagen concentrations studied (1, 3, 4.5%, Figure 4.1.b), CLSM revealed
poorly organized collagen with denser arrays of shorter fibers replacing the long fibers seen
at lower concentrations. Inhomogeneous organization of collagen gels prepared from high-
concentration solutions was also seen by transmission electron microscopy (Figure 4.2) as
dense, short-banded structures alongside unbanded filamentous structures. These
observations agree with previous reports that at concentrations higher than 0.5%, collagen
gels in vitro are formed of a mixture of banded fibrils and filamentous structures [27]. All
these gels had an apparent pore size roughly equal to or greater than the -0.5 tm spatial
resolution of the microscope.
Figure 4.1 (a and b) Confocal reflectance microscopy of 0.24% (a) and 4.5% (b) collagengels. Note the long collagen fibers in a in comparison to the shorter collagen fibers in b(bar = 10 m). (c) Second harmonic image of 0.04% collagen gel subsequentlycentrifuged to high concentration. Note the retention of long fibers as in (a) (bar = 10gim).
72
Figure 4.2. Electron microscopy of 4.5% collagen gels (a), and organization of fibrillarcollagen in the periphery of the U87 tumor (b) with a high collagen content (estimated at4.5%). In the collagen gel, compact banded collagen fibrils are find adjacent tofilamentous structures that are less organized. In the tumor periphery, compact collagenfibrils are also associated with less organized fibrils separated by larger interfibrillarspaces (Bar ::= 500 nm).
When low-concentration collagen solutions were gelated and then centrifuged to a
higher concentration, a dense mat of highly fibrillar collagen was formed (Figure 4.1.c) with
many long fibers compressed close together, with an interfibrillar spacing close to or smaller
than the -0.5 m resolution of the microscope. Note that the presence of organized structures
does not preclude the existence of unpolymerized collagen in what appear to be void spaces.
4.3.2 Collagen gels significantly hinder molecular diffusion.
Diffusion data obtained in collagen gels prepared from solutions of various concentrations
are shown in Figure 4.3.a, along with data for diffusion in saline and in HA solution. Results
of a one-sample t-test on slopes of diffusion coefficient vs. collagen concentration for
representative tracer molecules (dextran 4K, BSA, dextran 2M) verified that the diffusion
coefficients decrease significantly (p<0.05) with increasing collagen content. The
73
hydrodynamic radius, Rh, of each molecule was determined from its diffusion coefficient in
solution, Do, and the Stokes-Einstein relation, under the assumption that the molecule
assumes a spherical configuration:
kBT
6:qRl/,
where kB is Boltzmann's constant, kB=1.38 x 10-23 J/deg; T is temperature in K, and It is the
viscosity of water (0.8705 cP at T = 299 K).
For reference., correction to 37°C of the diffusion data of Shenoy and Rosenblatt [28]in 30
mg/ml succinylated collagen solution yields comparable results with D37oc=2.2 x 10-7 cm2/s
for BSA (Rh=4nm). and D37-c=2.0 x 10-7 cm2/s for 69kD dextran (Rh=6nm). The linearity of
the data sets indicates that the different classes of tracer particles (globular proteins, dextrans,
liposomes) behave similarly in our experiments, so that particle conformation and interaction
74
A BIn- 5
i I
a 0o. 0.8
0.6Z0
*" 0.40
0.2o
a . o
E
10.6
* 10-7
o
§ 10'
:3,-X1cz 10-70 00
39 0'
1 10 100 0 1 10 100
Hydrodynamic radius, RH, nm Hydrodynamic radius, RH, nm
Figure 4.3 FRAP data for diffusion coefficients of tracer molecules at 370C in saline, 0.4%HA, and 0.24. 1, 3, and 4.5% collagen gels. (a) Diffusion coefficients (D) as a function of tracermolecule hydrodynamic radius (Rh). Lines represent linear fits to data. (b) Diffusional hindrance(D/Do. where Do is diffusion coefficient in saline) as a function of tracer molecule hydrodynamicradius. Dotted lines represent least-square-error fits to effective medium model (mean ± SD).
with the matrix do not introduce experimental confounds. In Figure 4.3.b, we plot the ratio
of the diffusion coefficients obtained in gels to those in free solution as a function of the
experimental hydrodynamic radius, to more clearly illustrate the hindrance presented by the
gels. The data clearly indicate that at physiologically relevant concentrations (1-4.5%),
collagen poses a significant barrier to diffusive transport. HA solutions at 0.05% (0.5 mg/ml)
showed statistically significantly less diffusive hindrance relative to the >1% collagen
physiological gels studied here (p < 0.001 for BSA). This HA concentration used was chosen
to correspond to the HA content of the four tumors under consideration (see below). At much
higher HA concentrations (0.4%), we found significant diffusive hindrance (D/DO -0.56+0.11
for IgG, D/DO -0.27+0.04 for 2,000,000 MW Dextran), equivalent to that found in previous
studies [29] (data not shown).
4.3.3 Gel diffusion data closely match previous measurements in tumors.
We studied gels prepared from 1% (10 mg/ml), 3% (30 mg/ml), and 4.5% (45 mg/ml)
solutions specifically to allow comparison with diffusion data obtained by Netti et al. (2000)
[ 0]and Pluen et al. (2001)[10] in the following tumors implanted in mouse dorsal chambers:
human colon adenocarcinoma LS174T, mammary carcinoma MCAIV, human soft tissue
sarcoma HSTS-26T, and human glioblastoma U87. Measurements by Netti et al. of collagen
and HA content in tumors are given in Table 1. IM concentrations in these tumors are
estimated by approximating the interstitial volume fraction of the tumor as f=0.20 [30] and
assuming that (1) matrix components are distributed throughout the interstitial volume, and
(2) tissue density is -1 g/ml. Although the interstitial volume fraction will vary between
tumors, reaching up to 50% (unpublished data) and matrix component distribution is not
uniform within a given tumor, these approximations provide a rough basis for comparison.
75
Tumor Type Collagen HA Content IM Collagen IM HAContent (mg/g (mg/g wet (mg/ml IM) (mg/ml IM)
wet tissue) tissue)MCaIV 1.8 ± 0.5 0.16 0.03 9.0 2.5 0.80 0.15LS174T 1.8± 0.5 0.11 0.02 9.0 2.5 0.55 0.10U87 8.9 i 4.2 0.11 i 0.03 44.5 i 21 0.55 0.15HSTS26T 5.8 ± 1.1 0.16 ± 0.02 29 ± 5.5 0.80 ± 0.10Table 4.1 Interstitial matrix composition of human and murine tumors grown in mousedorsal chambers (based on data of Netti et al.. 2000[ 10])
To compare diffusion in gels and tumors, we also account for the tortuosity of the
interstitial space resulting from cellular obstacles, as illustrated in Figure 4.4. Diffusion along
an interstitial path with tortuosity r is reduced according to: Dl,=Dge/ - ' [31, 32].
Tortuosity is difficult to measure and exhibits inter and intratumor variation. In the absence
of detailed data on the tortuosity of the tumor types in question, the tortuosity of a well-
packed system of cells can be estimated theoretically, although such a theoretical estimate is
a possible source of error. Analytical and numerical calculations have yielded the
value =2X1/2 for two-dimensional diffusion in arrays of cells with negligible intercellular
spacing, and for diffusion in a two-dimensional isotropic pore network [33, 34]. We use this
value to adjust gel data for comparison with tumor tissue data, because the FRAP technique
measures two-dimensional radial diffusion.
Figure 4.4 Schematic of the tortuous path encountered by molecules diffusing in theinterstitial matrix between tumor cells. Tortuosity is defined as the ratio of effective pathlength to linear path length (L/Lo).
76
In Figure 4.5.a-c, we compare the adjusted gel diffusion coefficients to the data of
Pluen et al. (200 1)[1 1] in tumors of comparable collagen content. Overall, the gel and tumor
data match well, especially considering the absence of other matrix components in the gel
and the likely differences in collagen organization and distribution between tumors and gels.
The absence of other matrix components may explain the faster decrease of D with Rh in
tumors than in gels. The difference in slopes is reflected in Figure 4.5.d, which shows an
increase in the effective tortuosity,r* = JD1M/D8el with particle size. The effective tortuosity,
r*, is the value of the tortuosity necessary to completely account for the difference between
the gel and tumor diffusion coefficients, and reflects effects beyond the geometric
considerations discussed above.
4.3.4 Gelation of a collagen solution does not significantly affect its
diffusional hindrance.
Diffusion coefficients were measured in collagen samples pre- and post-gelation.
Measurements were obtained pre-gelation at 12-17°C and corrected to 370 C using the
Stokes-Einstein equation. Confocal reflectance images of collagen solutions verified a lack of
observable structure in pregelation samples (figure not shown), which was further confirmed by
optical density measurements, which were equivalent to those obtained in water. Pre- and post-
gelation diffusion coefficients were determined for collagen concentrations of 0-4.5%, from
multiple measurements within the same sample pre- and post-gelation and are shown in
Figure 4.6. No significant difference was detected between diffusion coefficients pre- and
77
post-gelation at any of the concentrations of collagen studied, after correction for temperature
and viscosity using the Stokes-Einstein relation.
a _ b104LSI74T.1ICANIV,U7,:u AdSTSA
if1- to ,u-
'' - 10;10,1 0 . .E. 1i .',. rim0
104
lo -lO
!tyddami radiusRnlj~~~~~~~~~~HdrdnmcrduR.n0..4
-
1 04 I I *
· -' f i -'"'111 " ·. . . . . =I
1 10 100 1 10 100
Hydrodynamic radius, R, nm Hydrodynamic radius, R,. nm
Figure 4.5 (a-c) Comparison of tortuosity-corrected diffusion data in gels to diffusiondata in tumors from Netti et al., 2000 and Pluen et al.. 2001 [10, 11]. Corrected diffusioncoefficient is calculated as D/T, using estimate I = sqrt(2). Comparisons are showbetween: 1% gels (open circle ) and data for LS 1 74T, MCAIV, and U87cw (closedcircle) (a): 3°%/ gels (diamond) and HSTS26T (black-lozenge ) (b); and 4.5% gels(triangle ) and U87dc (black-triangle) (c). (d) Effective tortuosity necessary to account fordiscrepancy between uncorrected gel data (Dgel) and tumor data (DIM) as a function oftracer molecule hydrodynamic radius. Values are calculated as T = (Dg,,/DlM)1/2 from
linear fits of Del (Figure 4.3.a) and DINT (Figure 4.5, a-c) data.
78
,,;d '; )
- hlgh (45i )- · MLnt-Carto cstimaw
i
I
-c~------�
.
b
10Hydrodynamic radius, R., nm
E
.t4104
% ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~P~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ i
t
11$x,,Q·~ ~ ~~~~~~~~ . ll
lrag uHydrodynamic radius, R, nm
d104.
C144.
. 4
10,0IE
10 100
Hydrodnmamic radius. R,, nm
i
t i~~~~~~~~~~~~~~~~~~~~~~~%.*# ml v\\*
IL
1 10
Hydrodynamic radius, R., nm
Figure 4.6 Comparison of diffusion data before and after incubation (gelation) at 370Cfor free solution (a), 0.24% collagen gel (b), 1% collagen gel (c), and 4.5% collagen gel(d). Preincubation data (open circles) obtained at 12-17°C and corrected to 37°C usingthe Stokes-Einstein equation, and postincubation data (closed circles) obtained at 37°Crepresent multiple measurements in the same sample. Averaged data (closed squares)obtained from multiple samples and presented earlier in Figure 4.3 are provided forreference.
4.3.5 Diffusion in gels prepared by centrifuging low-concentration gels
does not match diffusion in gels prepared directly from high-concentration
solutions
The diffusion coefficient of 2,000,000 MW dextran was measured in -20% collagen gels
prepared by centrifuging previously polymerized 0.04% gels. The measured value of 4.72 ±r~"'" ",'""" 3r""V' V'"V"Y VVI Y VU·II· · LUn U ULI 1 ·1
79
.U;W
'aU
a
-4
-;E
o
IM
_l
"e
1
10 4
104
10 4
C
10 4
10'
·a
¾*
,1 'O .
:'I
1
---
1.7 x 10-8 cm2 s -1 was significantly faster (p < 0.01) than the value of 7.8 ± 5.3 x 10-9 cm 2s - 1
measured in collagen gels prepared by direct gelation of 4.5% collagen solutions as
discussed above.
4.3.6 The effective medium model underpredicts the permeability of
collagen gels.
The Darcy permeability of 1%, 3%, and 4.5% collagen gels was determined experimentally
and also estimated from diffusion data using the effective medium model. Curve-fits of the
diffusion data to the model are shown in Figure 4.3.b and the experimental measurements
and model estimates of the Darcy permeability are compared in Figure 4.7.a. The
experimental values and model estimates agree only for the 1% gels. Above this
concentration, the experimental measurements are increasingly greater than the model
estimates, with an order of magnitude difference for the 4.5% gels. This difference in
permeability values translates into a difference in pore size as estimated by the Carman-
Kozeny model, as shown in Figure 4.7.b.
4.3.7 Measured permeability of gels does not correspond to tumor
permeability.
The permeability of gels correlated inversely with collagen content, whereas the permeability
of tumors with corresponding collagen content did not (Figure 4.8.a). To compare the
permeability measurements in collagen gels with the published measurements in tumors, the
gel measurements must be adjusted by the area fraction (fA) in a tumor slice and the
tortuosity, or increased length of the fluid path through the slice. Adjusting the gel data by
80
Ktumor=Keil f/T, where the interstitial area fraction is estimated at fA=0.2 and the theoretical
estimate =21/2 is used for the tortuosity, we obtained the data shown in Figure 4.8.b
alongside published tumor measurements. Collagen gels corrected for the absence of cells are
significantly less permeable than tumors of comparable collagen content, although direct
comparison may be complicated by the use of different permeability measurement techniques
for gels and tumors, and by intratumoral ECM heterogeneity.
81
A B1000
10' - : .
100 -.. 100 . s-
- 10i1 2 3 4 1 2 3 4
Percent collagen, g/l00ml Percent collagen, g/100ml
Figure 4.7 (a) Comparison of experimental measurements (closed circle) and model-based predictions (open circles) of Darcy permeability as a function of collagen gelconcentration,. Model predictions are obtained from application of the effective mediummodel to diffusion data (see curve fits. Figure 4.3.b). (b) Comparison of theoretical poresize predicted by Carman-Kozeny model from experimental measurements of Darcypermeability (closed circle), and from effective medium estimates of permeability fromdiffusion data (open circle).
E
'i2 1000
0
A Bfc1 -)4.
1W
; t I · ·- · · · · · · ·a·.·
't'. 0 0 0too
10
1% 3% 4.i% 1% 3% 4.5%
Percent collagen, glOOml Percent collagen, g/lOnml
Figure 4.8 (a) Comparison of Darcy permeability measurements in tumors and gels ofmeasured collagen concentrations. Confined compression measurements of K in MCaIV,LS174T (1), HSTS26T (1), and U87 tumors [10], micropipette measurements of K inLS 174T (2) tumors [5], and pressure gradients across clamped HSTS26T (2) tumor tissuesections [35]. (b) Comparison of Darcy permeability measured in tumors to those in gelswhen corrected for area fraction and tortuosity.
4.4 Discussion
4.4.1 Collagen can account for most of the diffusional hindrance measured
in tumors studied.
Collagen significantly impedes diffusion, and the extent to which it does so, when corrected for
the tortuosity of the interstitium, is consistent with diffusion data obtained in tumors of
comparable collagen content (Figure 4.5.a-c). Note that the slope of the diffusion data differs
between gel and tumor data sets. This phenomenon is also seen as an increase with molecular
size of the effective tortuosity in tissue (Figure 4.5.d), and has been observed in studies of
diffusion in the brain 321. In tumors, matrix components other than collagen could affect this
slope by differentially affecting the diffusion of small versus large molecules. Heterogeneity of
collagen structure and distribution in tumors, as shown by Pluen et al. (2001)[1 may also
82
differentially affect particles of different sizes. Thus the effective tortuosity in a tumor scales
with particle size and is heterogeneous, depending on the local tissue composition and structure.
Our results suggest that diffusion in pure collagen gels mimics that in the tumor IM
over the wide range of particle sizes studied. However, extrapolating these results to particles
with a hydrodynamic radius larger than 2,000,000 MW dextrans may not be justified. The
Carman-Kozeny estimates of pore size and the linearity of the diffusion data sets suggest that
the particles we used are smaller than the effective pore sizes of the gels studied. As particle
sizes approach the effective pore size of the media, the fine structure of the matrix is expected to
critically influence transport hindrance, and in vitro gels may no longer capture the in vivo
behavior. Rusakov and Kullmann (1998)[36] argued that large molecules comparable to the
pore size experience greater hindrance due to viscous interactions unaccounted for in tortuosity
corrections. Matrix pore size is expected to be different in gels than in tumors, where factors
such as compaction of collagen fibrils by fibroblasts [20, 37, 38] and additional IM molecules
such as decorin 139] play a role. Thus, although the agreement between the gel and tumor
measurements is surprisingly good, these results should not be extrapolated to larger particle
sizes.
4.4.2 Unassembled collagen is implicated in the diffusive hindrance of
pure collagen gels
After correction for the effect of temperature on viscosity and molecular motion, there was
no significant difference in diffusion between collagen solutions and collagen gels gelated
from equivalent concentrations. These data are consistent with those of Shenoy and
Rosenblatt (1995)128], where solutions of succinylated collagen at room temperature were
capable of significantly slowing diffusion. This fact, combined with imaged pore sizes that
appear too large (several hundreds of nanometers) to significantly hinder diffusion,
suggests that unassembled collagen in the void spaces of these gels plays a role in hindering
83
diffusion.
Note that gels formed by gelation of different concentrations of collagen are not
simply more or less concentrated versions of the same structure. The highly fibrillar
network formed from the gelation of low-concentration collagen solutions is qualitatively
different from the dense array of short fibers and partially formed structures generated upon
gelation of high-concentration collagen solutions. When gels formed from low-
concentration collagen solutions (-0.04%) are subsequently centrifuged to higher
concentrations (-20%) than the gels formed by direct gelation of high-concentration
solutions (-4.5%), the resultant gel retains its original highly fibrillar structure, but the long
fibers are significantly compacted, forming a dense mat. Not surprisingly, these qualitatively
different gels prepared by centrifugation postgelation do not reproduce the diffusive
hindrance of gels prepared by simple gelation, exhibiting a significantly higher diffusion
coefficient for 2,000,000 MW dextran. The compaction of the array of long fibers initially
formed at low concentrations could be markedly inferior to that of the dense array of short
fibers and partially formed structures generated by gelating a high-concentration solution.
Additionally, it is known that the partitioning of collagen between assembled and
unassembled states varies with the concentration at which the gel is polymerized [27]. We
conclude that the poorly assembled gels formed by simple polymerization of collagen
solutions and containing that proportion of unassembled collagen dictated by the
concentration at time of gelation are the gels that quantitatively mimic the diffusive
hindrance of tumor interstitium of equivalent collagen concentration.
Although these gels quantitatively mimic the diffusive hindrance of the tumor
interstitium, this does not mean that these gels completely reproduce the interstitial matrix at
a molecular level. Other matrix molecules are certainly present in vivo, and the structure of
collagen assembled in vivo is likely to differ from that assembled in vitro. However, the
poorly assembled gels studied here do have structural similarities to the collagen of the
tumor interstitium, which is poorly organized in comparison to normal tissue. Pluen et al.
84
(2001)1111 reported that subcutaneous U87 tumors stain positively for collagen type I in the
tumor center where only few fibrils were detected by EM visualization, whereas the
periphery of U87 and other tumor types showed a high density of collagen fibrils. These
results suggest that unassembled molecules between the fibers of the interstitial matrix can
influence the diffusion of macromolecules in vivo just as they seem to do in vitro. In pure
collagen type I gels, these unassembled molecules can only be collagen type I, while in vivo,
these unassembled molecules may include other matrix molecules, such as nonfibrillar
collagen type I, other collagen types, or HA.
4.4.3 At concentrations relevant to the tumors studied, pure collagen is a
major diffusive barrier and offers more hindrance than pure hyaluronan
The diffusion data attest to the ability of collagen gels at concentrations comparable to those
of the tumor IM to significantly hinder diffusive transport (Figure 4.3). In contrast, HA
solutions at concentrations comparable to the tumors analyzed here (0.05%) pose a weaker
barrier to diffusion. For 3% and 4.5% collagen gels, the diffusive barrier offered by HA
(i.e., D/Do) is far less than that offered by collagen, suggesting that in tumors with these
collagen concentrations (e.g., HSTS26T and U87), collagen alone can account for the
diffusive hindrance in the tumor. For the lowest collagen concentration gels (1%), the
barrier offered by HA is over half the barrier offered by collagen, suggesting that in tumors
with this collagen concentration (e.g., LS 174T) HA may have some influence on diffusive
hindrance.
This finding does not apply to tissues with higher HA content, including the tumor
spheroids studied by Davies et al. (2002)[40] and other GAG-rich tissues, such as cartilage.
Furthermore, the pure HA solutions do not replicate possible in vivo interactions between
different species of matrix molecules (e.g., Turley et al., 1985[41]), which may affect transport
properties.
85
4.4.4 Collagen gels pose a greater diffusive than hydraulic barrier
Data collected from several organs have indicated that permeability is inversely
correlated to collagen content [91. We have found the same trend in collagen gels.
However, the permeability values in tumors did not match the data in collagen gels
quantitatively, nor did they show the qualitative inverse correlation with collagen content.
Furthermore, when the data for collagen gels were adjusted for area fraction and
tortuosity in tumors, the permeability was higher in tumors than in gels of comparable
concentration. The differences in permeability could be due partially to measurement
techniques. Even within tumors, the confined compression technique used by Netti et al.
(2000)1 101 predicted significantly higher hydraulic conductivity compared to the
micropipette approach [5] and clamp methods [35]. The lack of correlation between
collagen and permeability observed by Netti et al. in tumors suggests a more important
contribution from other matrix molecules.
Estimates of gel permeability based on the effective medium model matched
experimental measurements of permeability only for 1% collagen gels (Figure 4.7). At
greater concentrations, the diffusion-based effective medium model values increasingly
underestimated the true permeability. In contrast, the model was reported to be accurate for
agarose gels 126], and underestimated diffusion coefficients in various other gels, a deviation
qualitatively opposite to that observed here [24]. In general, discrepancies between gel
measurements and effective medium model predictions may result from model assumptions
of fiber rigidity, immobility, and homogeneity. Furthermore, the effective medium model
empirically relates two fundamentally different modes of transport (convection and
86
diffusion), which can be differentially regulated. The accuracy of the effective medium
prediction at low collagen concentration and the increasing discrepancy at higher collagen
concentrations may also indicate that high concentrations of poorly organized collagen pose a
greater barrier to diffusion than to convection. This argument is also supported by the
observation that diffusional hindrance in tumors correlates with collagen content 111],
whereas the measured permeability of tumors does not [10].
4.5 Conclusions
In conclusion, our data show that collagen at physiological concentrations presents a major
barrier to molecular diffusion, especially for larger particles. Furthermore, theoretical
correction of gel diffusion data for the effects of in vivo tortuosity yielded good agreement
with in vivo measurements in tumors of comparable collagen concentration. The diffusive
hindrance data combined with imaging of the gels and permeability measurements suggest
that unassembled collagen in the void spaces of the gel plays a role in hindering diffusion. In
vivo, this role may be played by unassembled collagen or other matrix molecules. These
findings support our hypothesis that collagen is a major contributor to diffusive hindrance in
tumors. In addition, it suggests that in vitro gel models can be used to investigate diffusion in
tissues, with theoretical correction for issues such as tortuosity providing the necessary
bridge between the in vivo and in vitro measurements. This work has important implications
for drug delivery in tumors and for tissue engineering, where transport in collagen-based
tissue replacements or scaffolds is an important design consideration. Furthermore,
interfering with collagen synthesis or reducing collagen content may improve drug delivery
to tumors 1421.
87
4.6 References
I. Alberts, B., D. Bray, J. Lewis, M. Raff, K. Roberts, and J. Watson, ExtracellularMatrix of Animals, in Molecular Biology of the Cell, B. Alberts, D. Bray, J.Lewis, M. Raff, K. Roberts, and J. Watson, Editor. 1994, Garland Publishing:New York. p. 971-995.
2. Gabizon, A., D. Goren, R. Cohen, and Y. Barenholz, 1998. "Development ofliposomal anthracyclines: from basics to clinical applications." J Control Release,53(1-3): p. 275-9.
3. Kulkarni, S.B., G.V. Betageri, and M. Singh, 1995. "Factors affectingmicroencapsulation of drugs in liposomes." J Microencapsul, 12(3): p. 229-46.
4. Costantini, L.C., J.C. Bakowska, X.O. Breakefield, and 0. Isacson, 2000. "Genetherapy in the CNS." Gene Ther, 7(2): p. 93-109.
5. Boucher, Y., C. Brekken, P.A. Netti, L.T. Baxter, and R.K. Jain, 1998."Intratumoral infusion of fluid: estimation of hydraulic conductivity andimplications for the delivery of therapeutic agents." British Journal of Cancer,78(11): p. 1442-14448.
6. Jain, R.K., 1999. "Transport of molecules, particles, and cells in solid tumors."Annual Review of Biomedical Engineering, 01: p. 241-263.
7. Netti, P.A., et al., 1999. "Enhancement of fluid filtration across tumor vessels:implication for delivery of macromolecules." Proc Natl Acad Sci U S A, 96(6): p.3137--42.
8. Gribbon, P.M., A. Maroudas, K.H. Parker, and C.P. Winlove, Water and solutetransport in the extracellular matrix: physical principles and macromoleculardeterminants, in Connective tissue biology : integration and reductionism, R.K.Reed and K. Rubin, Editors. 1998, Portland: London; Miami. p. 95-124.
9. Levick, J.R., 1987. "Flow through interstitium and other fibrous matrices." Q JExp Physiol, 72(4): p. 409-37.
10. Netti, P.A., D.A. Berk, M.A. Swartz, A.J. Grodzinsky, and R.K. Jain, 2000. "Roleof extracellular matrix assembly in interstitial transport in solid tumors." CancerRes, 60(9): p. 2497-503.
11. Pluen, A., et al., 2001. "Role of tumor-host interactions in interstitial diffusion ofmacromolecules: cranial vs. subcutaneous tumors." Proceedings of the NationalAcademy of Sciences of the United States of America, 98(8): p. 4628-33.
12. Jain, R.K., 1998. "The next frontier of molecular medicine: delivery oftherapeutics." Nat Med, 4(6): p. 655-7.
13. Sano, A., T. Hojo, M. Maeda, and K. Fujioka, 1998. "Protein release fromcollagen matrices." Adv Drug Deliv Rev, 31(3): p. 247-266.
14. Williams, R.M., W.R. Zipfel, and W.W. Webb, 2001. "Multiphoton microscopyin biological research." Curr Opin Chem Biol, 5(5): p. 603-8.
15. Berk, D.A., F. Yuan, M. Leunig, and R.K. Jain, 1993. "Fluorescencephotobleaching with spatial Fourier analysis: Measurement of diffusion in light-scattering media." Biophysical Journal, 62: p. 2428-36.
88
16. Berk, D.A., F. Yuan, M. Leunig, and R.K. Jain, 1997. "Direct in vivomeasurement of targeted binding in a human tumor xenograft." Proceedings ofthe National Academy of Sciences, USA, 95: p. 1785-90.
17. Chang, Y.S., et al., 2000. "Effect of vascular endothelial growth factor on culturedendothelial cell monolayer transport properties." Microvasc Res, 59(2): p. 265-77.
18. Johnson, E.M. and W.M. Deen, 1996. "Hydraulic permeability of agarose gels."AIChE Journal, 42(5): p. 1220-1224.
19. Cheng, P.C., and R. G. Summers, Image contrast in confocal microscopy, inHandbook of Biological Confocal Microscopy, J.B. Pawley, Editor. 1990, PlenumPress: New York. p. 170-196.
20. Friedl, P., K. Maaser, C.E. Klein, B. Niggemann, G. Krohne, and K.S. Zanker,1997. "Migration of highly aggressive MV3 melanoma cells in 3-dimensionalcollagen lattices results in local matrix reorganization and shedding of 2 and 131integrins and CD44.." Cancer Research, 57: p. 2061-2070.
21. Brightman, A.O., B.P. Rajwa, J.E. Sturgis, M.E. McCallister, J.P. Robinson, andS.L. Voytik-Harbin, 2000. "Time-lapse confocal reflection microscopy ofcollagen fibrillogenesis and extracellular matrix assembly in vitro." Biopolymers,54(3):: p. 222-34.
22. Phillips, R.J., W.M. Deen, and J.F. Brady, 1989. "Hindered transport of sphericalmacro-molecules in fibrous membranes and gels." AIChE Journal, 35(11): p.1761- 1769.
23. Solomentsev, Y.E. and J.L. Anderson, 1996. "Rotation of a sphere in Brinkmanfluids." Physics of Fluids, 8(4): p. 1119.
24. Phillips, R.J., 2000. "A hydrodynamic model for hindered diffusion of proteinsand micelles in hydrogels." Biophys J, 79(6): p. 3350-3.
25. Carman, P.C., 1937. "Fluid flow through granular beds." Trans. Inst. Chem. Eng.,15: p. 150-166.
26. Pluen, A., P.A. Netti, R.K. Jain, and D.A. Berk, 1999. "Diffusion ofmacromolecules in agarose gels: comparison of linear and globularconfigurations." Biophys J, 77(1): p. 542-52.
27. Williams, B.R., R.A. Gelman, D.C. Poppke, and K.A. Piez, 1978. "Collagen fibrilformation. Optimal in vitro conditions and preliminary kinetic results." JBiolChem, 253(18): p. 6578-85.
28. Shenoy, V. and J. Rosenblatt, 1995. "Diffusion of macromolecules in colagen andhyaluronic acid, rigid-rod - flexible polymer, composite matrices."Macromolecules, 28: p. 8751-58.
29. De Smedt, S.C., A. Lauwers, J. Demeester, Y. Engelborghs, G. De Mey, and M.Du, 1994. "Structural information on hyaluronic acid solutions as studied byproble diffusion experiments." Macromolecules, 27: p. 141-146.
30. Jain, R.K., 1987. "Transport of molecules in the tumor interstitium: a review."Cancer Res, 47(12): p. 3039-51.
31. Nicholson, C. and J.M. Phillips, 1981. "Ion diffusion modified by tortuosity andvolume fraction in the extracellular microenvironment of the rat cerebellum." JPhysiol (Lond), 321: p. 225-57.
32. Nicholson, C. and E. Sykova, 1998. "Extracellular space structure revealed bydiffusion analysis." Trends in Neuroscience, 21: p. 207-215.
89
33. Blum, J.J., G. Lawler, M. Reed, and I. Shin, 1989. "Effect of cytoskeletalgeometry on intracellular diffusion." Biophys J, 56(5): p. 995-1005.
34. Chen, K.C. and C. Nicholson, 2000. "Changes in brain cell shape create residualextracellular space volume and explain tortuosity behavior during osmoticchallenge." Proc Natl Acad Sci U S A, 97(15): p. 8306-11.
35. Griffon-Etienne, G., Y. Boucher, C. Brekken, H.D. Suit, and R.K. Jain, 1999."Taxane-induced apoptosis decompresses blood vessels and lowers interstitialfluid pressure in solid tumors: clinical implications." Cancer Res, 59(15): p. 3776-82.
36. Rusakov, D.A. and D.M. Kullmann, 1998. "Geometric and viscous components ofthe tortuosity of the extracellular space in the brain. " Proc Natl Acad Sci U S A,95(15): p. 8975-80.
37. Guidry, C. and F. Grinnell, 1987. "Heparin modulates the organization ofhydrated collagen gels and inhibits gel contraction by fibroblasts." Journal of CellBiology, 104: p. 1097-1103.
38. Huang-Lee, L.L.H., J.H. Wu, and M.E. Nimni, 1994. "Effects of hyaluronan oncollagen fibrillar matrix contraction by fibroblasts." Journal of BiomedicalMaterials Research, 28: p. 123-132.
39. Pins, G.D., D.L. Christiansen, R. Patel, and F.H. Silver, 1997. "Self-assembly ofcollagen fibers. Influence of fibrillar alignment and decorin on mechanicalproperties." Biophys J, 73(4): p. 2164-72.
40. Davies, C.d.L., D. Berk, A. Pluen, and R.K. Jain, 2000. "Correlation betweendiffusion of IgG and extracellular matrix in rhabdomyosarcomas growing astumors in dorsal chambers or multicellular spheroids." in preparation.
41. Turley, E.A., C.A. Erickson, and R.P. Tucker, 1985. "The retention andultrastructural appearances of various extracellular matrix molecules incorporatedinto three-dimensional hydrated collagen lattices." Dev Biol, 109(2): p. 347-69.
42. McKee, T.D., A. Pluen, Y. Boucher, S. Ramanujan, E. N. Unemori, B. Seed, andR. K. Jain, 2001. "Relaxin increases the transport of large molecules in highcollagen content tumors." Proceedings of the American Association for CancerResearch, 42: p. 30.
90
Chapter 5
Dynamic Imaging of Collagen in vivo
using Second Harmonic Generation
5.1 Introduction
Collagen content and structure are key determinants of macromolecular transport in
tumors[1-3]. We propose that the penetration of therapeutic molecules could be estimated
based on the correlation between collagen content and diffusive transport[l]. We also
propose that drug penetration in tumors could be improved by administering agents that
modify the matrix and increase diffusion. Testing these hypotheses would require a routine,
noninvasive technique to monitor the collagen content and structure of tumors in vivo.
Collagen is known to induce SHG[4-7]. Here, we obtain high-resolution images of fibrillar
collagen in tumors in vivo using SHG. SHG imaging offers many advantages: SHG is an
intrinsic signal and does not require the addition of extrinsic dyes; the signal and background
are better than those of autofluorescence imaging; nonlinear excitation permits three-
dimensional resolution in vivo[8], and the SHG emission wavelength scales with the
excitation wavelength, allowing spectral separation between signals from fibrillar collagen
and other fluorophores.
91
5.2 Materials and Methods
5.2.1 Surgery and Imaging
Mouse mammary adenocarcinoma MCaIV and human colon adenocarcinoma LS 1 74T,
melanoma MU89 and soft tissue sarcoma HSTS26T were grown in dorsal skinfold
chambers in severe combined immunodeficient mice[ 1]. All experiments were done with
approval of the Institutional Animal Care and Use Committee. Images were obtained
using a custom-built multiphoton laser scanning microscope[9, 10]. Images of the SHG
signal were obtained using 810 nm excitation and 405DF30 emission filters (Chroma).
Spectra were generated using a focal spectrum analyzer based on a multiphoton laser
scanning microscope (12-nm spectral resolution). The short wavelength signal from the
fibrillar structures in tumor in vivo was located at exactly half the excitation wavelength
and had an extremely narrow bandwidth. However, at longer wavelengths, there was a
broad autofluorescence peak generated mainly by punctate structures. The narrow
bandwidth of the short wavelength peak, the fact that it was located at half the excitation
wavelength and the fact that it shifted center wavelength when the excitation wavelength
shifted were all consistent with SHG.
5.2.2 In vitro SHG and autofluorescence signals
Collagen I gels (Cohesion Technologies) were prepared at concentrations that reproduced
tumor matrix (15-65 mg/ml)[3]. Coverslips were coated with mouse ultrapure laminin
and mouse collagen IV (BD Biosciences) at a concentration of 10 mug/cm2 and were
92
dried. Coverslips were coated with Matrigel (BD Biosciences) at a concentration of 12
mg/ml using the manufacturer's thick gel protocol. The mean SHG signal at 810 nm
excitation was determined by generating a series of five image stacks of each gel with a
times5 objective, each stack consisting of five 2-mm times 1.33-mm images spaced 20
mum apart, using identical imaging conditions (equal laser power, photomultiplier tube
voltage and so on). Maximum intensity projections of each stack were generated and
mean pixel counts were calculated. For the matrix materials that formed only thin layers
on the coverslip, optical sections were generated at the optimum focus, determined using
770 nm excitation and a 400-nm to 500-nm filter, allowing detection of faint
autofluorescence.
5.2.3 In vivo SHG signal of different tumor types
The mean SHG signal of five specimens of three tumor types was calculated by acquiring
multiple three-dimensional image stacks as described above. Maximum intensity
projections of each stack were generated and a two-dimensional montage of the visible
tumor surface was formed (Figure 5.1 .a). An outline was drawn around the tumor area
and the mean pixel count in that area was calculated.
5.2.4 Collagen quantification with immunostaining and elastin staining
Tumors (six LS 174T; five MU89) were grown in the dorsal skinfold chamber until they
reached approximately 3-4 mm in diameter. Excised tumors were fixed in 4%
paraformaldehyde, embedded in optimum cutting temperature compound (Sakura
Finetek) and cut into sections 10 mum in thickness oriented perpendicular to the surface.
93
Collagen I staining and quantification were done as described before[2]. This generated
41 images for LS 174T and 52 for MU89. To identify elastin, adjacent sections from an
MU89 tumor embedded in paraffin were prepared. One section was stained with the
Weigert stain and the adjacent section was treated with a bath of xylene and a series of
ethanol baths at decreasing concentrations to remove paraffin and was used for spectral
analysis.
5.2.5 Diffusion measurements
Diffusion coefficients were measured by FRAP[1-3] seven times in each of four LS174T
and five Mu89 samples, and four to eight times in control and relaxin-treated HSTS26T
tumors.
5.2.6 Enzyme dynamics
The dynamic action of collagenase I was monitored in vivo by removal of the dorsal
skinfold chamber coverslip and pipetting of -100 gll Clostridium collagenase (0%, 1%
and 10% solutions in saline; Sigma). Consecutive SHG stacks were obtained every 5 min
using a X20 objective, 0.5 numerical aperture H20 lens, each stack consisting of 30
images spaced 5 glm apart, covering an area of 3.03 x105 im2. Relaxin was delivered by
Alzet 1002 osmotic pumps (Durect) loaded with 100 gl recombinant human relaxin (5
mg/ml) in mice bearing 14-day-old HSTS26T tumors. SHG stacks were generated every
2-3 d for 2 weeks. Maximum-intensity projections were generated and mean pixel counts
in the area of the image were calculated to yield the average SHG signal. Average fiber
lengths were determined by binarizing maximum-intensity projections of the first image
94
stack in a time series with a threshold chosen to set 90% of the image equal to zero. The
resultant image was inspected to choose the five brightest fibers. The three-dimensional
end-to-end length of each fiber was measured in the original image by determining the
shortest distance between a fiber's ends using Scion Image software (Scion). An 'end' was
defined as the point at which the fiber was no longer visible or bifurcated. A fiber's length
therefore changed if it became cleaved, its ends shortened, its branching structure
changed or it curled into a loop. Fiber lengths were measured in all stacks until their
location could no longer be recognized. Hence, drastically changed morphology in a
region prevented fiber tracking in that region, thereby underestimating average changes
in fiber length of a highly dynamic population. Lines were drawn around regions of
interest encompassing selected fibers to measure the average pixel counts of the
individual fibers.
5.2.7 Statistics
Statistical significance was determined using Student's t-test. Equality of variances was
tested using an F-test or analysis of variance. All values were expressed as mean + s.e.m.
95
5.3 Results
5.3.1 Validation of collagen imaging by SHG in tumors
We validated the utility of SHG imaging in tumors by implanting Mu89 human
melanomas in the dorsal skinfold chamber of immunodeficient mice. We generated high-
contrast images of fibrillar structures (Figure 5.1 .a,b), whose emission spectra indicated
that the fibrillar structures were imaged by SHG[4] (Fig. 5.1 .c).
Figure 5.1. SHG imaging of tumors in vivo a) Second-harmonic signal in a Mu89melanoma grown in the dorsal skinfold chamber of a severe combined immunodeficientmouse, image to left. This image was a montage of 12 separate images, each of whichwas a maximum intensity projection of 5 images obtained at 20 [m steps. The imageshown is 6.6 mm in width. (b) Second-harmonic signal with highlighted vessels. Vesselswere highlighted with an intravenous injection of 0.1 ml tetramethylrhodamine-dextran(10 mg/ml; red pseudocolor). SHG signal. green pseudocolor. There was nocolocalization of SHG signal with the borders of blood vessels. The image shown is 275pim in width.
96
0.4
0.3
0.2
0.1
Tumor Spectrum0.5
en
._c
EM,
.74
.9Uj
410 430 450 470 490
Wavelength, nm
Figure 5.1. SHG imaging of tumors in vivo (c) Average spectra of light generated with810 nm excitation of an approximately 0.25-mm 2 region of a Mu89 melanoma in thedorsal skinfold chamber of an immunodeficient mouse.
To determine the origin of the SHG signal in tumors, we did four studies. First, we
imaged gels made of collagen I or Matrigel (a mouse tumor basement membrane extract)
and layers of collagen IV and laminin deposited on coverslips, all at tumor
concentrations. Using identical SHG conditions, we obtained distinct SHG images of a
fibrillar meshwork from the collagen I gel (data not shown), but no signal from the other
preparations (average SHG pixel count/SHG pixel count of collagen fibers 9.3 x10 3).
Second, we noted SHG structures colocalizing with fluorescently labeled antibody to
collagen I in tumor sections. A highly fibrillar subpopulation of antibody-labeled
structures produced SHG (Fig. 5.2.a).
97
I I II e-810 nm excitation
VI
---- 7
iii
II 1,P
f
C
7C"��- -I__
TM
MMM"
k
i
n v
370 390 510 530
Figure 5.2. SHG images of fibrillar collagen I in tumors Second-harmonic signalcolocalized with staining with antibody to collagen I in a tumor section 10 pm inthickness. Left, antibody to collagen I conjugated with FITC (red pseudocolor) and 4,6-diamidino-2-phenylindole (green pseudocolor); right, SHG (red pseudocolor) and 4,6-diamidino-2-phenylindole (green pseudocolor). The SHG signal appeared as narrowfibers that formed a subpopulation of the antibody-stained structures. Each image was amaximum intensity projection of five images spaced 5 plm apart, and are 275 plm inwidth. The surface of the tumor facing the 'window' of the dorsal skinfold chamber wasto the right in each image.
98
._-.-__
C
E.._
._
m
0
EW
0.8
0.6
0.4
0.2
370 390
I o Elastin ring, 810 nm excitation I
410 430 450 470 490 510 530
Wavelength, nm
Figure 5.2 SHG images of fibrillar collagen I in tumors (b) Elastin did not generatesignificant SHG in tumors. Left, fixed section of a Mu89 mouse melanoma stained withWeigert stain and imaged with transmitted light. Dark brown, elastin; red arrow, thinelastin band around the peritumor arteriole. Right, spectrum of the selected band in anadjacent unstained section. Elastin did not generate significant SHG at 405 nm, half theexcita
r lgure .zL. aHnr images o iDrlllar collagen 1 in tumors (c) SHG was better thanautofluorescence in imaging fibrillar collagen in tumors. Left, image generated with SHG(81 0-nm excitation), showing fibrillar collagen with high signal and low background.Right. image generated at the same location with 770-nm excitation and 400- to 500-nmemission (optimum for autofluorescence from collagen), dominated by a bright punctatebackground from nonfibrillar autofluorescent molecules.
99
I IiI
i I i iI i �I iI I i iI I I i- iI 11I ii II
iIi I
II---- - L--- --i
i i
i I---- I
I I / �I I
___I__J_.___-.__ i.__~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
l
�-----
1
_-- _- -_
I I I I IU In
Third, we noted blood vessels in tumors and normal tissue in dorsal skinfold chambers
but did not find SHG signal forming a sheath around blood vessels (Fig. 5.1 .b). A sheath
of mainly nonfibrillar collagen IV forms the basement membrane of capillaries, post-
capillary venules and arterioles[ 11]. Around peritumor arterioles in fixed sections, we
noted an autofluorescent ring whose excitation and emission peaks and physiological
distribution were consistent with those of the elastin layer. The elastin layer did not
generate detectable SHG, however (Fig. 5.2.b).
Fourth, we demonstrated the advantage of SHG imaging in tumors in vivo by comparing
SHG and autofluorescence of collagen I (ref. [12]; Fig. 5.2.c). In collagen gels, distinct
autofluorescence imaging of collagen is possible[5], but in tumors in vivo the situation is
much more complicated. We noted bright punctate autofluorescence from other intrinsic
fluorophores in the tissue, which overwhelmed the autofluorescence from collagen fibers
(Fig. 5.2.c), whereas collagen fibers were distinctly visible in the SHG image of the same
region.
These observations indicated that SHG signal in tumors in vivo readily imaged a highly
fibrillar subpopulation of collagen I and did not image elastin, collagen IV or other
basement membrane components. In vivo images of collagen in tumors are detected using
the same objective lens used to provide excitation light (epidetection mode). A relatively
small fraction of SHG light is expected to be transmitted back to the objective lens[4];
most SHG light is expected to be transmitted in the direction of the excitation light[8].
100
Unfortunately, imaging with transmitted light in tumors in vivo is extremely inefficient
because tumors are more than 1 mm in thickness and, depending on the surgical
preparation, can have the animal's skin, other tissues or whole body on the other side of
the tumor.
5.3.2 Relationship of tumor SHG to diffusive transport
The total collagen content of tumors can serve as a predictor of diffusive transport[ 1]. We
hypothesized that the SHG signal from fibrillar collagen could be used to easily and
noninvasively assess the penetrability of tumors. SHG signal depends on the orientation
of the fiber relative to the plane of polarization of the laser. It also depends on the local
spacing of fibers, scaling linearly with fiber spacing for widely dispersed fibers and
quadratically with fiber spacing when several fibers are contained in one focal volume[8,
13]. Tumor images showed a random distribution of fiber orientations and a wide variety
of interfiber spacings in tumors (Fig. 5.1.a,b). Hence, it was difficult to predict how the
average SHG signal of a tumor would scale with total collagen content. To determine
this, we made collagen I gels with a range of concentrations corresponding to the tumor
models studied here (see Methods)[1, 3]. We collected the average SHG signal over wide
areas of the gel to obtain an average over the various fiber spacings and orientations (see
Methods and Fig. 5.3.a). The average SHG signal scaled with approximately the first
power of the total collagen content (power-law exponent, 0.91). The relationship between
gel SHG signal and concentration did not depend on the numerical apertures of the range
of lenses used here (numerical apertures, 0.15-0.5; P > 0.2; n = 10).
101
-- y = 0.84443* xA(0.91021) R= 0.99466
.zZ1Z11._
,, I
30 40 50 60 7 D
Collagen Gel Concentration, mg/ml Mu89 LS1 74T MCalV
Figure 5.3 Dependence of SHG signal on tumor type.
Second-harmonic signal scaled linearly with collagen gel concentration. Collagen gels wereprepared that reproduced tumor diffusive hindrance over a range of concentrations equal to thetumors studied here. The average SHG signal generated from these gels scaled with the firstpower of the collagen concentration (1 8-24 measurements at each concentration; best-fit powerlaw exponent = 0.91; Ri = 0.995). (b) Histogram of average SHG signals, which varied withtumor type. NMu89 melanomas, which have high collagen content and low macromoleculardiffusion, had a high SHG signal. MCaIV mammary adenocarcinomas and LS174T colonadenocarcinornas, which have identical, low collagen content and identical, highmacromolecular diffusion, had identical low SHG signal. Although the difference between themean SHG signal of LS 1 74T and Mu89 was statistically significant (P < 0.05), the differencebetween MCalV and Mu89 was just below significance (P = 0.064), and the difference betweenLS 174T and MCaIV was not statistically significant.
We next measured the SHG signal from tumors in an identical way. We examined three
tumor types, MCaIV, LS174T and Mu89, because of known differences in their collagen
content[l. 2]. The SHG signal varied with tumor type, with the signal of MCaIV being
about equal to that of LS 174T. which was less than that of Mu89 (Fig. 5.3.b). We then
compared this result to published values of collagen content or to measurements of
collagen by immunohistochemical staining of tissue sections. We found diffusion
102
An
.
-C
,) U-
10-
CelbL.
2[5.
10 2n
- e - t ---':-"- Iij . _ _ 75
lT
*'i"t_-I - - rTI _i l _er
c L2-4 _
--_ -- -rl--L.-
· l i I
, , 1 ~; - : I-c
-CL
l l l-;i ic
-t
I l . . . .
I I
-- --
coefficients in published reports or measured using fluorescence recovery after
photobleaching[ 1, 2] (Table 1). In both cases (LS174T:MCaIV and LS174T:Mu89), the
ratio of SHG signals was not statistically different from the ratio of collagen
quantification (P > 0.05) and correlated with the relative diffusive hindrance in these
tumor types (MCaIV being about equal to LS 174T, which was less than Mu89). These
results showed that in three diverse tumor types, our ability to detect tumor collagen with
SHG allowed us to simply and rapidly quantify relative collagen content and estimate
drug delivery characteristics noninvasively, obviating the need for biopsies, sectioning or
staining.
Tumor types SHG ratio Collagen ratio Diffusion
coefficient ratio
LS174T:MCaIV 0.75 + 0.46 1.0 0.22 0.96 ± 0.21
(hydroxyproline (FRAP
content from measurements from
published reports)' published reports)'
LS174T:Mu89 0.29 ±0.16 0.41 0.073 1.45 ± 0.33
(immunohistochemical (FRAP
staining measurements
Table 5.1. Comparison of SHG ratios with collagen and diffusion coefficient ratios
103
5.3.3 Dynamic imaging of collagen modification
We hypothesized that SHG allowed dynamic imaging of collagen modification in vivo.
To test this, we applied bacterial collagenase to Mu89 melanomas and imaged the tumors
(Fig. 5.4.a). The SHG signal of collagen fibers disappeared with a single exponential
decay consistent with Michaelis-Menten kinetics. The exponential decay times were 3.7 x
103 min, 1.2 x 102 min, and 9.9 min for 0%, 1% and 10% collagenase, respectively. The
mean collagen fiber length did not change significantly (P > 0.05), consistent with the
possibility that a free enzyme attacked multiple sites along a fiber, 'dissolving' it instead
of cleaving the fiber at one location. Although the unknown constant of proportionality
between local in vivo SHG signal and local in vivo collagen concentration prevented an
exact calculation of the kinetic constants for the enzymatic reaction, the exponential
decay time provided direct measurement of relative enzymatic efficacy. The exponential
decay time for SHG loss scaled linearly with collagenase concentration, at least up to
10% collagenase, indicating that these concentrations were in the linear range of the
dose-response curve. The considerable loss of SHG signal from tumors after collagenase
application was consistent with the substantial (approximately twofold) increase in
diffusion coefficient measured in tumors treated in an identical way[ 1].
104
105
I i I I I I
y 1.0016 * A(-0.00S2382x R 0.98432
-- y = 1.0038 eA(-0.10086x) R 0.99095
- - y = 1.0466' e^(0.00027271x) R= 0.24034
C_ C~o- . ~j--48.4 "-4- r Or-
1%10%
. conte!
, , , I I ,
10
(A
M 1-
I.
(w
c 0.10)Ulm
C3
0.0
0.01-20 0 20 40 60 80 100 120
Time (min)Figure 5.4. Effect of collagenase and relaxin on tumor collagen dynamics.(a) SHG noninvasively quantified enzymatic degradation of tumor collagen in vivo.Different concentrations of collagenase were used to degrade collagen of Mu89melanomas in the dorsal skinfold chamber. Loss of SHG signal, monitored afterapplication, showed a single exponential decay time that varied linearly with collagenaseconcentration. No change in fiber length occurred while the fibers were visible.
- �----
_!_
-
L
|1 i _
] J | , , ;
_ | . |.
Figure 5.4. Effect of collagenase and relaxin on tumor collagen dynamics.(b) SHG dynamically imaged the effects of chronic relaxin treatment. Five maximum-
intensity projections of the same region of collagen fibers in a Mu89 tumor were obtainedevery 3 d after the beginning of relaxin treatment, showing the effect of chronic relaxintreatment on the structure and brightness of preexisting fibers. The original width of eachregion was 70 pin. A representative region from a placebo treated mouse is shown belowthe relaxin treated image. for comparison.
106
_
_-
I .x
1
C 0.8
"O
0 0.6N
E 0.4L.
z0.2
n
-2 0 2 4 6 8 10 12 14 16 18
Time (days)Figure 5.4. Effect of collagenase and relaxin on tumor collagen dynamics.(c) Chronic relaxin treatment altered the characteristic length of collagen fibers. Theaverage lengths of collagen fibers in the relaxin group (8 mice, 77 fibers) and in controls(8 mice, 103 fibers) were monitored for 12 d during relaxin treatment. The average fiberlength decreased in each case, and relaxin treatment induced a significantly greaterdecrease than no relaxin (control; placebo; P < 0.05).
5.3.4 Relaxin enhances transport in tumors
Application of bacterial collagenase was 'proof of principle' that SHG imaging allowed
dynamic monitoring of collagen modification in vivo. We next used this technique to test
relaxin, an agent that can be used in a clinical setting[14]. The nontoxic hormone relaxin107
4 1)
is secreted by women during pregnancy to induce upregulation of various matrix-
degrading enzymes[ 15] known as matrix metalloproteinases. Consequently, we
hypothesized that chronic relaxin treatment would degrade the tumor matrix and improve
macromolecular diffusion in tumors.
To test this, we implanted relaxin-loaded osmotic pumps in immunodeficient mice
bearing 2-week-old human HSTS26T sarcomas in dorsal skinfold chambers. We then
obtained images of tumors for 12 d using SHG. The average SHG signal of both the
relaxin-treated and control mice did not change significantly from the first to the last day
of the experiment (P > 0.05; n = 8 for both). Furthermore, the average SHG signal of
relaxin-treated mice on day 12 was not significantly different from that of control mice;
the mean pixel count (normalized to 1 at day 0) was 1.77 + 0.28 for treated mice and 1.35
± 0.16 for control mice (P > 0.05; n = 8 for both).
Average lengths of preexisting collagen fibers underwent a statistically significant
decrease from the first day to the last day of the experiment in both groups (P < 0.05; n =
8 for both; Fig. 5.4.b,c). This decrease was significantly greater in the relaxin-treated
mice than in control mice (P < 0.05; n = 8 for both).
Finally, the SHG signal generated by individual pre-existing fibers decreased in the
relaxin-treated mice (P < 0.05; n = 14), whereas in the control mice the SHG signal did
not change significantly (P > 0.05, n = 14). The SHG signal in the relaxin-treated mice on
108
day 12 was significantly smaller than that of control mice (P < 0.05; 0.66 ± 0.07 versus
1.3 0.10; n = 14 fibers each, normalized to 1 at day 0).
We treated a cohort of HSTS26T-bearing mice with relaxin and assessed the evolution of
diffusive transport with fluorescence recovery after photobleaching (FRAP) after
treatment. We found statistically significant increases in the diffusion coefficients of IgG
and dextran 2,000,000 MW: IgG, 13.5 ± 5.6 x 10-8 cm2/s in relaxin-treated (n = 6) and 7.5
4: 2.5 x 10-8 cm2 /s in control (n = 4), P < 0.005; dextran 2,000,000 MW, 5.7 ± 1.5 x 10-9
cm 2/s in relaxin-treated (n = 6) and 2.0 ± 1.0 times 10-9 cm2/s in control (n = 5), P < 0.05.
5.4 Discussion
These data showed that over 2 weeks in control mice, neither the SHG signal averaged
over large regions of the tumor nor the brightness of individual pre-existing fibers
changed significantly. However, the average length of pre-existing fibers decreased
slightly. This represented the normal dynamic equilibrium of collagen turnover in a
tumor, in which old fibers are degraded through shortening and cleavage but the overall
level of collagen remains the same because of the appearance of new fibers. When mice
were treated with relaxin for 2 weeks, the average SHG signal remained the same as in
control mice, but the brightness and average length of individual pre-existing fibers
decreased significantly more than in control mice. Furthermore, the diffusion coefficients
of probe molecules increased, indicating that at a scale below optical resolution, the
matrix was loosened and hindrance decreased. In vivo, matrix-degrading enzymes (matrix
109
metalloproteinases) consist of both free and membrane-bound forms[ 16]. The membrane-
bound forms are bound to cells such as fibroblasts. The decrease in brightness of pre-
existing fibers indicated an increase in activity of free matrix metalloproteinases,
analogous to administration of free bacterial collagenase. The decrease in end-to-end
length of individual fibers, which was caused in part by localized cleavage of entire fibers
and loss of material from their visible ends, was consistent with fibroblast upregulation of
matrix metalloproteinases, which would locally cleave the fiber adjacent to the
fibroblasts. The decrease in end-to-end length was also caused by alterations in fiber
curvature and branching, consistent with an overall increase in the activity of fibroblasts,
which modify their surrounding matrix by tugging and repositioning fibers[ 17]. The fact
that the dynamic processes of matrix degradation and alteration were apparently
upregulated by relaxin administration, combined with the maintenance of the equilibrium
SHG signal, indicated that matrix production processes may also have been upregulated
in tumors. This has been seen in the uteri of mice[18] and rats[19] during relaxin
treatment. The new tumor matrix created by the acceleration of both degradation and
production, while the same equilibrium level of collagen was maintained, had a more
porous structure and hence weaker diffusive hindrance.
In conclusion, SHG imaging in tumors allowed visualization of fibrillar collagen
structure in vivo; this may lead to simple noninvasive estimation of drug accessibility in
tumors. Furthermore, SHG imaging allowed the noninvasive measurement of enzymatic
modification of tumor collagen. Finally, relaxin chronically decreased diffusive
hindrance in tumors, and we used SHG imaging to determine how this hormone altered
110
the tumor matrix, offering insight into the mechanisms by which it decreased diffusive
hindrance. This could provide a useful new technology to evaluate strategies for altering
the tumor extracellular matrix, to increase or decrease diffusive resistance, as studied
here. Although the device at present is suitable for tumors that grow in optically
accessible locations, with the availability of small hand-held laser-scanning
microscopesl[20] this approach could be adapted for use in clinics in the near future.
111
5.5 References
1. Netti, P.A., D.A. Berk, M.A. Swartz, A.J. Grodzinsky, and R.K. Jain, 2000. "Roleof extracellular matrix assembly in interstitial transport in solid tumors." CancerRes, 60(9): p. 2497-503.
2. Pluen, A., et al., 2001. "Role of tumor-host interactions in interstitial diffusion ofmacromolecules: cranial vs. subcutaneous tumors." Proceedings of the NationalAcademy of Sciences of the United States of America, 98(8): p. 4628-33.
3. Ramanujan, S., A. Pluen, T.D. McKee, E.B. Brown, Y. Boucher, and R.K. Jain,2002. "Diffusion and convection in collagen gels: implications for transport in thetumor interstitium." Biophys J, 83(3): p. 1650-60.
4. Williams, R.M., W.R. Zipfel, and W.W. Webb, 2001. "Multiphoton microscopyin biological research." Curr Opin Chem Biol, 5(5): p. 603-8.
5. Zoumi, A., A. Yeh, and B.J. Tromberg, 2002. "Imaging cells and extracellularmatrix in vivo by using second-harmonic generation and two-photon excitedfluorescence." Proc Natl Acad Sci U SA, 99(17): p. 11014-9.
6. Campagnola, P.J., A.C. Millard, M. Terasaki, P.E. Hoppe, C.J. Malone, and W.A.Mohler, 2002. "Three-dimensional high-resolution second-harmonic generationimaging of endogenous structural proteins in biological tissues." Biophys J, 82(1Pt 1): p. 493-508.
7. Freund, I., M. Deutsch, and A. Sprecher, 1986. "Connective tissue polarity.Optical second-harmonic microscopy, crossed-beam summation, and small-anglescattering in rat-tail tendon." Biophys J, 50(4): p. 693-712.
8. Moreaux, L., O. Sandre, and J. Mertz, 2000. "Membrane imaging by second-harmonic generation microscopy." Journal of the Optical Society of America B:Optical Physics, 17(10): p. 1685-1694.
9. Brown, E.B., R.B. Campbell, Y. Tsuzuki, L. Xu, P. Carmeliet, D. Fukumura, andR.K. .lain, 2001. "In vivo measurement of gene expression, angiogenesis andphysiological function in tumors using multiphoton laser scanning microscopy."Nat Med, 7(7): p. 864-8.
10. Jain, R.K., L.L. Munn, and D. Fukumura, 2002. "Dissecting tumourpathophysiology using intravital microscopy." Nat Rev Cancer, 2(4): p. 266-76.
11. Fleischmajer, R., J.S. Perlish, E.D. MacDonald, 2nd, A. Schechter, A.D.Murdoch, R.V. Iozzo, and Y. Yamada, 1998. "There is binding of collagen IV tobeta 1 integrin during early skin basement membrane assembly." Ann N YAcadSci, 857: p. 212-27.
12. Agarwal, A., M.L. Coleno, V.P. Wallace, W.Y. Wu, C.H. Sun, B.J. Tromberg,and S.C. George, 2001. "Two-photon laser scanning microscopy of epithelial cell-modulated collagen density in engineered human lung tissue." Tissue Eng, 7(2): p.191-202.
13. Stoller, P., K.M. Reiser, P.M. Celliers, and A.M. Rubenchik, 2002. "Polarization-modulated second harmonic generation in collagen." Biophys J, 82(6): p. 3330-42.
112
14. Seibold, J.R., et al., 2000. "Recombinant human relaxin in the treatment ofscleroderma. A randomized, double-blind, placebo-controlled trial." Ann InternMed, 132(11): p. 871-9.
15. Unemori, E.N. and E.P. Amento, 1990. "Relaxin Modulates Synthesis andSecretion of Procollagenase and Collagen By Human Dermal Fibroblasts."Journal of Biological Chemistry, 265(18): p. 10681-10685.
16. Egeblad, M. and Z. Werb, 2002. "New functions for the matrix metalloproteinasesin cancer progression." Nat Rev Cancer, 2(3): p. 161-74.
17. Grinnell, F., 2000. "Fibroblast-collagen-matrix contraction: growth-factorsignalling and mechanical loading." Trends Cell Biol, 10(9): p. 362-5.
18. Bylander, J.E., E.H. Frieden, and W.C. Adams, 1987. "Effects of porcine relaxinsupon uterine hypertrophy and protein metabolism in mice." Proc Soc Exp BiolMed, 185(1): p. 76-80.
19. Frieden, E.H. and W.C. Adams, 1985. "Stimulation of rat uterine collagensynthesis by relaxin." Proc Soc Exp Biol Med, 180(1): p. 39-43.
20. Helmchen, F., M.S. Fee, D.W. Tank, and W. Denk, 2001. "A miniature head-mounted two-photon microscope. high-resolution brain imaging in freely movinganimals." Neuron, 31(6): p. 903-12.
113
Chapter 6
Improving Gene Therapy
6.1 Introduction
Oncolytic vectors are mutant viruses that replicate in tumor cells preferentially over
normal cells and have shown promise in the treatment of various preclinical tumor
modelsl 1, 2]. Oncolytic viral therapy employs a novel method of tumor destruction
mediated by viral replication and selective lysis of cancer cells[3, 41. The creation of
more oncolytic virus by infected tumor cells and infectious spread from one cell to the
next result in improved performance over more passive forms of therapeutic delivery[5,
6]. Early phase human clinical trials of G207, an oncolytic HSV vector, for the treatment
of recurrent malignant glioblastomas have demonstrated both safety and efficacy[7].
However, the inability to efficiently propagate throughout the tumor and infect cells
distant from the injection site limits the capacity of oncolytic viruses to achieve
consistent therapeutic responses[8].
Viral therapeutics are orders of magnitude larger than traditional
chemotherapeutics, and thus may encounter transport limitations not associated with
those drugs. Quantitative studies have yet to be performed to determine the interstitial
barriers to viral transport. In this study, we show that fibrillar collagen, previously found
114
to be the major barrier to the transport of large molecules in the tumor interstitium[9- 11 ,
also limits viral distribution within tumors. Direct degradation of the fibrillar collagen
network using collagenase improves viral distribution and leads to the improved
oncolytic viral therapy of tumors.
6.2 Materials and Methods
6.2.1 Cell culture
E5 and E26 cells (from Dr. Neal DeLuca, University of Pittsburgh[ 12]) were maintained
in DMEM growth medium supplemented with 200 tM L-glutamine (Invitrogen), 100
units/ml penicillin and 100 tg/ml streptomycin (Sigma, St. Louis, MO), and 10% fetal
bovine serum (Sigma) under standard cell culture conditions.
6.2.2 Viral vectors
The HSV-1 recombinant viruses used in this study were the replication defective mutant
Gal4 (ICP4-, lacZ+; from Dr. Neal DeLuca[13]) and MGH2 (ICP6-, Oc4.5', eGFP+; from
Dr. E. Antonio Chiocca and Dr. Yoshi Saeki; Tyminski et al., unpublished data). MGH2
is a replication conditional virus that is attenuated by deletion of two nonessential viral
functions, the ICP6 gene encoding the large subunit of ribonucleotide reductase and
634.5, a product known to overcome impaired viral protein synthesis in neurons by
inducing the dephosphorylation of eIf2c[14]. These deletions impair virus replication in
non-dividing cells, but allow virus replication in tumor cells.
115
Gal4 and MGH2 stocks were propagated in E5 and E26 cells, respectively, which
supply the HSV- 1 ICP4 protein (E5) or HSV ICP4 and ICP27 proteins (E26) in trans. To
obtain GFP-labelled HSV particles, E5 and E26 cells were transfected with a plasmid
(pVP 16-GFP[15]) encoding the fusion protein VP16-GFP and infected with Gal4 and
MGH2, respectively. After purification and concentration the number of DNA-containing
particles in each virus preparation was quantified by transduction assay counting lacZ-
positive cells for Gal4 and GFP-positive cells for MGH2 virus.
6.2.3 Dorsal skinfold window preparation
Human melanoma Mu89 cells were grown in dorsal skinfold chambers in severe
combined immunodeficient mice as described previously[16]. All animal experiments
were done with the approval of the Institutional Animal Care and Use Committee.
6.2.4 Injection and imaging of labeled vectors
For dorsal chamber tumor studies, HSV vectors labeled with VP16-GFP were mixed with
either 0.2 Ftg/'tl bacterial collagenase (Sigma) or PBS, to a final titer of 106 t.u./l. One
microliter of virus was loaded into a glass micropipette with a beveled tip measuring 25-
30 microns in diameter. The micropipette was connected to a Harvard syringe pump
apparatus and the fluid was infused into the tumor at constant pressure (- I/l0min).
Images were obtained using a custom built multiphoton laser scanning microscope[17.
Images of the SHG signal[10] were obtained using a 435DF30 emission filter, and of the
GFP using a 525DF100 emission filter, with input excitation at 880 nm, and a high pass
475 dichroic filter to separate the two signals.
116
6.2.5 Image analysis
Multiphoton images were analyzed to determine the relative localization of collagen (red
pixels) and injected particles (viral vectors, green pixels; dextran, blue pixels). Pixel
intensities were spatially compared along lines drawn perpendicular to the periphery of
virus containing regions. For both viral vectors and dextran, analysis was performed for 3
injections in separate tumors, and for 20 images taken at different depths in each tumor.
As the spatial variable, the pixel intensities were plotted as a function of the relative
distance from the observed interface with fibrillar collagen.
The area of viral vector distribution following intratumoral injection was
quantified as follows. A maximum intensity projection of 10 images was performed to
create a single image for each injection site. An outline of the area of viral distribution
was drawn on these images and the area was calculated with imaging software (ImageJ).
6.2.6 Flank tumor growth delay
Human melanoma Mu89 cells were implanted subcutaneously in the flank of SCID mice,
and allowed to reach an average volume of 100 mm3, at which point mice were
randomized into 4 groups (6-7 animals per group) and given 10 dtl intratumoral injections
of either PBS; 1.0 tg collagenase; 106 t.u. MGH2; or a mixture 106 t.u. MGH2 and 1.0 itg
collagenase. A second equivalent injection was performed two days later. Mice were
examined daily and tumor volume measured every 2-3 days. Tumor volume was
calculated according to the formula volume = jTAB2/6, where A and B are the maximum
and minimum diameters, respectively. Mice died from the natural progression of their
117
disease process, or were euthanized when (a) tumor mass exceeded a size of 2,000 mm3
or (b) premorbid behavior (imminent death from lethargy, respiration depression, and/or
severe weight loss) was noted.
6.2.7 Immunostaining
Two days after viral and/or collagenase treatment of tumors, mice were euthanized and
tumors were removed and snap frozen in liquid nitrogen. Frozen tissue was embedded in
OCT and sectioned such that a 10 tm section was kept every 300 tm throughout the
tumor volume. Tissue sections were stained for tegument and envelope virion proteins
using anti-HSV primary antibody (AB1125, Chemicon International) and as secondary
anti-rabbit Alexa-conjugated antibody, counterstained for nuclei (DAPI), and imaged for
GFP expression and fluorescent markers using confocal microscopy.
6.2.8 Statistical analysis
Data are expressed as mean + SEM. Statistical significance between groups was
determined by an unpaired Student t-test. Statistical analysis was performed using
StatView 4.51 software (Abacus Concepts Inc.). Differences were considered statistically
significant for P < 0.05.
118
6.3 Results
6.3.1 Distribution of virion particles is hindered by collagen rich regions
In order to quantify virus distribution following direct injection, one microliter containing
106 viral transducing units (t.u.) of VP16-GFP labeled non-replicative HSV-1 (Gal4;
kindly provided by Dr. Neal DeLuca[l131) virions (150nm in diameter) were directly
injected into Mu89 human melanomas grown in dorsal skin windows in SCID mice.
Multiphoton imaging performed in vivo approximately 30 minutes following injection
revealed that viral particles distributed mostly near the site of injection. Second harmonic
generation (SHG) was used to simultaneously image fibrillar collagen at the injection
site. Viral particles distributed primarily within collagen free areas of the tumor, with
limited penetration into collagen rich regions (Figure 6.1.a). To quantify viral
penetration, pixel counts of collagen (SHG) and virus (GFP) were measured in one image
slice along lines drawn perpendicular to the periphery of virus containing regions (an
example line is shown in Figure 6.1.a). Averaging over many lines revealed an inverse
correlation between collagen and viral particles, such that a sharp decrease in virus signal
corresponded to an increase in the amount of fibrillar collagen present (Figure 6.1.c).
While collagen has previously been shown to hinder the interstitial transport of
macromolecules[9, 11], nearly complete exclusion to this extent has not been seen. In
order to directly compare viral distribution in the interstitial matrix with another
macromolecular tracer, Cascade-blue conjugated 2x106 molecular weight dextran tracer
molecules (RH - 20 nm) were co-injected with HSV vectors. While the dextran
penetrated into collagen rich regions, viral particles were excluded (Figure 6. 1.b,d).
119
C -n p,.tretIonl 9 d t copeaeun@In "150
15'
sa5
a
' 9
2I I"e
I 731
nI,,~135
-t .S-b -n a l o 0diotsm ftom tce. pm -S 2S so 73dmc cm Ittmo, m
Figure 6.1. Viral vector distribution following intratumoral injection. (a,b) Multiphotonimages of Mu89 melanomas 30 minutes after intratumoral injection of VP16-GFP labeledGal4 vectors (green), either alone (a) or with Cascade blue-conjugated dextran (blue) (b).Second-harmonic generation (SHG) signal denotes fibrillar collagen (red pseudocolor). HSVvectors localized in extracellular spaces around individual tumor cells and were excluded byareas of intense SHG signal. In contrast, the smaller dextran tracer penetrated regions rich infibrillar collagen.. (c,d) Relative localization of collagen and injected particles determined bypixel analysis. Spatial comparison of pixel intensities was performed for collagen (red pixels)and either viral particles (green pixels; c) or dextran (blue pixels; d). Analysis was performedalong lines drawn perpendicular to the border of SHG signal, and mean values plotted. Arepresentative image and line are shown for each case (a,b). Collagen and viral localization inthe tumor are anti-correlative
120
I
.l d
E 7-.+05
ua 6.E+05 -
*.; 5.E+05 -L.
r- 4.E+05 -
'. 3.E+05 -
C _,z :I r ._ Z.t+Ub I
., 1.E+05 -C
_ O.E+00 -
F *
T
X Control injection Collagenase injection(n=10) (n=4)
Figure 6.1 (e) Multiphoton image of viral vector distribution following co-injection withcollagenase. This area of distribution is outlined in blue for a representative injection of viruswith collagnease, to the left. The image to the right is a representative image for vectorsinjected alone outlined in blue. (f) Comparison of the area of viral vector distributionfollowing intratumoral injection. Areas measured from a maximum intensity projection of 10images taken -3() minutes following injection. Collagenase co-injection resulted in a 3-foldincrease in the area of viral distribution (P < 0.05).
121
r i
6.3.2 Disruption of the collagen network results in improved virus
distribution and gene expression
In order to test whether disruption of the collagen network could improve the penetration
of viral particles into the tumor, the viral vectors were co-injected with bacterial
collagenase (0.2 tg/tl). Collagenase increased the area of viral distribution by 3-fold
compared to control injections (Figure 6. 1.f). Rather than distributing into restricted
regions bounded by fibrillar collagen, as in the case of injection without collagenase,
vectors spread more uniformly from the injection site upon collagenase treatment (Figure
6. I .e, shown next to representative control injection).
6.3.3 Collagenase enhances the efficacy of oncolytic viral therapy
The oncolytic virus MGH2 (kindly provided by Drs. E. Antonio Chiocca and Yoshi
Saeki, Ohio State University) has the same backbone as G207[14, 18], but carries GFP as
a reporter gene instead of lacZ. MGH2 replicates in Mu89 melanoma cells in culture,
resulting in GFP expression and cell lysis within 24-48 hours (data not shown). To test
whether fibrillar collagen would also limit the spread of actively replicating viral particles
within the tumor, 106 t.u. MGH2 were injected into Mu89 tumors grown in dorsal
window chambers in SCID mice and GFP expression was imaged (Figure 6.2). As seen
previously, the initial distribution of viral particles was limited by fibrillar collagen (data
not shown). Twenty-four hours later, the area of infection was localized in only a small
proportion (-15%) of the entire tumor mass, corresponding to the site of injection (Figure
6.2.b). Even 11 days following the initial injection of MGH2, viral vectors could not
122
penetrate sufficiently to infect the entire tumor mass (Figure 6.2.a). No significant
treatment response was observed in any of the tumors injected with MGH2 alone.
In contrast, when the same amount of oncolytic virus was co-injected with
collagenase (0.2 tg/tl), the initial viral distribution was greater relative to virus alone
(data not shown), and this translated into an improved area of tumor cell infection (Figure
6.2.b). Therapeutic response was observed in all four collagenase co-treated tumors, with
nearly complete regression in two cases (Figure 6.2.b). Due to the time limitations in
using this particular tumor window model, we were not able to monitor the mice for a
longer period of time to follow tumor regression.
123
Figure 6.2. Effect of collagenase on oncolytic viral therapy. Mu89 melanomasimplanted in the dorsal skinfold chamber of SCID mice were treated with the oncolyticvector MGH2 in combination with PBS (left panels) or collagenase (right panels). (a)Fluorescent and b) brightfield and fluorescent microscopic images of tumors following atimecourse after injection with oncolytic virus. Infection of tumor cells was detected byexpression of the reporter gene GFP (encoded by the virus). Co-injection of MGH2 andcollagenase resulted in a greater distribution of infected cells, relative to injection ofMGH2 alone. At 11 days, nearly complete regression of the tumor (as evidenced byabsence of tumor vasculature) was achieved with MGH2 and collagenase co-injection,while no significant change in volume was observed with MGH2 treatment alone.Extent of tumor outlined in blue to guide the eye.
124
We then tested if the co-injection of collagenase and MGH2 would increase the
therapeutic efficacy of MGH2 over longer time intervals. When Mu89 tumors growing in
the flank of SCID mice reached 100 mm3, they were injected intratumorally with either 1
ytg collagenase, 106 t.u. MGH2, or both collagenase and MGH2, followed by similar
injections two days later. As a control, tumors were injected with PBS alone. The time for
the tumor to reach ten times the initial volume (mean ± SEM) was compared for each
group (Figure 6.3). If the tumors failed to reach ten times the initial volume due to
morbidity, the time to their last measurement was used as a conservative approximation
of growth delay. Both collagenase treatment alone (19 ± 1 days) and MGH2 injection
alone (27 ± 3 days) had no significant effect on tumor growth compared to PBS control
(24 ± 3 days) (P > 0.05, both cases). In the group treated with MGH2 alone, one tumor
showed marked regression, but recurred after 10 days. However, co-injection of MGH2
with collagenase (50 ± 9 days) significantly delayed the growth of tumors compared to all
other treatment groups (P < 0.05 for all cases). In this group two out of seven tumors
failed to grow to 200 mm3 even 60 days after treatment and apparently complete
regression of the tumor was observed in another animal, although it recurred 20 days
later.
125
Flank Tumor Growth4 A
12
0.
U-N
U)L0E
I-
10)
A 8
L 6U
4
2
00 10 20 30 40 50 60
Days
Figure 6.3. Effect of collagenase on MGH2-induced tumor growth delay. Tumorswere grown subcutaneously in the hind flank of SCID mice. When tumors reach -100mm3, animals were divided into four groups (n = 6-7) and treated twice (day 0 and day 2)with 10 tl of PBS (green), collagenase (0. 1 g/tl) (black), MGH2 (106 t.u.) in PBS(blue), or MGH2 (106 t.u.) and collagenase (0.1 Itg/Ltl) in PBS (red). Tumor volumeswere measured every 2-3 days and the time to reach a given volume was expressed asmean ± SEM for each group. The time to reach ten times the initial volume wascompared. There was no significant difference between PBS (23 ± 3 days) and eithercollagenase treatment alone (19 + 1 days) or MGH2 alone (27 + 3 days) (P > 0.05 forboth cases). However, MGH2 and collagenase co-treatment induced a significant tumorgrowth delay (50 + 8 days) relative to all other groups (P < 0.05 for all cases).
126
6.3.4 Improved efficacy is due to initial improved distribution of viral
particles
In order to investigate the mechanism of improved efficacy, tumors were treated as
before with MGH2, either alone or with collagenase, and analyzed two days after the
second injection. To determine viral distribution, tissue sections were stained for
structural virion proteins, counterstained for nuclei (DAPI), and imaged for GFP
expression using confocal microscopy. As expected, the immunostaining revealed the
presence of HSV virion particles within and surrounding cells expressing GFP (Figure
6.4.a,b). In tumors treated with MGH2 alone, virion particles and infected cells were
distributed only along the 500 /m width needle track (data not shown). In contrast, for
MGH2 and collagenase treatment, a diffuse distribution of infected cells was observed
throughout the entire tumor section, spanning an area of up to 3 x 7 mm (data not shown).
Imaging of sections at later times showed that virus was able to continue to spread within
the tumor, but not within collagen containing areas at the edge of the tumor (data not
shown).
127
Figure 6.4. Immunostaining analysis of tumor cell infection. Representative tissuesections of Mu89 flank tumors injected with either MGH2 alone (a) or MGH2 andcollagenase (b), were labeled with anti-HSV antibodies. GFP expression from MGH2-infected cells (green), HSV proteins detected with Alexa-conjugated secondary Ab (red),and nuclear stain DAPI (blue) are shown. Slightly more cells are stained for viralparticles than those expressing GFP. This is probably due to either the delay betweeninfection and expression or the overwhelming signal from new viral proteins synthesizedin infected cells. In the absence of collagenase treatment, viral particles and infected cellsare localized in dense clusters at the site of injection. Collagenase co-injection results ininfection of tumor cells dispersed throughout the tumor.
6.4 Discussion
The development of strategies to improve both the initial vector distribution within
tumnors and the ability of these vectors to propagate through the entire tumor mass is
critical to the success of oncolytic viral therapy[l 1. Our results demonstrate the important
role that fibrillar collagen plays in regulating both of these processes, and ultimately in
determining therapeutic efficacy. We have previously shown that fibrillar collagen is the
major barrier to the transport of macromolecules through the extracellular matrix of
tumors[9], an effect that increases with larger particle size[l 1. In the present study we
128
observed that whereas smaller tracers (2x106 MW dextran, RH-20 nm, as well as IgG,
RH-5 nm, data not shown) distributed relatively uniformly within the tumor following
injection, the vast majority of HSV virions (150 nm in diameter) were located only in
collagen-free areas. The absence of virus penetration into fibrillar collagen rich areas
suggests that: the effective pore size cutoff of the collagen network is smaller than the size
of viral particles. This finding has far-reaching implications: while many tumor models in
rodents consist of fast growing tumors that lack a significant collagen network, many
tumors in humans show extensive stromal infiltration with extracellular matrix and
collagen deposition[19, 20]. Thus preclinical models must take in to account the
deposition of extracellular matrix in order to properly mimic human disease.
Oncolytic vectors are thought to overcome some of the delivery issues faced by
non-replicating viral vectors through their ability to propagate on site in tumors (thereby
amplifying the input dose) and spread from tumor cell to tumor cell. However, we found
that the collagen network restricted the distribution of the intratumorally injected
oncolytic vector MGH2 and limited the area of tumor cell infection. Even several weeks
after treatment, tumor cell infection remained confined to a small area and the tumor
continued to grow (Figure 6.2.a, data not shown). Co-injection of MGH2 with
collagenase resulted in a broad, uniform distribution of viral particles and infected cells
(Figure 6.2.b), with substantial tumor regression and improved efficacy in a flank tumor
model. The dispersed distribution of virus following collagenase co-injection can lead to
improved therapeutic outcome in several ways: (1) the increased initial virion distribution
improves the chance that viral vectors can penetrate all regions of the tumor; (2) the
occurrence of multiple infections of the same tumor cell decreases, while the number of
129
distinct tumor cell infections increases; and (3) once the virus replicates and lyses the cell
it has infected, it has access to a greater number of previously uninfected neighboring
cells. All together, these processes can lead to increased oncolytic activity, as shown
schematically in Figure 6.5.
Control Injection
Initial virusdistributionSee figure la, le
Initial cellinfectionSee figure 2c, 2d
First viralreplicationSee figure 4a, 4b
Secondary cellinfection
i ...7 .Figure 6.5. A representative model of improvement in oncolytic viral distributionand tumor cell infection by collagenase treatment. Following direct intratumorinjection, viral spread (red area) is limited by fibrillar collagen (red lines) and results in acluster of infected cells (light green). The collagen network also restricts the distributionof subsequent viral progeny and tumor cell infection beyond the initial injection site isnot achieved. In contrast, co-injection of virus with collagenase results in a more diffusedistribution of viral particles and a greater number of initially infected cells (light green).Viral particles released by these cells have greater access to neighboring uninfected cells.This process results in more widespread secondary infection (dark green) and ultimatelygreater therapeutic efficacy.
130
Researchers have developed other methods to try to overcome the limited
distribution of oncolytic vectors in tumors[21]. One such method is the use of multiple
injections, either on successive days or with fractionation of the initial dose at multiple
sitesl22, 23]. However, in the absence of extracellular matrix-modification, the viral
distribution at each individual injection site would still be limited by collagen fibers.
Indeed, a phase II trial with an oncolytic adenoviral vector showed limited improvement
in efficacy even with daily injections that included fractionation[241. As an alternative to
increasing viral distribution, combination therapy with either radiation or chemotherapy
is often employed to improve oncolytic activity[25, 26]. Collagenase treatment is
compatible with combination therapy and could further improve efficacy. Indeed, this
may be a complementary therapy for combination with radiation, which can induce
fibrosis and lead to an increase in interstitial collagen[27].
In the present study, however, it was also noted that intratumoral haemorrhages
occurred in many of the tumors treated with collagenase. While bleeding from
collagenase treatment alone did not affect tumor growth in either the dorsal chamber
(data not shown) or flank models, this phenomenon demonstrates the complex
interactions between the extracellular matrix and cells within the tumor, including both
tumor cells and host endothelial cells. Furthermore, it is possible that collagenase
treatment of tumors may increase the risk of metastasis. The development of this matrix-
modulating technique for clinical applications may require the use of specific matrix
proteases, such as MMP-8, which degrades collagen and decreases metastasis[28, 291.
131
In conclusion, we determined that even with the on-site generation of viral
particles provided by the replication-competent nature of oncolytic viruses, there still
exist barriers, namely the collagen network, that are sufficient to prevent viral spread
throughout the entire tumor. Disruption of the collagen network within tumors leads to an
increase in both initial vector distribution and subsequent propagation of virus through
the tumor mass, resulting in significantly improved therapeutic outcome. This is a
powerful result since it applies to all viral particles and gene delivery stratagies, as well
as imaging systems involving the use of nano-particulates[30] - as all run into the
problem of insufficient delivery to the target cells. Furthermore, the method of
modification can be versatile: any technique that decreases the collagen content of tumors
would be useful. These findings open the way for increasing the potency of gene therapy
in cancer and other diseases.
132
6.5 References
1. Everts, B. and H.G. van der Poel, 2005. "Replication-selective oncolytic virusesin the treatment of cancer." Cancer Gene Ther, 12(2): p. 141-61.
2. Martuza, R.L., A. Malick, J.M. Markert, K.L. Ruffner, and D.M. Coen, 1991."Experimental therapy of human glioma by means of a genetically engineeredvirus mutant." Science, 252(5007): p. 854-6.
3. Kirn, D., R.L. Martuza, and J. Zwiebel, 2001. "Replication-selective virotherapyfor cancer: Biological principles, risk management and future directions." NatMed, 7(7): p. 781-7.
4. Chiocca, E.A., 2002. "Oncolytic viruses." Nat Rev Cancer, 2(12): p. 938-50.5. Lee, C.T., et al., 2004. "Combination therapy with conditionally replicating
adenovirus and replication defective adenovirus." Cancer Res, 64(18): p. 6660-5.6. Ichikawa, T. and E.A. Chiocca, 2001. "Comparative analyses of transgene
delivery and expression in tumors inoculated with a replication-conditional or -defective viral vector." Cancer Res, 61(14): p. 5336-9.
7. Markert, J.M., et al., 2000. "Conditionally replicating herpes simplex virusmutant, G207 for the treatment of malignant glioma: results of a phase I trial."Gene Ther, 7(10): p. 867-74.
8. Harrison, D., H. Sauthoff, S. Heitner, J. Jagirdar, W.N. Rom, and J.G. Hay, 2001."Wild-type adenovirus decreases tumor xenograft growth, but despite viralpersistence complete tumor responses are rarely achieved--deletion of the viralElb-19-kD gene increases the viral oncolytic effect." Hum Gene Ther, 12(10): p.1323-32.
9. Netti, P.A., D.A. Berk, M.A. Swartz, A.J. Grodzinsky, and R.K. Jain, 2000. "Roleof extracellular matrix assembly in interstitial transport in solid tumors." CancerRes, 60(9): p. 2497-503.
10. Brown, E., T. McKee, E. diTomaso, A. Pluen, B. Seed, Y. Boucher, and R.K.Jain, 2003. "Dynamic imaging of collagen and its modulation in tumors in vivousing second-harmonic generation." Nat Med, 9(6): p. 796-800.
11. Pluen, A., et al., 2001. "Role of tumor-host interactions in interstitial diffusion ofmacromolecules: cranial vs. subcutaneous tumors." Proc Natl Acad Sci U S A,98(8): p. 4628-33.
12. Samaniego, L.A., A.L. Webb, and N.A. DeLuca, 1995. "Functional interactionsbetween herpes simplex virus immediate-early proteins during infection: geneexpression as a consequence of ICP27 and different domains of ICP4. " J Virol,69(9): p. 5705-15.
13. Grondin, B. and N. DeLuca, 2000. "Herpes simplex virus type 1 ICP4 promotestranscription preinitiation complex formation by enhancing the binding of TFIIDto DNA. " J Virol, 74(24): p. 11504-10.
14. Kramm, C.M., et al., 1997. "Therapeutic efficiency and safety of a second-generation replication-conditional HSV 1 vector for brain tumor gene therapy."Hum Gene Ther, 8(17): p. 2057-68.
15. Bearer, E.L., X.O. Breakefield, D. Schuback, T.S. Reese, and J.H. LaVail, 2000."Retrograde axonal transport of herpes simplex virus: evidence for a single
133
mechanism and a role for tegument. " Proc Natl Acad Sci U S A, 97(14): p. 8146-50.
16. Leunig, M., F. Yuan, M.D. Menger, Y. Boucher, A.E. Goetz, K. Messmer, andR.K. Jain, 1992. "Angiogenesis, microvascular architecture, microhemodynamics,and interstitial fluid pressure during early growth of human adenocarcinomaLS 174T in SCID mice." Cancer Res, 52(23): p. 6553-60.
17. Brown, E.B., R.B. Campbell, Y. Tsuzuki, L. Xu, P. Carmeliet, D. Fukumura, andR.K. Jain, 2001. "In vivo measurement of gene expression, angiogenesis andphysiological function in tumors using multiphoton laser scanning microscopy."Nat Med, 7(7): p. 864-8.
18. Mineta, T., S.D. Rabkin, T. Yazaki, W.D. Hunter, and R.L. Martuza, 1995."Attenuated multi-mutated herpes simplex virus-i for the treatment of malignantgliomas. " Nat Med, 1(9): p. 938-43.
19. Elenbaas, B. and R.A. Weinberg, 2001. "Heterotypic signaling between epithelialtumor cells and fibroblasts in carcinoma formation." Exp Cell Res, 264(1): p. 169-84.
20. Martinez-Hernandez, A., 1988. "The extracellular matrix and neoplasia. " LabInvest, 58(6): p. 609-12.
21. Jia, W. and Q. Zhou, 2005. "Viral vectors for cancer gene therapy: viraldissemination and tumor targeting." Curr Gene Ther, 5(1): p. 133-42.
22. Kirn, D., 2001. "Clinical research results with d11520 (Onyx-015), a replication-selective adenovirus for the treatment of cancer: what have we learned?" GeneTher, 8(2): p. 89-98.
23. Currier, M.A., L.C. Adams, Y.Y. Mahller, and T.P. Cripe, 2005. "Widespreadintratumoral virus distribution with fractionated injection enables local control oflarge human rhabdomyosarcoma xenografts by oncolytic herpes simplex viruses."Cancer Gene Ther.
24. Nemunaitis, J., et al., 2001. "Phase II trial of intratumoral administration ofONYX-015, a replication-selective adenovirus, in patients with refractory headand neck: cancer." J Clin Oncol, 19(2): p. 289-98.
25. Khuri, F.R., et al., 2000. "a controlled trial of intratumoral ONYX-015, aselectively-replicating adenovirus, in combination with cisplatin and 5-fluorouracil in patients with recurrent head and neck cancer." Nat Med, 6(8): p.879-85.
26. Kim, S.H., et al., 2005. "Combination of mutated herpes simplex virus type 1(G207 virus) with radiation for the treatment of squamous cell carcinoma of thehead and neck." Eur J Cancer, 41(2): p. 313-22.
27. Znati, C.A., et al., 2003. "Irradiation reduces interstitial fluid transport andincreases the collagen content in tumors." Clin Cancer Res, 9(15): p. 5508-13.
28. Agarwal. D., S. Goodison, B. Nicholson, D. Tarin, and V. Urquidi, 2003."Expression of matrix metalloproteinase 8 (MMP-8) and tyrosinase-relatedprotein-i (TYRP-1) correlates with the absence of metastasis in an isogenichuman breast cancer model." Differentiation, 71(2): p. 114-25.
29. Montel, V., J. Kleeman, D. Agarwal, D. Spinella, K. Kawai, and D. Tarin, 2004."Altered metastatic behavior of human breast cancer cells after experimental
134
manipulation of matrix metalloproteinase 8 gene expression." Cancer Res, 64(5):p. 1687--94.
30. Ferrari, M., 2005. "Cancer nanotechnology: opportunities and challenges." NatRev Cancer, 5(3): p. 161-171.
135
Chapter 7
Conclusions / Future Directions
7.1 Introduction
In this thesis I have investigated the transport of macromolecules, liposomes and gene
therapeutic particles within tumors, in order to determine how to improve delivery of
these agents to tumor cells. Here I will summarize the conclusions and propose future
directions for the two following areas of my thesis: 1) Understanding the composition
and transport properties of the tumor extracellular matrix, and 2) A comparison of the
matrix modifying treatments used in this study, with the goal of considering practical
approaches to improving therapeutic transport within tumors.
136
7.2 Diffusive transport mechanisms within the tumor
extracellular matrix
The tumor interstitial matrix is a complex mixture of glycosaminoglycans, collagens, and
associated proteoglycans whose formation depends on the interactions between the tumor
cells and host cells present in or invading into the tumor[ 1-4]. The tumor matrix is highly
heterogeneous in its composition, which varies depending on the interaction between the
particular tumor cells and the host within which it is growing. For this reason, it is
important to investigate tumor models that are orthotopic - implanted into the same host
tissue from which the tumor initially arose[5]. The tumor models HSTS26T, a soft tissue
sarcoma, and Mu89, a melanoma, have been used repeatedly in my work because they
are both orthotopic to the subcutaneous tissue present in the dorsal chambers used in my
studies. The tumor matrix is also heterogeneous due to the amorphous character of tumor
cell growth, an uncontrolled and highly disordered process in which solid stresses
imposed by the cancer cells has been shown to influence tumor blood flow[6], and
extracellular matrix production[7]. As such, it can be difficult to relate analytical models
of transport to the diffusion coefficients observed within the tumor interstitial matrix. We
have attempted to quantify the composition of the interstitial matrix in this work in order
to have a more accurate picture of at least the overall composition of the tumor
extracellular matrix. The tumor interstitial matrix was analyzed using biochemical means
by Netti et al.,[8] (see figure 1.1), indicating that collagen, detected by the presence of
hydroxyproline within tissues, is more abundant on a per-mass basis than
glycosaminoglycans. This does not necessarily point to any conclusions regarding
collagen's influence on transport in vivo, however, since small amounts of137
glycosaminoglycan chains (such as hyaluronan) have the capacity to adsorb large
quantities of water, and as such have been shown to affect interstitial fluid flow within
tissues[9- 11 ]. What is more indicative of the influence of collagen on interstitial
diffusion is the observed correlation between higher collagen content, and reduced
diffusion coefficients of IgG within the extracellular space[8]. This was confirmed for a
broad range of macromolecular tracers in the work presented in chapter 3. The concept
of cell tortuosity was introduced to be able to separate geometric effects (imposed by
tumor cells on tracer molecules) from viscous effects (imposed by the tumor extracellular
matrix on tracer molecules)[12, 13]. While correcting for the presence of cells is
necessary to relate diffusion within model gels to diffusion within the tumor matrix, as
mentioned in chapters 3 and 4, the division of transport into simply two components,
namely geometric and viscous, may not be sufficient in some cases, for example for
molecules that approach the size of the interfibrillar space between collagen fibrils. For
molecules significantly below this size, the collagen fiber would appear as any other part
of the matrix would, as a purely viscous barrier to diffusion. However, for large
molecules that approach or exceed this size, the collagen fiber would then appear as an
impenetrable (or highly impermeable) object, shifting its definition to a geometric barrier
to diffusion. This was apparently the case when we were imaging the viral distribution
within the tumor - the collagen network appeared to be significantly impermeable to the
viral particles. The viral particle diameter was measured as approximately 300nm in size
using laser scattering techniques, indeed larger than the interfibrillar space. To
investigate the topic of the amount of extracellular matrix space available to tracer
particles of different sizes, one potential study to perform would be the co-injection of a
138
number of different particles of varying sizes, labeled with separate distinguishable
fluorophores. Quantum dots, which are semiconductor nanoparticles, offer great promise
in this regard, as it is possible to tune the wavelength of these particles such that it is
possible to distinguish many more individual populations of particles than are possible
using conventional fluorophores. The use of these tracer particles would permit the
simultaneous imaging of the exclusion of large molecules and the penetration of small
molecules into collagen fibers, which could be imaged using second harmonic
generation. This would allow a more direct determination of the "pore size" of the
collagen within the tumor interstitial matrix. While the search for the description of a
definitive "pore size" associated with the tumor interstitium, or components of the tumor
interstitium such as collagen, is promising, the argument also exists that molecular
motion will always be possible along certain preferential pathways within the tumor
matrix. An alternate way to describe the reduction of movement through the tumor
interstitium due to the gradual exclusion of accessible volume is to describe the diffusion
as occurring on a fractal substrate, which is equivalent to describing the diffusion as
falling into the regime of anomalous subdiffusion. This mechanism of transport has been
described for an increasing number of gel[14, 15] and drug delivery systems[16], and is
frequently mentioned when referring to the transport of molecules within the crowded
cytoplasm of cells[17-19]. While anomalous subdiffusion has been characterized in these
systems, it remains to be seen whether this is the case in the tumor interstitium, as we
currently do not have enough data to be able to either confirm or deny this mechanism of
transport.
139
7.3 Matrix modifying treatments and cancer therapy
Despite the complexities of understanding transport within the tumor interstitial matrix,
the attempts we have made to modify the content or structure of collagen within the
tumor interstitial matrix have resulted in increased transport of tracer molecules or viral
particles. Direct application of bacterial collagenase to tumors by Netti et al.,[8] resulted
in an increase in the diffusion coefficients of IgG in HSTS tumors of 100% after 24
hours. Relaxin treatment significantly increased the diffusion coefficients of both IgG
and dextran 2,000,000 MW in HSTS and Mu89 tumors. And finally application of
bacterial collagenase simultaneously with viral therapy resulted in improved penetration
of viral particles within the tumor mass.
While these results are promising for the improved delivery of macromolecules,
particles and gene vectors to tumors, at the same time care must be taken to consider the
effects these matrix modifying treatments themselves will have on the tumor progression.
As mentioned previously, many matrix metalloproteases have been implicated in tumor
invasion and metastatic progression[20-22]. In fact, a number of inhibitors of matrix
metalloproteases have been developed with the hope of being able to use them clinically
to prevent tumor invasion. While these inhibitors of matrix metalloproteases have
generally done poorly in clinical trials[23], nevertheless the dogma in the field has been
to inhibit rather than encourage matrix degradation. However, this is not universally the
case - while there are some matrix metalloproteases, such as MMP-2 and MMP-9, that
have been shown to increase the invasiveness of cancer cells[24], and have been
implicated in increasing the metastatic progression of tumors, there are also others, such
as MMP-8, that have been shown to both degrade collagen and decrease metastasis[25,
140
26]. The clinical application of this research should take care in choosing the appropriate
matrix modifying therapy in order to improve penetration of a therapeutic agent, while
not promoting increased invasiveness of the tumor. For example, relaxin has also been
shown to increase angiogenesis and the expression of angiogenic factors in a number of
different animal and cell culture models[27, 28]. Thus, care must be taken to ensure that
the positive effects of relaxin, in improving the transport of macromolecular therapies to
tumors, do enough good to justify the use of a potentially pro-angiogenic molecule in
tumor therapy. While bacterial collagenase is a highly nonspecific enzyme, and is thus
unlikely to increase metastasis in the same way that human-derived MMPs would, it
would nevertheless be important to test whether bacterial collagenase would have an
effect on the metastatic progression of cancer. Bacterial collagenase is also capable of
inducing an immune response, which would reduce its effectiveness after administration
of multiple doses. A future direction to more effectively improve the distribution of viral
vectors, especially oncolytic viral vectors, within tumors, would be to insert a gene
encoding for the matrix modifying treatment of choice, for example MMP-8, into the
gene therapeutic vector. Thus, as the virus would infect cells and lyse them, it would
simultaneously express the matrix modifying enzyme, which would reciprocally aid in
the spread of the viral particles to more of the tumor mass.
141
7.4 Conclusions
In conclusion, in chapter 3, we measured diffusion coefficients of macromolecules and
liposomes in tumors growing in cranial windows (CWs) and dorsal chambers (DCs) by
fluorescence recovery after photobleaching. For the same tumor types, diffusion of large
molecules was significantly faster in CW than in DC tumors. The greater diffusional
hindrance in DC tumors was correlated with higher levels of collagen type I and its
organization into fibrils. For molecules with diameters comparable to the interfibrillar
space the diffusion was 5- to 10-fold slower in DC than in CW tumors. I. The slower
diffusion in DC tumors was associated with a higher density of host stromal cells that
synthesize and organize collagen type I.
In chapter 4, diffusion coefficients of tracer molecules in collagen type I gels prepared
from 0-4.5% w/v solutions were measured by fluorescence recovery after
photobleaching. When adjusted to account for in vivo tortuosity, diffusion coefficients in
gels matched previous measurements in human tumor xenografts with equivalent
collagen concentrations. In contrast, hyaluronan solutions hindered diffusion to a lesser
extent when prepared at concentrations equivalent to those reported in these tumors.
Collagen permeability, determined from flow through gels under hydrostatic pressure,
was compared with predictions obtained from application of the Brinkman effective
medium model to diffusion data. Permeability predictions matched experimental results
at low concentrations, but underestimated measured values at high concentrations.
Permeability measurements in gels did not match previous measurements in tumors.
Visualization of gels by transmission electron microscopy and light microscopy revealed
142
networks of long collagen fibers at lower concentrations along with shorter fibers at high
concentrations. Negligible assembly was detected in collagen solutions pregelation.
However, diffusion was similarly hindered in pre and postgelation samples. Comparison
of diffusion and convection data in these gels and tumors suggests that collagen may
obstruct diffusion more than convection in tumors.
In chapter 5, we show that it is possible to optically image fibrillar collagen in tumors
growing in mice using second-harmonic generation (SHG). Using this noninvasive
technique, we estimated relative diffusive hindrance, quantified the dynamics of collagen
modification after pharmacologic intervention and provided mechanistic insight into
improved diffusive transport induced by the hormone relaxin.
And in chapter 6, we show that the spread of oncolytic viral vectors within tumors is
limited by the fibrillar collagen in the extracellular matrix. Thus, tumor cells in
inaccessible regions continue to grow, remaining out of the range of viral infection, and
tumor eradication cannot be achieved. Matrix modification with bacterial collagenase
upon initial virus injection results in a significant improvement in the range of viral
distribution within the tumor. This results in an extended range of infected tumor cells,
and improved virus propagation, ultimately leading to enhanced therapeutic outcome.
Thus, in this work we have shown that fibrillar collagen is an important barrier to the
macrmolecular transport and viral distribution within tumors, and matrix modifying
treatments that degrade the fibrillar collagen within tumors can significantly enhance the
143
penetration of large molecular therapeutics, ultimately resulting in an improved
therapeutic response. These findings have significant implications for drug delivery in
tumors and for tissue engineering applications.
144
7.5 References
1. Clarijs, R., D.J. Ruiter, and R.M. De Waal, 2003. "Pathophysiologicalimplications of stroma pattern formation in uveal melanoma." J Cell Physiol,194(3): p. 267-71.
2. Tuxhorn, J.A., G.E. Ayala, and D.R. Rowley, 2001. "Reactive stroma in prostatecancer progression." J Urol, 166(6): p. 2472-83.
3. Pluen, A., et al., 2001. "Role of tumor-host interactions in interstitial diffusion ofmacromolecules: cranial vs. subcutaneous tumors." Proceedings of the NationalAcademy of Sciences of the United States of America, 98(8): p. 4628-33.
4. Micke, P. and A. Ostman, 2004. "Tumour-stroma interaction: cancer-associatedfibroblasts as novel targets in anti-cancer therapy?" Lung Cancer, 45 Suppl 2: p.S 163 -75.
5. Killion, J.J., R. Radinsky, and I.J. Fidler, 1998. "Orthotopic models are necessaryto predict therapy of transplantable tumors in mice." Cancer Metastasis Rev,17(3): p. 279-84.
6. Padera, T.P., B.R. Stoll, J.B. Tooredman, D. Capen, E. di Tomaso, and R.K. Jain,2004. "Pathology: cancer cells compress intratumour vessels." Nature, 427(6976):p. 695.
7. Koike, C., et al., 2002. "Solid stress facilitates spheroid formation: potentialinvolvement of hyaluronan." Br J Cancer, 86(6): p. 947-53.
8. Netti, P.A., D.A. Berk, M.A. Swartz, A.J. Grodzinsky, and R.K. Jain, 2000. "Roleof extracellular matrix assembly in interstitial transport in solid tumors." CancerRes, 60(9): p. 2497-503.
9. Flessner, M.F., 2001. "The role of extracellular matrix in transperitoneal transportof water and solutes. " Perit Dial Int, 21 Suppl 3: p. S24-9.
10. Coleman, P.J., D. Scott, R.M. Mason, and J.R. Levick, 2000. "Role of hyaluronanchain length in buffering interstitial flow across synovium in rabbits." J Physiol,526 Pt 2: p. 425-34.
11. Bert, J. and R.K. Reed, 1998. "Hyaluronan, hydration and flow conductivity of ratdermis." Biorheology, 35(3): p. 211-9.
12. Sykova, E., 2004. "Extrasynaptic volume transmission and diffusion parametersof the extracellular space." Neuroscience, 129(4): p. 861-76.
13. Nicholson, C., 2005. "Factors governing diffusing molecular signals in brainextracellular space." J Neural Transm, 112(1): p. 29-44.
14. Masuda, A., K. Ushida, and T. Okamoto, 2005. "New fluorescence correlationspectroscopy enabling direct observation of spatiotemporal dependence ofdiffusion constants as an evidence of anomalous transport in extracellularmatrices." Biophys J, 88(5): p. 3584-91.
15. Fatin-Rouge, N., K. Starchev, and J. Buffle, 2004. "Size effects on diffusionprocesses within agarose gels." Biophys J, 86(5): p. 2710-9.
16. Saltzman, W.M. and R. Langer, 1989. "Transport rates of proteins in porousmaterials with known microgeometry. " Biophys J, 55(1): p. 163-71.
145
17. Tolic-Norrelykke, I.M., E.L. Munteanu, G. Thon, L. Oddershede, and K. Berg-Sorensen, 2004. "Anomalous diffusion in living yeast cells." Phys Rev Lett, 93(7):p. 078102.
18. Wong, .Y., M.L. Gardel, D.R. Reichman, E.R. Weeks, M.T. Valentine, A.R.Bausch, and D.A. Weitz, 2004. "Anomalous diffusion probes microstructuredynamics of entangled F-actin networks." Phys Rev Lett, 92(17): p. 178101.
19. Weiss, M., M. Elsner, F. Kartberg, and T. Nilsson, 2004. "Anomaloussubdiffusion is a measure for cytoplasmic crowding in living cells." Biophys J,87(5): p. 3518-24.
20. Hofmann, U.B., R. Houben, E.B. Brocker, and J.C. Becker, 2005. "Role of matrixmetalloproteinases in melanoma cell invasion." Biochimie, 87(3-4): p. 307-14.
21. Egeblad, M. and Z. Werb, 2002. "New functions for the matrix metalloproteinasesin cancer progression." Nat Rev Cancer, 2(3): p. 161-74.
22. Lynch, C.C. and L.M. Matrisian, 2002. "Matrix metalloproteinases in tumor-hostcell communication." Differentiation, 70(9-10): p. 561-73.
23. Overall, C.M. and C. Lopez-Otin, 2002. "Strategies for MMP inhibition in cancer:innovations for the post-trial era." Nat Rev Cancer, 2(9): p. 657-72.
24. Rudek, M.A., J. Venitz, and W.D. Figg, 2002. "Matrix metalloproteinaseinhibitors: do they have a place in anticancer therapy?" Pharmacotherapy, 22(6):p. 705-20.
25. Montel, V., J. Kleeman, D. Agarwal, D. Spinella, K. Kawai, and D. Tarin, 2004."Altered metastatic behavior of human breast cancer cells after experimentalmanipulation of matrix metalloproteinase 8 gene expression." Cancer Res, 64(5):p. 1687-94.
26. Agarwal, D., S. Goodison, B. Nicholson, D. Tarin, and V. Urquidi, 2003."Expression of matrix metalloproteinase 8 (MMP-8) and tyrosinase-relatedprotein- I (TYRP-1) correlates with the absence of metastasis in an isogenichuman breast cancer model." Differentiation, 71(2): p. 114-25.
27. Unemori, E.N., et al., 2000. "Relaxin induces vascular endothelial growth factorexpression and angiogenesis selectively at wound sites." Wound Repair Regen,8(5): p. 361-70.
28. Gavino, E.S. and D.E. Furst, 2001. "Recombinant relaxin: a review ofpharmacology and potential therapeutic use." BioDrugs, 15(9): p. 609-14.
146
MITLibrariesDocument Services
Room 14-055177 Massachusetts AvenueCambridge, MA 02139Ph: 617.253.5668 Fax: 617.253.1690Email: [email protected]://libraries. mit. edu/docs
DISCLAIMER OF QUALITY
Due to the condition of the original material, there are unavoidableflaws in this reproduction. We have made every effort possible toprovide you with the best copy available. If you are dissatisfied withthis product and find it unusable, please contact Document Services assoon as possible.
Thank you..
Some pages in the original document contain colorpictures or graphics that will not scan or reproduce well.