04 octobre 2012

Towards optimization of Acute Myeloid Leukemia treatment: a data-driven model of cell population dynamics

Annabelle Ballesta, Faten Mehri, Xavier Dupuis, Pierre Hirsh, Ruoping Tang, Jean-Pierre Marie, Jean Clairambault

04 Octobre 2012

OUTLINE Introduction on Acute Myeloid Leukemia

1. Cell Dynamics in the absence of drugs

1. Experimental results for Patient #7

2. Time- and age-structured mathematical model

3. Fit to experimental results for Patient #7

2. Cell Dynamics in presence of Anticancer drugs

(Aracytine and AC220)

04/10/2012Annabelle Ballesta

Normal Hematopoiesis


Hematopoietic Stem Cell

Acute Myeloid Leukemia


Hematopoietic Stem Cell

Acute Myeloid Leukemia


• Blockage in the differenciation of progenitors of the myeloid linage.

• Cancer cells proliferate in the bone marrow and eventually invade the blood and other organs.

• AMLs are the most frequent leukemias in adults. They are associated to a high mortality (40 000/year in Europe).

-> optimizing therapies against AML still a clinical challenge

Cell Dynamics in the absence of drugs

1

Annabelle Ballesta

Experimental protocol

04/10/2012

• Experiments on AML patient blood sample.

• Assumption: only cancer progenitors in blood, healthy progenitors being in the bone marrow.

• Three membrane markers to characterize cell populations: CD 34, CD 38 and CD 33

Surface marker

• Three cell population: from immature to mature cells

Able to self-renew, to differenciate

Able to differenciate

Unable to differenciate

Three Cell compartments

Cancer Hematopoietic stem cells CD34+/CD38-

Cancer progenitors CD38+/CD33-

Mature cancer cells ”blast”

CD38+/CD33+


Annabelle Ballesta


04/10/2012

• J0 8am: Blood sample collection

• J0: Sort white blood cells by Ficoll

• J0: Sort CD34/38/33+ cell population by immunomagnetic technique

• J0 2pm to J5: cell culture in standard medium supplemented with

growth factors (SCF, G-CSF, Il-3, Flt-3)

Patient #7: Markers at J0

• Markers:

• ->cell sorting of CD 38+ population

- +CD34 97.34 2.65

CD33- CD33+CD38+ 10.84 69.62CD38- 16.33 3.2


• Methods: count using the Malassez cell

• Average of 2 to 3 independant measurements


Patient #7: Cell count

0 1000 2000 3000 4000 5000 6000 7000 80000

200000

400000

600000

800000

1000000

1200000

Cell number (control conditions)

Time (min)


0 1000 2000 3000 4000 5000 6000 7000 800070

75

80

85

90

95

Viable cells (Annexin -/PI-)

• Methods: Annexin/ Propidium iodure (PI)

• Average of 2 to 3 independant measurements

Time (min)

Patient #7: Cell Death

Annabelle Ballesta 04/10/2012

J1 J2 J3 J4

G0 (%) G1 (%) S (%) G2/M (%)

J0 79.9 20.1 0 0J1 51.4 16.73 31.41 0.46J2 21.9 34.52 42.82 0.76J3 22 39.18 38.7 0.05J4 45.6 21.22 33.02 0.07J5 64.46 0.64 34.48 0

Patient #7: Cell cycle (PI+Ki67)


0 1000 2000 3000 4000 5000 6000 7000 80000

10

20

30

40

50

60

70

80

90

100

CD38+/33+controlCD38+/33- control

Patient #7: CD 38 and CD 33 markers

Time (min)

Mathematical model


• One population : CD 38+• Model is structured in time and age• 4 phases: G0 (r), G1 (g), S (s), G2/M (m)

• NB: model incorporates cell cycle phases in view of modeling of phase-specific anticancer drugs.

g r

γ

Tg

Tr

sTs

mTm

γ γ γ

Mathematical model


• Equation for g(t,a):

• Initial condition in age:

• Initial condition in time:

Mathematical model


• Tr, Tg, Ts, Tm: Transitions functions in the form:

• -> 3 parameters to be estimated for each phase= 12 parameters

• Initial instant: cells in G0 and G1, same age assumed for all cells, age to be estimated

Parameter estimation


• 16 parameters to be estimated in total: 12 for transition functions, age in G0 and G1 at t=0, 2 death rates gamma and delta.

• Least square approach, minimization task performed with the CMAES algorithm

Results for Patient #7: cell number



Results for Patient #7: cell death


Results for Patient #7: cell cycle

G0 G1, S , G2/M


Results for Patient #7: parameter values

a_min_r= 30.2 hµ_r= 53σ_r= 7.1

a_min_g= 18.9 hµ_g= 27.7σ_g=7

a_min_s= 28.3 hµ_s=39.9σ_s=-=6.59

a_min_m= 0.9 hµ_m=0.16σ_m=0.05

a0_G0= 53 ha0_G1= 235 h

γ=0.002 h-1δ=0 h-1

Conclusions and Perspectives


• Experimental characterization of cell dynamics in control conditions for Patient #7, mathematical model calibrated to data achieve a satisfying fit.

• Perspective: model two populations (CD 38+/CD 33- and CD38+/33+) to improve fit.

• Same modeling approach for other patient data (10 patients)

Cell Dynamics in presence of Aracytin (ARA-C) and FLt 3 inhibitor AC220

2

Annabelle Ballesta

AML therapeutics

04/10/2012

• Aracytine (ARA-C) widely used in clinics against AML.

• Aracytine targets cells in S-phase.

Annabelle Ballesta

Cell dynamics : cell count

04/10/2012

Temps (min)

Patient #7: CD38+ population

0 1000 2000 3000 4000 5000 6000 7000 80000

200000

400000

600000

800000

1000000

1200000

nombre de cellules controlARA0.5ARA1ARA2


Cell dynamics: Annexin/PI

Temps (min)

% d

e ce

llule

s A

nnex

in-/I

P-

0 1000 2000 3000 4000 5000 6000 7000 80000

10

20

30

40

50

60

70

80

90

100

nombre de cellules controlARA0.5ARA1ARA2

Forte mort cellulaire due à l’ARA-C, dépendante du temps d’exposition (pas de la dose pour ce patient)

Patient #7: CD38+ population

Annabelle Ballesta

Cell dynamics: cell cycle analysis

04/10/2012

J1 J2 J3

Patient #7: CD38+ population, ARA-C 0.5ng/mL

G0 G1 S G2/MJ0 80 20 0 0

J1 90,25 1,52 8,23 0

J2 91,95 0,26 7,79 0

J3 95,9 4,1 0 0

Annabelle Ballesta

Cell dynamics: differenciation

04/10/2012

0 1000 2000 3000 4000 5000 6000 7000 80000

200000

400000

600000

800000

1000000

1200000

CD38+/33+ Nbre cellulesCD38+/33- Nombre celluleARA 0.5 CD38+/33+ARA à.5 CD38+/33-

Temps (min)

Patient #7: CD38+ population, ARA-C 0.5ng/mL


Cell dynamics in presence of Flt3 inhibitor AC220

• Flt 3 is atyrosine kinase which is often mutated in AML cells, giving a proliferative advantage

• AC220 inhibits Flt3 activty.


Cell dynamics : cell count

Patient #8 (53LAM2012), polpulation CD38+/33-

Temps (min)

0 1000 2000 3000 4000 5000 6000 7000 80000

200000

400000

600000

800000

1000000

1200000

nombre de cellules controlAC50 microMAC200 microMAC1000 microM


Cell dynamics : Annexin/PI

Patient #8 (53LAM2012), population CD38+/33-

Temps (min)0 1000 2000 3000 4000 5000 6000 7000 8000

0

20

40

60

80

100

120

33- ControlAC50AC200AC1000

Annabelle Ballesta

Cell dynamics: cell cycle analysis

04/10/2012

Patient #8 (53LAM2012), Flt3-, polpulation CD38+/33-G0 G1 S G2/M

J0 87,7 6,43 5,87 0

J1 70,2 7,21 22,59 0

J2 28,8 43,22 24,79 3,19

J3 23,8 51,54 22,1 2,56

J4 77,14 21,18 1,68

J5 79,56 19,04 1,4

G0 G1 S G2/MJ0 87,7 6,43 5,87 0

J1 90.9 0 9,7 0

J2 75.7 0 33,48 0,77

J3 50,4 23,22 23,33 3,05

J4 81,85 16,31 1,84

J5 83,51 13,81 2,68

Normal conditions

AC-220, 1000µM


Conclusions and Perspectives

Cytotoxic activity of ARA-C on AML cells

Cytostatic activity of AC220 on AML cells

Next:

Mathematically Model ARA-C and AC220 activities.

Perform optimization procedure to optimize co-

administration.

Thank you

www.inria.fr

AML: FAB Classification

FAB Maturation

M0 Indifférenciée

M1 Myéloblastique sans différenciation

M2 Myéloblastique avec différenciation

M 3 Promyélocytaire

M4 Myélomonocytaire

M5 Monoblastique et monocytaire

M6 érythroleucémique

M7 mégacaryocytoblastique

M0

M1

M2

M3

M4 M5a

M5b

M6 M7

Expression des antigènes: CD33, CD13, CD117, CD65, CD14, MPO


Annabelle Ballesta


04/10/2012

Ficoll on blood samples to isolate white blood cells:

Annabelle Ballesta

Cell sorting by immunomagnetic technique:

Antibody against CD 34, CD38 and CD33


04/10/2012

Specific antibody

Magnetic particule

Surface antigen

Introduction rates: functions


Adimy et al., J of Biological systems, 2008 :

04 octobre 2012

Documents

cell death

cell culture

cell populations

cell cycle phases

sort cd343833 cell population

cd38 model

sort white blood cells

aml patient blood sample