04 pl 3 2-omics and injury - · pdf filematas, kasiske, hunsicker, gaston, mannon, cecka,...
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![Page 1: 04 PL 3 2-Omics and Injury - · PDF fileMatas, Kasiske, Hunsicker, Gaston, Mannon, Cecka, Gourishankar, Halloran, Rush. DeKAF Study ... Microsoft PowerPoint - 04 PL 3 2-Omics and Injury](https://reader033.vdocuments.us/reader033/viewer/2022052708/5a72acc07f8b9ac0538ddbd1/html5/thumbnails/1.jpg)
“Omics” technologies and allograft injury
David Rush
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100
50
70
80
90
60
40
0 1 2 3 4 5 6 7 8 9 10 11 12
Time (years)
Graft survival by year of transplantation
Kasiske et al, Am J Transplant (2005); 5:1405
Gra
ft s
urv
iva
l (%
)
1999-2002
1995-19981990-1994
1984-1989 Slope: -2.2 ± 7.22 mL/min/1.73m2/year
Slope: -1.4 ± 10.9 mL/min/1.73m2/year
DeKAF (Decline in Kidney Allograft Function) Study Group
Slope: -0.90 ± 0.18 mL/min/1.73m2/year (aging)*
*Rowe et al, J Gerontol (1976); 31:155
Matas, Kasiske, Hunsicker, Gaston, Mannon, Cecka, Gourishankar, Halloran, Rush
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DeKAF Study
Prospective/cross-sectional study (~ 5000 patients)
• Participating centres: Minneapolis (Matas, PI; Kasiske), Mayo
Clinic (Cosio, Grande), Iowa (Hunsicker), Alabama (Gaston, Mannon), UCLA (Cecka), Alberta (Gourishankar, Halloran), Manitoba (Rush)
• Renal Biopsies done “for cause” (n ~ 800) – (Mayo Clinic)
• Urine magnetic resonance done in Winnipeg
Deterioration of Kidney Allograft Function
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DeKAF: Hypotheses
1) Progressive graft dysfunction is due to ongoing active injury, and is not necessarily the consequence of past events;
2) There are discrete, definable entities responsible for injury, that lead to chronic graft deterioration and late graft loss;
3) These entities can be differentiated by means of clinical, laboratory, and pathologic studies;
4) Accurate diagnosis offers the best hope for the development of interventional trials.
Gourishankar el al, Am J Transplant (2010); 10: 324
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Enrollment: Prospective and Cross-sectional cohorts
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Depiction of clusters – “cluster clocks”
Legend
Each spoke represents
a Banff score, except
Length of spokes = %
patients with finding
No spoke = Banff 0
…. = Banff 1
---- = Banff 2
= Banff 3
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Inflammation in areas of fibrosis (“iatr”; A)
and tubulitis in atrophic tubules (“tatr”; B)
A B
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Inflammation in
areas of scar
Has previously
been excluded
from Banff
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Original DeKAF clusters (n = 265; now 370)
Cluster 1 – mild
inflammation;
mild fibrosis
Cluster 2 – ai,
at, iatr, tatr; mild
fibrosis
Cluster 3 – ai,
at, iatr, cg;
fibrosis
Cluster 5 – no
ai, at; only iatr,
tatr; fibrosis
PTC in several
clusters
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Actuarial Graft Survival
by Cluster and by “iatr” score
Matas et al Am J Tansplant (2010)
Mannon et al Am J Tansplant (2010)
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gDNA
acgtacca
aggtaacg
cggtttttcgt
gtatctccctt
20,000–30,000
Genes
mRNA
cDNA Microarrays
> 100,000
mRNAs
Systems Biology Approach:Profiling all Components in a Sample
Protein
Mass Spectroscopy
> 1,000,000
Proteins
1H Magnetic Resonance
Spectroscopy
Metabolism
Low MW Compounds
(<5kD)
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Mueller et al, Am J Transplant (2007); 7: 2712-2722
Microarray analysis of rejection using pathogenesis based transcripts (PBT)
CTL-transcripts
IFN-g transcripts
Transporter
transcripts
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4.56 4.36
4.15
3.95
3.74
3.54
3.34
3.13
2.93
2.72
2.52
2.31
2.11
1.91
1.70
1.50
1.29
1.09
PPM
The Human Metabolome Database: Biofluid Urine
785 metabolites identified.
444 have 1H-NMR referenced
spectral peaks.
Wishart et al, Nucleic Acids Research; 37: D603-D610, 2009 (updated 2011)
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Urine Metabolomics: Methodology
• The large number of data points in MR spectra requires
an informatics approach:
• The strategy for “pattern recognition” has 4 stages:
– 1) Pre-processing : Area normalization, peak alignment
– 2) Feature selection: Identification of maximally discriminating
averaged subregions of the spectra;
– 3) Classifier development: With these subregions,
crossvalidated linear discriminant analysis classifiers are
developed
– 4) Accurate visualization of resultsSomorjai RL et al. Artificial Intelligence Methods and Tools for Systems Biology (Dubitzky W and
Azuaje F, (eds.)), Computational Biology Series, Vol. 5 Springer pp. 67-85 (2004)
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Urine spectra from DeKAF patients
• Matching urine spectra/biopsy pairs (u/b) studied to
date are 457:
– 102 u/b from patients with varying degrees of fibrosis but no
inflammation;
– 150 u/b from patients with varying degrees of fibrosis and
severe inflammation in both normal and atrophic
parenchyma;
– 108 u/b from patients with varying degrees of fibrosis and
minor inflammation mostly in atrophic parenchyma;
– 97 u/b from patients with transplant glomerulopathy.
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Moreso et al, Am J Transplant (2006), 6:747
Cosio et al, Am J Transplant (2005) 5: 2464
The combination of inflammation
and fibrosis detected on protocol
biopsy identifies grafts at ↑ risk
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1. Can Urine MR spectra distinguish between IF with severe
inflammation and IF without inflammation
• One hundred (100) patients with IF plus severe inflammation and
68 patients with IF minus inflammation were used for the training
set; and 50 and 34 independent patients, respectively, were used as
the test set.
• The 3,300 data point data set of the average spectra was analyzed
(100 points at a time) and the best classifier was developed on the
training set. The classifier was validated with the independent test
set.
• Visualization of the data was done using the “class-proximity
plane” graphic.
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Conclusions
• Urine magnetic resonance spectroscopy (UMRS) distinguishes IF with severe inflammation from IF without inflammation with ~90% accuracy.
• The extent of inflammation (severe vs. minor) can also be accurately determined by UMRS with ~90% accuracy.
• Similarly, IF without inflammation can be distinguished from minor inflammation by UMRS with ~ 90% accuracy.
• Validation of UMRS signatures for other allograft pathologies – e.g. transplant glomerulopathy – is in progress.
• The non-invasive nature of UMRS will allow for repeat testing to evaluate changes in spectra and their correlation with specific interventions and their outcomes in prospective studies.
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Thank you!