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A comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer

A comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer. May 16, 2013 Levi Waldron Supervisors: Curtis Huttenhower and Giovanni Parmigiani. Harvard School of Public Health Department of Biostatistics. Dana Farber Cancer Institute

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A comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer

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  1. A comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer May 16, 2013 Levi Waldron Supervisors: Curtis Huttenhower and Giovanni Parmigiani Harvard School of Public Health Department of Biostatistics Dana Farber Cancer Institute Biostatistics and Computational Biology

  2. Predictive modeling for translational genomics • Measure Xij (gene expression, mutations, …) • Predict Yj (survival, treatment response, …)

  3. Training + Validation Cross-validation to estimate prediction accuracy Independent Validation trainingtest Dataset 2 Dataset 3 Lasso Ridge Elastic Net Random Forests Support Vector Machine K Nearest Neighbors Supervised PCA Linear Discriminant Analysis Boosting / Bagging Insert Favorite Method Here • Need a new cohort of patients • Can use public data

  4. Prognostic gene signatures of ovarian cancer Objectives: Assess the reproducibility of published prognostic gene expression models Evaluate published models using publicly available data Improve on models using all publicly available data Validate promising models in FFPE specimens from GOG-218 bevacizumabphase-III clinical trial 4 With Michael Birrer, MD (MGH)

  5. Machine syntax check 23 ovarian cancer microarray studies CURATION ✔ ✔ Human double check Available in Bioconductor (v2.12): > source("http://bioconductor.org/biocLite.R") > biocLite("curatedOvarianData") Y Download expression data Y (f) RMA re-normalization Affymetrix platform Raw data? N N Collapse probesets to genes Automatically build documented curatedOvarianData R package B.F. Ganzfried, M. Riester, B. Haibe-Kains, T. Risch, S. Tyekucheva, I. Jazic, X. V. Wang, M. Ahmadifar, M. Birrer, G. Parmigiani, C. Huttenhower, L. Waldron. curatedOvarianData: Clinically Annotated Data for the Ovarian Cancer Transcriptome (DATABASE 2013).

  6. Meta-analysis overview Literature review Prognostic models curatedOvarianData 101 papers from Pubmed search Five review papers Standardized clinical annotation and gene ID 23 studies, 2,908 samples Inclusion Criteria • Training sample size > 40 • Focus on late-stage serous • Multivariate model • Continuous risk score • Claims to predict survival • Possible to reproduce model Inclusion Criteria • Sample size > 40 • Primary tumors • Overall survival available • Events (deaths) > 15 • Late stage, high grade tumors • Serous subtype 14 prediction models implemented 100 pages documentation survHDBioconductor package 10 datasets, 1,386 samples

  7. Assessment of prognostic signatures Validation Statistics: 14 Models in 10 Datasets C-Index = Pr(g(Z1)>g(Z2) | T2>T1) T1, T2 = times to death of two patients g(Z1), g(Z2)= predicted risk scores C=0.5 expectation for random prediction C=1 if the exact order of all deaths is predicted 14 prognostic signatures Forest plot Kaplan-Meier estimate Study Survival 10 microarray datasets C-Index L. Waldronet al. A comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer. Submitted. Time

  8. Assessment of prognostic models Validation Statistics: 14 Models in 10 Datasets 14 prognostic signatures 10 microarray datasets L. Waldronet al. A comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer. Submitted.

  9. Assessment of prognostic models Validation Statistics: 14 Models in 10 Datasets Cancer Genome Atlas Research Network. Nature. 2011 474(7353):609-15. Integrated genomic analyses of ovarian carcinoma. 14 prognostic signatures 193 263 Bonomeet al. Cancer Res. 2008 68(13):5478-86. A gene signature predicting for survival in suboptimallydebulked patients with ovarian cancer. 10 10 microarray datasets L. Waldronet al. A comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer. Submitted.

  10. A little gene overlap corresponds to substantial risk score similarity Correlations Risk scores Gene overlap

  11. Assessment of prognostic models Validation Statistics: 14 Models in 10 Datasets Dressmanet al. J ClinOncol. 2007 25(5):517-25. An integrated genomic-based approach to individualized treatment of patients with advanced-stage ovarian cancer. Baggerlyet al. J ClinOncol. 2008 26(7):1186-7. Run batch effects potentially compromise the usefulness of genomic signatures for ovarian cancer. 14 prognostic signatures Dressmanet al. J ClinOncol. 2012 30(6):678. Retraction. 10 microarray datasets L. Waldronet al. A comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer. Submitted.

  12. Assessment of prognostic models Validation Statistics: 14 Models in 10 Datasets • Conclusions: • Validation datasets can be biased • Most models make better predictions than random • Large, consortium studies performed best • None of these models are ready for the clinic 14 prognostic signatures L. Waldronet al. A comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer. Submitted.

  13. Assessment of gene signatures(not models) • Start with a signature defined as a list of genes • Fit a simple prediction algorithm (β = ±1) • Compute “leave-one-in” matrix of C-statistics • Repeat with random gene sets Test sets Training sets

  14. Assessment of gene signatures About half of gene signatures provide prognostic “value added” over 97.5% of gene random signatures L. Waldronet al. A comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer.

  15. Prediction of surgical debulkability • Standard treatment includes surgical debulking, but it is suboptimal for ~50% cases • What if we could predict suboptimal debulking from the biopsy? M. Riester, W. Wei, L. Waldron, A. C. Culhane, L. Trippa, F. Michor, C. Huttenhower, G. Parmigiani, M. Birrer. Risk prediction for late-stage ovarian cancer by meta-analysis of 1,622 patient samples: Biologic and Clinical Correlations.

  16. Validation of a meta-analysis discovery: prediction of suboptimal debulking Stage 1: public data 200-gene signature

  17. Validation of a meta-analysis discovery:prediction of suboptimal debulking qRT-PCR 8-gene signature 78 new specimens from Bonomeet al. study Compare to AUC ~ 0.6 in microarray validation

  18. Validation of a meta-analysis discovery:prediction of suboptimal debulking 179 new specimens from tissue microarray Immunohistochemistry 3-protein signature POSTN Immunohistochemistry Number of Cases - Compare to AUC ~ 0.6 in microarray validation + +++ ++

  19. Outlook: Meta-analysis and Validation • Meta-analysis for prediction modeling works • Provides sample size • Identifies and mitigates dataset-specific bias • qRT-PCR and protein assays can dramatically improve prediction accuracy • Model testing in meta-analysis by: • “leave-one-dataset-in” cross-validation • “leave-one-dataset-out” cross-validation

  20. Reproducible analysis

  21. Thank you Giovanni Parmigiani lab Markus Riester, Dave Zhao, CristianTomasetti, EmmanueleMazzola, Jie Ding, SvitlanaTyekucheva, Victoria Wang, Ina Jazic, Ben Ganzfried, RomiMagori-Cohen Curtis Huttenhower lab Nicola Segata, Tim Tickle, XochitlMorgan, Daniela Boernigen, Eric Franzosa, Brian Palmer, Joseph Moon, Emma Schwager, Jim Kaminski, Craig Bielski, VagheeshNarasimhan • MGH – Boston • Michael Birrer • Dana-Farber Cancer Institute • Lorenzo Trippa • University of Montreal • Benjamin Haibe-Kains

  22. HR increases with training sample size for most test sets

  23. RNA-seq vs. microarray validationTCGA validation dataset

  24. Manuscripts and publications • B.F. Ganzfried* and M. Riester*, B. Haibe-Kains, T. Risch, S. Tyekucheva, I. Jazic, X. V. Wang, M. Ahmadifar, M. Birrer, G. Parmigiani, C. Huttenhower, L. Waldron. curatedOvarianData: Clinically Annotated Data for the Ovarian Cancer Transcriptome (DATABASE 2013). • L. Waldron, B. Haibe-Kains, A. C. Culhane, M. Riester, J. Ding, V. Wang, S. Tyekucheva, C. Bernau, T. Risch, B. Ganzfried, C. Huttenhower, M. Birrer, G. Parmigiani. A comparative meta-analysis of prognostic gene signatures for late-stage ovarian cancer (submitted). • M. Riester, W. Wei, L. Waldron, A. C. Culhane, L. Trippa, F. Michor, C. Huttenhower, G. Parmigiani, M. Birrer. Risk prediction for late-stage ovarian cancer by meta-analysis of 1,622 patient samples: Biologic and Clinical Correlations (submitted). • D. Zhao, C. Huttenhower, G. Parmigiani, L. Waldron. Mas-o-menos: asimple sign average method for discrimination in genomic data analysis (submitted, preprint at http://biostats.bepress.com/harvardbiostat/paper158/). • L. Trippa, L. Waldron, C. Huttenhower, G. Parmigiani. Cross-study validation of prediction methods. (submitted).

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