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Statistics in Biosciences

, Volume 9, Issue 1, pp 1–12 | Cite as

IPI59: An Actionable Biomarker to Improve Treatment Response in Serous Ovarian Carcinoma Patients

  • J. Choi
  • S. Ye
  • K. H. Eng
  • K. Korthauer
  • W. H. Bradley
  • J. S. Rader
  • C. Kendziorski
Article
  • 200 Downloads

Abstract

Despite improvements in operative management and therapies, overall survival rates in advanced ovarian cancer have remained largely unchanged over the past three decades. Although it is possible to identify high-risk patients following surgery, the knowledge does not provide information about the genomic aberrations conferring risk, or the implications for treatment. To address these challenges, we developed an integrative pathway-index model and applied it to messenger RNA expression from 458 patients with serous ovarian carcinoma from the Cancer Genome Atlas project. The biomarker derived from this approach, IPI59, contains 59 genes from six pathways. As we demonstrate using independent datasets from six studies, IPI59 is strongly associated with overall and progression-free survival, and also identifies high-risk patients who may benefit from enhanced adjuvant therapy.

Keywords

Adjuvant Therapy Ovarian Cancer Patient Serous Ovarian Carcinoma Affymetrix Human Genome Lasso Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors thank Drs. Michael Newton and Ning Leng for suggestions that improved the manuscript. This research was supported by NIH GM102756, NIH U54 AI117924, NIH K01LM012100, and the Clinical and Translational Science Institute of Southeastern Wisconsin (NIH UL1RR031973). Results were generated in part using data from the Cancer Genome Atlas (TCGA) pilot project established by the NCI and NHGRI. Information about TCGA and the investigators and institutions who constitute the TCGA research network can be found at http://cancergenome.nih.gov/.

Supplementary material

12561_2016_9144_MOESM1_ESM.docx (715 kb)
Supplementary material 1 (docx 714 KB)

References

  1. 1.
    Siegel R, Naishadham D, Jemal A (2013) Cancer statistics. CA Cancer J Clin 63:11–30. doi: 10.3322/caac.21166 CrossRefPubMedGoogle Scholar
  2. 2.
    Tewari D, Java JJ, Salani R et al (2015) Long-term survival advantage and prognostic factors associated with intraperitoneal chemotherapy treatment in advanced ovarian cancer: a gynecologic oncology group study. J Clin Oncol 33:1460–1466. doi: 10.1200/JCO.2014.55.9898 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Tothill RW, Tinker AV, George J et al (2008) Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin Cancer Res 14:5198–5208. doi: 10.1158/1078-0432.CCR-08-0196 CrossRefPubMedGoogle Scholar
  4. 4.
    Bild AH, Yao G, Chang JT et al (2006) Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439:353–7. doi: 10.1038/nature04296 ADSCrossRefPubMedGoogle Scholar
  5. 5.
    Mehta S, Shelling A, Muthukaruppan A et al (2010) Predictive and prognostic molecular markers for cancer medicine. Ther Adv Med Oncol 2:125–148. doi: 10.1177/1758834009360519 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Services H (2007) GEO?: the gene expression omnibus. Gene Expr 23:2–3Google Scholar
  7. 7.
    Konstantinopoulos PA, Spentzos D, Karlan BY et al (2010) Gene expression profile of BRCAness that correlates with responsiveness to chemotherapy and with outcome in patients with epithelial ovarian cancer. J Clin Oncol 28:3555–61. doi: 10.1200/JCO.2009.27.5719 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Lisowska KM, Olbryt M, Dudaladava V et al (2014) Gene expression analysis in ovarian cancer: faults and hints from DNA microarray study. Front Oncol 4:6. doi: 10.3389/fonc.2014.00006 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Ferriss JS, Kim Y, Duska L et al (2012) Multi-gene expression predictors of single drug responses to adjuvant chemotherapy in ovarian carcinoma: predicting platinum resistance. PLoS One 7:e30550. doi: 10.1371/journal.pone.0030550 ADSCrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Bonome T, Levine DA, Shih J et al (2008) A gene signature predicting for survival in suboptimally debulked patients with ovarian cancer. Cancer Res 68:5478–5486. doi: 10.1158/0008-5472.CAN-07-6595 CrossRefPubMedGoogle Scholar
  11. 11.
    Yoshihara K, Tajima A, Yahata T et al (2010) Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets. PLoS One 5:e9615. doi: 10.1371/journal.pone.0009615 ADSCrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Ganzfried BF, Riester M, Haibe-Kains B et al. (2013) curatedOvarianData: clinically annotated data for the ovarian cancer transcriptome. Database (Oxford). doi: 10.1093/database/bat013
  13. 13.
    Irizarry RA, Hobbs B, Collin F et al (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264. doi: 10.1093/biostatistics/4.2.249 CrossRefPubMedzbMATHGoogle Scholar
  14. 14.
    Tucker SL, Gharpure K, Herbrich SM et al (2014) Molecular biomarkers of residual disease after surgical debulking of high-grade serous ovarian cancer. Clin Cancer Res 20:3280–3288. doi: 10.1158/1078-0432.CCR-14-0445 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Chang S-J, Bristow RE, Ryu H-S (2012) Impact of complete cytoreduction leaving no gross residual disease associated with radical cytoreductive surgical procedures on survival in advanced ovarian cancer. Ann Surg Oncol 19:4059–4067. doi: 10.1245/s10434-012-2446-8 CrossRefPubMedGoogle Scholar
  16. 16.
    du Bois A, Reuss A, Pujade-Lauraine E et al (2009) Role of surgical outcome as prognostic factor in advanced epithelial ovarian cancer: a combined exploratory analysis of 3 prospectively randomized phase 3 multicenter trials: by the Arbeitsgemeinschaft Gynaekologische Onkologie Studiengruppe Ovarialkarzin. Cancer 115:1234–1244. doi: 10.1002/cncr.24149 CrossRefPubMedGoogle Scholar
  17. 17.
    Wang S, Nan B, Zhu J, Beer DG (2008) Doubly penalized Buckley–James method for survival data with high-dimensional covariates. Biometrics 64:132–140. doi: 10.1111/j.1541-0420.2007.00877.x MathSciNetCrossRefPubMedzbMATHGoogle Scholar
  18. 18.
    Eng KH, Wang S, Bradley WH et al (2013) Pathway index models for construction of patient-specific risk profiles. Stat Med 32:1524–1535. doi: 10.1002/sim.5641 MathSciNetCrossRefPubMedGoogle Scholar
  19. 19.
    Bair E, Tibshirani R (2004) Semi-supervised methods to predict patient survival from gene expression data. PLoS Biol 2:E108. doi: 10.1371/journal.pbio.0020108 CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Gui J, Li H (2005) Penalized Cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Bioinformatics 21:3001–3008. doi: 10.1093/bioinformatics/bti422 CrossRefPubMedGoogle Scholar
  21. 21.
    Ishwaran H, Kogalur UB, Gorodeski EZ et al (2010) High-dimensional variable selection for survival data. J Am Stat Assoc 105:205–217. doi: 10.1198/jasa.2009.tm08622 MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Ma S, Huang J (2007) Clustering threshold gradient descent regularization: With applications to microarray studies. Bioinformatics 23:466–472. doi: 10.1093/bioinformatics/btl632 CrossRefPubMedGoogle Scholar
  23. 23.
    Yuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J R Stat Soc Ser B Stat Methodol 68:49–67. doi: 10.1111/j.1467-9868.2005.00532.x MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Zhao P, Rocha G, Yu B (2009) The composite absolute penalties family for grouped and hierarchical variable selection. Ann Stat 37:3468–3497. doi: 10.1214/07-AOS584 MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Luan Y, Li H (2008) Group additive regression models for genomic data analysis. Biostatistics 9:100–113. doi: 10.1093/biostatistics/kxm015 CrossRefPubMedzbMATHGoogle Scholar
  26. 26.
    Gu Z, Wang J (2013) CePa: an R package for finding significant pathways weighted by multiple network centralities. Bioinformatics 29:658–660. doi: 10.1093/bioinformatics/btt008 CrossRefPubMedGoogle Scholar
  27. 27.
    Wang S, Nan B, Zhu N, Zhu J (2009) Hierarchically penalized Cox regression with grouped variables. Biometrika 96:307–322. doi: 10.1093/biomet/asp016 MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Simon N, Friedman J (2013) A sparse-group lasso. J Comput Graph Stat 22:231–245. doi: 10.1080/10618600.2012.681250 MathSciNetCrossRefGoogle Scholar
  29. 29.
    Kanehisa M, Goto S (2000) KEGG: Kyoto Encyclopaedia of Genes and Genomes. Nucl Acids Res 28:27–30. doi: 10.1093/nar/28.1.27 CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Kanehisa M, Goto S (2011) Integrated genomic analyses of ovarian carcinoma. Nature 474:609–615. doi: 10.1038/nature10166 CrossRefGoogle Scholar
  31. 31.
    Verhaak R, Tamayo P (2012) Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. J Clin Investig 123:1–9. doi: 10.1172/JCI65833DS1 Google Scholar

Copyright information

© International Chinese Statistical Association 2016

Authors and Affiliations

  • J. Choi
    • 1
  • S. Ye
    • 1
  • K. H. Eng
    • 1
  • K. Korthauer
    • 1
  • W. H. Bradley
    • 2
  • J. S. Rader
    • 2
  • C. Kendziorski
    • 1
  1. 1.University of Wisconsin MadisonMadisonUSA
  2. 2.Medical College of WisconsinMilwaukeeUSA

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