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

Author

Listed:
  • J. Choi

    (University of Wisconsin Madison)

  • S. Ye

    (University of Wisconsin Madison)

  • K. H. Eng

    (University of Wisconsin Madison)

  • K. Korthauer

    (University of Wisconsin Madison)

  • W. H. Bradley

    (Medical College of Wisconsin)

  • J. S. Rader

    (Medical College of Wisconsin)

  • C. Kendziorski

    (University of Wisconsin Madison)

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.

Suggested Citation

  • J. Choi & S. Ye & K. H. Eng & K. Korthauer & W. H. Bradley & J. S. Rader & C. Kendziorski, 2017. "IPI59: An Actionable Biomarker to Improve Treatment Response in Serous Ovarian Carcinoma Patients," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 1-12, June.
  • Handle: RePEc:spr:stabio:v:9:y:2017:i:1:d:10.1007_s12561-016-9144-1
    DOI: 10.1007/s12561-016-9144-1
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    References listed on IDEAS

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    1. Ishwaran, Hemant & Kogalur, Udaya B. & Gorodeski, Eiran Z. & Minn, Andy J. & Lauer, Michael S., 2010. "High-Dimensional Variable Selection for Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 205-217.
    2. S. Wang & B. Nan & N. Zhu & J. Zhu, 2009. "Hierarchically penalized Cox regression with grouped variables," Biometrika, Biometrika Trust, vol. 96(2), pages 307-322.
    3. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
    4. J Stuart Ferriss & Youngchul Kim & Linda Duska & Michael Birrer & Douglas A Levine & Christopher Moskaluk & Dan Theodorescu & Jae K Lee, 2012. "Multi-Gene Expression Predictors of Single Drug Responses to Adjuvant Chemotherapy in Ovarian Carcinoma: Predicting Platinum Resistance," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-9, February.
    5. Sijian Wang & Bin Nan & Ji Zhu & David G. Beer, 2008. "Doubly Penalized Buckley–James Method for Survival Data with High-Dimensional Covariates," Biometrics, The International Biometric Society, vol. 64(1), pages 132-140, March.
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