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 and C. Kendziorski ()
Additional contact information
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

Statistics in Biosciences, 2017, vol. 9, issue 1, No 1, 12 pages

Abstract: 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 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12561-016-9144-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stabio:v:9:y:2017:i:1:d:10.1007_s12561-016-9144-1

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/12561

DOI: 10.1007/s12561-016-9144-1

Access Statistics for this article

Statistics in Biosciences is currently edited by Hongyu Zhao and Xihong Lin

More articles in Statistics in Biosciences from Springer, International Chinese Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().