title: Optimized Prediction of Extreme Treatment Outcomes in Ovarian Cancer.
abstract: Ovarian cancer is the fifth leading cause of death among female cancers. Front-line therapy for ovarian cancer is platinum-based chemotherapy. However, the response of patients is highly nonuniform. The TCGA database of serous ovarian carcinomas shows that ~10% of patients respond poorly to platinum-based chemotherapy, with tumors relapsing in seven months or less. Another 10% or so enjoy disease-free survival of three years or more. The objective of the present research is to identify a small number of highly predictive biomarkers that can distinguish between the two extreme responders and then extrapolate to all patients. This is achieved using the lone star algorithm that is specifically developed for biological applications. Using this algorithm, we are able to identify biomarker panels of 25 genes (of 12,000 genes) that can be used to classify patients into one of the three groups: super responders, medium responders, and nonresponders. We are also able to determine a discriminant function that can divide the entire patient population into two classes, such that one group has a clear survival advantage over the other. These biomarkers are developed using the TCGA Agilent platform data and cross-validated on the TCGA Affymetrix platform data, as well as entirely independent data from Tothill et al. The P-values on the training data are extremely small, sometimes below machine zero, while the P-values on cross-validation are well below the widely accepted threshold of 0.05.
authors.affiliation: Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX, USA.
authors.forename: Burook
authors.initials: B
authors.lastname: Misganaw
authors.affiliation: IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA.
authors.forename: Eren
authors.initials: E
authors.lastname: Ahsen
authors.affiliation: Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX, USA.
authors.forename: Nitin
authors.initials: N
authors.lastname: Singh
authors.affiliation: Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX, USA.
authors.forename: Keith A
authors.initials: KA
authors.lastname: Baggerly
authors.affiliation: Graduate Student, The University of Texas Graduate School of the Biomedical Sciences, Houston, TX, USA.
authors.forename: Anna
authors.initials: A
authors.lastname: Unruh
authors.affiliation: The University of Texas Southwestern Medical Center, Dallas, TX, USA.
authors.forename: Michael A
authors.initials: MA
authors.lastname: White
authors.affiliation: Cecil and Ida Green Chair in Systems Biology Science, Erik Jonsson School of Engineering and Computer Science, University of Texas at Dallas, Richardson, TX, USA.
authors.forename: M
authors.initials: M
authors.lastname: Vidyasagar
keywords: ovarian cancer; platinum chemotherapy; prediction of patient response