Oncology Commons^™

Analytic Lymph Node Number Establishes Staging Accuracy By Occult Tumor Burden In Colorectal Cancer., Terry Hyslop, David S. Weinberg, Stephanie Schulz, Alan Barkun, Scott A. Waldman

Department of Pharmacology and Experimental Therapeutics Faculty Papers

BACKGROUND AND OBJECTIVES: Recurrence in lymph node-negative (pN0) colorectal cancer suggests the presence of undetected occult metastases. Occult tumor burden in nodes estimated by GUCY2C RT-qPCR predicts risk of disease recurrence. This study explored the impact of the number of nodes analyzed by RT-qPCR (analytic) on the prognostic utility of occult tumor burden.

METHODS: Lymph nodes (range: 2-159) from 282 prospectively enrolled pN0 colorectal cancer patients, followed for a median of 24 months (range: 2-63), were analyzed by GUCY2C RT-qPCR. Prognostic risk categorization defined using occult tumor burden was the primary outcome measure. Association of prognostic variables and risk category …

Survival Associated Pathway Identification With Group Lp Penalized Global Auc Maximization., Zhenqiu Liu, Laurence S Magder, Terry Hyslop, Li Mao

Department of Pharmacology and Experimental Therapeutics Faculty Papers

It has been demonstrated that genes in a cell do not act independently. They interact with one another to complete certain biological processes or to implement certain molecular functions. How to incorporate biological pathways or functional groups into the model and identify survival associated gene pathways is still a challenging problem. In this paper, we propose a novel iterative gradient based method for survival analysis with group Lp penalized global AUC summary maximization. Unlike LASSO, Lp (p < 1) (with its special implementation entitled adaptive LASSO) is asymptotic unbiased and has oracle properties 1. We first extend Lp for individual gene identification to group Lp penalty for pathway selection, and then develop a novel iterative gradient algorithm for penalized global AUC summary maximization (IGGAUCS). This method incorporates the genetic pathways into global AUC summary maximization and identifies survival associated pathways instead of individual genes. The tuning parameters are determined using 10-fold cross validation with training data only. The prediction performance is evaluated using test data. We apply the proposed method to survival outcome analysis with gene expression profile and identify multiple pathways simultaneously. Experimental results with simulation and gene expression data demonstrate that the proposed procedures can be used for identifying important biological pathways that are related to survival phenotype and for building a parsimonious model for predicting the survival times.