Perou, C. M. | Search bioRxiv

bioRxiv2018

An Integrative Boosting Approach for Predicting Survival Time With Multiple Genomics Platforms

Recent technological advances have made it possible to collect multiple types of genomics data on the same set of patients. It is of great interest to integrate multiple genomics data types together for predicting disease outcomes. We propose a variable selection method, termed Integrative Boosting (I-Boost), that makes proper use of all available clinical and genomics data in predicting individual patient survival time. Through simulation studies and applications to data sets from The Cancer Genome Atlas, we demonstrate that I-Boost provides substantially higher prediction accuracy than existing variable selection methods. Using I-Boost, we show that (1) the integration of multiple genomics platforms with clinical variables significantly improves the prediction accuracy for survival time over the use of clinical variables alone; (2) gene expression values are typically more prognostic of survival time than other genomics data types; and (3) gene modules/signatures are at least as prognostic as the collection of individual gene expression data.

bioinformatics↗

bioRxiv2017

A class of GATA3 mutation reprograms the breast cancertranscriptional network through gain and loss of function

GATA3 is frequently mutated in breast cancer; these mutations are widely presumed to be loss of function. Here, we address molecular alterations downstream of a novel class of GATA3 mutations, revealing both gain and loss of function. Mutation of one allele of GATA3 led to loss of binding and decreased expression at a subset of genes, including Progesterone Receptor. At other loci, associated with epithelial to mesenchymal transition, gain of binding at a novel sequence motif correlated with increased gene expression. These results demonstrate that not all GATA3 mutations are equivalent and that these mutations impact breast cancer through gain and loss of function.

cell biology↗