Ricerc@Sapienza

The Cancer Genome Atlas (TCGA) cancer genomicsdataset includes over 10,000 tumor-normal exomepairs across 33 different cancer types, in total >400TB of raw data files requiring analysis. Here wedescribe the Multi-Center Mutation Calling in Multi-ple Cancers project, our effort to generate a compre-hensive encyclopedia of somatic mutation calls forthe TCGA data to enable robust cross-tumor-typeanalyses. Our approach accounts for varianceand batch effects introduced by the rapid advance-ment of DNA extraction, hybridization-capture,sequencing, and analysis methods over time.

Precision oncology uses genomic evidence to match patients with treatment but often fails to
identify all patients who may respond. The transcriptome of these “hidden responders” may reveal
responsive molecular states. We describe and evaluate a machine-learning approach to classify
aberrant pathway activity in tumors, which may aid in hidden responder identification. The
algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across
The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer.