Oral Poster Presentations

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OP09 A new molecular signature approach for prediction of driver cancer pathways from transcriptional data Unable to attend:
Date: Sunday, July 12, 10:50 am - 11:10 amRoom: Wicklow Hall 2B
Boris Reva, Mount Sinai School Of Medicine, United States
Dmitry Rykunov, Mount Sinai School Of Medicine, United States
Andrew Usilov, Mount Sinai School Of Medicine, United States
Hui Li, Mount Sinai School Of Medicine, United States
Eric Schadt, Mount Sinai School Of Medicine, United States
Assigning cancer patients to the most effective treatments requires an understanding of the molecular basis of their disease. While DNA-based molecular profiling approaches have flourished over the past several years to transform our understanding of driver pathways across a broad range of tumors, a systematic characterization of key driver pathways based on RNA data has not been undertaken.

Here we introduce a new approach to predict the status of driver cancer pathways based on weighted sums of gene expressions or signature functions derived from RNA sequencing data. To identify the driver cancer pathways of interest, we mined DNA variant data from TCGA and nominated driver alterations in seven major cancer pathways in breast, ovarian, and colon cancer tumors. The activation status of these driver pathways were then characterized using RNA sequencing data by constructing signature functions in training datasets and then testing the accuracy of the signatures in test datasets.

The signature functions perform well in separation tumors with nominated active pathways from tumors with no genomic signs of activation (average AUC equals to 0.83) systematically exceeding the accuracies obtained by the SVM method that we employed as a control approach. A typical pathway signature is composed of ~20 biomarker genes that are unique to a given pathway and cancer type. Our results confirm that driver genomic alterations are distinctively displayed at the transcriptional level and that the transcriptional signatures can generally provide an alternative to DNA sequencing methods in detecting specific driver pathways.