NetSig

NetSig integrates protein interaction networks with tumor exome data to identify and prioritize potential cancer driver genes.

Key Features:

• Integration of Data: Combines protein interaction networks with tumor exome data from 4,742 samples.
• Classification Accuracy: Demonstrates high accuracy in classifying known driver genes across various tumor types, achieving 60% success in tested cases.
• Predictive Capability: Predicts new cancer driver gene candidates, identifying 62 potential drivers for further investigation.

Scientific Applications:

• Tumor Genomics Research: Expands discovery in cancer genomics by providing a computational approach to identify novel oncogenes.
• Experimental Validation: Supports in vivo experimental frameworks to assess tumorigenic potential, with predicted candidates inducing tumors at rates comparable to known oncogenes and higher than random genes.

Methodology:

NetSig employs a robust statistical integration method to combine molecular network information with tumor genome/exome data, and it reanalyzes tumor-inducing candidates in patient cohorts (oncogene-negative lung adenocarcinomas) to identify gene amplifications such as AKT2 and TFDP2.