Abstract
We present a new method (SPARROW) to identify genes driving expression changes in cancer genome evolution from genome-wide expression data. The identified genes, which we characterize as expression drivers, reveal known driver mutations in multiple human cancers and a potential biomarker for response to a chemotherapy drug. SPARROW identifies a new link between cancer expression variation and driver events, where driver events are difficult to detect from sequence data due to a large number of passenger mutations and lack of comprehensive sequence information from a large number of samples. SPARROW outperforms popular methods for inferring gene expression networks across many benchmarks including known drivers, disease-gene annotations and survival-associated genes. We demonstrate that when applied to acute myeloid leukemia expression data, SPARROW identifies an apoptotic biomarker (PYCARD) for an investigational drug obatoclax. The PYCARD and obatoclax association is validated in 30 AML patient samples.