Integration of Genomic Data Enables Selective Discovery of Breast Cancer Drivers

Identifying driver genes in cancer remains a crucial bottleneck in therapeutic development and basic understanding of the disease. We developed Helios, an algorithm that integrates genomic data from primary tumors with data from functional RNAi screens to pinpoint driver genes within large recurrently amplified regions of DNA. Applying Helios to breast cancer data identified a set of candidate drivers highly enriched with known drivers (p < 10−14). Nine of ten top-scoring Helios genes are known drivers of breast cancer, and in vitro validation of 12 candidates predicted by Helios found ten conferred enhanced anchorage-independent growth, demonstrating Helios’s exquisite sensitivity and specificity. We extensively characterized RSF-1, a driver identified by Helios whose amplification correlates with poor prognosis, and found increased tumorigenesis and metastasis in mouse models. We have demonstrated a powerful approach for identifying driver genes and how it can yield important insights into cancer. [Display omitted] •Integrating data helps pinpoint SCNA drivers with high precision•Systematic validation confirms 16 of 18 Helios predicted SCNA drivers•Helios expands the set of known drivers in breast cancer by greater than two times•RSF1 is an oncogene that promotes metastasis in breast cancer A new algorithm called Helios can pinpoint driver mutations within somatic copy number alterations (SCNA) with high precision, offering an in silico tool of identification of driver genes in cancer.