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Cell. 2014 Dec 4;159(6):1461-75. doi: 10.1016/j.cell.2014.10.048. Epub 2014 Nov 26.

Integration of genomic data enables selective discovery of breast cancer drivers.

Author information

1
Department of Biological Sciences and Department of Systems Biology, Columbia University, New York, NY 10027, USA; Department of Computer Science, Columbia University, New York, NY 10027, USA.
2
Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY 10029, USA.
3
Department of Biological Sciences and Department of Systems Biology, Columbia University, New York, NY 10027, USA.
4
Icahn School of Medicine at Mount Sinai, The Mount Sinai Hospital, New York, NY 10029, USA. Electronic address: jose.silva@mssm.edu.
5
Department of Biological Sciences and Department of Systems Biology, Columbia University, New York, NY 10027, USA. Electronic address: dpeer@biology.columbia.edu.

Abstract

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.

PMID:
25433701
PMCID:
PMC4258423
DOI:
10.1016/j.cell.2014.10.048
[Indexed for MEDLINE]
Free PMC Article
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