Abstract 4275: Identifying cancer driver mutations in clinical sequencing data
Authors: Tenghui Chen, Hao Zhao, Yong Mao, Yuan Qi, Agda Karina Eterovic, Kenna R. Mills Shaw, Stacy L. Moulder, Michael A. Davies, John F. Degroot, Scott E. Kopetz, Funda Meric-Bernstam, Gordon B. Mills, Ken Chen
Journal: Cancer Research (2014)

Abstract Background Cancer therapy is challenged by diverse molecular implementations of oncogenic processes and by variations in therapeutic responses. So far, whole genome sequencing (WGS) and whole exome sequencing (WES) has been implemented in both research and clinical settings to identify oncogenic events in cancer genomes. However, a large number of passenger mutations have been identified and true driver mutations have been disguised. Distinguishing “driver” events from “passenger” events will be a key challenge for the realization of targeted therapy. To elucidate driver mutations that are clinically actionable, the Institute for Personalized Cancer Therapy at MD Anderson cancer center has developed a NGS clinical sequencing platform, T200, to sequence 202 cancer-related genes at high depth in thousands of cancer patients at MD Anderson. Materials and Methods The T200 mutation data, including single nucleotide variants (SNV, variant allele frequency &amp;gt;=1%) and copy number variations (CNV), of more than 500 cancer patients treated in MD Anderson Cancer Center were utilized in our study. Results 1. By analyzing the SNV data, we identified several sets of co-occurring genes across all cancers. Some sets such as KRAS/APC/SMAD4 contain driver genes from multiple signaling pathways, which may indicate essential mechanisms for tumor development and nominate targets for combinational therapy. 2. We elucidated mutual-exclusive mutations that were from genes in the same or different pathways. Several sets of well-known mutual-exclusive SNVs were verified in our analysis such as BRAF/NRAS SNVs in melanoma. We also illustrated novel sets of mutual-exclusive mutational events, such as IDH1/PTEN/PPP1R3A SNVs in brain tumors and EGFR/FGFR3/GNAS/NOTCH4 CNVs in all cancers. 3. SNVs of more than 10 genes were enriched in specific cancer types, such as IDH1 and NF1 in brain tumor, BRAF and MITF in melanoma. CNVs of more than 50 genes were found enriched in one cancer type, such as PDGFRA amplifications in brain tumors and IL6R amplifications in breast cancer. We observed not only expected cancer specific mutations such as BRAF SNVs but also novel mutations such as NF1 SNVs and PDGFRA amplifications. 4. We performed mutual-exclusivity and cancer type enrichment analysis on SNV hotspots. More than 20 SNV hotspots were elucidated, such as BRAF(V600E) in melanoma, IDH1(R132H) in brain tumors, KRAS(G12D) in colorectal cancer, and PIK3CA(H1047L) in breast cancer. In addition, multiple SNV hotspots were found to occur mutual-exclusively, such as BRAF(V600E)/IDH1(R132H)/MPL(L532V) in brain tumors. Conclusion Deep target sequencing enables us to systematically determine the potential driver mutation events (including low allele frequency SNVs), which not only help us characterize the landscape of cancer genomic alterations, but also provide comprehensive patient molecular profile to facilitate clinical decision-making and novel clinical trial design. Citation Format: Tenghui Chen, Hao Zhao, Yong Mao, Yuan Qi, Agda Karina Eterovic, Kenna R. Mills Shaw, Stacy L. Moulder, Michael A. Davies, John F. Degroot, Scott E. Kopetz, Funda Meric-Bernstam, Gordon B. Mills, Ken Chen. Identifying cancer driver mutations in clinical sequencing data. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4275. doi:10.1158/1538-7445.AM2014-4275