Person: Cherniack, Andrew
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Publication Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas
(2018) Way, Gregory P.; Sanchez-Vega, Francisco; La, Konnor; Armenia, Joshua; Chatila, Walid K.; Luna, Augustin; Sander, Chris; Cherniack, Andrew; Mina, Marco; Ciriello, Giovanni; Schultz, Nikolaus; Sanchez, Yolanda; Greene, Casey S.SUMMARY Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these “hidden responders” may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA) PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders.
Publication Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas
(2018) Knijnenburg, Theo A.; Wang, Linghua; Zimmermann, Michael T.; Chambwe, Nyasha; Gao, Galen F.; Cherniack, Andrew; Fan, Huihui; Shen, Hui; Way, Gregory P.; Greene, Casey S.; Liu, Yuexin; Akbani, Rehan; Feng, Bin; Donehower, Lawrence A.; Miller, Chase; Shen, Yang; Karimi, Mostafa; Chen, Haoran; Kim, Pora; Jia, Peilin; Shinbrot, Eve; Zhang, Shaojun; Liu, Jianfang; Hu, Hai; Bailey, Matthew H.; Yau, Christina; Wolf, Denise; Zhao, Zhongming; Weinstein, John N.; Li, Lei; Ding, Li; Mills, Gordon B.; Laird, Peter W.; Wheeler, David A.; Shmulevich, Ilya; Monnat, Raymond J; Xiao, Yonghong; Wang, ChenSUMMARY DNA damage repair (DDR) pathways modulate cancer risk, progression, and therapeutic response. We systematically analyzed somatic alterations to provide a comprehensive view of DDR deficiency across 33 cancer types. Mutations with accompanying loss of heterozygosity were observed in over 1/3 of DDR genes, including TP53 and BRCA1/2. Other prevalent alterations included epigenetic silencing of the direct repair genes EXO5, MGMT, and ALKBH3 in ~20% of samples. Homologous recombination deficiency (HRD) was present at varying frequency in many cancer types, most notably ovarian cancer. However, in contrast to ovarian cancer, HRD was associated with worse outcomes in several other cancers. Protein structure-based analyses allowed us to predict functional consequences of rare, recurrent DDR mutations. A new machine-learning-based classifier developed from gene expression data allowed us to identify alterations that phenocopy deleterious TP53 mutations. These frequent DDR gene alterations in many human cancers have functional consequences that may determine cancer progression and guide therapy.