Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas
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Way Gregory P., Sanchez-Vega Francisco, La Konnor, Armenia Joshua, Chatila Walid K., Luna Augustin, Sander Chris, Cherniack Andrew D., Mina Marco, Ciriello Giovanni, Schultz Nikolaus, Caesar-Johnson Samantha J., Demchok John A., Felau Ina, Kasapi Melpomeni, Ferguson Martin L., Hutter Carolyn M., Sofia Heidi J., Tarnuzzer Roy, Wang Zhining, Yang Liming, Zenklusen Jean C., Zhang Jiashan (Julia), Chudamani Sudha, Liu Jia, Lolla Laxmi, Naresh Rashi, Pihl Todd, Sun Qiang, Wan Yunhu, Wu Ye, Cho Juok, Defreitas Timothy, Frazer Scott, Gehlenborg Nils, Getz Gad, Heiman David I., Kim Jaegil, Lawrence Michael S., Lin Pei, Meier Sam, Noble Michael S., Saksena Gordon, Voet Doug, Zhang Hailei, Bernard Brady, Chambwe Nyasha, Dhankani Varsha, Knijnenburg Theo, Kramer Roger, Leinonen Kalle, Liu Yuexin, Miller Michael, Reynolds Sheila, Shmulevich Ilya, Thorsson Vesteinn, Zhang Wei, Akbani Rehan, Broom Bradley M., Hegde Apurva M., Ju Zhenlin, Kanchi Rupa S., Korkut Anil, Li Jun, Liang Han, Ling Shiyun, Liu Wenbin, Lu Yiling, Mills Gordon B., Ng Kwok-Shing, Rao Arvind, Ryan Michael, Wang Jing, Weinstein John N., Zhang Jiexin, Abeshouse Adam, Armenia Joshua, Chakravarty Debyani, Chatila Walid K., de Bruijn Ino, Gao Jianjiong, Gross Benjamin E., Heins Zachary J., Kundra Ritika, La Konnor, Ladanyi Marc, Luna Augustin, Nissan Moriah G., Ochoa Angelica, Phillips Sarah M., Reznik Ed, Sanchez-Vega Francisco, Sander Chris, Schultz Nikolaus, Sheridan Robert, Sumer S. Onur, Sun Yichao, Taylor Barry S., Wang Jioajiao, Zhang Hongxin, Anur Pavana, Peto Myron, Spellman Paul, Benz Christopher, Stuart Joshua M., Wong Christopher K., Yau Christina, Hayes D. Neil, Parker Joel S., Wilkerson Matthew D., Ally Adrian, Balasundaram Miruna, Bowlby Reanne, Brooks Denise, Carlsen Rebecca, Chuah Eric, Dhalla Noreen, Holt Robert, Jones Steven J. M., Kasaian Katayoon, Lee Darlene, Ma Yussanne, Marra Marco A., Mayo Michael, Moore Richard A., Mungall Andrew J., Mungall Karen, Robertson A. Gordon, Sadeghi Sara, Schein Jacqueline E., Sipahimalani Payal, Tam Angela, Thiessen Nina, Tse Kane, Wong Tina, Berger Ashton C., Beroukhim Rameen, Cherniack Andrew D., Cibulskis Carrie, Gabriel Stacey B., Gao Galen F., Ha Gavin, Meyerson Matthew, Schumacher Steven E., Shih Juliann, Kucherlapati Melanie H., Kucherlapati Raju S., Baylin Stephen, Cope Leslie, Danilova Ludmila, Bootwalla Moiz S., Lai Phillip H., Maglinte Dennis T., Van Den Berg David J., Weisenberger Daniel J., Auman J. Todd, Balu Saianand, Bodenheimer Tom, Fan Cheng, Hoadley Katherine A., Hoyle Alan P., Jefferys Stuart R., Jones Corbin D., Meng Shaowu, Mieczkowski Piotr A., Mose Lisle E., Perou Amy H., Perou Charles M., Roach Jeffrey, Shi Yan, Simons Janae V., Skelly Tara, Soloway Matthew G., Tan Donghui, Veluvolu Umadevi, Fan Huihui, Hinoue Toshinori, Laird Peter W., Shen Hui, Zhou Wanding, Bellair Michelle, Chang Kyle, Covington Kyle, Creighton Chad J., Dinh Huyen, Doddapaneni Harshavardhan, Donehower Lawrence A., Drummond Jennifer, Gibbs Richard A., Glenn Robert, Hale Walker, Han Yi, Hu Jianhong, Korchina Viktoriya, Lee Sandra, Lewis Lora, Li Wei, Liu Xiuping, Morgan Margaret, Morton Donna, Muzny Donna, Santibanez Jireh, Sheth Margi, Shinbrot Eve, Wang Linghua, Wang Min, Wheeler David A., Xi Liu, Zhao Fengmei, Hess Julian, Appelbaum Elizabeth L., Bailey Matthew, Cordes Matthew G., Ding Li, Fronick Catrina C., Fulton Lucinda A., Fulton Robert S., Kandoth Cyriac, Mardis Elaine R., Mclellan Michael D., Miller Christopher A., Schmidt Heather K., Wilson Richard K., Crain Daniel, Curley Erin, Gardner Johanna, Lau Kevin, Mallery David, Morris Scott, Paulauskis Joseph, Penny Robert, Shelton Candace, Shelton Troy, Sherman Mark, Thompson Eric, Yena Peggy, Bowen Jay, Gastier-Foster Julie M., Gerken Ma
ISSN: 2211-1247
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.
