한빛사 논문
Joo Sang Lee1,2,3,*, Nishanth Ulhas Nair1, Gal Dinstag4, Lesley Chapman1, Youngmin Chung2, Kun Wang1, Sanju Sinha1, Hongui Cha3, Dasol Kim2, Alexander V. Schperberg5, Ajay Srinivasan6, Vladimir Lazar7, Eitan Rubin8, Sohyun Hwang9, Raanan Berger10, Tuvik Beker4, Ze’ev Ronai11, Sridhar Hannenhalli1, Mark R. Gilbert12, Razelle Kurzrock7,13, Se-Hoon Lee3,14, Kenneth Aldape15, Eytan Ruppin1,16,*
1Cancer Data Science Lab, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
2Next Generation Medicine Lab, Department of Artificial Intelligence & Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
3Department of Digital Health & Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Samsung Medical Center, Sungkyunkwan University, Seoul 06351, Republic of Korea
4Pangea Therapeutics, Ltd., Tel Aviv 6971003, Israel
5Department of Mechanical and Aerospace Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
6Research and Innovations Group, Datar Cancer Genetics Limited, Nasik, Maharashtra 422010, India
7Worldwide Innovative Network (WIN) Association-WIN Consortium, Villejuif 94801, France
8The Center for Evolutionary Genomics and Medicine, Ben-Gurion University of the Negev, Beersheva 8410501, Israel
9Department of Pathology, CHA University, CHA Bundang Medical Center, Seongnam 13497, Republic of Korea
10Cancer Center, Chaim Sheba Medical Center, Tel Hashomer 5262000, Israel
11Cancer Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
12Neuro-Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
13Moore Cancer Center, University of California, San Diego, San Diego, CA 92037, USA
14Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea
15Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
16Lead contact
*Corresponding author
Abstract
Precision oncology has made significant advances, mainly by targeting actionable mutations in cancer driver genes. Aiming to expand treatment opportunities, recent studies have begun to explore the utility of tumor transcriptome to guide patient treatment. Here, we introduce SELECT (synthetic lethality and rescue-mediated precision oncology via the transcriptome), a precision oncology framework harnessing genetic interactions to predict patient response to cancer therapy from the tumor transcriptome. SELECT is tested on a broad collection of 35 published targeted and immunotherapy clinical trials from 10 different cancer types. It is predictive of patients’ response in 80% of these clinical trials and in the recent multi-arm WINTHER trial. The predictive signatures and the code are made publicly available for academic use, laying a basis for future prospective clinical studies.
Keywords : precision oncology, synthetic lethality, synthetic rescues, transcriptomics, cancer immunotherapy, patient stratification
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