Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: results from the randomised phase III SAMIT trial.

Autor:	Sundar R; Department of Haematology-Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore.; Yong Loo Lin School of Medicine, National University of Singapore, Singapore.; Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore.; The N.1 Institute for Health, National University of Singapore, Singapore., Barr Kumarakulasinghe N; Department of Haematology-Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore., Huak Chan Y; Biostatistics Unit, Yong Loo Lin School of Medicine, National University Singapore, Singapore., Yoshida K; Department of Surgical Oncology, Gifu University Graduate School of Medicine, Gifu, Japan., Yoshikawa T; Department of Gastric Surgery, National Cancer Center Hospital, Tokyo, Japan., Miyagi Y; Kanagawa Cancer Center Research Institute, Yokohama, Japan., Rino Y; Department of Surgery, Yokohama City University, Yokohama, Japan., Masuda M; Department of Surgery, Yokohama City University, Yokohama, Japan., Guan J; Department of Clinical Biostatistics, Graduate School of Medicine, Kyoto University, Kyoto, Japan., Sakamoto J; Tokai Central Hospital, Kakamigahara, Japan., Tanaka S; Department of Clinical Biostatistics, Graduate School of Medicine, Kyoto University, Kyoto, Japan., Tan AL; Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore., Hoppe MM; Cancer Science Institute of Singapore, National University of Singapore, Singapore., Jeyasekharan AD; Department of Haematology-Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore.; Cancer Science Institute of Singapore, National University of Singapore, Singapore., Ng CCY; Laboratory of Cancer Epigenome, Department of Medical Sciences, National Cancer Centre Singapore, Singapore., De Simone M; InSilico Genomics, Phoenix, Arizona, USA., Grabsch HI; Department of Pathology, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands.; Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK., Lee J; Department of Medicine, Division of Hematology-Oncology, Samsung Medical Center, Gangnam-gu, Republic of Korea., Oshima T; Department of Gastrointestinal Surgery, Kanagawa Cancer Center, Yokohama, Japan gmstanp@duke-nus.edu.sg oshimat@kcch.jp tuburayaa@gmail.com., Tsuburaya A; Department of Surgery, Ozawa Hospital, Odawara, Japan gmstanp@duke-nus.edu.sg oshimat@kcch.jp tuburayaa@gmail.com., Tan P; Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore gmstanp@duke-nus.edu.sg oshimat@kcch.jp tuburayaa@gmail.com.; Cancer Science Institute of Singapore, National University of Singapore, Singapore.; Genome Institute of Singapore, Singapore.; SingHealth/Duke-NUS Institute of Precision Medicine, National Heart Centre Singapore, Singapore.; Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Jazyk:	angličtina
Zdroj:	Gut [Gut] 2022 Apr; Vol. 71 (4), pp. 676-685. Date of Electronic Publication: 2021 May 12.
DOI:	10.1136/gutjnl-2021-324060
Abstrakt:	Objective: To date, there are no predictive biomarkers to guide selection of patients with gastric cancer (GC) who benefit from paclitaxel. Stomach cancer Adjuvant Multi-Institutional group Trial (SAMIT) was a 2×2 factorial randomised phase III study in which patients with GC were randomised to Pac-S-1 (paclitaxel +S-1), Pac-UFT (paclitaxel +UFT), S-1 alone or UFT alone after curative surgery. Design: The primary objective of this study was to identify a gene signature that predicts survival benefit from paclitaxel chemotherapy in GC patients. SAMIT GC samples were profiled using a customised 476 gene NanoString panel. A random forest machine-learning model was applied on the NanoString profiles to develop a gene signature. An independent cohort of metastatic patients with GC treated with paclitaxel and ramucirumab (Pac-Ram) served as an external validation cohort. Results: From the SAMIT trial 499 samples were analysed in this study. From the Pac-S-1 training cohort, the random forest model generated a 19-gene signature assigning patients to two groups: Pac-Sensitive and Pac-Resistant. In the Pac-UFT validation cohort, Pac-Sensitive patients exhibited a significant improvement in disease free survival (DFS): 3-year DFS 66% vs 40% (HR 0.44, p=0.0029). There was no survival difference between Pac-Sensitive and Pac-Resistant in the UFT or S-1 alone arms, test of interaction p<0.001. In the external Pac-Ram validation cohort, the signature predicted benefit for Pac-Sensitive (median PFS 147 days vs 112 days, HR 0.48, p=0.022). Conclusion: Using machine-learning techniques on one of the largest GC trials (SAMIT), we identify a gene signature representing the first predictive biomarker for paclitaxel benefit. Trial Registration Number: UMIN Clinical Trials Registry: C000000082 (SAMIT); ClinicalTrials.gov identifier, 02628951 (South Korean trial). Competing Interests: Competing interests: RS: Advisory board: BMS, Merck, Eisai, Bayer, Taiho; honoraria for talks: MSD, Eli Lilly, BMS, Roche, Taiho; Travel funding: Roche, Astra Zeneca, Taiho, Eisai; Research funding: Paxman Coolers, MSD. These are outside the submitted work.TO: Research Funding: Taiho pharmaceutical, Chugai pharmaceutical, Ono pharmaceutical, Daiitisankyo pharmaceutical, Nippon Kayaku and Eli Lilly Japan K. K. Lecture fees: Nippon Kayaku, Ono pharmaceutical and Bristol-Myers Squibb K. K. Speaker Bureau: Taiho pharmaceutical, Chugai pharmaceutical, Ono pharmaceutical, Bristol-Myers Squibb K. K and Eli Lilly Japan K. K. These are outside the submitted work. TY: Lecture fees from: MSD, ONO, BMS, Taiho, Chugai, Daiichi-Sankyo, Lilly, Johnson & Johnson, Covidien and Olympus. Personal grant from Lilly. These are outside the submitted work. KY: Personal fees from Taiho Pharm and Bristol-Myers Squibb, during the conduct of the study; grants and personal fees from Asahi Kasei Pharma, Chugai Pharm., Covidien Japan, Daiichi Sankyo, Eisai, Eli Lilly Japan, Johnson & Johnson, MerkSerono, MSD, Nippon Kayaku, Novartis, Ono Pharm., Otsuka Pharm., Sanofi, Tsumura, Yakult Honsha, Takeda Pharm., grants from Abbott, Abbvie, Astellas, Biogen Japan, Celgene, GlaxoSmithKline, KCI, Kyowa Kirin, Meiji Seika Pharma, Toray Medical, Koninklijke Philips, personal fees from AstraZeneka, Denka, EA Pharma, Olympus, Pfizer, Sanwa Kagaku Kenkyusho, SBI Pharma, Teijin Phamra, TERUMO. These are outside the submitted work. YR: Speaker Bureau from; Daiichi-Sankyo, Johnson & Johnson, Otsuka, Lilly, Taiho pharmaceutical, Bristol-Myers Squibb. Research Funding: Taiho pharmaceutical, Abbott, Asahi Kasei, Daiichi-Sankyo, Tsumura & Co., Covidien, Zeria pharmaceutical, Otsuka, EA Pharma, Johnson & Johnson. These are outside the submitted work. YM: Lecture fees from AstraZeneca, Taiho, Chugai, and Daiichi-Sankyo. Consigned research fund from Toso company, Japan. These are outside the submitted work. MM: Research Funding from Chugai pharmaceutical, Teijin pharmaceutical, Daiitisankyo pharmaceutical, Takeda pharmaceutical, Terumo, Japan Lifelin, Senkod. These are outside the submitted work. ST: Lecture fee: Bayer Yakuhin, Amgen Astellas BioPharma K.K. Consultation fee: Boehringer Ingelheim. These are outside the submitted work. ADJ: honoraria from AstraZeneca, Janssen and MSD, travel funding from Perkin Elmer, and research funding from Janssen. These are outside the submitted work. HG: honoraria for participation in an expert meeting from MSD. These are outside the submitted work. AL-KT: Lecture fees Chugai Pharmaceutical. These are outside the submitted work. PT: Travel: Illumina, Research funding: Thermo Fisher, Kyowa Hakko Kirin. These are outside the submitted work. (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)
Databáze:	MEDLINE
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