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Machine-learning model derived gene signature predictive of paclitaxel survival benefit in gastric cancer: results from the randomised phase III SAMIT trial.
Sundar, Raghav; Barr Kumarakulasinghe, Nesaretnam; Huak Chan, Yiong; Yoshida, Kazuhiro; Yoshikawa, Takaki; Miyagi, Yohei; Rino, Yasushi; Masuda, Munetaka; Guan, Jia; Sakamoto, Junichi; Tanaka, Shiro; Tan, Angie Lay-Keng; Hoppe, Michal Marek; Jeyasekharan, Anand D; Ng, Cedric Chuan Young; De Simone, Mark; Grabsch, Heike I; Lee, Jeeyun; Oshima, Takashi; Tsuburaya, Akira; Tan, Patrick.
Afiliación
  • Sundar R; Department of Haematology-Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore.
  • Barr Kumarakulasinghe N; Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
  • Huak Chan Y; Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore.
  • Yoshida K; The N.1 Institute for Health, National University of Singapore, Singapore.
  • Yoshikawa T; Department of Haematology-Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore.
  • Miyagi Y; Biostatistics Unit, Yong Loo Lin School of Medicine, National University Singapore, Singapore.
  • Rino Y; Department of Surgical Oncology, Gifu University Graduate School of Medicine, Gifu, Japan.
  • Masuda M; Department of Gastric Surgery, National Cancer Center Hospital, Tokyo, Japan.
  • Guan J; Kanagawa Cancer Center Research Institute, Yokohama, Japan.
  • Sakamoto J; Department of Surgery, Yokohama City University, Yokohama, Japan.
  • Tanaka S; Department of Surgery, Yokohama City University, Yokohama, Japan.
  • Tan AL; Department of Clinical Biostatistics, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
  • Hoppe MM; Tokai Central Hospital, Kakamigahara, Japan.
  • Jeyasekharan AD; Department of Clinical Biostatistics, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
  • Ng CCY; Program in Cancer and Stem Cell Biology, Duke-NUS Medical School, Singapore.
  • De Simone M; Cancer Science Institute of Singapore, National University of Singapore, Singapore.
  • Grabsch HI; Department of Haematology-Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore.
  • Lee J; Cancer Science Institute of Singapore, National University of Singapore, Singapore.
  • Oshima T; Laboratory of Cancer Epigenome, Department of Medical Sciences, National Cancer Centre Singapore, Singapore.
  • Tsuburaya A; InSilico Genomics, Phoenix, Arizona, USA.
  • Tan P; Department of Pathology, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands.
Gut ; 71(4): 676-685, 2022 04.
Article en En | MEDLINE | ID: mdl-33980610
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).
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Gástricas / Adenocarcinoma Tipo de estudio: Clinical_trials / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Gut Año: 2022 Tipo del documento: Article País de afiliación: Singapur Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Gástricas / Adenocarcinoma Tipo de estudio: Clinical_trials / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: Gut Año: 2022 Tipo del documento: Article País de afiliación: Singapur Pais de publicación: Reino Unido