Journal for ImmunoTherapy of Cancer (Jan 2019)

Clinicopathological and molecular features of responders to nivolumab for patients with advanced gastric cancer

  • Saori Mishima,
  • Akihito Kawazoe,
  • Yoshiaki Nakamura,
  • Akinori Sasaki,
  • Daisuke Kotani,
  • Yasutoshi Kuboki,
  • Hideaki Bando,
  • Takashi Kojima,
  • Toshihiko Doi,
  • Atsushi Ohtsu,
  • Takayuki Yoshino,
  • Takeshi Kuwata,
  • Akihito Tsuji,
  • Kohei Shitara

DOI
https://doi.org/10.1186/s40425-019-0514-3
Journal volume & issue
Vol. 7, no. 1
pp. 1 – 8

Abstract

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Abstract Background Clinicopathological and molecular features of responders to nivolumab for advanced gastric cancer (AGC) are not well understood. Methods Patients (pts) with AGC who were treated with nivolumab after two or more chemotherapy regimens in a single institution from September 2017 to May 2018 were enrolled in this study. PD-L1 expression in tumor cells (TC) and mismatch repair (MMR) were analyzed by immunohistochemistry. Epstein-Barr virus (EBV) was detected by in situ hybridization. Cancer genome alterations were evaluated by a next-generation sequencing-based panel. High tumor mutation burden (TMB) was defined as more than 10 mutations/megabase. Results A total of 80 pts were analyzed in this study. Tumor response was evaluated in 72 pts with measurable lesions and 14 pts (19%) had an objective response. Overall response rate (ORR) was significantly higher in pts with ECOGPS 0 in those with PS 1 or 2, MMR-deficient (MMR-D) in those with MMR-proficient (MMR-P), PD-L1+ in TC in those with PD-L1- in TC and PIK3CA mutation in those with PIK3CA wild-type. ORR was 31% in pts with at least one of the following factors; MMR-D, high TMB, EBV+ and PD-L1+ in TC vs. 0% in those without these factors. Progression-free survival was significantly longer in pts with PS 0 than in those with PS 1 or 2, MMR-D than in those with MMR-P, and PD-L1+ in TC than in those with PD-L1- in TC. Conclusions Some features were associated with favorable response to nivolumab for AGC. Combining these features might be useful to predict efficacy.

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