ESMO Gastrointestinal Oncology (Mar 2024)

VOLTAGE-2: multicenter phase II study of nivolumab monotherapy in patients with mismatch repair-deficient resectable locally advanced rectal cancer

  • H. Bando,
  • Y. Tsukada,
  • S. Kumagai,
  • Y. Miyashita,
  • A. Taketomi,
  • S. Yuki,
  • Y. Komatsu,
  • T. Akiyoshi,
  • E. Shinozaki,
  • Y. Kanemitsu,
  • A. Takashima,
  • M. Shiozawa,
  • A. Shiomi,
  • K. Yamazaki,
  • N. Matsuhashi,
  • H. Hasegawa,
  • T. Kato,
  • E. Oki,
  • M. Fukui,
  • M. Wakabayashi,
  • N. Fuse,
  • H. Nishikawa,
  • M. Ito,
  • T. Yoshino

Journal volume & issue
Vol. 3
p. 100031

Abstract

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Background: Neoadjuvant radiotherapy and chemotherapy, followed by surgical resection, are standard treatments for locally advanced rectal cancer (LARC). Emerging evidence has shown the efficacy of anti-programmed cell death protein 1 (anti-PD-1) therapy for patients with mismatch repair-deficient (dMMR) colorectal cancer, particularly in managing metastatic disease. Several ongoing clinical trials evaluating the efficacy of anti-PD-1 therapy in patients with dMMR LARC have reported outstanding responses. Patients and methods: Here, we present the VOLTAGE-2 study (EPOC 2201), a non-randomized, single-arm, phase II trial that aims to investigate the efficacy and safety of nivolumab monotherapy for 1 year in patients with dMMR-resectable LARC. Patients with clinical complete response (cCR) or near-complete response (nCR) will be observed with non-operative management (NOM) using the Memorial Sloan Kettering Regression Schema.The primary endpoint will be investigator-determined 2-year cCR rate for nivolumab monotherapy. We will investigate the surrogacy of circulating tumor DNA assay as a cCR using whole-genome sequencing (WGS)-based molecular residual disease (MRD) assay and will evaluate the biomarkers of the response to anti-PD-1 antibody using whole-exome sequencing (WES) plus whole-transcriptome sequencing (WTS)-based tumor genomics and immune microenvironment evaluations. We plan to carry out spatiotemporal trans-omics analyses using artificial intelligence and deep learning-driven genomics, transcriptomics, radiomics, pathomics, colonoscopic imaging, quality of life, and clinical features.

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