Predicting Chemotherapy Responsiveness in Gastric Cancer Through Machine Learning Analysis of Genome, Immune, and Neutrophil Signatures

Authors

Shota Sasagawa
Yoshitaka Honma
Xinxin Peng
Kazuhiro Maejima
Koji Nagaoka
Yukari Kobayashi
Ayako Oosawa
Todd A Johnson
Yuki Okawa
Han Liang
Kazuhiro Kakimi
Yasuhide Yamada
Hidewaki Nakagawa

Publication Date

3-1-2025

Journal

Gastric Cancer

Abstract

Background: Gastric cancer is a major oncological challenge, ranking highly among causes of cancer-related mortality worldwide. This study was initiated to address the variability in patient responses to combination chemotherapy, highlighting the need for personalized treatment strategies based on genomic data.

Methods: We analyzed whole-genome and RNA sequences from biopsy specimens of 65 advanced gastric cancer patients before their chemotherapy treatment. Using machine learning techniques, we developed a model with 123 omics features, such as immune signatures and copy number variations, to predict their chemotherapy outcomes.

Results: The model demonstrated a prediction accuracy of 70-80% in forecasting chemotherapy responses in both test and validation cohorts. Notably, tumor-associated neutrophils emerged as significant predictors of treatment efficacy. Further single-cell analyses from cancer tissues revealed different neutrophil subgroups with potential antitumor activities suggesting their usefulness as biomarkers for treatment decisions.

Conclusions: This study confirms the utility of machine learning in advancing personalized medicine for gastric cancer by identifying tumor-associated neutrophils and their subgroups as key indicators of chemotherapy response. These findings could lead to more tailored and effective treatment plans for patients.

Keywords

Humans, Stomach Neoplasms, Machine Learning, Neutrophils, Male, Female, Middle Aged, Aged, Biomarkers, Tumor, Precision Medicine, Antineoplastic Combined Chemotherapy Protocols, Prognosis, Gastric cancer, Chemotherapy, Whole-genome sequencing., RNA sequencing, Machine learning, Tumor-associated neutrophils, Personalized medicine

DOI

10.1007/s10120-024-01569-4

PMID

39621213

PMCID

PMC11842519

PubMedCentral® Posted Date

12-2-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes