Predicting chemotherapy responsiveness in gastric cancer through machine learning analysis of genome, immune, and neutrophil signatures

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2024年12月2日

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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

摘要

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.