Sasagawa Shota, Honma Yoshitaka, Peng Xinxin, Maejima Kazuhiro, Nagaoka Koji, Kobayashi Yukari, Oosawa Ayako, Johnson Todd A, Okawa Yuki, Liang Han, Kakimi Kazuhiro, Yamada Yasuhide, Nakagawa Hidewaki
Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
Department of Head and Neck, Esophageal Medical Oncology, National Cancer Center Hospital, Tokyo, Japan.
Gastric Cancer. 2025 Mar;28(2):228-244. doi: 10.1007/s10120-024-01569-4. Epub 2024 Dec 2.
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.
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.
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.
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.
胃癌是一项重大的肿瘤学挑战，在全球癌症相关死亡率原因中排名靠前。开展本研究是为了解决患者对联合化疗反应的变异性问题，强调基于基因组数据制定个性化治疗策略的必要性。
我们分析了65例晚期胃癌患者化疗前活检标本的全基因组和RNA序列。利用机器学习技术，我们开发了一个具有123个组学特征（如免疫特征和拷贝数变异）的模型，以预测他们的化疗结果。
该模型在测试和验证队列中预测化疗反应的准确率为70%-80%。值得注意的是，肿瘤相关中性粒细胞成为治疗疗效的重要预测指标。对癌组织进行的进一步单细胞分析揭示了具有潜在抗肿瘤活性的不同中性粒细胞亚群，表明它们作为治疗决策生物标志物的有用性。
本研究通过将肿瘤相关中性粒细胞及其亚群确定为化疗反应的关键指标，证实了机器学习在推进胃癌个性化医疗方面的实用性。这些发现可能为患者带来更具针对性和有效的治疗方案。
