Evidence map›Paper›PMID 39621213›Full record

ArticleGastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association2025

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

Shota Sasagawa, Yoshitaka Honma, Xinxin Peng, Kazuhiro Maejima, Koji Nagaoka, Yukari Kobayashi, Ayako Oosawa, Todd A Johnson, Yuki Okawa, Han Liang and 3 more

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Article in Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Shota SasagawaLaboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
Yoshitaka HonmaDepartment of Head and Neck, Esophageal Medical Oncology, National Cancer Center Hospital, Tokyo, Japan.
Xinxin PengPrecision Scientific (Beijing) Ltd, Beijing, 100085, China.
Kazuhiro MaejimaLaboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
Koji NagaokaDepartment of Immunotherapeutics, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, 113-8655, Japan.
Yukari KobayashiDepartment of Immunotherapeutics, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, 113-8655, Japan.
Ayako OosawaLaboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
Todd A JohnsonLaboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
Yuki OkawaLaboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan.
Han LiangDepartment of Bioinformatics and Computational Biology, Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
Kazuhiro KakimiDepartment of Immunotherapeutics, The University of Tokyo Hospital, Bunkyo-ku, Tokyo, 113-8655, Japan.
Yasuhide YamadaDepartment of Head and Neck, Esophageal Medical Oncology, National Cancer Center Hospital, Tokyo, Japan.
Hidewaki NakagawaLaboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan. hidewaki@riken.jp.ORCID 0000-0003-1807-772X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGastric 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.

methodsWe 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.

resultsThe 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.

conclusionsThis 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.

Indexed as

Antineoplastic Combined Chemotherapy ProtocolsMachine LearningNeutrophilsStomach NeoplasmsAgedBiomarkers, TumorFemaleHumansMaleMiddle AgedPrecision MedicinePrognosisBiomarkers, TumorChemotherapyGastric cancerMachine learningPersonalized medicineRNA sequencingTumor-associated neutrophilsWhole-genome sequencing.

Identifiers

PMID39621213
PMCPMC11842519

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.