Genomic and clinical parallels between US and Japanese gastric cancers: a propensity score-matched cohort study
Bibliographic record
Abstract
INTRODUCTION: Gastric cancer incidence, risk factors, and survival outcomes differ significantly between Japan and the United States. These disparities have led to the belief that gastric cancer represents biologically distinct diseases across regions. However, direct genomic comparisons of tumours from these populations have not been performed. The aim of this study was to compare the genomic and clinical characteristics of gastric cancers in patients from the US and Japan following curative-intent resection. METHODS: A retrospective cohort study of patients who underwent curative-intent gastrectomy between 2010 and 2019 at Memorial Sloan Kettering (MSK, n = 142) and Fujita Health University (FHU, n = 108), with ≥5 years of follow-up, was conducted. Tumour samples underwent targeted sequencing. Clinical and genomic data were compared between unmatched and propensity score-matched (PSM) cohorts, matched by age, sex, clinical T/N-category, and tumour location (n = 58 each). RESULTS: Commonly altered genes included TP53 (60%), ARID1A (17%), ERBB2 (14%), CCNE1 (13%), and KRAS (12%). MSK tumours showed higher rates of microsatellite instability (MSI-high; 22.4% versus 5.2%, P = 0.013) and KMT2D mutations (18% versus 5%, P < 0.05). Otherwise, gene- and pathway-level alterations were similar across unmatched, microsatellite stable only, and PSM cohorts. Five-year overall survival in PSM cohorts was comparable (MSK 60% versus FHU 69.4%, P = 0.548). Peritoneal recurrence was more common in the MSK cohort (47% versus 34%), but recurrence patterns were not associated with distinct genomic profiles. CONCLUSION: After adjustment for clinical covariates, US and Japanese gastric cancers exhibit comparable genomic landscapes and survival, supporting the relevance of clinical trial data across geographic settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".