The Tumor Immune Microenvironment Drives Survival Outcomes and Therapeutic Response in an Integrated Molecular Analysis of Gastric Adenocarcinoma
Bibliographic record
Abstract
PURPOSE: We performed an integrated analysis of molecular classification systems proposed by The Cancer Genome Atlas (TCGA), the Asian Cancer Research Group (ACRG), and the tumor microenvironment (TME) score to identify which classification scheme(s) are most promising to pursue in subsequent translational investigations. EXPERIMENTAL DESIGN: Supervised machine learning classifiers were created using 10-fold nested cross-validation for TCGA, ACRG, and TME subtypes and applied to 2,202 patients with gastric cancer from 11 separate publicly available datasets. Overall survival was assessed with a multivariable Cox proportional hazards model. A propensity score-matched analysis was performed to evaluate the subgroup effect of adjuvant chemotherapy on molecular subtypes. A public external cohort comprised of metastatic gastric cancer treated with immunotherapy was used to externally validate the molecular subtypes. RESULTS: Classification models for TCGA, ACRG, and TME achieved an accuracy ± SD of 89.5% ± 0.04, 84.7% ± 0.04, and 89.3% ± 0.02, respectively. We identified the TME score as the only significantly prognostic classification system [HR, 0.54 (95% confidence interval [CI], 0.39-0.74); global Wald test P < 0.001]. In our subgroup analysis, patients who received adjuvant chemotherapy achieved greater survival with increasing TME score (HR, 0.47; 95% CI, 0.29-0.74; interaction P < 0.05). The combination of TME-high and microsatellite instability scores significantly outperformed microsatellite instability as a univariable predictor of immunotherapy response. CONCLUSIONS: We conclude that the TME score is a predominate driver of prognosis as well as chemotherapy- and immunotherapy-related outcomes in gastric cancer. This article provides a foundation for additional analyses and translational work.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 source (direct Gemma or distilled Codex), 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".