The tumour microenvironment score outperforms established molecular classifiers as a prognostic factor for disease-free interval and disease-specific survival in non-metastatic gastric cancer
Bibliographic record
Abstract
INTRODUCTION Molecular classification in gastric cancer has identified relevant disease heterogeneity with prognostic implications. However, limited comparative analysis of molecular classification systems has occurred. We assessed the effect of the Tumour Microenvironment Score (TME), the Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ACRG) classification systems on disease-free interval (DFI) and disease-specific survival (DSS) in Stage I-III gastric cancer. METHODS Previously characterized machine learning models were used to assign TCGA, ACRG and TME molecular classes to stage I-III patients in the ACRG and TCGA datasets (n=523). DFI and DSS was assessed using univariable and multivariable Cox Proportional Hazards models. RESULTS A multivariable Cox model including TCGA, ACRG and TME subtypes showed that only a high TME score was associated with improved DFI (HR 0.22 [95% CI 0.10, 0.49]; p < 0.001) and DSS (HR 0.2 [95% CI 0.09-0.43]; p < 0.001). The significant effect of TME High score was maintained after sensitivity analysis that adjusted for stage, age, sex, chemotherapy, radiation, tumour location, and study (DFI: TME High HR 0.33 [95% CI 0.15, 0.73)]; p < 0.01 and DSS: TME High HR 0.21 [95% CI 0.08, 0.52)]; p < 0.001). CONCLUSIONS In an integrated analysis comparing TCGA, ACRG and TME scores, a high TME score is the only independent molecular prognostic factor for DFI and DSS in non-metastatic gastric cancer. Additional investigation into implications of the heterogeneity of the TME score relative to the TCGA and ACRG classifications may yield additional insight into gastric cancer biology and treatment. Synopsis We compared the prognostic relevance of multiple molecular classification methods in gastric cancer using a previously developed machine learning model. The Tumour Microenvironment Score, which indicates an active immune microenvironment, was the only significant independent prognostic factor for disease-free interval and disease-specific survival.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".