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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 Tumour Microenvironment Score (TME) 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 gastric cancer patients 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 ± standard deviation 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% 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 (MSI) scores significantly outperformed MSI as a univariable predictor of immunotherapy response., Conclusions: We conclude that the Tumour Microenvironment Score is a predominate driver of prognosis as well as chemotherapy and immunotherapy-related outcomes in gastric cancer. This paper provides a foundation for additional analyses and translational work.

Background: Cross-platform normalization seeks to minimize technological bias between microarray and RNAseq whole-transcriptome data. Incorporating multiple gene expression platforms permits external validation of experimental findings, and augments training sets for machine learning models. Here, we compare the performance of Feature Specific Quantile Normalization (FSQN) to a previously used but unvalidated and uncharacterized method we label as Feature Specific Mean Variance Normalization (FSMVN). We evaluate the performance of these methods for bidirectional normalization in the context of nested feature selection., Results: FSQN and FSMVN provided clinically equivalent bidirectional model performance with and without feature selection for colon CMS and breast PAM50 classification. Using principal component analysis, we determine that these methods eliminate batch effects related to technological platforms. Without feature selection, no statistical difference was identified between the performance of FSQN and FSMVN of cross-platform data compared to within-platform distributions. Under optimal feature selection conditions, balanced accuracy was FSQN and FSMVN were statistically equivalent to the within-platform distribution performance in multivariable linear regression analysis. FSQN and FSMVN also provided similar performance to within-platform distributions as the number of selected genes used to create models decreases., Conclusions: In the context of generating supervised machine learning classifiers for molecular subtypes, FSQN and FSMVN are equally effective. Under optimal modeling conditions, FSQN and FSMVN provide equivalent model accuracy performance on cross-platform normalization data compared to within-platform data. Using cross-platform data should still be approached with caution as subtle performance differences may exist depending on the classification problem, training, and testing distributions., (© 2024. The Author(s).)

Recent advances in our understanding of gastric cancer biology have prompted a shift towards more personalized therapy. However, results are based on population-based survival analyses, which evaluate the average survival effects of entire treatment groups or single prognostic variables. This study uses a personalized survival modelling approach called individual survival distributions (ISDs) with the multi-task logistic regression (MTLR) model to provide novel insight into personalized survival in gastric adenocarcinoma. We performed a pooled analysis using 1043 patients from a previously characterized database annotated with molecular subtypes from the Cancer Genome Atlas, Asian Cancer Research Group, and tumour microenvironment (TME) score. The MTLR model achieved a 5-fold cross-validated concordance index of 72.1 ± 3.3%. This model found that the TME score and chemotherapy had similar survival effects over the entire study time. The TME score provided the greatest survival benefit beyond a 5-year follow-up. Stage III and Stage IV disease contributed the greatest negative effect on survival. The MTLR model weights were significantly correlated with the Cox model coefficients (Pearson coefficient = 0.86, p < 0.0001). We illustrate how ISDs can accurately predict the survival time for each patient, which is especially relevant in cases of molecular subtype heterogeneity. This study provides evidence that the TME score is principally associated with long-term survival in gastric adenocarcinoma. Additional external validation and investigation into the clinical utility of this ISD model in gastric cancer is an area of future research.