The Tumor Immune Microenvironment Drives Survival Outcomes and Therapeutic Response in an Integrated Molecular Analysis of Gastric Adenocarcinoma.

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.

Feature-specific quantile normalization and feature-specific mean-variance normalization deliver robust bi-directional classification and feature selection performance between microarray and RNAseq data.

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).

Individual Survival Distributions Generated by Multi-Task Logistic Regression Yield a New Perspective on Molecular and Clinical Prognostic Factors in Gastric Adenocarcinoma.

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.

Establishment of CD8+ T Cell Thymic Central Tolerance to Tissue-Restricted Antigen Requires PD-1.

Highly self-reactive T cells are censored from the repertoire by both central and peripheral tolerance mechanisms upon receipt of high-affinity TCR signals. Clonal deletion is considered a major driver of central tolerance; however, other mechanisms such as induction of regulatory T cells and functional impairment have been described. An understanding of the interplay between these different central tolerance mechanisms is still lacking.

Increased CD4/CD8 Lymphocyte ratio predicts favourable neoadjuvant treatment response in gastric cancer: A prospective pilot study.

Background: Despite optimal neoadjuvant chemotherapy only 40% of gastric cancer tumours achieve complete or partial treatment response. In the absence of treatment response, neoadjuvant chemotherapy in gastric cancer contributes to adverse events without additional survival benefit compared to adjuvant treatment or surgery alone. Additional strategies and methods are required to optimize the allocation of existing treatment regimens such as FLOT chemotherapy (5-Fluorouracil, Leucovorin, Oxaliplatin and Docetaxel).