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.

A first-in-human phase I trial of daily oral zelenirstat, a N-myristoyltransferase inhibitor, in patients with advanced solid tumors and relapsed/refractory B-cell lymphomas.

Myristoylation, the N-terminal addition of the fatty acid myristate to proteins, regulates membrane-bound signal transduction pathways important in cancer cell biology. This modification is catalyzed by two N-myristoyltransferases, NMT1 and NMT2. Zelenirstat is a first-in-class potent oral small molecule inhibitor of both NMT1 and NMT2 proteins.

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.

A phase 2, multicenter, open-label study of anti-LAG-3 ieramilimab in combination with anti-PD-1 spartalizumab in patients with advanced solid malignancies.

Ieramilimab, a humanized anti-LAG-3 monoclonal antibody, was well tolerated in combination with the anti-PD-1 antibody spartalizumab in a phase 1 study. This phase 2 study aimed to further investigate the efficacy and safety of combination treatment in patients with selected advanced (locally advanced or metastatic) solid malignancies. Eligible patients with non-small cell lung cancer (NSCLC), melanoma, renal cell carcinoma (RCC), mesothelioma, and triple-negative breast cancer (TNBC) were grouped depending on prior anti-PD-1/L1 therapy (anti-PD-1/L1 naive or anti-PD-1/L1 pretreated).