cancer

Framework: This paper introduces DeepProfile, an unsupervised deep-learning model using an ensemble of Variational Autoencoders (VAEs) to create robust and biologically interpretable low-dimensional latent spaces from 50,211 transcriptomes across 18 human cancers.
Key Biological Findings: Universal latent variables across all 18 cancers are driven by genes controlling immune cell activation, while cancer-specific variables define molecular subtypes.
Clinical Associations: DeepProfile linked gene expression to clinical outcomes, finding that Tumour Mutation Burden (TMB) is associated with cell-cycle pathways, and patient survival correlates with DNA-mismatch repair and MHC class II antigen presentation pathway activity.

Current Landscape: Treatment for advanced gastric cancer is rapidly evolving beyond chemotherapy, with approved targeted agents including Trastuzumab (HER2), Zolbetuximab (CLDN18.2), and Pembrolizumab/Nivolumab (PD-L1).
Emerging Targets: Key areas of development include novel Antibody-Drug Conjugates (ADCs), the anti-FGFR2b antibody Bemarituzumab, and bispecific antibodies to overcome resistance mechanisms and improve response rates.
Precision Medicine Goal: The future of GC therapy relies on integrating multi-omics analysis (biomarkers) with functional drug testing on patient-derived models (organoids) to enable individualized treatment selection and improve patient survival.