AI and Cancer Research ── Cancer biology ── 2026-11-05
Cancer molecular biology and AI
- Abstract
Precision oncology requires accurate molecular insights, yet obtaining these directly from genomics is costly and time-consuming for broad clinical use. Predicting complex molecular features and patient prognosis directly from routine whole-slide images (WSI) remains a major challenge for current deep learning methods. Here we introduce PathLUPI, which uses transcriptomic privileged information during training to extract genome-anchored histological embeddings, enabling effective molecular prediction using only WSIs at inference. Through extensive evaluation across 49 molecular oncology tasks using 11,257 cases among 20 cohorts, PathLUPI demonstrated superior performance compared to conventional methods trained solely on WSIs. Crucially, it achieves AUC $\geq$ 0.80 in 14 of the biomarker prediction and molecular subtyping tasks and C-index $\geq$ 0.70 in survival cohorts of 5 major cancer types. Moreover, PathLUPI embeddings reveal distinct cellular morphological signatures associated with specific genotypes and related biological pathways within WSIs. By effectively encoding molecular context to refine WSI representations, PathLUPI overcomes a key limitation of existing models and offers a novel strategy to bridge molecular insights with routine pathology workflows for wider clinical application.
Journal IF-equivalent: 18.1 (OpenAlex 2-year mean citedness, value as of 2026-10-06, retrieved 2026-10-07; not the official Clarivate IF)Reference: Jin C, Zhou F, Yu Y, Ma J, Wang Y, Xu Y, et al. Genome-anchored foundation model embeddings improve molecular prediction from histology images. Nat Commun. 2026 Nov 2 [Epub ahead of print]. doi:10.1038/s41467-026-78947-2.Checked: Abstract only - Abstract
Major Finding: Proximity mapping identifies CDCP1 as a tumor-associated proximity antigen partner of EGFR, wherein co-targeting of EGFR and CDCP1 boosts tumor cell killing across multiple modalities. Concept: Photocatalytic proximity labeling and graph-based machine learning map tumor surface protein networks. Impact : The concept of tumor-associated proximity antigens is a promising tool to identify novel and tumor-specific therapeutic co-targets.
Journal IF-equivalent: 8.0 (OpenAlex 2-year mean citedness, value as of 2026-11-03, retrieved 2026-11-04; not the official Clarivate IF)Reference: [No authors listed]. Spatial Proximity Mapping Identifies Vulnerabilities for Therapeutic Co-Targeting. Cancer Discov. 2026 Oct 8;16(11):OF1-OF1. doi:10.1158/2159-8290.cd-rw2026-090. PMID: 42847768.Checked: Abstract only