AI and Cancer Research (Top Journals) ── Cancer biology ── 2026-09-30
New papers on cancer and AI from 26 medical, oncology and general journals with an impact factor of 10 or more, collected from the journals' own websites and selected by Jev (an AI that makes language judgements): is it original research, is AI central, which of six themes, and does it concern treatment or trials. Summaries are written from each abstract only and make no claim about efficacy or safety.
Cancer molecular biology and AI
SpaCEy is an explainable graph neural network that models tissues as spatial graphs from marker expression, without predefined cell-type labels, to predict overall survival and disease progression. In a lung cancer spatial proteomic cohort it identified patterns associated with progression, and across breast cancer datasets it stratified patients by overall survival and highlighted contributing protein markers.
UniCure combines biological and chemical foundation models to predict drug-induced transcriptomic responses and rank drugs. Trained on 1.9 million perturbation profiles, it was fine-tuned on 345 patient-derived tumor-like cluster profiles and assessed on 396 real-world clinical profiles, with experimental checks in cell line and patient-derived models.
The authors generated over 38 million temporal protein-abundance measurements from systematically perturbed breast cancer cell lines and built ProteinTalks, a virtual cell model pretrained on these trajectories. It was applied to drug efficacy and synergy prediction, resistance-associated proteins and patient stratification, extending to organoids and clinical biopsies, generally exceeding the selected benchmarks under the evaluated protocols.
COMPASS is described as a pan-cancer foundation model that predicts immunotherapy outcomes from bulk transcriptomic data by routing input through human-readable concepts. The concept bottleneck architecture is reported to improve outcome prediction while providing biological interpretation. This item is a research-highlight summary of a primary study.
HisToSpatialCNV is a multiscale deep-learning framework that infers spatial copy number variations from H&E images using graph neural networks and multihead self-attention. On HER2+ breast, skin and brain cancer datasets it outperformed existing spatial gene expression inference methods, enabled subclone identification and phylogenetic reconstruction, and identified spatial-molecular subtypes with distinct survival in TCGA HER2+ data.
Thousands of AI-designed minibinders were screened by mammalian cell-surface display, yielding several high-affinity PD-L1 binders but far fewer against CD276 and VTCN1. Chai-1 ipTM scores with ESM embeddings correlated with binding success. Some binders trafficked poorly in chimeric antigen receptors; a genetic-algorithm redesign preserving the interface revealed an isoelectric point window improving CAR expression and target-selective killing.
stPainter is a conditional generative model with a latent diffusion architecture, pretrained on a pan-cancer scRNA-seq atlas, that reconstructs expanded expression profiles from sparse spatial transcriptomics without matched references or retraining. Across six cancer datasets it supported fine-grained subpopulation clustering and pathway enrichment, and comparison with CODEX proteomics indicated regional agreement.
VirTues is a foundation model for spatial proteomics that learns marker-aware, multi-scale representations of proteins, cells, niches and tissues from multiplex imaging. One pretrained backbone supports marker reconstruction, cell segmentation and typing, niche annotation, biomarker discovery and patient stratification. In triple-negative breast cancer its biomarkers predicted anti-PD-L1 chemo-immunotherapy response and stratified disease-free survival in an independent cohort.
Oncoformer, a multimodal transformer trained on the COMPASS cohort (3.67 million individuals, 17.7 million visits) and validated on external cohorts including UK Biobank, combines longitudinal EHR data with chest X-rays. Reported results include pan-cancer diagnosis (AUROC 0.956), cancer prediction up to one year before diagnosis (AUROC 0.869), stage inference (mean AUROC >0.90), and recurrence-free survival stratification.
CenSegNet is a deep learning framework for segmenting centrosomes and epithelial architecture. Applied to tissue microarrays of 911 breast cancer cores from 127 patients, it quantified numerical and structural centrosome abnormalities, which showed distinct spatial distributions and age-dependent dynamics and were associated with tumour grade, hormone receptor status, genomic alterations and nodal involvement.
GeneLLM is a transformer model that processes nucleotide sequences of plasma cfRNA reads directly, bypassing gene-level annotation. In a multi-centre cohort it reached ROC-AUC values of 0.9250 to 0.9962 across several cancers, with comparable performance at one-sixth of the typical sequencing depth. The work reports classification performance, not clinical outcomes.
COMPASS is a pan-cancer foundation model that predicts immune checkpoint inhibitor response from bulk tumour transcriptomes via a concept bottleneck of 44 immune concepts. Trained on 10,184 tumours across 33 cancer types, it outperformed 22 methods on average across 16 clinical cohorts covering seven cancers and six ICIs, and predicted responders had longer overall survival.