Decoding the ERS-CAF immunoregulatory axis via multimodal AI and its pan-cancer prognostic and therapeutic predictive value.
retrospective_cohort · Level III
Where this comes from
- Record sourced from PubMed, PMID 41617967.
- Also identified by DOI 10.1038/s41746-026-02388-w and PMC identifier 12960699.
- Licence recorded as CC BY-NC-ND.
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Abstract
Endoplasmic reticulum stress-related cancer-associated fibroblasts (ERS-CAF) remodel the tumor microenvironment and drive immune exclusion and therapy resistance in chordoma, yet routine and non-invasive readouts of this biology are lacking. We hypothesized that standard pre-operative MRI and H&E whole-slide images (WSI) encode image-based surrogates of ERS-CAF-driven immunoregulation that can be learned and generalized across cancers. Three bulk-transcriptomic reference scores were defined for surrogate supervision, capturing ERS-program activity, ERS-CAF-immuneligand-receptor crosstalk and microenvironmental heterogeneity. In 126 chordoma cases, a stage-wise multimodal framework integrating calibrated WSI attention, gated radiopathomic fusion and domain alignment showed strong concordance with molecular profiles, independent prognostic value and biologically specific localization to fibrotic immune-excluded regions. These associations were generalized in zero-shot analyses to the TCGA pan-cancer. An MRI-only distilled model preserved most predictive performance with substantial gains in efficiency, supporting scalable non-invasive clinical application.