Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability

• URL: http://arxiv.org/abs/2604.05478v3
• Date: Thu, 16 Apr 2026 01:24:44 GMT
• Title: Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability
• Abstract summary: We benchmark nine state-of-the-art transcriptomic ICI response predictors, five bulk RNA-seq-based models, and four scRNA-seq-based models.<n>Overall, predictive performance was modest; bulk RNA-seq models performed at or near chance level across most cohorts.<n> Pathway-level analyses revealed sparse and inconsistent biomarker signals across models.
• Score: 15.181294821978902
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Immune checkpoint inhibitors (ICIs) have transformed cancer therapy; yet substantial proportion of patients exhibit intrinsic or acquired resistance, making accurate pre-treatment response prediction a critical unmet need. Transcriptomics-based biomarkers derived from bulk and single-cell RNA sequencing (scRNA-seq) offer a promising avenue for capturing tumour-immune interactions, yet the cross-cohort generalisability of existing prediction models remains unclear.We systematically benchmark nine state-of-the-art transcriptomic ICI response predictors, five bulk RNA-seq-based models (COMPASS, IRNet, NetBio, IKCScore, and TNBC-ICI) and four scRNA-seq-based models (PRECISE, DeepGeneX, Tres and scCURE), using publicly available independent datasets unseen during model development. Overall, predictive performance was modest: bulk RNA-seq models performed at or near chance level across most cohorts, while scRNA-seq models showed only marginal improvements. Pathway-level analyses revealed sparse and inconsistent biomarker signals across models. Although scRNA-seq-based predictors converged on immune-related programs such as allograft rejection, bulk RNA-seq-based models exhibited little reproducible overlap. PRECISE and NetBio identified the most coherent immune-related themes, whereas IRNet predominantly captured metabolic pathways weakly aligned with ICI biology. Together, these findings demonstrate the limited cross-cohort robustness and biological consistency of current transcriptomic ICI prediction models, underscoring the need for improved domain adaptation, standardised preprocessing, and biologically grounded model design.

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