[2604.05478] Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability

Quantitative Biology > Genomics arXiv:2604.05478 (q-bio) [Submitted on 7 Apr 2026] Title:Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability Authors:Yuheng Liang, Lucy Chuo, Ahmadreza Argha, Nona Farbehi, Lu Chen, Roohallah Alizadehsani, Mehdi Hosseinzadeh, Amin Beheshti, Thantrira Porntaveetusm, Youqiong Ye, Hamid Alinejad-Rokny View a PDF of the paper titled Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability, by Yuheng Liang and 10 other authors View PDF 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 this http URL 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-se...