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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-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.
| Subjects: | Genomics (q-bio.GN); Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.05478 [q-bio.GN] |
| (or arXiv:2604.05478v3 [q-bio.GN] for this version) | |
| https://doi.org/10.48550/arXiv.2604.05478 arXiv-issued DOI via DataCite |
From: Hamid Rokny [view email]
[v1]
Tue, 7 Apr 2026 06:18:59 UTC (1,203 KB)
[v2]
Wed, 15 Apr 2026 05:13:17 UTC (1,203 KB)
[v3]
Thu, 16 Apr 2026 01:24:44 UTC (1,203 KB)
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