CheckDyn: a multi-cohort computational framework for profiling treatment-induced immune checkpoint dynamics and predicting adaptive resistance to immune checkpoint blockade

Adaptive resistance limits durable benefit from immune checkpoint blockade (ICB) in the majority of cancer patients, yet the transcriptomic dynamics of the broader checkpoint landscape during treatment remain poorly characterized across tumor types. Here we present CheckDyn, a multi-cohort computational framework that profiles paired pre- and post-treatment transcriptomes to quantify treatment-induced changes across 38 immune checkpoint and exhaustion-associated genes and to predict adaptive resistance.

We integrated publicly available RNA-seq and scRNA-seq data from 64 paired tumor samples spanning melanoma, basal cell carcinoma, and non-small-cell lung cancer (GSE91061, GSE120575, GSE123813, GSE176021), applying pseudo-bulk aggregation, Z-score batch correction, and Stouffer meta-analysis for cross-cohort harmonization. Paired Wilcoxon signed-rank testing and linear mixed-effects meta-analysis identified LAG3 (log2FC = 0.596, padj = 0.015), PDCD1 (log2FC = 0.810, padj = 0.003), TOX2 (log2FC = 0.605, padj = 0.003), CD274 (log2FC = 0.402, padj = 0.015), and IDO1 (log2FC = 0.381, padj = 0.026) as consistently upregulated post-treatment across cohorts.

Co-expression network analysis revealed extensive rewiring, with ENTPD1 (ΔDegree = +0.297) emerging as the largest hub-degree shift, suggesting a shift toward metabolic immune suppression.