Resistance to oxaliplatin treatment limits therapeutic benefit in gastric cancer (GC) and remains difficult to predict from bulk biomarkers. We aimed to identify oxaliplatin resistance-driving malignant tumor states at single-cell resolution and translate them into clinically tractable predictors. Differential expression in oxaliplatin-resistant versus sensitive GC organoids was used to derive a resistance signature, which was evaluated in bulk GC organoid cohorts alongside computational estimate on oxaliplatin sensitivity. Single-cell RNA sequencing (scRNA-seq) was used to localize resistance programs to malignant epithelial states. A multi-constraint integration framework (Clinical-Transcriptomic Integration for Validation - Consensus Resistance Program, CTIV-CRP) was developed to prioritize candidates based on tumor enrichment, pharmacologic association, and reproducible prognostic relevance. Radiation sensitive1(RAD1) was further validated using public protein data, an independent neoadjuvant cohort assessed by tumor regression, and patient-derived organoid and xenograft models. Resistance-associated activity associated with a malignant epithelial state (C2). CTIV-CRP distilled a core program in which RAD1 emerged as a consistent surrogate, aligning with resistance signatures and oxaliplatin sensitivity estimates. Clinical and experimental validations supported the association between RAD1 and poor therapeutic response. CTIV-CRP connects single-cell malignant states to clinically relevant resistance phenotypes and nominates RAD1 as a candidate resistance biomarker in GC.
No. 2 Wenbo Road, Zhangqiu District, Jinan, Shandong
Qilu Normal University · Genelibs Bioinformatics Lab
750 Shunhua Rd, Jinan
2F, Bldg F, University Science Park
Tel: 0531-88819269
Follow our WeChat subscription account for real-time updates and the latest in medical and biological research.
Business Email
E-mail: product@genelibs.com