Spatial transcriptomic analysis of 53 pre-treatment biopsies from LARC patients using GeoMx technology
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ABSTRACT: Locally Advanced Rectal cancer (LARC) is treated using neoadjuvant chemoradiotherapy (nCRT) followed by surgery. On average 15% of tumors at surgery will present a pathological complete response (pCR). Increasing pCR can be done at the cost of treatment intensification usually combining nCRT and chemotherapy which comes with higher toxicity. However, many LARC patients do not respond to the current neoadjuvant treatments while experiencing important treatment related side effects. Predicting pCR following nCRT opens the door to non-surgical management - the watch and wait strategy and avoid escalation as well as sparring non-responders from nCRT in patients not eligible for intensification. Using RNA expression data, several groups have defined predictive signatures, however, none have made it into clinical practice. A comprehensive differential expression analysis was conducted across six Gene Expression Omnibus (GEO) datasets, followed by integration with four microarray datasets for refinement. A machine learning approach was employed to identify a 186-gene signature predictive of response to nCRT, validated using cross-validation. Gene set enrichment analysis (GSEA) was performed to explore biological pathways associated with treatment response. Spatial transcriptomic profiling of pre-treatment biopsies further examined gene expression in tumor and stroma compartments and identified eight compartment-specific genes significantly associated with response, with tumor-associated genes showing greater predictive value than stroma-associated genes. This study identifies a novel 186-gene signature associated with nCRT response in LARC, with potential clinical utility in guiding personalized treatment. Further validation in larger cohorts and exploration of spatial biomarkers are needed to confirm its clinical relevance.
ORGANISM(S): Homo sapiens
PROVIDER: GSE279942 | GEO | 2025/09/25
REPOSITORIES: GEO
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