123 - Transcriptomic Profiling Reveals Predictive Markers of Response to Concurrent Chemoradiotherapy plus Immune Checkpoint Inhibition in Cervical Cancer
Presenter(s)
Y. J. Cao, J. Chen, Z. Yuan, C. Li, and S. Y. Huo; Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer and Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
Purpose/Objective(s): Concurrent chemoradiotherapy (CCRT) remains a cornerstone of treatment for locally advanced cervical cancer (LACC). However, a substantial proportion of patients exhibit resistance, leading to recurrence. The integration of immune checkpoint inhibitors (ICIs) with CCRT has emerged as a promising strategy to improve outcomes, but predictive biomarkers are needed to optimize patient selection. This study aimed to identify transcriptomic signatures associated with response to CCRT plus ICI in LACC.
Materials/Methods: We performed RNA sequencing on pre-treatment tumor biopsies from 71 LACC patients undergoing curative-intent CCRT plus or minus toripalimab, a PD-1 inhibitor, between January 2020 and June 2022. Patients were classified as complete responders (CR) or non-complete responders (non-CR) according to iRECIST criteria. Differential expression analysis, pathway enrichment, and tumor microenvironment (TME) profiling were conducted using DESeq2, GSEA, ESTIMATE, and CIBERSORT. A LASSO-Cox regression model was developed using the TCGA-CESC dataset to predict overall survival (OS) based on CCRT-related genes. Model performance was validated using the CGCI-HTMCP-CC dataset. Single-cell RNA sequencing data from a subset of patients (n=6) were analyzed to determine the cellular distribution of model genes.
Results: Differential expression analysis identified 21 genes upregulated in CR tumors and 133 genes upregulated in non-CR tumors (|log2FoldChange|=1, P<0.05). Pathway analysis revealed enrichment of B-cell signaling in CR tumors and neutrophil-related pathways in non-CR tumors. Non-CR tumors exhibited higher expression of immune checkpoint genes. A three-gene prognostic model (BCAT1, ITGA5, CXCL2) demonstrated robust performance in predicting OS in both the training (AUC=0.82) and validation cohorts (AUC=0.79). Single-cell RNA sequencing revealed that these genes are predominantly expressed in myeloid and endothelial cells.
Conclusion: Transcriptomic profiling identified distinct molecular features associated with response to concurrent chemoradiotherapy plus immune checkpoint inhibition in cervical cancer. A three-gene prognostic model independently predicted OS and may guide personalized treatment strategies for LACC patients. These findings support further exploration of chemo-radio-immunotherapy combinations to improve outcomes in LACC.