贝伐珠单抗反应基因表达预测因子的开发

Background: Bevacizumab, an anti-vascular endothelial growth factor (VEGF) monoclonal antibody, inhibits tumor angiogenesis and is used to treat several advanced-stage cancers, such as glioblastoma, ovarian, lung, and colorectal cancers. More recently, it has been incorporated into treatment regimens for locally advanced nasopharyngeal carcinoma (NPC). Despite its widespread adoption, no reliable biomarker has been developed to predict the treatment outcome. Moreover, bevacizumab treatment carries risks such as bleeding, hypertension, and proteinuria. Therefore, selecting patients for bevacizumab addition to already intensive chemotherapy regimens remains a clinical challenge. Methods: We obtained 19 formalin-fixed paraffin-embedded (FFPE) biopsies from a completed phase 2 clinical trial (NCT01309633) for locally advanced NPC, where patients received bevacizumab prior to standard concurrent chemoradiation. Based on haematoxylin and eosin (H&E) identification, tumor epithelial regions were isolated by laser-capture microdissection, with biological replicates when available. RNAseq libraries were prepared with an in-house RNAseq technique optimized for FFPE tissues. Differential expression between complete response (CR, n=11) and partial response (PR, n= 8) tumors was assessed, and a bevacizumab response gene signature was derived. Gene signature scores were computed using single-sample gene set enrichment analysis (ssGSEA) and gene set variation analysis (GSVA). Results: After quality control, 37 tumor epithelial libraries were analyzed (21 CR and 16 PR). We identified 58 differentially expressed genes (p.adj<0.05), with 44 upregulated in PR. These genes were related to extracellular matrix remodelling, vascular endothelia, and immune response. GSEA using cell-type signature gene sets or Curated Cancer Cell Atlas gene sets from MSigDB showed enrichment of inflamed fibroblasts, endothelial, and stromal cell types in PR, and cell-cycle programs in CR. Therefore, the 44-gene panel was used as a gene signature to predict reduced response to bevacizumab. Both ssGSEA and GSVA scores robustly separated PR from CR (p<0.001). These findings suggest that stromal/vascular inflammation in the tumor epithelial regions is associated with reduced bevacizumab response. Conclusions: In this study, we derived a 44-gene tumor epithelial signature that predicts reduced response to bevacizumab in locally advanced NPC and confirmed its performance with ssGSEA and GSVA. The signature captures stromal, endothelial, and immune-inflammatory programs within tumor epithelial regions and informs patient selection for anti-angiogenic therapy. Our current work includes validation in independent patient cohorts across multiple cancer types to improve clinical utility and generalizability.