FPSIR predicts clinical therapeutic responses and survival outcomes in patients with metastatic colorectal cancer undergoing first-line bevacizumab-containing chemotherapy

Background Identifying patients who are most likely to benefit from the combination of bevacizumab and chemotherapy (Bev/CT) is essential for the optimal management of metastatic colorectal cancer (mCRC). The aim of this study is to investigate the utility of chronic inflammatory biomarkers in predicting clinical response to Bev/CT and outcomes in patients with mCRC. Materials and methods This study enrolled 364 patients with mCRC undergoing first-line Bev/CT therapy. The patients were randomly assigned to discovery (n=249) and validation (n=115) cohorts, maintaining an approximate 2:1 ratio. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO)-penalized Cox regression and random survival forest (RSF), were employed to identify significant inflammatory biomarkers. Logistic regression, Kaplan-Meier survival analysis, and Cox regression analyses were conducted to evaluate the associations between the clinical outcomes and a product of fibrinogen-pre-albumin ratio and systematic inflammatory ratio (SIR) (FPSIR) and clinical outcomes. The primary endpoints included clinical disease control rate (DCR) and progression-free survival (PFS), 2-year overall survival (OS) was designated as a secondary endpoint. Results Following the integration of inflammatory biomarkers identified through the LASSO and RSF algorithms, FPSIR was independently associated with PFS in both the discovery ( p log-rank