Abstract
The standard therapy for locally unresectable advanced non-small cell lung cancer (NSCLC) is comprised of chemoradiotherapy (CRT) before immunotherapy (IO) consolidation. However, how to predict treatment outcomes and recognize patients that will benefit from IO remain unclear. This study aimed to identify prognostic biomarkers by integrating computed tomography (CT)-based radiomics and genomics. Specifically, our research involved 165 patients suffering from unresectable Stage III NSCLC. Cohort 1 (IO following CRT) was divided into D1 (n = 74), D2 (n = 32), and D3 (n = 26) sets, and the remaining 33 patients treated with CRT alone were grouped in D4. According to the CT images of primary tumor regions, radiomic features were analyzed through the least absolute shrinkage and selection operator (LASSO) regression. The Rad-score was figured out to forecast the progression-free survival (PFS). According to the Rad-score, patients were divided into high and low risk groups. Next-generation sequencing was implemented on peripheral blood and tumor tissue samples in the D3 and D4 cohorts. The maximum somatic allele frequency (MSAF) about circulating tumor DNA levels was assessed. Mismatch repair and switching/sucrose non-fermenting signaling pathways were significantly enriched in the low-risk group compared to the high-risk group (p < 0.05). Moreover, patients with MSAF ≥ 1% and those showing a decrease in MSAF after treatment significantly benefited from IO. This study developed a radiomics model predicting PFS after CRT and IO in Stage III NSCLC and constructed a radio-genomic map to identify underlying biomarkers, supplying valuable insights for cancer biology.