Plasma proteomics profiling identifies predictive biomarkers for immunotherapy response in small-cell lung cancer

Immune checkpoint inhibitors have improved clinical outcomes for small-cell lung cancer (SCLC), but patient responses vary widely, and predictive biomarkers remain inadequate.

Longitudinal plasma samples were collected from SCLC patients before and during chemo-immunotherapy treatment, and quantified using mass spectrometry. Machine learning models were used to identify predictive biomarkers of chemo-immunotherapy response in both discovery and validation cohorts.

A total of 118 SCLC patients treated with anti-programmed death-ligand 1 (anti-PD-L1) antibodies plus chemotherapy were enrolled. Proteomic profiling revealed that responders exhibited upregulation of proteins associated with IL-17 and JAK-STAT signaling pathways, while non-responders showed enrichment in PI3K-Akt and HIF-1 pathways. A least absolute shrinkage and selection operator (LASSO)-based machine learning model incorporating three proteins-vasorin (VASN), par-3 family cell polarity regulator (PARD3), and prostaglandin E synthase 3 (PTGES3) (termed VPP model)-was developed to predict treatment response. In the training cohort (N = 42), the VPP model achieved an area under the curve (AUC) of 0.846 (95 % confidence interval (CI): 0.723-0.968, P < 0.001). In the validation cohort (N = 40), the model yielded an AUC of 0.821 (95 % CI: 0.686-0.955, P < 0.001), indicating robust predictive performance. Based on the VPP model, the response rates were 80 % in the low-risk group and 20 % in the high-risk group (χ² = 12.1, P < 0.001), with median progression-free survival of 6.87 and 3.97 months, respectively (hazard ratio = 0.39; 95 % CI: 0.19-0.81; P = 0.004). In an external independent cohort (N = 36) using the enzyme-linked immunosorbent assay method, the model also demonstrated excellent predictive performance, with an AUC of 0.859 (95 % CI: 0.730-0.981; P < 0.001).

Our findings demonstrate that the VPP model, identified through plasma proteomics profiling, serves as a predictive biomarker for response to anti-PD-L1 plus chemotherapy in SCLC patients.