Integrating clinico-pathological features and longitudinal liquid biopsy results to predict clinical benefit from immune checkpoint Inhibitors in advanced Non-Small Cell Lung Cancer patients

Abstract

Abstract Background: Immune checkpoint inhibitors (ICIs) are widely used in non-oncogene addicted Non-small cell lung cancer and characterized by high heterogeneity in clinical benefit. Different combination strategies are available in first-line setting and PD-L1 is the only predictive marker used in clinical practice. Methods: Patients with NSCLC treated with ICI single-agent according to clinical practice were prospectively enrolled. Liquid biopsy was performed at the time of first dose administration (T1), after 3 weeks (T2) and at the time of radiological evaluation (T3). Cell free DNA (cfDNA) was quantified (ng/ml) by qPCR and analysed by NGS targeted panel. Molecular variables used for association with outcome endpoints were: cfDNA quantification as static parameter, dynamic cfDNA change (DT2-T1), variant allele frequency of the gene with the highest frequency at base line (MaxVAF) and dynamic maxVAF change (DT2-T1). Cox regression analysis was used to build integrated predictive models. Results: 113 patients were included. At multivariate analysis, PD-L1 negativity, T1 cfDNA, cfDNA increase (DT2-T1), and maxVAF at T2 were significantly associated with shorter PFS; PD-L1 negativity, squamous histology, T1 cfDNA, increase of cfDNA (DT2-T1), and maxVAF at T2 were significantly associated with worse OS. Integrated model permitted to build a nomogram and establish three groups of patients deriving different clinical benefit from ICI. The model was tested in patients expressing PD-L1 ³50% and treated with first-line pembrolizumab (n=57) and was able to identify elevated maxVAF at T2 and increase (DT2-T1) of cfDNA as independently associated with worse PFS; higher levels of maxVAF at T2 and increase (DT2-T1) of cfDNA with worse OS. Derived integrated model was able to identify patients with different clinical benefit (high, intermediate, low risk). Conclusions: We developed an integrated nomogram to stratify NSCLC patients deriving different clinical benefit from ICIs which outperforms individual predictive markers.