Identification of predictive biomarkers of immunotherapy response in non-small cell lung cancer by multiparametric analysis

Abstract

Non-Small-Cell Lung Cancer (NSCLC) leads cancer incidence and mortality. Immune checkpoint blockers showed promising results, yet responders cannot be accurately selected as robust predictive biomarkers are lacking. Our hypothesis is that molecular aberrations of tumor cells impact the immune microenvironment. This association may be crucial to predict response to immunotherapy. We evaluated a retrospective cohort of clinically annotated early stage NSCLC (N=196). We described its immune profile by immunohistochemistry and an immune markers RNAseq panel, and its molecular profile by Next Generation Sequencing (DNA/RNAseq). We defined novel groups of patients with distinct molecular features and two main immunophenotypes. Immune hot tumors had higher infiltration of CD4+, CD8+ and CD20+ cells, while cold tumors accrued mutations in ARID1A, CARD11, CCND1, FGF3, FGF10, FGF19, MYC, NF1, NOTCH1, PIK3CA, PIK3CB and TFRC, dependent on histology. These features, together with the differential expression of immune markers, defined potential predictive biomarkers. Hot tumors were identified by genes related to adaptive immune response (CD28, FCRLA, JCHAIN, LY9, MS4A1, SLAMF7, TNFRSF9, TNFRSF17), antigen presentation (CD1C, CD1D), immune chemotaxis (CCL20, CCR4, CCR6) and immune regulation (CD53, PDCD1, SH2D1A). Cold tumors were defined by genes involved in angiogenesis (VEGFA), cell cycle (BUB1, CCNB2, FOXM1, MAD2L1, TOP2A), growth/survival (IGF1R), DNA replication/repair (KIAA0101) and iron metabolism (HMBS, TFRC). We defined novel patient groups in NSCLC based on multiparametric features and identified potential predictive biomarkers of response to immunotherapy.