Immune cells of the tumor microenvironment are central but erratic targets for immunotherapy. The aim of this study was to characterize novel patterns of immune cell infiltration in non-small cell lung cancer (NSCLC) in relation to its molecular and clinicopathologic characteristics. Lymphocytes (CD3+, CD4+, CD8+, CD20+, FOXP3+, CD45RO+), macrophages (CD163+), plasma cells (CD138+), NK cells (NKp46+), PD1+, and PD-L1+ were annotated on a tissue microarray including 357 NSCLC cases. Somatic mutations were analyzed by targeted sequencing for 82 genes and a tumor mutational load score was estimated. Transcriptomic immune patterns were established in 197 patients based on RNA sequencing data. The immune cell infiltration was variable and showed only poor association with specific mutations. The previously defined immune phenotypic patterns, desert, inflamed, and immune excluded, comprised 30, 13, and 57% of cases, respectively. Notably, mRNA immune activation and high estimated tumor mutational load were unique only for the inflamed pattern. However, in the unsupervised cluster analysis, including all immune cell markers, these conceptual patterns were only weakly reproduced. Instead, four immune classes were identified: (1) high immune cell infiltration, (2) high immune cell infiltration with abundance of CD20+ B cells, (3) low immune cell infiltration, and (4) a phenotype with an imprint of plasma cells and NK cells. This latter class was linked to better survival despite exhibiting low expression of immune response-related genes (e.g. CXCL9, GZMB, INFG, CTLA4). This compartment-specific immune cell analysis in the context of the molecular and clinical background of NSCLC reveals two previously unrecognized immune classes. A refined immune classification, including traits of the humoral and innate immune response, is important to define the immunogenic potency of NSCLC in the era of immunotherapy.

Checkpoint inhibitors have been approved for the treatment of non-small cell lung cancer (NSCLC). However, only a minority of patients demonstrate a durable clinical response. PD-L1 scoring is currently the only biomarker measure routinely used to select patients for immunotherapy, but its predictive accuracy is modest. The aim of our study was to evaluate a proteomic assay for the analysis of patient plasma in the context of immunotherapy. Pretreatment plasma samples from 43 NSCLC patients who received anti-PD-(L)1 therapy were analyzed using a proximity extension assay (PEA) to quantify 92 different immune oncology-related proteins. The plasma protein levels were associated with clinical and histopathological parameters, as well as therapy response and survival. Unsupervised hierarchical cluster analysis revealed two patient groups with distinct protein profiles associated with high and low immune protein levels, designated as “hot” and “cold”. Further supervised cluster analysis based on T-cell activation markers showed that higher levels of T-cell activation markers were associated with longer progression-free survival (PFS) (p < 0.01). The analysis of single proteins revealed that high plasma levels of CXCL9 and CXCL10 and low ADA levels were associated with better response and prolonged PFS (p < 0.05). Moreover, in an explorative response prediction model, the combination of protein markers (CXCL9, CXCL10, IL-15, CASP8, and ADA) resulted in higher accuracy in predicting response than tumor PD-L1 expression or each protein assayed individually. Our findings demonstrate a proof of concept for the use of multiplex plasma protein levels as a tool for anti-PD-(L)1 response prediction in NSCLC. Additionally, we identified protein signatures that could predict the response to anti-PD-(L)1 therapy.

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