15 Integration of multiple immune-associated biomarkers facilitates classification of solid tumors by primary immune escape mode and prediction of patient outcomes

Background Many individual biomarkers describe the idiosyn-crasies of each tumor and its interactions with the tumor microenvironment (TME). However, tumors often evade immunotherapy through multiple immune escape mechanisms. Here, we present a method of integrating immune and neo-plastic biomarkers that classify tumor and immune activity in the TME. Methods Standard-of-care comprehensive genomic and immune profiling was performed on 5450 FFPE tumors representing 39 histologic types, assessing expression levels of 395 immune genes and >500 tumor-associated genes. From this data, three previously published gene expression signatures were calcu-lated: cell proliferation (CP), tumor immunogenic signature (TIGS), and cancer testis antigen burden (CTAB). PD-L1 status of each tumor was assessed by IHC, and tumor mutational burden (TMB) was calculated. Principle component analysis (PCA) and unsupervised clustering revealed four distinct bio-logical groups. Subsequently, a nearest neighbor method was used to classify an immune checkpoint inhibitor (ICI) treated 242-patient validation cohort (Lung cancer, melanoma and renal cell carcinoma) into these groups, the association between these groups and ICI treatment response was deter-mined by overrepresentation analysis, and overall survival was assessed using Kaplan-Meyer and CoxPH analyses. Results PCA and clustering generated four groups: 1) Tumor-dominant, exhibiting high CTAB, TMB, and CP, and low PD-L1 and TIGS; 2) Proliferative, exhibiting high CP and low TIGS, PD-L1, CTAB, and TMB; 3) Inflamed, exhibiting high TIGS and low CP, PD-L1, CTAB, and TMB; and 4) Checkpoint, exhibiting high PD-L1, TIGS, and TMB,