Abstract 5893: Comprehensive proteogenomic analysis and classification of lung adenocarcinoma

Lung adenocarcinoma is a highly lethal tumor that displays extensive molecular heterogeneity of which deep characterization may drive therapeutic development and improve clinical outcomes. Through the Applied Proteogenomics Organizational Learning and Outcomes (APOLLO) research network, we utilized five molecular profiling technologies (DNA whole genome sequencing, RNA sequencing, total and phospho-proteomics by mass spectrometry, and reverse phase protein arrays [RPPA]) to characterize a longitudinally-annotated cohort of 87 lung adenocarcinomas. Through whole genome sequencing, we identified molecular signatures from patterns of somatic SNVs, indels, and large structural alterations that stratified tumors into three groups associated with patient smoke exposures. We also identified TP53, EGFR, KRAS, and STK11 as recurrently mutated genes, which together represent 80% of the cohort, in addition to genes mutated in smaller cohort subsets (e.g. RBM10), fusion genes, and pathogenic germline variants. To characterize tumor proteomes, we quantified >7,000 proteins and >10,000 phosphopeptides by mass spectrometry and >300 species by RPPA. Matched RNAs and proteins were typically positively correlated across samples (median ρ = 0.49). Through quantitative trait loci analyses, we identified genes whose RNA and protein expression levels were significantly modified by somatic mutations. We then classified tumors into RNA expression subtypes and found coordinated proteogenomic alterations and distinct clinical associations: terminal respiratory unit subtype – EGFR mutations and RNA/protein overexpression, acinar histology, non-smokers; proximal-proliferative subtype – STK11 mutations and RNA/protein underexpression, high smoking signature; proximal-inflammatory subtype – high tumor mutational burden. We also identified phospho-peptide signatures associated with these subtypes, including downregulation of CDK1/2 targets in terminal respiratory unit tumors. Protein co-expression network analysis discovered biologically-diverse pathway activities of the RNA expression subtypes. To interrogate somatic mutations in the context of molecular pathways, we projected DNA alterations onto known interaction networks and identified four subtypes with markedly distinct proteomic and microenvironment characteristics. Finally, several molecular characteristics were found to significantly predict patient outcomes, including RNA expression subtype classification against metastasis-fr