Abstract 6340: Deep, unbiased and peptide-centric plasma proteomics with differential analysis of proteoforms enabling proteogenomic studies of NSCLC at scale

Abstract Introduction: Comprehensive assessment of the proteome remains elusive because of proteoforms arising from alternative splicing, allelic variation, and protein modifications. Characterization of the variable protein forms, or proteoforms will expand our understanding of the molecular mechanisms underlying diseases, however requires unbiased protein coverage at sufficient scale. Scalable, deep and unbiased proteomics studies have been impractical due to cumbersome and lengthy workflows required for complex samples, like blood plasma. Here, we demonstrate the power of Proteograph in a proof-of-concept proteogenomic analysis of 80 healthy controls and 61 early-stage non-small-cell lung cancer (NSCLC) samples to dissect differences between protein isoforms arising from alternative gene splicing, as well as the identification of novel peptides arising from allelic variation. Materials, Methods and Results: Processing the 141 plasma samples with Proteograph yielded 21,959 peptides corresponding to 2,499 protein groups. Using peptides with significant abundance differences (p < 0.05; Benjamini-Hochberg corrected), we extracted proteins comprised of peptides where at least one peptide had significantly higher plasma abundance, and another significantly lower plasma abundance in controls vs. cancer, resulting in a set of putative proteoforms. For three of these proteins, the abundance variation is possibly explained by underlying protein isoforms. To identify protein variants, we performed exome sequencing on 29 individuals from the NSCLC study, created personalized mass spectrometry search libraries for each individual, and identified 464 protein variants. Conclusions: Proteograph can generate unbiased and deep plasma proteome profiles that enable identification of protein variants and peptides present in plasma, at a scale sufficient to enable population-scale proteomic studies. Citation Format: Margaret Donovan, Henry Huang, John Blume, Marwin Ko, Ryan Benz, Theodore Platt, Juan Cuevas, Serafim Batzoglou, Asim Siddiqui, Omid Farokhzad. Deep, unbiased and peptide-centric plasma proteomics with differential analysis of proteoforms enabling proteogenomic studies of NSCLC at scale [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6340.