Abstract 6339: Deep plasma proteomics at scale enabling proteogenomic analyses in a lung cancer (NSCLC) study

Abstract Our ~20,000 genes encode over one million protein variants, given alternative splice forms, allelic variation, and protein modification. Though large-scale genomics studies have expanded our understanding of biology, similarly, scaled deep and untargeted proteomics studies of biofluids have remained impractical due to complex workflows. To address this need, we have previously described Proteograph࣪, a novel platform that leverages the protein-corona interactions of nanoparticles for deep and untargeted proteomic sampling at scale. Using Proteograph࣪ in a non-small cell lung cancer (NSCLC) cohort, we previously conducted a deep interrogation of plasma from 141 subjects: 61 early-stage NSCLC subjects and 80 non-cancer controls. We identified 2,499 plasma proteins, with 1,992 present in ≥ 25% of the samples. Leveraging this data, we created a biomarker classifier distinguishing NSCLC from controls with area under the receiver operating characteristic curve of 0.911. In this study, we now re-analyze the data with the more sensitive DIA-NN software to enhance protein depth while preserving the accuracy of the classifier. In addition, to show the added value of our platform in combination with genomic data, we integrate previously sequenced exome data with our proteomic data to build a multi-modal proteogenomic deep learning classifier. Our results outline proteogenomic workflows for robust biomarker discovery and cohort subtyping. The Proteograph࣪ platform interrogates the plasma proteome at previously impractical combinations of scale, depth and coverage, and enables the development of improved classification models and the study of proteogenomics. 1Blume et al. Nat. Comm. (2020) Citation Format: Mahdi Zamanighomi, Harendra Guturu, Jian Wang, Amir Alavi, Tristan Brown, Daniel Hornburg, Moaraj Hasan, Shadi Ferdosi, Khatereh Motamedchaboki, Margaret Donovan, Theodore Platt, Ryan Benz, Asim Siddiqui, Serafim Batzoglou. Deep plasma proteomics at scale enabling proteogenomic analyses in a lung cancer (NSCLC) study [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 6339.