Abstract 2537: Application of the proteograph to the identification of differential protein isoform plasma abundance in early lung cancer vs. healthy controls
Authors: Siddiqui Asim , Blume John E. , Donovan Margaret K. , Ko Marwin , Benz Ryan W. , Platt Theodore L. , Cuevas Juan C. , Batzoglou Serafim , Farokhzad Omid C. 
Published: July 2021

Introduction: Our ~20,000 genes encode over ~1 million protein variants because of alternative splice forms, allelic variation and protein modification. Large-scale genomics studies over the last decade have markedly increased our understanding of cancer biology through analysis of both tissue and biofluids. However, similarly scaled deep and unbiased proteomics studies has been impractical due to the complex workflows required in biofluids. We have previously described Proteograph, a novel platform that leverages the nano-bio interactions of nanoparticles for deep and unbiased proteomic sampling at scale. As proof-of-concept we demonstrated the utility of Proteograph to deeply interrogate the plasma of 141 subjects, 80 healthy controls and 61 early-stage non-small cell lung cancer (NSCLC) subjects, to create a plasma biomarker classifier for the detection of NSCLC versus healthy control with AUC of 0.91 1. Here, we present a further analysis of this data to dissect differences between patients and controls in plasma abundance of protein isoforms arising from alternative gene splicing.

Methods and Results: In the prior study1, we identified 1,664 proteins per individual with a median detection of 7 peptides per protein. Here, we searched for peptides that had differential abundance between controls and cancer (p &lt; 0.05; Benjamini-Hochberg corrected). Next, we filtered for proteins comprising sets of peptides where at least one peptide had significantly higher and another significantly lower plasma abundance in controls vs. cancer, resulting in a set of 16 proteins.

For 3 of these 16 proteins, the differential abundance of the peptides can be explained by differential abundance of the underlying protein isoforms. For example, one of the proteins, BMP1 comprises four protein coding isoforms. Two of these isoforms are substantially longer (~400-800 residues) than the other two isoforms covering additional exons. Peptides mapping to exons that cover all four protein isoforms have higher abundance in cancer relative to controls, whereas peptides mapping to exons that cover only the two longer isoforms have higher abundance in healthy controls. BMP1 is known to play a dual role in cancer, acting as both suppressor and activator2 and this differential pattern of isoform abundance may shed further light into BMP1's role in cancer.

Discussion: We demonstrate that peptide level measurement of the plasma proteome enables quantification of differential isoform abundance patterns, which are inaccessible to prior methods of lesser scale, depth or coverage compared to the Proteograph platform. By extending our approach to include additional features such as protein amino acid variants and PTMs, we anticipate deepening this connection enabling proteogenomics.

1Blume et al. Nat Comm (2020)

2Bach et al Mol Ther Oncolytics (2018).

Citation Format: Asim Siddiqui, John E. Blume, Margaret K. Donovan, Marwin Ko, Ryan W. Benz, Theodore L. Platt, Juan C. Cuevas, Serafim Batzoglou, Omid C. Farokhzad. Application of the proteograph to the identification of differential protein isoform plasma abundance in early lung cancer vs. healthy controls [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2537.