Precision proteogenomics reveals pan-cancer impact of germline variants.

in Cell by Fernanda Martins Rodrigues, Nadezhda V Terekhanova, Kathleen J Imbach, Karl R Clauser, Myvizhi Esai Selvan, Isabel Mendizabal, Yifat Geffen, Yo Akiyama, Myranda Maynard, Tomer M Yaron, Yize Li, Song Cao, Erik P Storrs, Olivia S Gonda, Adrian Gaite-Reguero, Akshay Govindan, Emily A Kawaler, Matthew A Wyczalkowski, Robert J Klein, Berk Turhan, Karsten Krug, D R Mani, Felipe da Veiga Leprevost, Alexey I Nesvizhskii, Steven A Carr, David Fenyö, Michael A Gillette, Antonio Colaprico, Antonio Iavarone, Ana I Robles, Kuan-Lin Huang, Chandan Kumar-Sinha, François Aguet, Alexander J Lazar, Lewis C Cantley, Urko M Marigorta, Zeynep H Gümüş, Matthew H Bailey, Gad Getz, Eduard Porta-Pardo, Li Ding,

TLDR

  • A study investigating the impact of germline variants on cancer patients' proteomes using precision peptidomics identified variants affecting proteomic features and predicted destabilizing events in specific protein targets.
  • The study highlights the importance of considering germline genetics in cancer diagnosis and treatment, and has the potential to identify new therapeutic targets.

Abstract

We investigate the impact of germline variants on cancer patients' proteomes, encompassing 1,064 individuals across 10 cancer types. We introduced an approach, "precision peptidomics," mapping 337,469 coding germline variants onto peptides from patients' mass spectrometry data, revealing their potential impact on post-translational modifications, protein stability, allele-specific expression, and protein structure by leveraging the relevant protein databases. We identified rare pathogenic and common germline variants in cancer genes potentially affecting proteomic features, including variants altering protein abundance and structure and variants in kinases (ERBB2 and MAP2K2) impacting phosphorylation. Precision peptidome analysis predicted destabilizing events in signal-regulatory protein alpha (SIRPA) and glial fibrillary acid protein (GFAP), relevant to immunomodulation and glioblastoma diagnostics, respectively. Genome-wide association studies identified quantitative trait loci for gene expression and protein levels, spanning millions of SNPs and thousands of proteins. Polygenic risk scores correlated with distal effects from risk variants. Our findings emphasize the contribution of germline genetics to cancer heterogeneity and high-throughput precision peptidomics.

Overview

  • The study investigated the impact of germline variants on cancer patients' proteomes, analyzing 1,064 individuals across 10 cancer types.
  • The researchers introduced 'precision peptidomics' to map coding germline variants onto peptides from patients' mass spectrometry data, exploring potential effects on post-translational modifications, protein stability, and allele-specific expression.
  • The study identified rare pathogenic and common germline variants affecting proteomic features, including alterations to protein abundance and structure, and predicted destabilizing events in specific protein targets.

Comparative Analysis & Findings

  • Rare pathogenic and common germline variants were identified in cancer genes, potentially impacting proteomic features such as protein abundance and structure.
  • Precise peptidome analysis predicted destabilizing events in signal-regulatory protein alpha (SIRPA) and glial fibrillary acid protein (GFAP), relevant to immunomodulation and glioblastoma diagnostics, respectively.
  • Genome-wide association studies identified quantitative trait loci for gene expression and protein levels, spanning millions of SNPs and thousands of proteins.

Implications and Future Directions

  • The study highlights the contribution of germline genetics to cancer heterogeneity, emphasizing the importance of considering germline variants in cancer diagnosis and treatment.
  • Precision peptidomics has the potential to identify specific protein targets for further investigation and therapeutic development.
  • Future studies could utilize machine learning algorithms to predict the functional effects of germline variants and integrate genomic data with epigenetic and environmental factors to understand cancer development.
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