Proteogenomic analysis prioritises functional single nucleotide variants in cancer samples

Area of Science:

• Proteogenomics
• Cancer Genomics
• Mass Spectrometry

Background:

• Massively parallel DNA sequencing detects thousands of single nucleotide variants (SNVs) in cancer.
• Functional analysis of SNVs often relies on in silico predictions with limited experimental validation.

Purpose of the Study:

• To investigate the utility of mass spectrometry-based proteomics for functional annotation of SNVs in cancer.
• To identify novel protein-level consequences of SNVs, including phosphorylation events.

Main Methods:

• Constructed a custom database of single amino acid variant (SAAV) peptides using RNA-seq and whole genome sequencing (WGS) data from Jurkat cells.
• Analyzed Jurkat proteomics and phosphoproteomics datasets to identify SAAV-containing peptides and phosphopeptides.
• Integrated proteogenomics data for functional annotation of SNVs.

Main Results:

• Identified over 1,000 SAAV-containing peptides in Jurkat cell proteomics datasets.
• Detected a truncated form of the splicing regulator YTHDC1 at the protein level.
• Identified 24 SAAVs impacting phosphorylation, including a novel phosphorylation site created by a SAAV in SF3B1, a leukemia-associated protein.

Conclusions:

• Demonstrated the potential of phosphoproteomics to identify novel phosphorylation events created by SAAVs.
• Revealed functional mutations impacting the splicing pathway in cancer cells.
• Highlighted the benefits of integrative proteogenomics for high-throughput SNV functional annotation.