NEW YORK – A team led by researchers at Washington University in St. Louis has used a "precision proteomics" approach to study how common and rare germline variants influence protein expression, stability, structure, and posttranslational modifications in cancer patients.
"Our study adds new understanding about the genetic variants that increase cancer risk, which could contribute to the accuracy of polygenic risk scores in the future," senior and co-corresponding author Li Ding, a researcher at Washington University and assistant director of its McDonnell Genome Institute, said in an email, adding that the work "advances our understanding of inherited cancer susceptibility and informs the development of personalized strategies for cancer prevention and treatment."
As part of a National Cancer Institute-supported effort known as the Clinical Proteomic Tumor Analysis Consortium (CPTAC), the researchers analyzed multiomic data for tumor and matched normal samples for 1,064 individuals diagnosed with 10 cancer types, including mass spectrometry-based proteomic profiles, exome sequences, RNA sequence data, and phosphoproteomic data.
The team was able to tally the effects of nearly 337,500 protein-coding germline variants on protein expression and function, unearthing 119 pathogenic or likely pathogenic variants influencing RNA or protein expression.
"Amid mutational chaos, the germline plays a critical role that can enable or constrain the evolution of cancer, dictating the odds of many clinically relevant phenomena: from cancer driver mutations to immune responses against cancer cells," the authors wrote in their study, published in Cell on Monday.
They noted that a "deeper understanding, afforded by proteomics, illuminates this complexity, unveiling altered protein function as pivotal in carcinogenesis."
Along with protein abundance, structure, and phosphorylation shifts associated with rare variants in genes such as BRCA, ERBB2, or MAP2K2, the team spelled out the potential proteomic effects of common germline variants.
"While these common variants may not significantly increase cancer risk on their own, they can act collectively to influence key cancer pathways," Ding said.
By bringing in whole-genome sequence, expression quantitative trait loci (QTL), protein QTL, and other data, the team also started to tease out the influence of germline variants on gene regulation, peptide expression, and protein stability, and posttranslational modifications that are expected to influence cancer progression and trajectory.
"Our findings suggest that germline variants are not solely associated with protein levels," the authors reported, "but may mediate [posttranslational modifications] on particular protein residues due to their changes in amino acid context, affecting oncogenic signaling pathways."
The researchers also identified proteomic shifts in tumors from specific cancer types in individuals carrying certain germline variants — from peptide abundance shifts in lung squamous cell carcinoma or glioblastoma samples from individuals carrying a SIRPA gene variant to germline variant-linked immune regulatory features.
They also used ancestry prediction and data from the gnomAD database to look at differences in allele frequency of cancer-related germline variants in and across European, admixed American, East Asian, South Asian, or African ancestry groups.
Together, the authors explained, their results "suggest that precision proteogenomics could inform patient risk stratification and prevention and interception approaches."
With that in mind, the investigators established cancer- and patient-specific polygenic risk scores (PRS), demonstrating that the scores could stratify patients by disease aggressiveness.
Members of the team are now working on untangling the cell type-specific effects of germline variants implicated in cancer risk or cancer progression.
"We are now actively investigating the functional impact of germline variants across different cell types using single-cell data," Ding said. "By integrating germline variant information with single-cell transcriptomic and epigenomic data, we aim to reveal how inherited variants influence gene regulation and cellular behavior."
More generally, she explained, the team hopes that the current work "lays the foundation for future studies and serves as a valuable resource for driving progress in precision oncology and genetic risk assessment."