Tumor heterogeneity has presented a significant challenge to the development of breast cancer therapeutics while emphasizing the need for precision medicine-based treatment strategies. To improve current understanding of the molecular characteristics of breast cancer cells, scientists at MIT’s Broad Institute, Baylor College of Medicine, and NYU Grossman School of Medicine employed a combinatorial approach towards breast cancer profiling by integrating proteomics with next-generation sequencing (NGS). Their approach has led to the proteogenomic characterization of one of the largest breast cancer cohorts to date and could serve as a valuable resource for cancer researchers and clinicians alike.
Working with 122 primary breast cancer samples, the researchers conducted mass-spectrometry-based proteomics in parallel with next-generation DNA and RNA sequencing. In addition to gene expression and protein quantification, the multi-omics dataset obtained also provided insights into post-translational protein modifications. Mutations in protein kinases, resulting in dysregulation of kinase activity, are very commonly observed in many types of cancers and are often oncogenic. The authors of this study discovered that recurrent somatic gene mutations in specific cancer subtypes could be linked to the phosphorylation states of protein kinases, potentially aiding the identification of novel therapeutic targets. For instance, GATA3 mutations were found to be associated with increased phosphorylation of MAST4 and DCLK, a previously unestablished association.
Another key feature of this study is that it provides the first well-characterized report of the tumor acetylome, in the case of breast cancer. Acetylation is an important posttranslational modification that affects chromatin plasticity and consequently, the functioning of proteins involved in cell proliferation, migration, and metabolic activities. Changes to the acetylation status of proteins have been associated with cancer. The acetlyproteomic data in this paper showed significant differences in the acetylation states of an array of proteins between the different breast cancer subtypes. Following up on these leads could provide insights into the metabolic vulnerabilities of cancer cells.
The authors of this paper have demonstrated how utilizing a multi-omics approach, such as proteogenomics, can help obtain a clearer picture of the tumor landscape. Though further work to validate potential drug targets is still required, the timely adoption of proteogenomics to study disease states such as cancer could bring us a step closer to realizing the dream of personalized medicine.
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