Genomic and Proteomic Data Reveal Breast Cancer Treatment Targets


Proteogenomic analysis expands knowledge into breast cancer therapy.

The first large scale breast cancer study integrating genomic and proteomic data (proteogenomic) provided a more complete picture of cancer biology than individual analysis, a recent study suggests.

The study, published in Nature, built on data from The Cancer Genome Atlas (TCGA) project to link DNA mutations with protein signaling, while also helping to identify which genes are drivers of cancer.

The TCGA project created an extensive catalog of somatic mutations found in cancer, but its effect on cellular functions or patient outcomes was unknown. Furthermore, not every mutated gene is considered a true driver of cancer, but merely passengers that have little functional consequences.

Some mutations can even be found in large DNA regions that are deleted or present in extra copies. To help narrow the list of genes and identify therapeutic targets, the activity of their protein products should be studied.

During the study, researchers used accurate mass, high-resolution mass spectrometry to analyze breast tumors. This technology gives researchers the ability to extend the coverage of the proteome deeper than what the traditional antibody-based methods could produce.

Researchers were able to scale their efforts and quantify more than 12,000 proteins and 33,000 phosphosites.

“Advances in sample handling and instrumentation have brought on a revolution in mass spectrometry-based proteomics,” said senior study author Steven Carr. “We can now apply that to the phosphoproteome, which is of central importance to understanding signaling in cancer and other diseases. Our approach produces robust and reproducible data, at a scale unachievable before.”

The new approach gives researchers the ability to study the cancer cell that the mutations altered, and analyze the integrated output of the cell’s proteins. This cuts down on endless experiments required in tumors that harbor many mutations to determine the effects of various combinations of mutations in a model system.

“There is great potential for new insights to come from the combined analysis of cancer proteomic and genomic data, as proteomic data can now reproducibly provide information about protein levels and activities that are difficult or impossible to infer from genomic data alone,” said researcher Douglas Lowy.

The results of the study revealed new signaling pathways and protein markers for breast cancer subtypes and tumors that carry mutations, such as PIK3CA and TP53. Furthermore, researchers were able to correlate copy number alterations in certain genes with protein levels, allowing them to identify 10 new candidate regulators, 2 of which called SKP1 and CETN3 that can be connected to EGFR.

Researchers used transcriptional (mRNA) profiling to divide breast cancer into 4 major subtypes that include: luminal A and B subtypes, basal-like tumors, and HER2-enriched tumors. The proteomic and phosphoproteomic data was used to recapitulate luminal and basal subtypes.

Furthermore, the method allowed researchers to identify a stromal-enriched cluster. When the tumors were clustered based on phosphorylation pathways, it highlighted a G-protein-coupled receptor subgroup that had not been seen using mRNA.

Scientists are hoping to identify a kinase protein that can be targeted with drugs in addition to HER2, which can currently be targeted with trastuzumab (Herceptin) in 20% of breast cancer patients with overexpressed HER2.

During the current study, researchers were able to conduct an outlier analysis of the phosphorylation states of kinase enzymes that highlight the kinases in breast cancer samples like CDK12, HER2, PAK1, PTK2, RIPK2, and TLK2.

“It's always been important to get through to the molecules at work in the cell, the proteins, and this integrative exercise really gives us a whole new understanding of the landscape,” said researcher Li Ding. “The proteogenomic approach shows potential for funneling down to a much smaller set of proteins and modifications that are the interesting drivers that we should think about from a therapeutic standpoint.”

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