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The Cancer Cell Map Initiative, developed by investigators at the University of California San Francisco and the University of California San Diego, has successfully charted how hundreds of genetic mutations involved in breast cancer and cancers of the head and neck affect the activity of proteins that ultimately lead to disease.
The Cancer Cell Map Initiative (CCMI), developed by investigators at the University of California San Francisco and the University of California San Diego, has successfully charted how hundreds of genetic mutations involved in breast cancer and cancers of the head and neck affect the activity of proteins that ultimately lead to disease.1
“We’re elevating the conversation about cancer from individual genes to whole protein complexes,” said Trey Ideker, PhD, a professor at the University of California San Diego School of Medicine and Moores Cancer Center, in a press release. “For years different groups have been discovering more and more gene mutations that are involved in cancers. But now we’re able to explain these mutations at the next level by looking at how the different gene mutations in different patients have the same downstream effects on the same protein machines. This is the first map of cancer from the protein complex lens.”
The goal of the CCMI was to map protein complexes comprised of approximately 60 genes commonly involved in breast cancer or in cancers of the head and neck. Investigators of the initiative also examined what the complexes looked like in healthy cells. The initiative also created maps of how protein complexes are affected by hundreds of different gene mutations in the 2 cancerous cell lines.1
For example, investigators used affinity purification plus mass spectrometry to catalog protein to protein interactions (PPIs) for 40 proteins that were altered in breast cancer. The study included multidimensional measurements across mutant and normal protein isoforms and across cancerous and noncancerous cellular contexts.2
A CCMI analysis of PIK3CA, a commonly mutated gene in breast cancer, found that the previously unidentified interacting proteins BPIFA1 and SCGB2A1 acted as potent negative regulators of the PI3K-AKT pathway in multiple breast cancer cell contexts. Investigators noted that this discovery provided new mechanistic and therapeutic insights into the regulation of this signaling pathway. Additionally, investigators found that the protein-coding gene UBE2N was a functionally relevant interactor of BRCA1 and could be a potential biomarker of response for some DNA repair targeted therapies.2
Approximately 79% of the PPIs identified in the study had not been reported previously and 81% are not shared across cell lines. Investigators noted that the breast cancer cell lines MCF7 and MDA-MB-231 were more often mutated in breast tumors compared with interacting proteins in nontumorigenic MCF10A cells, implying that proteins interacting with known cancer drivers could contribute to the onset of cancer. The study concluded that hierarchical maps of protein complexes and systems in both healthy and diseased cells could be used to stratify patients for known anticancer therapies and drive the discovery of therapeutic targets in breast cancer.2
In cancers of the head and neck, study authors used the same approach utilized in breast cancer to identify 771 PPIs from cancer and noncancerous cell states. Investigators analyzed 3 cell lines for 31 genes frequently altered in head and neck squamous cell carcinoma, as well as 16 PIK3CA mutations. Approximately 84% of these interactions had not been previously reported.3
Results from the study showed a previously unidentified association between FGFR tyrosine kinase 3 and the guanine-nucleotide exchange factor Daple that resulted in the activation of Gαi and PAK1/2, promoting cancer cell migration. Analysis with CCMI techniques also showed differences in PPIs among the 16 PIK3CA mutations.3
“We’re not only making connections between different genes and proteins but between different people and different disciplines,” said Nevan Krogan, PhD, director of the University of California San Francisco’s Quantitative Biosciences Institute, in the release. “Those collaborations have built up an infrastructure that allows them to integrate an array of types of information and push the boundaries of what’s possible in applying data science to complex diseases. We’re in the perfect position to take advantage of this revolution on every level. We can do such damage to cancer.”