The research is the work of the Cancer Cell Map Initiative (CCMI), a multi-institution research program founded in 2015 at the University of California, San Diego (UCSD) and San Francisco (UCSF) campuses.

From the gene level to the protein level

The CCMI approach seeks to gain a more expansive view of the activity underlying cancer by zooming in from the gene level to the protein level, which is far more detailed. "This is an entirely new way to do cancer research," Nevan Krogan, PhD, director of UCSF's Quantitative Biosciences Institute and co-senior author of the papers, said in a statement. "We realized we need another way to look at cancer that takes it a step beyond DNA."

Genes contain instructions for building proteins, which then interact with other proteins. When gene mutations cause disruptions, they are reflected in the interactions among protein complexes that regulate activities in the body or turn individual functions on or off. For example, if a gene mutation results in misshapen protein, it may not interact correctly with other proteins, causing a loss of function that, in some cases, can lead to cancer.

"We're elevating the conversation about cancer from individual genes to proteins, allowing us to look at how the varying mutations we see in patients can have the same effects on protein function," Trey Ideker, PhD, professor at the UCSD School of Medicine and co-senior author, said. "We've produced the first map looking at cancer through the lens of interactions between proteins."

Mapping protein-protein interactions

The CCMI scientists used affinity purification-mass spectrometry (AP-MS), a technique for isolating and identifying binding partners to a target protein, to catalog protein-protein interactions (PPIs) across mutant and normal protein forms and across cancerous and noncancerous cell lines. The targets were protein complexes formed by about 60 genes commonly involved in either breast cancer or cancers of the head and neck.

In the first paper (Science, October 1, 2021, Vol. 374:6563), CCMI scientists analyzed PPI data from head and neck squamous cells. By comparing the results from the cancerous and noncancerous cell lines, they identified hundreds of distinct cancer-specific interactions, some of which may be potential therapeutic targets. Of the 771 PPIs identified, 84% had not been previously reported in public databases.

In the same Science issue, another paper targeted 40 proteins associated with breast cancer. Again, the AP-MS process identified hundreds of PPIs, approximately 79% of which had not been previously reported. The study also identified two proteins connected to the tumor suppressor gene BRCA1, as well as two proteins that regulate PIK3CA.

A third paper from the issue combined the new PPI data from the first two CCMI papers with existing public data to generate a map of protein pathways that revealed previously hard-to-detect mutations potentially important in tumor metastasis.

Looking for new biomarkers and druggable targets

By revealing the protein biochemistry of tumor pathways, the PPI networks mapped by the CCMI team can facilitate cancer diagnosis and prognosis and the identification of druggable targets. For example, many of the biomarkers that physicians depend on to determine whether a particular cancer drug might benefit a patient are mutated genes. According to Ideker, however, understanding the interactions between protein complexes can reveal a new class of potential biomarkers.

"The problem is that we've only found a few genes that we can work with in this way to help guide prescription of an FDA-approved drug," he said. "Our studies provide a new definition of biomarkers based not on single genes or proteins but on the large, multiprotein complexes."

A specific example is given in the breast cancer paper, where the authors identified the enzyme ubiquitin conjugating enzyme E2 N (UBE2N) as a potential biomarker of response to poly (ADP-ribose) polymerase inhibitors (PARPi) and other therapies targeted at DNA repair.

"Identification of UBE2N as a potential biomarker for PARPi response could be clinically valuable to help stratify patients with UBE2N alteration for targeted therapy," they wrote.

Still missing: the specific pathways

In a perspective article (pp. 38-39) accompanying the CCMI papers in Science, Stanford University scientists Ran Cheng and Peter Jackson noted that the missing piece of the puzzle is a consolidated map that organizes specific mutations into pathways that drive tumor growth.

"A clearer picture would emerge if mechanisms critical for tumor growth were better consolidated into specific pathways," wrote Cheng and Jackson. "Identifying and consolidating these pathways and identifying how combinations of pathways drive cancer will simplify our search for effective cancer therapies."

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