Researchers at University of California San Diego and University of California San Francisco have mapped out how hundreds of mutations involved in two types of cancer affect the activity of discrete groups of proteins that are the ultimate actors behind the disease. The work points the way to identifying new precision treatments that may skirt side effects common with much current chemotherapy.
The effort, dubbed Cancer Cell Mapping Initiative (CCMI) https://ccmi.org, is led by Trey Ideker, PhD, professor at UC San Diego School of Medicine and Moores Cancer Center, and Nevan Krogan, PhD, director of the Quantitative Biosciences Institute at UCSF, who are co-senior authors on a set of three related studies that describe the map. The papers appear in the October 1, 2021 online issue of Science.
The bottom line is that we're elevating the conversation about cancer from individual genes to whole protein complexes. For years, different groups have been discovering more and more mutations that are involved in cancers, but in so many different genes that scientists can't make sense of it all. Now we're able to explain these mutations at the next level — by looking at how the different gene mutations in different patients actually have the same downstream effects on the same protein machines. This is the first map of cancer from the protein complex lens."
Trey Ideker, PhD, Professor, UC San Diego School of Medicine
Mapping protein mutations
Hierarchy of cancer protein systems: Each node represents a protein system carrying out cellular functions such as mobility or immune signaling. Nodes farther out on the branches represent systems with few proteins and highly specialized processes, while those closer to the root have many proteins and correspond to generalized processes. Darker colored systems, and their subsystems, are under selection in more tumor types.
DNA contains the instructions for building proteins, which then interact with other proteins, almost always in large groups called complexes. These protein complexes, in turn, make up most of the machinery of cells, dictating basic cell functions like feeding, growth and whether the cell develops into cancer. If the underlying DNA has a mutation, the resulting protein machines often will as well.
In cancers, a subset of genes is commonly mutated, Krogan said, and each of these genes can be mutated in hundreds of different ways. In addition, the function of a particular protein may be different in different types of cells, so a mutation in a breast cancer cell might have different effects on protein complexes than that same mutation in a cell in the throat.
CCMI's goal was to map the constellation of protein complexes formed by approximately 60 proteins commonly involved in either breast cancer or cancers of the head and neck, and to see what each looked like in healthy cells. Alongside that effort, they created maps of how protein complexes are affected by hundreds of different gene mutations in two cancerous cell lines.
"This is an exciting advance that not only provides a treasure trove of new protein-protein interactions, but also the computational tools to robustly analyze the data and put it in a meaningful context for others to use," said Shannon K. Hughes, PhD, deputy director of the Division of Cancer Biology at the National Cancer Institute, part of the National Institutes of Health, which funds CCMI. "The methodology can be expanded to other tumor types and other diseases, which is very exciting. Importantly, these large-scale, systems-level mapping endeavors require a strong collaborative team, which has clearly been demonstrated within the CCMI."
Expanding precision medicine
Currently, physicians look for a small number of mutated genes as biomarkers to decide whether or not to prescribe a particular drug. For instance, patients with breast cancer who have an alteration in their HER2 gene are given the medication Herceptin because that's what will work best for them.
"The problem is that there are still only a few genes that work in this way, providing reliable biomarkers that are clearly actionable with an FDA-approved drug," Ideker said. "Our studies provide a new definition of biomarkers based not on single genes or proteins but on large, multi-protein complexes."
Because each protein complex incorporates mutations from a larger collection of genes, it is typically relevant to more patients, Ideker said. For example, XRCC5 is a DNA-repair gene altered in just 2 percent of colon cancers, which limits the usefulness of this biomarker. Now, however, researchers can look at CCMI's new map of cancer protein complexes and see that XRCC5 is part of a 15-protein assembly altered in 14 percent of patients, and that these patients are typically very resistant to standard therapies.
Trey Ideker, PhD, professor at UC San Diego School of Medicine and Moores Cancer Center.
"There are many examples like this in our map," Ideker said. "The clear next step is to help scientists and physicians evaluate them for use in the clinic. This is one of the main reasons we have worked really hard to make the map easily accessible on the web."
"Indeed, by targeting simultaneously multiple components of these 'oncogenic networks,' our collaborative studies will pave the way for the development of more effective combination cancer therapies, while preventing treatment resistance," said co-author J. Silvio Gutkind, PhD, chair of the Department of Pharmacology at UC San Diego School of Medicine and associate director of basic science and co-director of the Head and Neck Cancer Center at Moores Cancer Center. "These studies in breast and oral cancer can now be expanded to most human malignancies."
The most powerful aspect of these extensive protein interaction maps is that they can shed the same light on many other conditions, Krogan said. For example, the team is also at work on similar studies of protein interactions in psychiatric and neurodegenerative disorders and infectious diseases.
Collaboration is key
The team sees the CCMI collaboration as the real source of strength behind the approach.
"We're not only making connections between different genes and proteins, but also between different people and different disciplines," Krogan said. "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. I couldn't be more excited than I am right now. We can do such damage to cancer."
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