A Computational Biologist Explains How Cancer Patient Data Leads to Personalized Medicine

Barry Taylor sitting at a desk.

Computational biologist Barry Taylor is looking for gene mutations that contribute to cancer.

Last year, Memorial Sloan Kettering announced the establishment of the Marie-Josée and Henry R. Kravis Center for Molecular Oncology (CMO). A wide-ranging effort to uncover the links between tumors’ molecular profiles and patients’ responses to therapy, the CMO aims to identify the functional significance of genetic alterations in tumors so that people with cancer can receive the most individualized treatments.

As part of this initiative, MSK has begun a program to analyze the tumors of patients with advanced cancers utilizing a sequencing test called MSK-IMPACT™, which looks for abnormalities in more than 400 of the most important cancer genes.

Computational biologist Barry Taylor is one of the Associate Directors of the CMO. We spoke with him about his work, including a study that his laboratory published earlier this week in Nature Biotechnology.

How are you working to uncover new mutations that might contribute to the development of cancer?

It’s becoming increasingly important for us to better understand the individual gene mutations that we’re now routinely seeing in the tumors of our active patients. While we have a very good understanding of a small number of those, we don’t yet know the function of the vast majority of mutations that we find. So we need to figure out how to distinguish those with biological and therapeutic significance to enable truly genome-directed precision oncology.

One way we can do that is by studying what are called mutational hotspots, which are areas of proteins that are frequently mutated. Selective, or evolutionary, pressure makes these mutations more common than what would occur randomly, suggesting that these mutations affect the function of the protein in some potentially important way. In fact, such mutations are a hallmark of the genomic basis of human cancer.

In our latest paper, Matthew Chang, a graduate student in my lab, collected the full genomic sequences of more than 11,000 tumors across more than 40 different kinds of cancer. The data came from a number of public databases, such as The Cancer Genome Atlas (TCGA) project [a multi-institutional initiative funded by the National Institutes of Health]. After pulling all this information together, he developed an algorithmic approach to identify the individual mutations that arise more frequently than we would expect without selective pressure.

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What did your team find?

What we got was a very large list — about 470 mutations. The most common ones were mutations we’ve been studying for years and know very well. But the vast majority of them were quite rare, many arising in fewer than ten patients out of 11,000.

Yet when they did arise, it was among many cancer types. They weren’t specific to breast cancer or lung cancer, for example. This reinforces the need to run the kinds of basket trials we’re now performing routinely at MSK, in which patients are given a targeted therapy based on the mutations in their tumors, rather than the locations of the tumors in their bodies. And although these mutations were individually rare, together rare mutations affect a sizable portion of cancer patients.

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How does this research relate to MSK-IMPACT testing?

We’ve already started the next phase of this research, and we’ve added 6,800 patients who have been analyzed to date with MSK-IMPACT to our database, as well as several other large studies. We now have data on about 20,000 cancer patients.

Adding MSK-IMPACT data will teach us so much more because the patients we analyze at MSK are very different from the ones in the public databases. TCGA focuses on patients with newly diagnosed tumors who generally haven’t been treated yet, while the patients who undergo analysis here at MSK have advanced, recurrent, or metastatic tumors that have been treated with many different therapies.

It’s a different clinical profile, and because of that we’re starting to uncover completely different patterns of mutational hotspots. These findings are extremely interesting for reasons that span from basic tumor biology to the clinic.

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How might this type of information be used?

We often treat patients with targeted therapies not on the basis of the gene in which a mutation lies, but instead based on the individual mutation affecting that gene. All mutations don’t do the same thing or affect the same protein in the same way. It’s this level of nuance that we need to start capturing before we can make real decisions on how to use all this genomic information clinically.

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How does the wider cancer research community benefit from these kinds of studies?

We’ve put all the data from our study into MSK’s cBioPortal for Cancer Genomics, which is available to researchers inside and outside MSK. This is the largest dataset of its kind currently in the literature, and I think people throughout the biomedical community will find great value in being able to analyze in different ways the data we were able to assemble for this study. People will also be able to use and extend the algorithm we developed to identify mutational hotspots in their own data.

In addition, I think researchers will learn a lot from the list of mutations themselves — for example, a mutation they have never seen before in a gene that they’ve long studied that may help to uncover new facets of its biology.

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What is the role of computational oncology in the CMO?

The focus of computational oncology, and our real goal, is to foster truly translational research that combines expertise in quantitative and computational biology with cancer biology and a detailed understanding of clinical oncology. The mission of the CMO is to bring these three areas of research together, to make discoveries of clinical significance and clinical actionability.

Projects that make use of data from public studies, CMO projects, and clinical MSK-IMPACT are the type of science the CMO was meant to cultivate and catalyze. And ultimately, these kinds of projects will lead to discoveries that can benefit cancer patients. 

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This study was funded by the National Institutes of Health under grants T32 GM007175, P30 CA82103, and P30 CA008748, and the Josie Robertson Foundation, the Prostate Cancer Foundation, the Sontag Foundation, and Cycle for Survival.