A deep (learning) dive into the roots of cancer

In a recent grant application to the National Institutes of Health, Petar Stojanov was required, among other things, to describe his “specific aims” as well as his background. It’s doubtful that the NIH reviewers would have considered Stojanov’s research agenda lacking in ambition, given its broad scope: to identify the genetic mutations that cause cancer and figure out how they cause it.

The reviewers, moreover, must have decided he had a credible chance of achieving these goals, or at least making progress toward their realization, as he was informed earlier this year that he had earned a coveted Pathway to Independence (K99) Award. As a result, Stojanov — a current Eric and Wendy Schmidt Center Postdoctoral Fellow at the Broad Institute of MIT and Harvard — will receive up to five years of research support, meaning he can devote himself fully to his scientific inquiries without having to worry about funding.

K99 grants help “outstanding” researchers transition from postdoctoral positions to running their own labs. In this next stage of his career, Stojanov will develop new methods in two types of machine learning: algorithms related to causality and deep generative models.

An early interest in computational biology

In some sense, Stojanov set off on the path that led him to this milestone when he was a high school student in Macedonia. A family friend told him that computational biology was becoming a hot area in science. Stojanov was immediately intrigued, he said, “for the same reason that has brought many people to this field — math and biology were my favorite subjects.” And here was a chance to combine his preferred disciplines into a unified course of study that might lead to an interesting career.

He spent his senior year of high school in Pelham, New York (where he lived with his family friend), as he’d always believed he “would have the best opportunities for innovation in the U.S.” A year later, he enrolled in Bard College, which had no courses, let alone a major, in computational biology. Stojanov stuck to his passion, nevertheless, taking the bulk of his classes in computer science, biology, mathematics, and chemistry. He gained hands-on experience in computational biology through summer research programs at George Washington University and the University of Maryland.

After graduating from Bard in 2010, he took a job in the laboratory of Gaddy Getz, director of the Broad’s Cancer Genome Computational Analysis Group. That’s where Stojanov got started on the two-pronged research track he’s still pursuing today: First, to figure out which mutations are present in cancerous tissue and, second, to determine which of those mutations actually spur our cells to multiply out of control and drive cancer. The standard approach at the time was to rely on statistical methodology, such as examining whether the number of mutations in a given gene was greater than would be expected from random processes, unrelated to cancer.

Stojanov spent four productive years at the Broad, coauthoring more than a dozen papers — four of which he was a lead author. He didn’t sleep much those days, mainly because he was “hungry for projects and never said no to an opportunity.” Yet, by the end of that tenure, he felt that his work in this area could benefit from additional training in computer science, which would enable him to bring new tools to the kinds of problems he’d been grappling with. In 2014, he entered a PhD program at Carnegie Mellon University, where he immersed himself in machine learning techniques and other emerging approaches in artificial intelligence. Although his graduate research had nothing to do with biology, he recognized that the methods he was learning, combined with statistics, might lead to breakthroughs in his previous cancer investigations.

Bringing ML to bear on cancer research

Stojanov returned to the Broad in 2021 and picked up in the Getz lab where he had left off — this time ready to unleash the full power of AI. Getz was eager to have him back, touting “the unique set of skills that Petar has,” given his prior experience in cancer research and his recently strengthened background in computer science. “And now,” Getz said, “he’s applying his expertise in machine learning to the search for the drivers of cancer.”

Just counting the number of mutations in a gene is not enough to reveal the mechanisms underpinning cancer, Stojanov explained. “That may tell you which mutations are most prevalent, and maybe the most important, but it still doesn’t tell you what they do.” To understand how a mutation affects a gene, you have to look at gene expression, the cellular process by which the information encoded in a gene is used to create proteins.

In his latest work at the Broad, Stojanov is focusing on two variables: gene mutations, which can be gleaned from DNA sequencing data, and gene expression expression (which can be obtained from RNA sequencing data by measuring the amount of RNA, a gene-decoding molecule, in the cell). He then uses a set of machine learning tools called causal inference and discovery algorithms to uncover the “causal relationships” between these two variables – mutations and expression.

“The idea is to show that some aspects of gene expression are the consequences of mutations,” he said.  

The only causal relationships he cares about are those associated with cancer. While sorting through DNA and RNA sequencing data from thousands of cancer patients, he’s looking for patterns. In particular, he said, “we might find mutations that influence patients with the same cancer type (or subtype), in the same way.”

As an intermediate step, Stojanov relies on a related class of machine learning-based tools, so-called deep generative models, which basically takes abstract (“high-dimensional”) information processed by computers and represents it in a form that is meaningful to humans. If you have mutation and expression data for 20,000 genes, he said, these models offer a way to summarize that vast amount of data in terms of the concepts you’re interested in, such as biological processes or cell subtypes that might be impacted by cancer.

The ultimate goal is to learn as much as possible about this multifaceted disease — how and where it starts and progresses. “To really understand what’s going on,” Stojanov said, “we need an interpretable map that shows which processes are affected by what mutations.”

Existing techniques can only get you so far

Eric and Wendy Schmidt Center co-director Caroline Uhler is excited by the prospect of “getting at the causal genes, which contain the mutations that drive cancer. "Once you have that,” she said, “you’re in a much better position to think about effective therapies. That’s really the promise of this work.”

Stojanov’s current research is, admittedly, at an early stage. He has a solid base of experience to draw on, and he’s picked out a set of tools, in the form of machine learning algorithms, that are poised to advance our knowledge base. The big challenge, Uhler pointed out, is that “existing techniques can only get you so far. Petar has to build on these methods and develop new algorithms in order to solve the important biological questions he plans to address.”

Stojanov is mindful of the hard work ahead and grateful that his burden has been eased by having several years of funding already secured. “This [K99] award gives you the ultimate amount of independence you can have as a postdoc,” he said.

When asked if getting the award is the best thing that could happen to someone in his position, embarking on such an ambitious enterprise, he replied, “Well, it’s certainly up there.”

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