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Med school identifies formula for success of cancer treatment

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Researchers at the Stanford School of Medicine developed a mathematical formula for correlating the response rate of human lung tumors to certain treatments during the sustained regression of certain cancers.

The development builds on the scientific insight that successful treatments do not necessarily immediately kill cancer-causing genes but rather slow rampant cell growth to shrink tumors.

Associate professor of oncology and senior author Dean Felsher led the team.

“It’s really just advanced high-school-level math,” Felsher said in the Medical School press release. “With some simple measurements, we found we can determine when a cancer is addicted to a particular cancer gene and will respond to therapy targeting that gene. I was astounded that it works.”

Felsher’s co-senior author was assistant professor of radiology David Paik.

The study applied a computation biology approach to a phenomenon known as “oncogene addiction,” defined as a cancer dependent on one cancer-causing gene. While most tumors build on the interaction of many cell mutations, some are reliant on a single mutated protein known as oncogene, and therefore regress quickly when that protein’s activity is manipulated.

Felsher and Paik induced tumors in animals and then blocked oncogene activity and monitored tumor regression. By precisely measuring death and survival signals, as well as activity levels of certain genes, they were able to mathematically map the effects of the treatment.

“Lots of people will respond to therapy at first, but many times they don’t get better,” Felsher said. “With a new therapy, would you rather wait four months and say, ‘Well, it’s kind of working,’ or is it better to know after a couple of weeks? We’ve found that the kinetics of regression can quickly predict whether the tumor is oncogene-addicted and likely to be treated successfully by targeted therapies.”

Researchers developed a differential equation relating the treatment’s progress and cell death. Their model successfully predicted which of 43 patients enrolled in a clinical trial had oncogene-addicted tumors. Though the predictions were retrospective and therefore did not influence actual treatment, the research has potential to identify the success of certain therapies and make recommendations for future patients as to whether their treatment is working.

“Our results may have provocative implications,” Felsher said. “We’ve learned that a key point that many people don’t realize is that it matters a lot how quickly the tumor is getting smaller. There’s a certain rate of regression where you’re never going to get rid of your cancer completely, but at another rate, you will. For oncogene-addicted tumors, it’s a very predictable kinetic response.”

The research was sponsored by the Radiological Society of North America, the Francis Family Foundation, the Henry S. Kaplan Fund, the Howard Hughes Medical Institute, Stanford MIPS, the National Institutes of Health, the Leukemia & Lymphoma Society, Burroughs Wellcome Fund and Damon Runyon Foundation.

– Ellora Israni