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New Stanford tool for genetic data analysis may improve cancer treatment


Statistics isn’t what normally comes to mind what people think of cancer research, but a new statistical tool developed at the Stanford University School of Medicine could smooth out some of the fits and starts that have plagued the effort to understand and treat the disease.

The tool is at the heart of a new study that divides similar-looking kidney tumors into subtypes depending on which of thousands of genes are turned on or off. The idea behind this and related studies of other types of cancer published over the past five years is that doctors can use the information to decide the most appropriate treatment strategy for each patient. Targeting the treatment to a patient’s specific cancer means quicker treatment and fewer side effects. Sounds good, right?

The problem is that in many cases the subtypes that turn up in one analysis are absent in follow-up studies, rendering this work clinically irrelevant. The new way of analyzing these cancer studies, published online in the Dec. 5 Public Library of Science-Medicine, should minimize these setbacks and help turn cancer research into cancer cures.

Robert Tibshirani, PhD, professor of health research and policy and of statistics, said part of the problem lies in how the scientists analyze the data. "A lot of people have applied old statistical tools to new data, and they don’t necessarily work," he said. The type of studies in question, called microarray studies, generates a veritable haystack of data. Most researchers search for genetic needles in that haystack. Sometimes they find the needles, but sometimes they accidentally mistake hay for a needle, confusing the entire field.

Tibshirani and his colleagues got into a debate in the pages of the New England Journal of Medicine in March over one such study that Tibshirani said used flawed statistical methods and therefore generated interesting but false conclusions. Tibshirani said his new tools for analyzing the data would help eliminate these misleading studies in the future and help the community of cancer researchers to focus on the most relevant data. He said the new tool he and his colleagues developed looks for larger groups of genes - equivalent to searching for pitchforks rather than needles - and therefore are more likely to turn up again in future studies. These groups represent biological pathways that are characteristically active or inactive within tumors.

In the first test of Tibshirani’s new tool, he and his colleagues James Brooks, MD, associate professor of urology, and Hongjuan Zhao, PhD, research associate, studied the most common form of kidney cancer, called renal cell carcinoma. This cancer kills nearly 95,000 people worldwide each year.

Brooks and Zhao analyzed the genes that were turned on or off in kidney tumors removed by their collaborators at Umea University in Sweden. Using Tibshirani’s approach they found 259 interesting genes. Whether these genes were being actively used by a particular cancer could reveal whether that cancer was likely to spread aggressively and need more vigorous treatment.

"Picking out who has a more or less aggressive cancer can help us decide how to follow that patient once we treat them," Brooks said. For example, somebody who has a less aggressive cancer may need fewer CAT scans after surgery. That would save the patient from needing to come to the hospital regularly for tests, prevent any problems from excessive exposure to radiation during the CAT scans and save money. Likewise, the information could help identify patients who need very aggressive treatment beyond surgery alone, Brooks said.

Eventually the group wants to narrow those 259 genes to a smaller subset that can accurately distinguish between cancers. Tibshirani said that he could only have developed this statistical tool at Stanford, where he can work closely with colleagues like Brooks and Zhao who are actively using microarrays to study cancer. "I think that our collaboration is an excellent example of Stanford’s strength in translational medicine," he said. Tibshirani, whose expertise is in biostatistics, said he needs input from cancer surgeons and researchers in order to tweak his statistical programs effectively.

The tool Tibshirani and his colleagues developed is now available to other researchers carrying out similar analyses. He hopes the tool catches on with researchers in the field and helps prevent some of the misleading studies that have been published in the past.

Amy Adams | EurekAlert!
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