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 an
Contact: Amy Adams
Stanford University Medical Center