Gene Profiles Might Help Guide Lung Cancer Care

SUNDAY, July 20 (HealthDay News) -- A sweeping genetic analysis suggests that the activity of certain genes might someday allow doctors to predict which lung cancer patients need more aggressive therapies and which do not.

But the findings also underscore the difficulty of making such predictions, especially in the case of people with the earliest forms of the disease, when aggressive therapies could be of greatest value.

The goal is to build effective predictors based on gene expression (activity) and use them prospectively to guide treatment decisions, experts said.

However, to do that, "you have to know what are the potential issues that might influence how well gene expression might predict," said researcher David Beer, a professor in the department of thoracic surgery at the University of Michigan. "I guess the bottom line from this study is that because of the heterogeneity of lung adenocarcinoma, it is not an easy problem. There are still significant issues."

Still, this study -- the most comprehensive yet to date -- could pave the way to more tailored lung cancer treatment based on gene expression profiles, said one expert.

"The goal is five years from now, if I had this data on a stage 1 or stage 2 lung cancer patient, that I could say, 'Hey, you have a very low-risk profile, you don't need chemotherapy' and vice-versa, of course," said Dr. Edward Kim, an assistant professor of medicine in the department of thoracic/head and neck oncology at the University of Texas M.D. Anderson Cancer Center, in Houston.

"It is no different from what we do for breast cancer, where we use certain markers to help doctors make decisions about what treatment they need," Kim said. "This is a step in that direction for lung cancer."

The results were published online July 20 inNature Medicine.

Beer, along with James Jacobson of the U.S. National Cancer Institute, led the study under the auspices of the NCI Director's Challenge Consortium for the Molecular Classification of Lung Adenocarcinoma, which also includes researchers at the H. Lee Moffitt Cancer Center in Tampa, Fla., the Memorial Sloan-Kettering Cancer Center in New York, the Dana-Farber Cancer Institute in Boston, and the Ontario Cancer Institute in Canada.

The consortium first compiled 442 lung adenocarcinoma samples from six institutions and then divided them into four test sets. For each sample, they collected gene expression data on some 22,000 genes found in these cancer samples. They also looked over clinical information, such as the stage of the cancer and the patients' outcomes.

Consortium members then used two of the test sets, including outcome data, to develop prognostic "classifiers" -- collections of genes whose changes in activity (expressing or producing proteins, for example), whether up or down, predict patient outcome.

Then, the researchers applied these classifiers -- eight were developed overall -- to the remaining two test sets in a so-called validation step. Unlike during the initial "training phase" of the study, patient outcome data at this stage was "blinded." That meant that the researchers had to let their gene signatures (with and without the aid of clinical data) predict patient outcome. Those predictions were then checked against the actual clinical data to measure their accuracy.