Gene Profiles Might Help Guide Lung Cancer Care

The results, said Beer, were mixed.

"We found some [classifiers] work well on one test set but not both, and very few worked well on both, and some of the published signatures did not work very well at all," he said.

Performance was better for tumors of all disease stages than when focusing exclusively on stage 1 disease, he noted. But, in most cases, the addition of clinical data substantially improved the predictions.

For Beer, the data highlight the difficulties of working with such a variable disease as lung cancer, which stems from both genetic and environmental (i.e., smoking) factors.

"It would be wonderful if this was very easy, and you could do it very accurately, but in reality it doesn't work as well as hoped, and we are trying to understand why that is the case," he said. "Why does it work well in some patients but not in others? How do you improve it? How do we identify genes that are prognostic for everybody, or at least for specific subgroups of patients?"

But Dr. Arul Chinnaiyan, a cancer microarray expert at the University of Michigan who was not affiliated with this research, praised the study's design -- particularly its size, use of blinded samples, and multi-institutional format. He also applauded the team's ability to develop and identify gene signatures that work across the various testing sites.

"Many biomarkers as developed often don't hold up across institutions," he said. "Early on, studies are done in an unblended way, at one institution. Often, when another researcher does this, it doesn't validate. That is what is so impressive, that it held up at all these institutions. That points to the robustness of the signature they identified, that it probably will hold up in a clinical setting."

Kim agreed that the study's strength lay in its numbers.

"This is extremely important [work] because they brought everyone together, they have 442 samples for which they have very good gene expression data and clinical data," he said. "And the goal is to grow this so it can be used in a prospective study and hopefully, then, be integrated into our daily clinical practice."

According to Chinnaiyan, the new data suggest that a lung cancer prognosis, like that of breast cancer, could be predicted from gene expression data via a diagnostic test. Two clinical tests, Agendia's MammaPrint and Genomic Health's Oncotype DX, already use the expression of 70 or 21 genes, respectively, to predict which breast cancer patients are likely to suffer a recurrence of disease, and thus might benefit from more aggressive therapies.

The hope is that similar strategies might work for an even bigger killer, lung cancer.

"This is very analogous," Chinnaiyan said.

More information

For more on lung cancer, visit the American Lung Association.

SOURCES: David Beer, Ph.D., professor, department of thoracic surgery, Cancer Center, University of Michigan, Ann Arbor; Edward Kim, M.D., assistant professor, medicine, department of thoracic/head and neck oncology, University of Texas M.D. Anderson Cancer Center, Houston; Arul M. Chinnaiyan, M.D., Ph.D., director, Michigan Center for Translational Pathology, investigator, Howard Hughes Medical Institute, and S.P. Hicks Endowed Professor of Pathology, University of Michigan Medical School, Ann Arbor; July 20, 2008,Nature Medicine, online