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Protein profile predicts prognosis for lung cancer


In the future, many cancer scientists and physicians believe, a "molecular fingerprint" of an individual´s cancer may be used to diagnose that patient´s disease and tailor therapy.

Researchers at Vanderbilt have moved a step closer to that scenario with the identification of a distinct pattern of expression of 15 proteins in lung cancers that can predict a poor prognosis or a good prognosis. All patients in the poor prognosis group had died one year after diagnosis, while all patients in the good prognosis group were still alive. Median survival, the point at which half the patients were still alive, was six months for the poor prognosis group, compared to 33 months for the good prognosis group. "If this pattern is confirmed in larger studies, its prognostic power exceeds that of virtually any previously published standard molecular marker," the authors write in the August 9 issue of The Lancet.

The scientists also demonstrate that protein profiles obtained from a tiny amount of tumor tissue – only 1 millimeter in diameter and only 1/1000 of a millimeter in thickness - can be used to predict risk that the cancer has spread to nearby lymph nodes.

"Involvement of lymph nodes is one of the most important factors in determining treatment strategies, so the clinical implications of these data could be significant," said Dr. David P. Carbone, Ingram Professor of Cancer Research, professor of Medicine and Cancer Biology. "Being able to use molecular markers to divide patients into high- or low-risk groups would also be very useful in determining treatment strategy."

Such a predictor could help patients and families, with their physicians, decide the most appropriate action, which could range from more aggressive therapy at the outset to avoiding therapies that are more likely to hurt quality of life for the patient than to extend that life.

The research involved investigators from the Vanderbilt-Ingram Cancer Center; Vanderbilt School of Medicine´s departments of Medicine, Preventive Medicine, Molecular Physiology and Biophysics, Cardiac and Thoracic Surgery, and Pathology; and Vanderbilt´s Mass Spectrometry Research Center. The project is part of Vanderbilt´s Specialized Program of Research Excellence (SPORE) in lung cancer, a major initiative funded by the National Cancer Institute.

Now that the human genome has been defined, proteomics – the study of the proteins that carry out the work of the cells at the instruction of the genes – is widely considered the next frontier in biomedical research. Vanderbilt has one of the strongest programs in the world in proteomics research, with the sophisticated equipment, informatics power and statistical expertise required to comprehensively analyze the activity of thousands of proteins at once.

The investigators used mass spectrometry and customized software to analyze samples from 79 lung tumors and 14 normal lung tissue. The investigators were able, based on differences in patterns of protein expression, to distinguish with 100 percent accuracy:

  • Lung tumor from normal lung;

  • Primary non-small cell lung cancer (NSCLC) from normal lung;

  • Primary NSCLC from cancer that had spread to the lungs from other organs; and

  • Adenocarcinomas from squamous cell carcinomas, squamous cell carcinomas from large cell carcinomas.

Predictions based on protein profiles were confirmed by pathological evaluation under a microscope. In one case, a large cell carcinoma may have been misclassified based on protein patterns as an adenocarcinoma, but the investigators report that this tumor may actually be an adenocarcinoma that is too poorly differentiated to identify as such under the microscope.

The investigators note that using protein profiles to make distinctions that are already apparent under the microscope offers little use in clinical care, although the approach is potentially useful in identifying novel therapeutic targets. However, the ability to use protein profiles to predict node involvement or to identify patients as high- or low-risk could have great implications for treatment strategies, Carbone said.

"Because such small tissue samples are needed, it would be of great interest to analyze protein expression patterns of tissue samples from needle aspirations or from different cell subtypes within the lung," Carbone said. "It also would be interesting to look for patterns associated with response to specific therapies, with smoking exposure, or with preneoplasia and the progression to cancer.

"If these data are confirmed using larger numbers of patients, this technology could have significant implications for the clinical management of non-small cell lung cancer."

Contact: Cynthia Floyd Manley,

Cynthia Floyd Manley | EurekAlert!
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