Two research teams have developed models for classifying the clinical outcomes of patients with nonsmall-cell lung cancer (NSCLC) using mass spectrometry techniques. The studies are published in the June 6 Journal of the National Cancer Institute.
Currently, clinicians do not have adequate methods for determining the prognosis of patients with NSCLC or for determining which patients will benefit from treatment with certain drugs. The new models could help physicians decide who will benefit from certain treatment options.
In one study, an international team led by David Carbone, M.D., Ph.D., of the Vanderbilt-Ingram Cancer Center in Nashville, developed an algorithm to predict the outcomes of NSCLC patients treated with the drugs gefitinib and erlotinib, two tyrosine kinase inhibitors. The algorithm places patients into categories indicating good or poor survival before treatment with one of the drugs and is based on the pattern of a group of proteins in the patients blood serum. The authors developed the algorithm on a group of patients with known outcomes then tested it on pretreatment serum for independent validation and control groups.
The researchers found that the algorithm could classify patients by their survival outcomes after treatment. In one validation group, patients who were predicted to have good outcomes survived for a median of 306 days, while those in the poor group survived a median of 107 days. By contrast, the algorithm did not correctly classify patients in control groups, who were not treated with the drug.
In the clinical development of biomarkers for the individualization of therapy, it is important to distinguish between those that can accurately classify patients according to whether they will benefit from an intervention and those that simply portend a favorable or unfavorable prognosis, independent of the planned intervention. Biomarkers predictive for survival benefit from an intervent
Contact: Liz Savage
Journal of the National Cancer Institute