“There’s a real clinical need to identify which patients will benefit from targeted therapies,” said David Carbone, M.D., Ph.D., Harold L. Moses Professor of Cancer Research at Vanderbilt-Ingram Cancer Center and the senior author of the study. Targeted cancer therapies are the newest generation of anti-cancer drugs. They affect proteins and signalling pathways that are selectively activated in certain malignant cells and not in normal cells. But this selectivity makes these therapies effective only for the subset of patients whose tumours are “driven” by the targeted pathways.
In the case of the epidermal growth factor (EGF) receptor tyrosine kinase inhibitors (TKIs) gefitinib (Iressa) and erlotinib (Tarceva), studies have demonstrated a survival benefit for 30 to 40 percent of lung cancer patients, but there has been no method for identifying these patients prior to treatment, Carbone said.
Using mass spectrometry, the researchers analyzed pre-treatment blood samples from 139 patients who had been treated with gefitinib, identified a pattern of eight proteins that was correlated with survival, and developed a prediction algorithm. They then tested the algorithm in two additional groups of patients.
“We classified the patients as being either likely to benefit (“good”) or not likely to benefit (“poor”) from the TKIs,” Carbone said. The method was highly successful in predicting a survival benefit. In the gefitinib-treated group, patients classified as “good” had a median survival of 207 days whereas those classified as “poor” had a median survival of 92 days. In the erlotinib-treated group, median survivals for “good” and “poor” groups were 306 and 107 days, respectively.
The fact that the signature, which was developed from gefitinib-treated patients, also accurately predicted a survival benefit for erlotinib-treated patients buoys Carbone’s confidence in the algorithm, he said, since these two drugs share a common mechanism of action.
COMPAMED.de; Source: Vanderbilt University Medical Center