New Algorithms Classify Outcomes Of Nonsmall Cell Lung Cancer Patients

Two research teams have developed models for classifying the clinical outcomes of patients with non-small-cell lung cancer (NSCLC) using mass spectrometry techniques. One team 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 patient's blood serum. In the second study, researchers analyzed protein patterns in NSCLC tumor tissue and normal lung tissue. The researchers identified a pattern that was associated with increased survival among NSCLC patients and may distinguish patients with poor prognosis from those with good prognosis.