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New Algorithms Classify Outcomes Of Nonsmall Cell Lung Cancer Patients

Science Daily — Two research teams have developed models for classifying the clinical outcomes of patients with non--small-cell lung cancer (NSCLC) using mass spectrometry techniques. 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 patient's 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 intervention are much more useful for guiding management," the authors write.

In an accompanying editorial, Ming-Sound Tsao, M.D., of Princess Margaret Hospital in Toronto, and colleagues discuss the need for further validation of the results to avoid the over-hype seen with previous blood serum biomarkers. "Taguchi [and colleagues] should be lauded for their efforts in assessing the reproducibility of their technique and results by performing independent tests on duplicate sample at two different institutions," the authors write.

In the second study, Kiyoshi Yanagisawa, M.D., Ph.D., of Nagoya University in Japan, and colleagues 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. They also tested their model on independent validation and control groups.

"Consequently, use of the [protein] signature to identify high-risk patients could reduce rates of both overtreatment and undertreatment and improve survival for NSCLC patients," the authors write.

The studies are published in the June 6 Journal of the National Cancer Institute.


Article1: Taguchi F, Solomon B, Gregorc V, Roder H, Gray R, Kasahara K, et al. Mass Spectrometry to Classify Non -- Small-Cell Lung Cancer Patients for Clinical Outcome After Treatment With Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors: A Multicohort Cross-Institutional Study. J Natl Cancer Inst 2007; 99:

Editorial: Tsao MS, Liu G, Shepherd FA. Serum Proteomic Classifier for Predicting Response to Epidermal Growth Factor Receptor Inhibitor Therapy: Have We Built a Better Mousetrap" J Natl Cancer Inst 2007; 99: 826-827

Article 2: Yanagisawa K, Tomida S, Shimada Y, Yatabe Y, Mitsudomi T, Takahashi T. A 25-Signal Proteomic Signature and Outcome for Patients With Resected Non -- Small-Cell Lung Cancer. J Natl Cancer Inst 2007; 99:858-867

Note: This story has been adapted from a news release issued by Journal of the National Cancer Institute.

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