Mass Spectroscopy
ARTICLE
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
Fumiko Taguchi, Benjamin Solomon, Vanesa Gregorc, Heinrich Roder, Robert Gray, Kazuo Kasahara, Makoto Nishio, Julie Brahmer, Anna Spreafico, Vienna Ludovini, Pierre P. Massion, Rafal Dziadziuszko, Joan Schiller, Julia Grigorieva, Maxim Tsypin, Stephen W. Hunsucker, Richard Caprioli, Mark W. Duncan, Fred R. Hirsch, Paul A. Bunn Jr, David P. Carbone
Downloaded from http://jnci.oxfordjournals.org/ by guest on September 13, 2012
Background
Some but not all patients with non–small-cell lung cancer (NSCLC) respond to treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). We developed and tested the ability of a predictive algorithm based on matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) analysis of pretreatment serum to identify patients who are likely to benefit from treatment with EGFR TKIs. Serum collected from NSCLC patients before treatment with gefitinib or erlotinib were analyzed by MALDI MS. Spectra were acquired independently at two institutions. An algorithm to predict outcomes after treatment with EGFR TKIs was developed from a training set of 139 patients from three cohorts. The algorithm was then tested in two independent validation cohorts of 67 and 96 patients who were treated with gefitinib and erlotinib, respectively, and in three control cohorts of patients who were not treated with EGFR TKIs. The clinical outcomes of survival and time to progression were analyzed. An algorithm based on eight distinct m/z features was developed based on outcomes after EGFR TKI therapy in training set patients. Classifications based on spectra acquired at the two institutions had a concordance of 97.1%. For both validation cohorts, the classifier identified patients who showed improved
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
Fumiko Taguchi, Benjamin Solomon, Vanesa Gregorc, Heinrich Roder, Robert Gray, Kazuo Kasahara, Makoto Nishio, Julie Brahmer, Anna Spreafico, Vienna Ludovini, Pierre P. Massion, Rafal Dziadziuszko, Joan Schiller, Julia Grigorieva, Maxim Tsypin, Stephen W. Hunsucker, Richard Caprioli, Mark W. Duncan, Fred R. Hirsch, Paul A. Bunn Jr, David P. Carbone
Downloaded from http://jnci.oxfordjournals.org/ by guest on September 13, 2012
Background
Some but not all patients with non–small-cell lung cancer (NSCLC) respond to treatment with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). We developed and tested the ability of a predictive algorithm based on matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS) analysis of pretreatment serum to identify patients who are likely to benefit from treatment with EGFR TKIs. Serum collected from NSCLC patients before treatment with gefitinib or erlotinib were analyzed by MALDI MS. Spectra were acquired independently at two institutions. An algorithm to predict outcomes after treatment with EGFR TKIs was developed from a training set of 139 patients from three cohorts. The algorithm was then tested in two independent validation cohorts of 67 and 96 patients who were treated with gefitinib and erlotinib, respectively, and in three control cohorts of patients who were not treated with EGFR TKIs. The clinical outcomes of survival and time to progression were analyzed. An algorithm based on eight distinct m/z features was developed based on outcomes after EGFR TKI therapy in training set patients. Classifications based on spectra acquired at the two institutions had a concordance of 97.1%. For both validation cohorts, the classifier identified patients who showed improved
References: Downloaded from http://jnci.oxfordjournals.org/ by guest on September 13, 2012 Methods JNCI | Articles 841 Downloaded from http://jnci.oxfordjournals.org/ by guest on September 13, 2012 842 Articles | JNCI Downloaded from http://jnci.oxfordjournals.org/ by guest on September 13, 2012