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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
Affiliations of authors: Departments of Medicine (FT, DPC), Medicine, Pulmonary Division (PPM), Biochemistry (RC), and Cancer Biology (DPC), Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN; Departments of Medical Oncology (BS, FRH, PAB), and Pediatrics (SWH, MWD), University of Colorado at Denver and Health Sciences Center, Aurora, CO; Department of Oncology, Scientific Institute University Hospital San Raffaele, Milan, Italy (VG, AS); Biodesix, Steamboat Springs, CO (HR, JG, MT); Department of Biostatistics and Computational Biology, ECOG Biostatistical Office, Boston, MA (RG); Department of Respiratory Medicine, Kanazawa University, Kanazawa, Japan (KK); Thoracic Oncology Center, Japanese Foundation for Cancer Research, Tokyo, Japan (MN); Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD (JB); Department of Medical Oncology, Azienda Ospedaliera di Perugia, Perugia, Italy (VL); Department of Medical Oncology, Medical University of Gdansk, Poland (RD); Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TX (JS)
Correspondence to: David P. Carbone, MD, PhD, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN 37232-6838 (e-mail: d.carbone{at}vanderbilt.edu).
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.
Methods: 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.
Results: 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 outcomes after EGFR TKI treatment. In one cohort, median survival of patients in the predicted "good" and "poor" groups was 207 and 92 days, respectively (hazard ratio [HR] of death in the good versus poor groups = 0.50, 95% confidence interval [CI] = 0.24 to 0.78). In the other cohort, median survivals were 306 versus 107 days (HR = 0.41, 95% CI = 0.17 to 0.63). The classifier did not predict outcomes in patients who did not receive EGFR TKI treatment.
Conclusion: This MALDI MS algorithm was not merely prognostic but could classify NSCLC patients for good or poor outcomes after treatment with EGFR TKIs. This algorithm may thus assist in the pretreatment selection of appropriate subgroups of NSCLC patients for treatment with EGFR TKIs.
| CONTEXT AND CAVEATS Prior knowledge Some patients with non–small-cell lung cancer respond to treatment with the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) gefitinib or erlotinib, but others do not. Clinical parameters alone are not sufficient to identify which patients are likely to benefit. Study design Matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry (MS) analysis of a training set of patients was used to develop an algorithm to classify patients as having "good" or "poor" outcomes after EGFR TKI treatment. The algorithm was then tested in several independent validation and control cohorts. Contribution The algorithm was able to classify patients in the validation cohorts in terms of their outcomes after treatment with gefitinib or erlotinib. In one validation set, the patients classified as "good" survived for a median of 306 days, whereas those classified as "poor" survived for a median of 107 days. The algorithm did not predict outcomes in control cohorts of patients who were not treated with EGFR TKIs. Implications The algorithm was able to classify patients according to their outcomes after EGFR TKI treatment. This classification algorithm, if confirmed in other cohorts, may help to identify appropriate subgroups of non–small-cell lung cancer patients for treatment with EGFR TKIs. Limitations Some studies have shown poor reproducibility of MALDI MS profiling, although this study reported that the profile was reproducible in different institutions. The identity of the proteins that make up the MALDI MS features in the classification algorithm is not known.
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Manuscript received November 20, 2006; revised March 26, 2007; accepted April 13, 2007.
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J Natl Cancer Inst 2007 99: 826-827.
J Natl Cancer Inst 2007 99: 825.
J Natl Cancer Inst 2007 99: 825.
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