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

Johns Hopkins University, Baltimore, Maryland, United States
CancerSpectrum Knowledge Environment (Impact Factor: 15.16). 07/2007; 99(11):838-46. DOI: 10.1093/jnci/djk195
Source: PubMed

ABSTRACT 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 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.
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.

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Available from: Vanesa Gregorc, Aug 21, 2015
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    • "Of note, serum proteome profiling by MALDI/SEL- DI mass spectrometry was used for the identification of multi-peptide signatures discriminating patients with NSCLC and healthy donors or patients with other malignancies (Patz et al., 2007; Yildiz et al., 2007; Han et al., 2008; Ocak et al., 2009; Pietrowska et al., 2012). Similarly, serum proteome profiling revealed proteome signature allowing for classification of NSCLC patients for good or poor outcome after treatment with EGFR inhibitors (Taguchi et al., 2007), which signature was a base for the prognostic and predictive VeriStrat test (Carbone et al., 2012). "
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    Acta biochimica Polonica 05/2014; · 1.39 Impact Factor
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    • "Spectral preprocessing was performed, which included background and noise estimation, background subtraction, alignment and normalisation to partial ion current before spectral analysis by the VeriStrat algorithm, which classifies each sample as VeriStrat Good, Poor or Indeterminate. All details of sample processing, spectral preprocessing and the classification algorithm, based on eight distinct m/z features have been fixed since development of the test in 2006 (Taguchi et al, 2007). The identity of the proteins that make up the MALDI-MS features used in the test are still under investigation. "
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    • "classified with the proteomic algorithm according to the protocol previously described [16]. Mass spectra for all the samples were generated on a Voyager DE-STR MALDI-TOF mass spectrometer (Applied Biosystems, Foster City, CA, USA). "
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