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
Journal of the National Cancer Institute (Impact Factor: 12.58). 07/2007; 99(11):838-46. DOI: 10.1093/jnci/djk195
Source: PubMed


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
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    • "In addition to nucleotide biomarkers, proteins in the blood may also provide valuable information for tumor evaluation before and after EGFR-targeted therapy. VeriStrat, a mass spectrometry-based high-throughput platform, can analyze proteomic profiles using only 5 μL serum from the patients and subsequently classify the patients for “good” or “poor” response to TKIs.175 To our knowledge, VeriStrat is the only liquid biopsy tool for NSCLC patient stratification before TKI treatment that has been validated in a Phase III clinical trial,176,177 which revealed its prognostic value in predicting overall survival and time to progression.177–180 "
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    ABSTRACT: Lung cancer, mostly nonsmall cell lung cancer, continues to be the leading cause of cancer-related death worldwide. With the development of tyrosine kinase inhibitors that selectively target lung cancer-related epidermal growth factor receptor mutations, management of advanced nonsmall cell lung cancer has been greatly transformed. Improvements in progression-free survival and life quality of the patients were observed in numerous clinical studies. However, overall survival is not prolonged because of later-acquired drug resistance. Recent studies reveal a heterogeneous subclonal architecture of lung cancer, so it is speculated that the tumor may rapidly adapt to environmental changes via a Darwinian selection mechanism. In this review, we aim to provide an overview of both spatial and temporal tumor heterogeneity as potential mechanisms underlying epidermal growth factor receptor tyrosine kinase inhibitor resistance in nonsmall cell lung cancer and summarize the possible origins of tumor heterogeneity covering theories of cancer stem cells and clonal evolution, as well as genomic instability and epigenetic aberrations in lung cancer. Moreover, investigational measures that overcome heterogeneity-associated drug resistance and new assays to improve tumor assessment are also discussed.
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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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    • "A serum mass spectrometry-based classifier assay segregates candidates for EGFRi therapy into “good” and “bad” prognosis groups [9]. In multiple studies of EGFRis, the patients in the two groups had divergent survival patterns, but not in NSCLC patients treated with cytotoxic therapy, suggesting that the serum proteomic profile is a predictive rather than prognostic marker. "
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