Serum Proteomic Classifier for Predicting Response to Epidermal Growth Factor Receptor Inhibitor Therapy: Have We Built a Better Mousetrap?

Abstract

The ability to predict treatment response and clinical (survival) outcomes is one of the two “holy grails” of cancer biomarker development, the other being early detection or risk stratification by population screening ( 1 ). In advanced non – small-cell lung cancer (NSCLC), small-molecule tyrosine kinase inhibitors (TKIs) of the epidermal growth factor receptor (EGFR) represent a breakthrough for targeted therapy, yet only a small proportion of patients (especially among non – East Asians) appear to benefit from these expensive agents ( 2 , 3 ). Previous studies have shown that EGFR immunohistochemistry, tyrosine kinase domain mutations, and/or EGFR gene copy number by fluorescent in situ hybridization (FISH) are biomarkers that are potentially useful to select patients more likely to benefit from EGFR TKIs ( 4 – 6 ). Aside from the incomplete validation of these markers for routine clinical implementation, mutation and FISH analyses are limited by tissue availability and, at times, technical feasibility ( 5 ). Therefore, a strongly predictive but simple blood-based test has great potential for becoming the ultimate biomarker for selecting patients who are likely to benefit from EGFR TKI therapy. In this issue of the Journal, Taguchi et al. ( 7 ) report a proteomic algorithm (signature) that classifi es patients according to their likely outcomes after EGFR TKI therapy. This algorithm was generated by matrix-assisted laser desorption ionization (MALDI) mass spectroscopy (MS) analysis of pretreatment sera or plasma of patients. The authors used a training set of 139 patients treated with second- or greater-line gefi tinib who were recruited from three separate institutions in Europe or Asia to discover eight MALDI signature peaks that form the predictive algorithm. This classifi er was then validated in two independent cohorts of gefi tinibor erlotinib-treated patients from Italy and the United States. In fact, this study marks the fi rst large-scale multi-institutional evaluation of an MS-based protein signature in the serum or plasma of NSCLC patients for predicting EGFR TKI – related clinical outcomes. The study showed that, despite variability in the source or nature of samples studied and in the spectra generated by different operators, the data preprocessing and normalization procedures were able to generate MALDI MS features that were reproducible in two independent laboratories. Furthermore, the clinical outcome classifi er (which classifi ed patients into good, poor, or undefi ned classes) was validated in two independent cohorts of patients receiving fi rst- or greater-line gefi tinib or erlotinib therapy. More important, the authors claim that the algorithm is predictive and not prognostic, in that its classifi cation capability failed in three separate cohorts of patients who were not treated with EGFR TKIs. The authors conclude that their eight-peak MALDI MS algorithm might assist in the pretreatment selection of appropriate subgroups of NSCLC pa tients for