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.

Introduction We investigated the predictive and prognostic effects of VeriStrat, a serum or plasma-based assay, on response and survival in a subset of patients enrolled on the NCIC Clinical Trials Group, BR.21 phase III trial of erlotinib versus placebo in previously treated advanced non–small-cell lung cancer patients. Methods Pretreatment plasma samples were available for 441 of 731 enrolled patients and were provided as anonymized aliquots to Biodesix. The VeriStrat test was performed in a Clinical Laboratory Improvement Act-accredited laboratory at Biodesix, Inc. (Boulder, CO). The results (Good, Poor) were returned to NCIC Clinical Trials Group, which performed all the statistical analyses. Results VeriStrat testing was successful in 436 samples (98.9%), with 61% classified as Good. VeriStrat was prognostic for overall survival in both erlotinib-treated patients and those on placebo, independent of clinical covariates. For VeriStrat Good patients, the median survival was 10.5 months on erlotinib versus 6.6 months for placebo (hazard ratio 0.63, 95% confidence interval 0.47–0.85, p = 0.002). For VeriStrat Poor patients, the median survival was 4 months for patients receiving erlotinib, and 3.1 months for placebo (hazard ratio: 0.77, 95% confidence interval 0.55–1.06, p = 0.11). VeriStrat was predictive for objective response (p = 0.002), but was not able to predict for differential survival benefit from erlotinib (interaction p = 0.48). Similar results were found for progression-free survival. Conclusion We were able to confirm that VeriStrat is predictive of objective response to erlotinib. VeriStrat is prognostic for both OS and progression-free survival, independent of clinical features, but is not predictive of differential survival benefit versus placebo.

A mass spectrometry analysis was performed using serum from patients receiving checkpoint inhibitors to define baseline protein signatures associated with outcome in metastatic melanoma. Pretreatment serum was obtained from a development set of 119 melanoma patients on a trial of nivolumab with or without a multipeptide vaccine and from patients receiving pembrolizumab, nivolumab, ipilimumab, or both nivolumab and ipilimumab. Spectra were obtained using matrix-assisted laser desorption/ionization time of flight mass spectrometry. These data combined with clinical data identified patients with better or worse outcomes. The test was applied to five independent patient cohorts treated with checkpoint inhibitors and its biology investigated using enrichment analyses. A signature consisting of 209 proteins or peptides was associated with progression-free and overall survival in a multivariate analysis. The test performance across validation cohorts was consistent with the development set results. A pooled analysis, stratified by set, demonstrated a significantly better overall survival for “sensitive” relative to “resistant” patients, HR = 0.15 (95% confidence interval: 0.06–0.40, P < 0.001). The test was also associated with survival in a cohort of ipilimumab-treated patients. Test classification was found to be associated with acute phase reactant, complement, and wound healing pathways. We conclude that a pretreatment signature of proteins, defined by mass spectrometry analysis and machine learning, predicted survival in patients receiving PD-1 blocking antibodies. This signature of proteins was associated with acute phase reactants and elements of wound healing and the complement cascade. This signature merits further study to determine if it identifies patients who would benefit from PD-1 blockade. Cancer Immunol Res; 6(1); 79–86. ©2017 AACR.

Background: We hypothesized that a serum proteomic profile predictive of survival benefit in non–small cell lung cancer patients treated with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI) reflects tumor EGFR dependency regardless of site of origin or class of therapeutic agent. Methods: Pretreatment serum or plasma from 230 patients treated with cetuximab, EGFR-TKIs, or chemotherapy for recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) or colorectal cancer (CRC) were analyzed by mass spectrometry. Each sample was classified into “good” or “poor” groups using VeriStrat, and survival analyses of each cohort were done based on this classification. For the CRC cohort, this classification was correlated with the tumor EGFR ligand levels and KRAS mutation status. Results: In the EGFR inhibitor–treated cohorts, the classification predicted survival (HNSCC: gefitinib, P = 0.007 and erlotinib/bevacizumab, P = 0.02; CRC: cetuximab, P = 0.0065) whereas the chemotherapy cohort showed no survival difference. For CRC patients, tumor EGFR ligand RNA levels were significantly associated with the proteomic classification, and combined KRAS and proteomic classification provided improved survival classification. Conclusions: Serum proteomic profiling can detect clinically significant tumor dependence on the EGFR pathway in non–small cell lung cancer, HNSCC, and CRC patients treated with either EGFR-TKIs or cetuximab. This classification is correlated with tumor EGFR ligand levels and provides a clinically practical way to identify patients with diverse cancer types most likely to benefit from EGFR inhibitors. Prospective studies are necessary to confirm these findings. Cancer Epidemiol Biomarkers Prev; 19(2); 358–65