Detection of tumor epidermal growth factor receptor pathway dependence by serum mass spectrometry in cancer patients

Abstract

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.

Figure 1

Kaplan-Meier plots for OS of patients with recurrent and/or metastatic HNSCC comparing the predictive groups of survival benefit, good and poor, determined by the MS profile, when treated with EGFRIs as previously described (24). A, a cohort of patients treated with gefitinib (n = 55); B, a cohort of patients treated with erlotinib and bevacizumab (n = 32); C, a cohort of patients treated with cetuximab (n = 21); and D, a cohort of patients treated with docetaxel-containing palliative chemotherapy as a control (n = 34).

Figure 2

A, Kaplan-Meier plot for PFS of patients with recurrent and/or metastatic CRC comparing the predictive groups of survival benefit, good and poor, determined by the MS profile, when treated with cetuximab as previously described (n = 88; refs. 16, 24). B, Kaplan-Meier plot for PFS based on KRAS mutation status in the subset of CRC patients with both KRAS and MS profile classification data (n = 64; ref. 16). C, Kaplan-Meier plot for PFS of patients based on combined KRAS mutation status and the good/poor classification: KRAS mutant (mut)/poor versus KRAS wild-type (wt)/good groups (log-rank P = 0.023; HR, 0.37; 95% CI, 0.16-0.88); KRAS-wt/good versus KRAS-wt/poor (log-rank P = 0.18; HR, 0.59; 95% CI, 0.27-1.27); KRAS-mut/good versus KRAS-mut/poor (log-rank P = 0.40; HR, 0.69; 95% CI, 0.29-1.62); KRAS-mut/good versus KRAS-wt/poor (log-rank P = 0.82; HR, 1.10; 95% CI, 0.49-2.44); KRAS-wt/good (n = 24), KRAS-wt/poor (n = 14), KRAS-mut/good (n = 12), KRAS-mut/poor (n = 14); and D, correlation between the expression levels of AREG and EREG in tumors determined by qRT-PCR and the good/poor classification determined by the proteomic profile (n = 60; Mann-Whitney U test P = 0.0053 and 0.0015, respectively).