TY - JOUR
T1 - Detection of tumor epidermal growth factor receptor pathway dependence by serum mass spectrometry in cancer patients
AU - Chung, Christine H.
AU - Seeley, Erin H.
AU - Roder, Heinrich
AU - Grigorieva, Julia
AU - Tsypin, Maxim
AU - Roder, Joanna
AU - Burtness, Barbara A.
AU - Argiris, Athanassios
AU - Forastiere, Arlene A.
AU - Gilbert, Jill
AU - Murphy, Barbara
AU - Caprioli, Richard M.
AU - Carbone, David P.
AU - Cohen, Ezra E.W.
PY - 2010/2
Y1 - 2010/2
N2 - 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 Veri Strat, 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.
AB - 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 Veri Strat, 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.
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U2 - 10.1158/1055-9965.EPI-09-0937
DO - 10.1158/1055-9965.EPI-09-0937
M3 - Article
C2 - 20086114
AN - SCOPUS:76149106147
SN - 1055-9965
VL - 19
SP - 358
EP - 365
JO - Cancer Epidemiology Biomarkers and Prevention
JF - Cancer Epidemiology Biomarkers and Prevention
IS - 2
ER -