RESUMEN
Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated â¼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, pâ=â1.0×10(-25)), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control.
Asunto(s)
Enfermedades Pulmonares Intersticiales/sangre , Enfermedades Pulmonares Intersticiales/complicaciones , Neoplasias Pulmonares/complicaciones , Neoplasias Pulmonares/tratamiento farmacológico , Proteómica/métodos , Quinazolinas/uso terapéutico , Pueblo Asiatico , Biomarcadores/sangre , Carcinoma de Pulmón de Células no Pequeñas/complicaciones , Carcinoma de Pulmón de Células no Pequeñas/tratamiento farmacológico , Cromatografía Liquida , Bases de Datos de Proteínas , Análisis Discriminante , Gefitinib , Humanos , Enfermedades Pulmonares Intersticiales/diagnóstico , Péptidos/sangre , Péptidos/aislamiento & purificación , Fenotipo , Control de Calidad , Reproducibilidad de los Resultados , Espectrometría de Masas en TándemRESUMEN
Personalized medicine allows the selection of treatments best suited to an individual patient and disease phenotype. To implement personalized medicine, effective tests predictive of response to treatment or susceptibility to adverse events are needed, and to develop a personalized medicine test, both high quality samples and reliable data are required. We review key features of state-of-the-art proteomic profiling and introduce further analytic developments to build a proteomic toolkit for use in personalized medicine approaches. The combination of novel analytical approaches in proteomic data generation, alignment and comparison permit translation of identified biomarkers into practical assays. We further propose an expanded statistical analysis to understand the sources of variability between individuals in terms of both protein expression and clinical variables and utilize this understanding in a predictive test.