Proteomic Patterns of Preinvasive Bronchial Lesions

Abstract

Purpose: A proteomics approach is warranted to further elucidate the molecular steps involved in lung tumor development. We asked whether we could classify preinvasive lesions of airway epithelium according to their proteomic profile. Experimental Design: We obtained matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry profiles from 10-m sections of fresh-frozen tissue samples: 25 normal lung, 29 normal bronchial epithelium, and 20 preinvasive and 36 invasive lung tumor tissue samples from 53 patients. Proteomic profiles were calibrated, binned, and normalized before analysis. We performed class comparison, class prediction, and supervised hierarchic cluster analysis. We tested a set of discriminatory features obtained in a previously published dataset to classify this independent set of normal, preinvasive, and invasive lung tissues. Results: We found a specific proteomic profile that allows an overall predictive accuracy of over 90% of normal, preinvasive, and invasive lung tissues. The proteomic profiles of these tissues were distinct from each other within a disease continuum. We trained our prediction model in a previously published dataset and tested it in a new blinded test set to reach an overall 74% accuracy in classifying tumors from normal tissues. Conclusions: We found specific patterns of protein expression of the airway epithelium that accurately classify bronchial and alveolar tissue with normal histology from preinvasive bronchial lesions and from invasive lung cancer. Although further study is needed to validate this approach and to identify biomarkers of tumor development, this is a first step toward a new proteomic characterization of the human model of lung cancer tumorigenesis. Keywords: early detection; mass spectrometry; preneoplasia; profilingThe discovery of preinvasive lesions in the high-risk population places patients at increased risk of developing lung cancer (1-3). Although the natural history of these preinvasive lesions is poorly understood, increasing evidence suggests that, in the absence of treatment, high-grade preinvasive lesions, most commonly found in patients with a prior history or with concomitant cancer, will develop into invasive carcinoma in 30 to 50% of the cases, whereas the vast majority of low-grade preinvasive lesions remain stable or regress on follow-up (4-7). In addition, nonstep- wise progression of preinvasive lesions makes prediction of lung cancer progression based on histologic grade unreliable. Therefore, the need for molecular biomarkers predictive of tumor progression is becoming more evident (8-10).In a recent report, we used conventional matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI MS) to generate protein profiles that accurately classify and predict histologic subgroups of lung cancer (11). Using biostatistical methods to select differentially expressed peaks (MS signals) and after the development of a class prediction model (12), 82 discriminatory signals were found to classify normal...

Lung Cancer Diagnosis from Proteomic Analysis of Preinvasive Lesions

“…To derive a bronchial epithelium-based signature predictive of lung cancer, results from two independent studies published from our laboratory were examined for top ranking discriminatory features ( m/z ) identified by MALDI MS. The first study led to features distinguishing normal lung from invasive lung tumors (n=93) (16), and the second study led to features distinguishing normal bronchial epithelium, preinvasive bronchial lesions and invasive lung tumors (n=51) (15). Nine features ( m/z ) were selected from these 144 patients as candidates of a novel signature from the intercept of best classifiers from these two previous studies based on their statistical significance with false discovery rate-adjusted p values <0.01 and based on the expert visual confirmation of the characteristics of the peak.…”

“…We recently identified patterns of protein expression in fresh frozen bronchial lesions obtained by autofluorescence bronchoscopy and resected lung tissue samples at different stages of tumor progression using matrix-assisted laser desorption ionization mass spectrometry (MALDI MS) (15). Our proteomic analysis generated signatures discriminating subtypes of lesions in a continuum from normal lung to invasive lung tumor.…”

“…To assess the probability of having a lung cancer based on the analysis of bronchial lesions, we are moving away from a pathological scale based on the grade of the lesions to a quantitative scale based on the proteomic analysis of these bronchial specimens. We selected 9 MALDI MS features (mass to charge ratio [ m/z ]) as a signature from two previous studies (15, 16). The novel proteomic signature of 9 features gives rise to a score based on MALDI MS signal intensity that is used as a continuous variable to predict the diagnosis of lung cancer and circumvents many of the problems associated with a histology-based assessment of the lesions.…”

Acyl-Coenzyme A–Binding Protein Regulates Beta-Oxidation Required for Growth and Survival of Non–Small Cell Lung Cancer

“…Early detection efforts in our laboratory have led to the discovery by matrix-assisted laser desorption/ionization mass-spectrometry (MALDI-MS) of ACBP as one of a six protein signature predicting the risk of having lung cancer in individuals with endobronchial lesions [1, 2]. This novel observation prompted us to investigate its role in lung cancer progression.…”

High-throughput molecular analysis in lung cancer: insights into biology and potential clinical applications

“…Investigators found a 75% accurate signature allowing lung tumours classification by histology [171]. They extended this approach to the analysis of preinvasive lesions to distinguish low-grade from high-grade preinvasive lesions [172]. These efforts have not yet lead to applications in clinical practice.…”