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Identification of Proteomic Features To Distinguish Benign Pulmonary Nodules from Lung Adenocarcinoma.

ABSTRACT: We hypothesized that distinct protein expression features of benign and malignant pulmonary nodules may reveal novel candidate biomarkers for the early detection of lung cancer. We performed proteome profiling by liquid chromatography-tandem mass spectrometry to characterize 34 resected benign lung nodules, 24 untreated lung adenocarcinomas (ADCs), and biopsies of bronchial epithelium. Group comparisons identified 65 proteins that differentiate nodules from ADCs and normal bronchial epithelium and 66 proteins that differentiate ADCs from nodules and normal bronchial epithelium. We developed a multiplexed parallel reaction monitoring (PRM) assay to quantify a subset of 43 of these candidate biomarkers in an independent cohort of 20 benign nodules, 21 ADCs, and 20 normal bronchial biopsies. PRM analyses confirmed significant nodule-specific abundance of 10 proteins including ALOX5, ALOX5AP, CCL19, CILP1, COL5A2, ITGB2, ITGAX, PTPRE, S100A12, and SLC2A3 and significant ADC-specific abundance of CEACAM6, CRABP2, LAD1, PLOD2, and TMEM110-MUSTN1. Immunohistochemistry analyses for seven selected proteins performed on an independent set of tissue microarrays confirmed nodule-specific expression of ALOX5, ALOX5AP, ITGAX, and SLC2A3 and cancer-specific expression of CEACAM6. These studies illustrate the value of global and targeted proteomics in a systematic process to identify and qualify candidate biomarkers for noninvasive molecular diagnosis of lung cancer.

SUBMITTER: Codreanu SG

PROVIDER: S-EPMC6339813 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Identification of Proteomic Features To Distinguish Benign Pulmonary Nodules from Lung Adenocarcinoma.

Codreanu Simona G SG Hoeksema Megan D MD Slebos Robbert J C RJC Zimmerman Lisa J LJ Rahman S M Jamshedur SMJ Li Ming M Chen Sheau-Chiann SC Chen Heidi H Eisenberg Rosana R Liebler Daniel C DC Massion Pierre P PP

Journal of proteome research 20170808 9

We hypothesized that distinct protein expression features of benign and malignant pulmonary nodules may reveal novel candidate biomarkers for the early detection of lung cancer. We performed proteome profiling by liquid chromatography-tandem mass spectrometry to characterize 34 resected benign lung nodules, 24 untreated lung adenocarcinomas (ADCs), and biopsies of bronchial epithelium. Group comparisons identified 65 proteins that differentiate nodules from ADCs and normal bronchial epithelium a ...[more]

PMID: 28731711

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Project description:BackgroundComputed tomography (CT) has been widely known to be the first choice for the diagnosis of solid solitary pulmonary nodules (SSPNs). However, the smaller the SSPN is, the less the differential CT signs between benign and malignant SSPNs there are, which brings great challenges to their diagnosis. Therefore, this study aimed to investigate the differential CT features between small (≤15 mm) benign and malignant SSPNs with different sizes.MethodsFrom May 2018 to November 2021, CT data of 794 patients with small SSPNs (≤15 mm) were retrospectively analyzed. SSPNs were divided into benign and malignant groups, and each group was further classified into three cohorts: cohort I (diameter ≤6 mm), cohort II (6 mm < diameter ≤8 mm), and cohort III (8 mm < diameter ≤15 mm). The differential CT features of benign and malignant SSPNs in three cohorts were identified. Multivariable logistic regression analyses were conducted to identify independent factors of benign SSPNs.ResultsIn cohort I, polygonal shape and upper-lobe distribution differed significantly between groups (all P<0.05) and multiparametric analysis showed polygonal shape [adjusted odds ratio (OR): 12.165; 95% confidence interval (CI): 1.512-97.872; P=0.019] was the most effective variation for predicting benign SSPNs, with an area under the receiver operating characteristic curve (AUC) of 0.747 (95% CI: 0.640-0.855; P=0.001). In cohort II, polygonal shape, lobulation, pleural retraction, and air bronchogram differed significantly between groups (all P<0.05), and polygonal shape (OR: 8.870; 95% CI: 1.096-71.772; P=0.041) and the absence of pleural retraction (OR: 0.306; 95% CI: 0.106-0.883; P=0.028) were independent predictors of benign SSPNs, with an AUC of 0.778 (95% CI: 0.694-0.863; P<0.001). In cohort III, 12 CT features showed significant differences between groups (all P<0.05) and polygonal shape (OR: 3.953; 95% CI: 1.508-10.361; P=0.005); calcification (OR: 3.710; 95% CI: 1.305-10.551; P=0.014); halo sign (OR: 6.237; 95% CI: 2.838-13.710; P<0.001); satellite lesions (OR: 6.554; 95% CI: 3.225-13.318; P<0.001); and the absence of lobulation (OR: 0.066; 95% CI: 0.026-0.167; P<0.001), air space (OR: 0.405; 95% CI: 0.215-0.764; P=0.005), pleural retraction (OR: 0.297; 95% CI: 0.179-0.493; P<0.001), bronchial truncation (OR: 0.165; 95% CI: 0.090-0.303; P<0.001), and air bronchogram (OR: 0.363; 95% CI: 0.208-0.633; P<0.001) were independent predictors of benign SSPNs, with an AUC of 0.869 (95% CI: 0.840-0.897; P<0.001).ConclusionsCT features vary between SSPNs with different sizes. Clarifying the differential CT features based on different diameter ranges may help to minimize ambiguities and discriminate the benign SSPNs from malignant ones.

Project description:BackgroundAs a rare subtype of lung adenocarcinoma, the diagnosis of pulmonary enteric adenocarcinoma (PEAC) remains challenging due to overlapping morphologic spectrum with lung metastatic colorectal cancer (lmCRC). However, the molecular features of PEAC as a separate lung cancer entity are poorly understood.MethodsWe performed whole-exome sequencing and targeted bisulfite sequencing of 32 PEAC and 30 lmCRC to improve differential molecular characterization of the two diseases. We used machine learning methods to select key markers and developed a diagnostic classifier. In addition, we validated the classifier in the internal test cohort and an independently recruited external validation cohort with 17 PEAC and 7 lmCRC.FindingsOur results showed that EGFR was the key driver mutation in PEAC but at a lower prevalence compared to typical lung adenocarcinomas, whereas ERBB2 and KRAS were more frequently observed in PEAC. By contrast, we observed significant enrichment of KRAS and APC mutations in lmCRC compared with PEAC. At the chromosome arm level, copy number variations in 13q, 14q, and 18p were the major chromosomal differences observed between PEAC and lmCRC. Furthermore, by comparing differentially methylated regions (DMRs), we established a neat DNA methylation-based classifier consisting of eight DMRs. This classifier correctly classified all samples in the training cohort and 95% of the samples in the internal test cohort. An external validation cohort of 24 cases recruited from multiple centers in China also reliably agreed with pathological diagnosis.InterpretationThese results provide solid evidence of PEAC-specific genomic characteristics and demonstrate the potential utility of DNA methylation markers for auxiliary diagnosis of PEAC and lmCRC.FundingThis work was supported by National key research and development project 2019YFC1315700, CAMS Key Laboratory of Translational Research on Lung Cancer (2018PT31035), and Beijing Natural Science Foundation (7222144).

Dataset Information

Identification of Proteomic Features To Distinguish Benign Pulmonary Nodules from Lung Adenocarcinoma.

Publications

Identification of Proteomic Features To Distinguish Benign Pulmonary Nodules from Lung Adenocarcinoma.

Similar Datasets

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