Dataset Information

Proteomic analysis of bronchoalveolar lavage fluid (BALF) from lung cancer patients using label-free mass spectrometry.

ABSTRACT:

Background

Lung cancer is the leading cause of cancer-related mortality in both men and women throughout the world. The need to detect lung cancer at an early, potentially curable stage, is essential and may reduce mortality by 20%. The aim of this study was to identify distinct proteomic profiles in bronchoalveolar fluid (BALF) and plasma that are able to discriminate individuals with benign disease from those with non-small cell lung cancer (NSCLC).

Methods

Using label-free mass spectrometry analysis of BALF during discovery-phase analysis, a significant number of proteins were found to have different abundance levels when comparing control to adenocarcinoma (AD) or squamous cell lung carcinoma (SqCC). Validation of candidate biomarkers identified in BALF was performed in a larger cohort of plasma samples by detection with enzyme-linked immunoassay.

Results

Four proteins (Cystatin-C, TIMP-1, Lipocalin-2 and HSP70/HSPA1A) were selected as a representative group from discovery phase mass spectrometry BALF analysis. Plasma levels of TIMP-1, Lipocalin-2 and Cystatin-C were found to be significantly elevated in AD and SqCC compared to control.

Conclusion

The results presented in this study indicate that BALF is an important proximal biofluid for the discovery and identification of candidate lung cancer biomarkers.

General significance

There is good correlation between the trend of protein abundance levels in BALF and that of plasma which validates this approach to develop a blood biomarker to aid lung cancer diagnosis, particularly in the era of lung cancer screening. The protein signatures identified also provide insight into the molecular mechanisms associated with lung malignancy.

SUBMITTER: Hmmier A

PROVIDER: S-EPMC5357681 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Publications

Proteomic analysis of bronchoalveolar lavage fluid (BALF) from lung cancer patients using label-free mass spectrometry.

Hmmier Abduladim A O'Brien Michael Emmet ME Lynch Vincent V Clynes Martin M Morgan Ross R Dowling Paul P

BBA clinical 20170306

<h4>Background</h4>Lung cancer is the leading cause of cancer-related mortality in both men and women throughout the world. The need to detect lung cancer at an early, potentially curable stage, is essential and may reduce mortality by 20%. The aim of this study was to identify distinct proteomic profiles in bronchoalveolar fluid (BALF) and plasma that are able to discriminate individuals with benign disease from those with non-small cell lung cancer (NSCLC).<h4>Methods</h4>Using label-free mass ...[more]

PMID: 28331811

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Project description:BackgroundDifferentiating malignant lung tumors from benign pulmonary nodules is a great challenge. While the analysis of bronchoalveolar lavage fluid (BALF) is used for diagnosing infections and interstitial lung diseases, there is limited evidence to support its use for lung cancer diagnosis. This study aimed to interrogate the potential of using BALF cell-free DNA (cfDNA) to discriminate malignant lesions from benign nodules.MethodsFifty-three patients with solid pulmonary nodules (≤2 cm) were prospectively enrolled, including 21 confirmed with benign disease and 32 with malignant tumors. Mutations were profiled for 30 tumor tissues and 40 BALFs. Paired BALFs and plasma from 48 patients underwent DNA methylation profiling. A methylome-based classification model was developed for BALF and plasma separately.ResultsAmong the 30 patients with paired tissues and BALFs, 96.7% and 70% had alterations detected from their tissues (79 alterations) and BALFs (53 alterations), respectively. Using tissues as references, BALFs revealed 14 new alterations and missed 41. BALF mutation displayed a sensitivity of 71%, specificity of 77.8%, and accuracy of 72.5% in detecting lung cancer. BALF methylation achieved an accuracy of 81.3%, with both sensitivity and specificity being 81%. Plasma methylation showed a 66.7% sensitivity, 71.4% specificity, and 68.8% accuracy. BALF methylation also demonstrated 82.4% sensitivity in stage I patients. Parallel bronchoscopy, lavage cytology, and bronchial brushing demonstrated an inferior sensitivity of 23%, 3.1%, and 9.7%, respectively, compared with BALF methylation and mutation (P<0.0001).ConclusionsBALF cfDNA can serve as a liquid biopsy media for both mutation and methylation profiling, demonstrating better sensitivities in distinguishing small malignant tumors from benign nodules than conventional methods.KeywordsLung cancer diagnosis; pulmonary nodule; bronchoalveolar lavage fluid (BALF); methylation; genomic mutation.

Project description:Liquid biopsy of cancers, detecting tumor-related information from liquid samples, has attracted wide attentions as an emerging technology. Our previously reported large-area PERFECT (Precise-Efficient-Robust-Flexible-Easy-Controllable-Thin) filter has demonstrated competitive sensitivity in recovering rare tumor cells from clinical samples. However, it is time-consuming and easily biased to manually inspect rare target cells among numerous background cells distributed in a large area (Φ ≥ 13 mm). This puts forward an urgent demand for rapid and bias-free inspection. Hereby, this paper implemented deep learning-based object detection for the inspection of rare tumor cells from large-field images of PERFECT filters with hematoxylin-eosin (HE)-stained cells recovered from bronchoalveolar lavage fluid (BALF). CenterNet, EfficientDet, and YOLOv5 were trained and validated with 240 and 60 image blocks containing tumor and/or background cells, respectively. YOLOv5 was selected as the basic network given the highest mAP@0.5 of 92.1%, compared to those of CenterNet and EfficientDet at 85.2% and 91.6%, respectively. Then, tricks including CIoU loss, image flip, mosaic, HSV augmentation and TTA were applied to enhance the performance of the YOLOv5 network, improving mAP@0.5 to 96.2%. This enhanced YOLOv5 network-based object detection, named as BALFilter Reader, was tested and cross-validated on 24 clinical cases. The overall diagnosis performance (~2 min) with sensitivity@66.7% ± 16.7%, specificity@100.0% ± 0.0% and accuracy@75.0% ± 12.5% was superior to that from two experienced pathologists (10-30 min) with sensitivity@61.1%, specificity@16.7% and accuracy@50.0%, with the histopathological result as the gold standard. The AUC of the BALFilter Reader is 0.84 ± 0.08. Moreover, a customized Web was developed for a user-friendly interface and the promotion of wide applications. The current results revealed that the developed BALFilter Reader is a rapid, bias-free and easily accessible AI-enabled tool to promote the transplantation of the BALFilter technique. This work can easily expand to other cytopathological diagnoses and improve the application value of micro/nanotechnology-based liquid biopsy in the era of intelligent pathology.

Dataset Information

Proteomic analysis of bronchoalveolar lavage fluid (BALF) from lung cancer patients using label-free mass spectrometry.

Background

Methods

Results

Conclusion

General significance

Publications

Proteomic analysis of bronchoalveolar lavage fluid (BALF) from lung cancer patients using label-free mass spectrometry.

Similar Datasets

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