Dataset Information

Sputum analysis by flow cytometry; an effective platform to analyze the lung environment.

ABSTRACT: Low dose computed tomography (LDCT) is the standard of care for lung cancer screening in the United States (US). LDCT has a sensitivity of 93.8% but its specificity of 73.4% leads to potentially harmful follow-up procedures in patients without lung cancer. Thus, there is a need for additional assays with high accuracy that can be used as an adjunct to LDCT to diagnose lung cancer. Sputum is a biological fluid that can be obtained non-invasively and can be dissociated to release its cellular contents, providing a snapshot of the lung environment. We obtained sputum from current and former smokers with a 30+ pack-year smoking history and who were either confirmed to have lung cancer or at high risk of developing the disease. Dissociated sputum cells were counted, viability determined, and labeled with a panel of markers to separate leukocytes from non-leukocytes. After excluding debris and dead cells, including squamous epithelial cells, we identified reproducible population signatures and confirmed the samples' lung origin. In addition to leukocyte and epithelial-specific fluorescent antibodies, we used the highly fluorescent meso-tetra(4-carboxyphenyl) porphyrin (TCPP), known to preferentially stain cancer (associated) cells. We looked for differences in cell characteristics, population size and fluorescence intensity that could be useful in distinguishing cancer samples from high-risk samples. We present our data demonstrating the feasibility of a flow cytometry platform to analyze sputum in a high-throughput and standardized matter for the diagnosis of lung cancer.

SUBMITTER: Bederka LH

PROVIDER: S-EPMC9385012 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Sputum analysis by flow cytometry; an effective platform to analyze the lung environment.

Bederka Lydia H LH Sanchez Jamila R JR Rebeles Jennifer J Araujo Patricia R PR Grayson Marcia H MH Lai Shao-Chiang SC DePalo Louis R LR Habib Sheila A SA Hill David G DG Lopez Kathleen K Patriquin Lara L Sussman Robert R Humphreys James J Reveles Xavier T XT Rebel Vivienne I VI

PloS one 20220817 8

Low dose computed tomography (LDCT) is the standard of care for lung cancer screening in the United States (US). LDCT has a sensitivity of 93.8% but its specificity of 73.4% leads to potentially harmful follow-up procedures in patients without lung cancer. Thus, there is a need for additional assays with high accuracy that can be used as an adjunct to LDCT to diagnose lung cancer. Sputum is a biological fluid that can be obtained non-invasively and can be dissociated to release its cellular cont ...[more]

PMID: 35976857

Similar Datasets

Project description:BackgroundLow-dose spiral computed tomography (LDCT) may not lead to a clear treatment path when small to intermediate-sized lung nodules are identified. We have combined flow cytometry and machine learning to develop a sputum-based test (CyPath Lung) that can assist physicians in decision-making in such cases.MethodsSingle cell suspensions prepared from induced sputum samples collected over three consecutive days were labeled with a viability dye to exclude dead cells, antibodies to distinguish cell types, and a porphyrin to label cancer-associated cells. The labeled cell suspension was run on a flow cytometer and the data collected. An analysis pipeline combining automated flow cytometry data processing with machine learning was developed to distinguish cancer from non-cancer samples from 150 patients at high risk of whom 28 had lung cancer. Flow data and patient features were evaluated to identify predictors of lung cancer. Random training and test sets were chosen to evaluate predictive variables iteratively until a robust model was identified. The final model was tested on a second, independent group of 32 samples, including six samples from patients diagnosed with lung cancer.ResultsAutomated analysis combined with machine learning resulted in a predictive model that achieved an area under the ROC curve (AUC) of 0.89 (95% CI 0.83-0.89). The sensitivity and specificity were 82% and 88%, respectively, and the negative and positive predictive values 96% and 61%, respectively. Importantly, the test was 92% sensitive and 87% specific in cases when nodules were < 20 mm (AUC of 0.94; 95% CI 0.89-0.99). Testing of the model on an independent second set of samples showed an AUC of 0.85 (95% CI 0.71-0.98) with an 83% sensitivity, 77% specificity, 95% negative predictive value and 45% positive predictive value. The model is robust to differences in sample processing and disease state.ConclusionCyPath Lung correctly classifies samples as cancer or non-cancer with high accuracy, including from participants at different disease stages and with nodules < 20 mm in diameter. This test is intended for use after lung cancer screening to improve early-stage lung cancer diagnosis. Trial registration ClinicalTrials.gov ID: NCT03457415; March 7, 2018.

Project description:Despite the remarkable evolution of flow cytometers, fluorescent probes, and flow cytometry analysis software, most users still follow the same ways for data analysis. Conventional flow cytometry analysis relies on the creation of dot plot sequences, based on two fluorescence parameters at a time, to evidence phenotypically distinct populations. Thus, reaching conclusions about the biological characteristics of the samples is a fragmented and challenging process. We present here the MCTA (Multiparametric Color Tendency Analysis), a method for data analysis that considers multiple labelings simultaneously, extending and complementing conventional analysis. The MCTA method executes the background fluorescence exclusion, spillover compensation, and a user-defined gating strategy for subpopulation analysis. The results are then presented in conventional FSC x SSC dot plots with statistical data. For each event, the method converts each of the multiple fluorescence colors under analysis into a vector, with longer vectors being attributed to more intense labelings. Then, the MCTA generates a resultant vector, which is therefore mostly influenced by predominant labelings. The radial position of this resultant vector corresponds to a resultant color, making it easy to visualize phenotypic modulations among cellular subpopulations. Besides, it is a deterministic method that quickly assigns a resulting color to all events that obey the gating strategy, with no polymeric regions defined by the user or downsampling. The MCTA application generates a single dot plot showing all events in the FCS file, but a resultant color is attributed only to those that obey the gating strategy. Therefore, it can also help to evidence rare events or unpredicted subpopulations naturally excluded from the regions defined by the user. We believe that the MCTA method adds a new perspective over multiparametric flow cytometry analysis while evidencing modulations of molecular labeling profiles based on multiple fluorescences. Availability and implementation: The instructions for the MCTA application is freely available at https://github.com/flowcytometry/MCTA.

Dataset Information

Sputum analysis by flow cytometry; an effective platform to analyze the lung environment.

Publications

Sputum analysis by flow cytometry; an effective platform to analyze the lung environment.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets