Detection of circulating genetically abnormal cells in peripheral blood for early diagnosis of non-small cell lung cancer.
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ABSTRACT:
SUBMITTER: Liu WR
PROVIDER: S-EPMC7606026 | biostudies-literature | 2020 Nov
REPOSITORIES: biostudies-literature
Publications
Detection of circulating genetically abnormal cells in peripheral blood for early diagnosis of non-small cell lung cancer.
Liu Wei-Ran WR Zhang Bin B Chen Chen C Li Yue Y Ye Xin X Tang Dong-Jiang DJ Zhang Jun-Cheng JC Ma Jing J Zhou Yan-Ling YL Fan Xian-Jun XJ Yue Dong-Sheng DS Li Chen-Guang CG Zhang Hua H Ma Yu-Chen YC Huo Yan-Song YS Zhang Zhen-Fa ZF He Shu-Yu SY Wang Chang-Li CL
Thoracic cancer 20200928 11
<h4>Background</h4>Circulating genetically abnormal cells (CACs) with specific chromosome variations have been confirmed to be present in non-small cell lung cancer (NSCLC). However, the diagnostic performance of CAC detection remains unclear. This study aimed to evaluate the potential clinical application of the CAC test for the early diagnosis of NSCLC.<h4>Methods</h4>In this prospective study, a total of 339 participants (261 lung cancer patients and 78 healthy volunteers) were enrolled. An a ...[more]
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Project description:PurposeAvailable biomarkers lack sensitivity for an early lung cancer. Circulating genetically abnormal cells (CACs) occur early in tumorigenesis. To determine the diagnostic value of CACs in blood detected by 4-color fluorescence in situ hybridization (FISH) for lung cancer.MethodsThis was a prospective study of patients with pulmonary nodules ≤ 30 mm detected between 10/2019 and 01/2020 at four tertiary hospitals in China. All patients underwent a pathological examination of lung nodules found by imaging and were grouped as malignant and benign. CACs were detected by 4-color FISH. Patients were divided into the training and validation cohorts. Receiver operating characteristics analysis was used to analyze the diagnosis value of CACs.ResultsA total of 205 participants were enrolled. Using a cut-off value of ≥ 3, blood CACs achieved areas under the curve (AUCs) of 0.887, 0.823, and 0.823 for lung cancer in the training and validation cohorts, and all patients, respectively. CACs had high diagnostic values across all tumor sizes and imaging lesion types. CACs were decreased after surgery (median, 4 vs. 1, P < 0.001) in the validation set. The CAC status between blood and tissues was highly consistent (kappa = 0.909, P < 0.001). The AUC of CAC (0.823) was higher than that of CEA (0.478), SCC (0.516), NSE (0.506), ProGRP (0.519), and CYFRA21-1 (0.535) (all P < 0.001).ConclusionCACs might have a high value for the early diagnosis of lung cancer. These findings might need to be validated in future studies. Evidence suggested homology in genetic aberrations between the CACs and the tumor cells.
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Project description:BackgroundLung cancer screening using low-dose computed tomography (LDCT) often leads to unnecessary biopsy because of the low specificity among patients with pulmonary nodules ≤10 mm. Circulating genetically abnormal cells (CACs) can be used to discriminate lung cancer from benign lung disease. To examine the diagnostic value of CACs in detecting lung cancer for patients with malignant pulmonary nodules ≤10 mm.MethodsIn this prospective study, patients with pulmonary nodules ≤10 mm who were detected at four hospitals in China from January 2019 to January 2020 were included. CACs were detected using fluorescence in-situ hybridization. All patients were confirmed as lung cancer or benign disease by further histopathological examination. Multivariable logistic regression models were established to detect the presence of lung cancer using CACs and other associated characteristics. Receiver operating characteristic analysis was used to evaluate the performance of CACs for lung cancer diagnosis.ResultsOverall, 125 patients were included and analyzed. When the cutoff value of CACs was >2, the sensitivity and specificity for lung cancer were 70.5 and 86.4%. Male (OR = 0.330, P = 0.005), maximum solid nodule (OR = 2.362, P = 0.089), maximum nodule located in upper lobe (OR = 3.867, P = 0.001), and CACs >2 (OR = 18.525, P < 0.001) met the P < 0.10 criterion for inclusion in the multivariable models. The multivariable logistic regression model that included the dichotomized CACs (>2 vs. ≤2) and other clinical factors (AUC = 0.907, 95% CI = 0.842-0.951) was superior to the models that only considered dichotomized CACs or other clinical factors and similar to the model with numerical CACs and other clinical factors (AUC = 0.913, 95% CI = 0.850-0.956).ConclusionCACs presented a significant diagnostic value in detecting lung cancer for patients with pulmonary nodules ≤10 mm.
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Project description:Circulating tumor DNA (ctDNA) is emerging as a key potential biomarker for post-diagnosis surveillance but it may also play a crucial role in the detection of pre-clinical cancer. Small-cell lung cancer (SCLC) is an excellent candidate for early detection given there are no successful therapeutic options for late-stage disease, and it displays almost universal inactivation of TP53. We assessed the presence of TP53 mutations in the cell-free DNA (cfDNA) extracted from the plasma of 51 SCLC cases and 123 non-cancer controls. We identified mutations using a pipeline specifically designed to accurately detect variants at very low fractions. We detected TP53 mutations in the cfDNA of 49% SCLC patients and 11.4% of non-cancer controls. When stratifying the 51 initial SCLC cases by stage, TP53 mutations were detected in the cfDNA of 35.7% early-stage and 54.1% late-stage SCLC patients. The results in the controls were further replicated in 10.8% of an independent series of 102 non-cancer controls. The detection of TP53 mutations in 11% of the 225 non-cancer controls suggests that somatic mutations in cfDNA among individuals without any cancer diagnosis is a common occurrence, and poses serious challenges for the development of ctDNA screening tests.
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Project description:Lung cancer is the leading cause of cancer-related deaths globally. Early detection of lung cancer can lead to more effective treatment and improved survival. Circulatory abnormal cells (CACs) with specific chromosomal variation may be used to diagnose lung cancer and to differentiate benign from malignant nodules. The value of CAC in precancer diagnosis, however, remains controversial. In this study, a systematic review and meta-analysis are conducted to clarify the diagnostic value of CAC in early-stage lung cancer. A systematic literature search was conducted using the following medical topic title terms and text-free words: "circulating genetically abnormal cells", "CACs", "liquid biopsy", "early lung cancer", "non-small cell lung cancer", "diagnostic accuracy", "sensitivity" and "specificity" in Science Direct, CNKI and Wanfang databases, respectively. Sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and area under the curve were analyzed by STATA15.0 (MP) software. Deek funnel plots were used to assess potential publication bias. Heterogeneity was tested using the I2 statistic and the Cochrane Q test. 7 major studies were included in this meta-analysis, and a total of 53728 participants were analyzed. In the diagnosis of early lung cancer, CAC had pooled sensitivity, specificity, and receiver operating characteristics of 0.80 (95% CI: 0.73-0.86), 0.85 (95% CI: 0.69-0.94), and 0.87 (95% CI: 0.84-0.90). The combined positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and diagnostic score were 23.36 (95% CI: 7.33-74.46), 5.42 (95% CI: 2.37-12.43), 0.23 (95% CI: 0.16-0.35) and 3.15 (95% CI: 1.99-4.31) respectively. Publication bias was not detected. The CAC is effective at detecting lung cancer in its early stages.
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2021-04-06 | GSE171517 | GEO
Project description:BackgroundDetection of lung cancer at an early stage by sensitive screening tests could be an important strategy to improving prognosis. Our objective was to identify a panel of circulating microRNAs in plasma that will contribute to early detection of lung cancer.Material and methodsPlasma samples from 100 early stage (I to IIIA) non-small-cell lung cancer (NSCLC) patients and 100 non-cancer controls were screened for 754 circulating microRNAs via qRT-PCR, using TaqMan MicroRNA Arrays. Logistic regression with a lasso penalty was used to select a panel of microRNAs that discriminate between cases and controls. Internal validation of model discrimination was conducted by calculating the bootstrap optimism-corrected AUC for the selected model.ResultsWe identified a panel of 24 microRNAs with optimum classification performance. The combination of these 24 microRNAs alone could discriminate lung cancer cases from non-cancer controls with an AUC of 0.92 (95% CI: 0.87-0.95). This classification improved to an AUC of 0.94 (95% CI: 0.90-0.97) following addition of sex, age and smoking status to the model. Internal validation of the model suggests that the discriminatory power of the panel will be high when applied to independent samples with a corrected AUC of 0.78 for the 24-miRNA panel alone.ConclusionOur 24-microRNA predictor improves lung cancer prediction beyond that of known risk factors.
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