Detection of circulating genetically abnormal cells using 4-color fluorescence in situ hybridization for the early detection of lung cancer

Detection and identification of circulating tumor cells in parathyroid tumors and correlation analysis with clinicopathological features

INTRODUCTION

The differential diagnosis of parathyroid carcinoma (PC)/parathyroid adenoma (PA) in parathyroid tumors is critical for their management and prognosis. Circulating tumor cells (CTCs) identification in the peripheral blood of parathyroid tumors remains unknown. In this study, we proposed to investigate the differences of CTCs in PC/PA and the relationship with clinicopathologic features to assess its relevance to PC and value in identifying PC/PA.

METHODS AND MATERIALS

Peripheral blood was collected from 27 patients with PC and 37 patients with PA treated in our hospital, and the number of chromosome 8 aberrant CTCs was detected by negative magnetic bead sorting fluorescence in situ hybridization (NE-FISH). The differences of CTCs in PC/PA peripheral blood were compared and their diagnostic efficacy was evaluated, and the correlation between CTCs and clinicopathological features of PC was further explored.

RESULTS

CTCs differed significantly in PC/PA (p = 0.0008) and were up-regulated in PC, with good diagnostic efficacy. CTCs combined with alkaline phosphatase (ALP) assay improved the diagnostic efficacy in identifying PC/PA (AUC = 0.7838, p = 0.0001). The number of CTCs was correlated with tumor dimensions, but not significantly correlated with clinical markers such as calcium and PTH and pathological features such as vascular invasion, lymph node metastasis and distant metastasis.

CONCLUSION

As a non-invasive liquid biopsy method, CTCs test combined with ALP test can be used as an important reference basis for timely and accurate identification and treatment of PC. It is of great significance to improve the current situation of PC diagnosis, treatment and prognosis.

Comparison of long-term outcomes of stereotactic body radiotherapy (SBRT) via Helical tomotherapy for early-stage lung cancer with or without pathological proof

BACKGROUND

Stereotactic body radio therapy (SBRT) has emerged as a standard treatment option for nonsurgical candidates with early-stage non-small cell lung cancer (NSCLC). Pathological proof is sometimes difficult to obtain in patients with solitary pulmonary nodules (SPNs). We aimed to compare the clinical outcomes of stereotactic body radiotherapy via helical tomotherapy (HT-SBRT) for early-stage lung cancer patients with or without a pathological diagnosis.

METHODS

Between June 2011 and December 2016, we treated 119 lung cancer patients with HT-SBRT, including 55 with a clinical diagnosis and 64 with a pathological diagnosis. Survival outcomes, including local control (LC), progression-free survival (PFS), cancer-specific survival (CSS), and overall survival (OS), were compared between two cohorts with and without a pathological diagnosis.

RESULTS

The median follow-up for the whole group was 69 months. Patients with a clinical diagnosis were significantly older (p = 0.002). No significant differences were observed between the clinical and pathological diagnosis cohorts in terms of the long-term outcome, with 5-year LC, PFS, CSS, and OS of 87% versus 83% (p = 0.58), 48% versus 45% (p = 0.82), 87% versus 84% (p = 0.65), and 60% versus 63% (p = 0.79), respectively. Recurrence patterns and toxicity were also similar.

CONCLUSIONS

Empiric SBRT appears to be a safe and effective treatment option in a multidisciplinary setting when patients with SPNs highly suggestive of malignancy are unable/refuse to obtain a definitive pathological diagnosis.

Potential clinical utility of liquid biopsy in early-stage non-small cell lung cancer

BACKGROUND

Liquid biopsy has been widely researched for early diagnosis, prognostication and disease monitoring in lung cancer, but there is a need to investigate its clinical utility for early-stage non-small cell lung cancer (NSCLC).

METHODS

We performed a meta-analysis and systematic review to evaluate diagnostic and prognostic values of liquid biopsy for early-stage NSCLC, regarding the common biomarkers, circulating tumor cells, circulating tumor DNA (ctDNA), methylation signatures, and microRNAs. Cochrane Library, PubMed, EMBASE databases, ClinicalTrials.gov, and reference lists were searched for eligible studies since inception to 17 May 2022. Sensitivity, specificity and area under the curve (AUC) were assessed for diagnostic values. Hazard ratio (HR) with a 95% confidence interval (CI) was extracted from the recurrence-free survival (RFS) and overall survival (OS) plots for prognostic analysis. Also, potential predictive values and treatment response evaluation were further investigated.

RESULTS

In this meta-analysis, there were 34 studies eligible for diagnostic assessment and 21 for prognostic analysis. The estimated diagnostic values of biomarkers for early-stage NSCLC with AUCs ranged from 0.84 to 0.87. The factors TNM stage I, T1 stage, N0 stage, adenocarcinoma, young age, and nonsmoking contributed to a lower tumor burden, with a median cell-free DNA concentration of 8.64 ng/ml. For prognostic analysis, the presence of molecular residual disease (MRD) detection was a strong predictor of disease relapse (RFS, HR, 4.95; 95% CI, 3.06-8.02; p < 0.001) and inferior OS (HR, 3.93; 95% CI, 1.97-7.83; p < 0.001), with average lead time of 179 ± 74 days between molecular recurrence and radiographic progression. Predictive values analysis showed adjuvant therapy significantly benefited the RFS of MRD + patients (HR, 0.27; p < 0.001), while an opposite tendency was detected for MRD - patients (HR, 1.51; p = 0.19). For treatment response evaluation, a strong correlation between pathological response and ctDNA clearance was detected, and both were associated with longer survival after neoadjuvant therapy.

CONCLUSIONS

In conclusion, our study indicated liquid biopsy could reliably facilitate more precision and effective management of early-stage NSCLC. Improvement of liquid biopsy techniques and detection approaches and platforms is still needed, and higher-quality trials are required to provide more rigorous evidence prior to their routine clinical application.

Construction and analysis of expression profile of exosomal lncRNAs in pleural effusion in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a highly malignant tumor with a very low five-year survival rate. In this study, we aimed to identify differentially expressed long-chain non-coding RNA (lncRNAs) and mRNAs from benign and malignant pleural effusion exosomes.

METHODS

We used gene microassay and quantitative real-time reverse transcription polymerase chain reaction (RT-qPCR) to detect and verify differentially expressed mRNAs and lncRNAs in benign and malignant pleural effusion exosomes. Gene Ontology (GO) functional significance and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway significance enrichment analyses were performed to identify the difference in biological processes and functions between different mRNAs. We selected the lncRNA ZBED5-AS1 with an upregulated differential fold of 3.003 and conducted a preliminary study on its cellular function.

RESULTS

Gene microassay results revealed that 177 differentially expressed lncRNAs were upregulated, and 215 were downregulated. The top 10 upregulated were FMN1, AL118505.1, LINC00452, AL109811.2, CATG00000040683.1, AC137932.1, AC008619.1, AL450344.1, AC092718.6, and ZBED5-AS1. The top 10 downregulated were TEX41, G067726, JAZF1-AS1, AC027328.1, AL445645.1, AL022345.4, AC008572.1, AC123777.1, AC093714.1, and PHKG1. For the mRNAs, 79 were upregulated, and 123 were notably downregulated. GO analysis revealed that the upregulated differential mRNAs were mainly involved in "cellular response to acidic pH" (biological processes), "endoplasmic reticulum part" (cellular components), and "at DNA binding, cyclase activity" (molecular functions). KEGG pathways were found to be related to V. cholerae infection, Parkinson's disease, and cell adhesion molecules. RT-qPCR showed that ZBED5-AS1 was highly expressed in LUAD tissues, cells, and benign and malignant pleural fluid exosomes. Overexpression of ZBED5-AS1 could significantly promote the proliferation, migration, invasion, and colony formation of LUAD cells, and knockdown had the opposite consequence.

CONCLUSION

The pleural effusion exosomes from patients with LUAD include several improperly expressed genes, and lncRNA-ZBED5-AS1 is a new biomarker that aids in our understanding of the occurrence and progression of LUAD.

Diagnostic value of circulating genetically abnormal cells to support computed tomography for benign and malignant pulmonary nodules

Background

The accuracy of CT and tumour markers in screening lung cancer needs to be improved. Computer-aided diagnosis has been reported to effectively improve the diagnostic accuracy of imaging data, and recent studies have shown that circulating genetically abnormal cell (CAC) has the potential to become a novel marker of lung cancer. The purpose of this research is explore new ways of lung cancer screening.

Methods

From May 2020 to April 2021, patients with pulmonary nodules who had received CAC examination within one week before surgery or biopsy at First Affiliated Hospital of Zhengzhou University were enrolled. CAC counts, CT scan images, serum tumour marker (CEA, CYFRA21–1, NSE) levels and demographic characteristics of the patients were collected for analysis. CT were uploaded to the Pulmonary Nodules Artificial Intelligence Diagnostic System (PNAIDS) to assess the malignancy probability of nodules. We compared diagnosis based on PNAIDS, CAC, Mayo Clinic Model, tumour markers alone and their combination. The combination models were built through logistic regression, and was compared through the area under (AUC) the ROC curve.

Results

A total of 93 of 111 patients were included. The AUC of PNAIDS was 0.696, which increased to 0.847 when combined with CAC. The sensitivity (SE), specificity (SP), and positive (PPV) and negative (NPV) predictive values of the combined model were 61.0%, 94.1%, 94.7% and 58.2%, respectively. In addition, we evaluated the diagnostic value of CAC, which showed an AUC of 0.779, an SE of 76.3%, an SP of 64.7%, a PPV of 78.9%, and an NPV of 61.1%, higher than those of any single serum tumour marker and Mayo Clinic Model. The combination of PNAIDS and CAC exhibited significantly higher AUC values than the PNAIDS (P = 0.009) or the CAC (P = 0.047) indicator alone. However, including additional tumour markers did not significantly alter the performance of CAC and PNAIDS.

Conclusions

CAC had a higher diagnostic value than traditional tumour markers in early-stage lung cancer and a supportive value for PNAIDS in the diagnosis of cancer based on lung nodules. The results of this study offer a new mode of screening for early-stage lung cancer using lung nodules.

A Classifier for Improving Early Lung Cancer Diagnosis Incorporating Artificial Intelligence and Liquid Biopsy

Lung cancer is the leading cause of cancer-related deaths worldwide and in China. Screening for lung cancer by low dose computed tomography (LDCT) can reduce mortality but has resulted in a dramatic rise in the incidence of indeterminate pulmonary nodules, which presents a major diagnostic challenge for clinicians regarding their underlying pathology and can lead to overdiagnosis. To address the significant gap in evaluating pulmonary nodules, we conducted a prospective study to develop a prediction model for individuals at intermediate to high risk of developing lung cancer. Univariate and multivariate logistic analyses were applied to the training cohort (n = 560) to develop an early lung cancer prediction model. The results indicated that a model integrating clinical characteristics (age and smoking history), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the training cohort (sensitivity 89.53%, specificity 81.31%, area under the curve [AUC] = 0.880). In the independent validation cohort (n = 168), this model had an AUC of 0.895, which was greater than that of the Mayo Clinic Model (AUC = 0.772) and Veterans' Affairs Model (AUC = 0.740). These results were significantly better for predicting the presence of cancer than radiological features and artificial intelligence risk scores alone. Applying this classifier prospectively may lead to improved early lung cancer diagnosis and early treatment for patients with malignant nodules while sparing patients with benign entities from unnecessary and potentially harmful surgery.

Clinical Trial Registration Number

ChiCTR1900026233, URL: http://www.chictr.org.cn/showproj.aspx?proj=43370.