Identification of circulating tumor cells using 4-color fluorescence in situ hybridization: Validation of a noninvasive aid for ruling out lung cancer in patients with low-dose computed tomography-detected lung nodules

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.

Cancer screening with multicancer detection tests: A translational science review

Multicancer detection (MCD) tests use a single, easily obtainable biospecimen, such as blood, to screen for more than one cancer concurrently. MCD tests can potentially be used to improve early cancer detection, including cancers that currently lack effective screening methods. However, these tests have unknown and unquantified benefits and harms. MCD tests differ from conventional cancer screening tests in that the organ responsible for a positive test is unknown, and a broad diagnostic workup may be necessary to confirm the location and type of underlying cancer. Among two prospective studies involving greater than 16,000 individuals, MCD tests identified those who had some cancers without currently recommended screening tests, including pancreas, ovary, liver, uterus, small intestine, oropharyngeal, bone, thyroid, and hematologic malignancies, at early stages. Reported MCD test sensitivities range from 27% to 95% but differ by organ and are lower for early stage cancers, for which treatment toxicity would be lowest and the potential for cure might be highest. False reassurance from a negative MCD result may reduce screening adherence, risking a loss in proven public health benefits from standard-of-care screening. Prospective clinical trials are needed to address uncertainties about MCD accuracy to detect different cancers in asymptomatic individuals, whether these tests can detect cancer sufficiently early for effective treatment and mortality reduction, the degree to which these tests may contribute to cancer overdiagnosis and overtreatment, whether MCD tests work equally well across all populations, and the appropriate diagnostic evaluation and follow-up for patients with a positive test.

Value of circulating tumor cell assisting low-dose computed tomography in screening pulmonary nodules based on existing liquid biopsy techniques: a systematic review with meta-analysis and trial sequential analysis

OBJECTIVE

This study aims to assess the diagnostic utility of circulating tumor cells (CTCs) in conjunction with low-dose computed tomography (LDCT) for differentiating between benign and malignant pulmonary nodules and to substantiate the foundation for their integration into clinical practice.

METHODS

A systematic literature review was performed independently by two researchers utilizing databases including PubMed, Web of Science, The Cochrane Library, Embase, and Medline, to collate studies up to September 15, 2023, that investigated the application of CTCs in diagnosing pulmonary nodules. A meta-analysis was executed employing Stata 15.0 and Revman 5.4 to calculate the pooled sensitivity, specificity, positive and negative likelihood ratios (PLR and NLR), diagnostic odds ratio (DOR), and the area under the receiver operating characteristic curve (AUC). Additionally, trial sequential analysis was conducted using dedicated TSA software.

RESULTS

The selection criteria identified 16 studies, encompassing a total of 3409 patients. The meta-analysis revealed that CTCs achieved a pooled sensitivity of 0.84 (95% CI 0.80 to 0.87), specificity of 0.80 (95% CI 0.73 to 0.86), PLR of 4.23 (95% CI 3.12 to 5.72), NLR of 0.20 (95% CI 0.16 to 0.25), DOR of 20.92 (95% CI 13.52 to 32.36), and AUC of 0.89 (95% CI 0.86 to 0.93).

CONCLUSIONS

Circulating tumor cells demonstrate substantial diagnostic accuracy in distinguishing benign from malignant pulmonary nodules. The incorporation of CTCs into the diagnostic protocol can significantly augment the diagnostic efficacy of LDCT in screening for malignant lung diseases.

Lung cancer clustering by identification of similarities and discrepancies of DNA copy numbers using maximal information coefficient

Lung cancer is the second most diagnosed cancer and the first cause of cancer related death for men and women in the United States. Early detection is essential as patient survival is not optimal and recurrence rate is high. Copy number (CN) changes in cancer populations have been broadly investigated to identify CN gains and deletions associated with the cancer. In this research, the similarities between cancer and paired peripheral blood samples are identified using maximal information coefficient (MIC) and the spatial locations with substantially high MIC scores in each chromosome are used for clustering analysis. The results showed that a sizable reduction of feature set can be obtained using only a subset of locations with high MIC values. The clustering performance was evaluated using both true rate and normalized mutual information (NMI). Clustering results using the reduced feature set outperformed the performance of clustering using entire feature set in several chromosomes that are highly associated with lung cancer with several identified oncogenes.

A Lightweight and Robust Framework for Circulating Genetically Abnormal Cells (CACs) Identification Using 4-Color Fluorescence In Situ Hybridization (FISH) Image and Deep Refined Learning

Circulating genetically abnormal cells (CACs) constitute an important biomarker for cancer diagnosis and prognosis. This biomarker offers high safety, low cost, and high repeatability, which can serve as a key reference in clinical diagnosis. These cells are identified by counting fluorescence signals using 4-color fluorescence in situ hybridization (FISH) technology, which has a high level of stability, sensitivity, and specificity. However, there are some challenges in CACs identification, due to the difference in the morphology and intensity of staining signals. In this concern, we developed a deep learning network (FISH-Net) based on 4-color FISH image for CACs identification. Firstly, a lightweight object detection network based on the statistical information of signal size was designed to improve the clinical detection rate. Secondly, the rotated Gaussian heatmap with a covariance matrix was defined to standardize the staining signals with different morphologies. Then, the heatmap refinement model was proposed to solve the fluorescent noise interference of 4-color FISH image. Finally, an online repetitive training strategy was used to improve the model's feature extraction ability for hard samples (i.e., fracture signal, weak signal, and adjacent signals). The results showed that the precision was superior to 96%, and the sensitivity was higher than 98%, for fluorescent signal detection. Additionally, validation was performed using the clinical samples of 853 patients from 10 centers. The sensitivity was 97.18% (CI 96.72-97.64%) for CACs identification. The number of parameters of FISH-Net was 2.24 M, compared to 36.9 M for the popularly used lightweight network (YOLO-V7s). The detection speed was about 800 times greater than that of a pathologist. In summary, the proposed network was lightweight and robust for CACs identification. It could greatly increase the review accuracy, enhance the efficiency of reviewers, and reduce the review turnaround time during CACs identification.

The presence of circulating genetically abnormal cells in blood predicts risk of lung cancer in individuals with indeterminate pulmonary nodules

PURPOSE

Computed tomography is the standard method by which pulmonary nodules are detected. Greater than 40% of pulmonary biopsies are not lung cancer and therefore not necessary, suggesting that improved diagnostic tools are needed. The LungLB™ blood test was developed to aid the clinical assessment of indeterminate nodules suspicious for lung cancer. LungLB™ identifies circulating genetically abnormal cells (CGACs) that are present early in lung cancer pathogenesis.

METHODS

LungLB™ is a 4-color fluorescence in-situ hybridization assay for detecting CGACs from peripheral blood. A prospective correlational study was performed on 151 participants scheduled for a pulmonary nodule biopsy. Mann-Whitney, Fisher's Exact and Chi-Square tests were used to assess participant demographics and correlation of LungLB™ with biopsy results, and sensitivity and specificity were also evaluated.

RESULTS

Participants from Mount Sinai Hospital (n = 83) and MD Anderson (n = 68), scheduled for a pulmonary biopsy were enrolled to have a LungLB™ test. Additional clinical variables including smoking history, previous cancer, lesion size, and nodule appearance were also collected. LungLB™ achieved 77% sensitivity and 72% specificity with an AUC of 0.78 for predicting lung cancer in the associated needle biopsy. Multivariate analysis found that clinical and radiological factors commonly used in malignancy prediction models did not impact the test performance. High test performance was observed across all participant characteristics, including clinical categories where other tests perform poorly (Mayo Clinic Model, AUC = 0.52).

CONCLUSION

Early clinical performance of the LungLB™ test supports a role in the discrimination of benign from malignant pulmonary nodules. Extended studies are underway.

Attention Mask R-CNN with edge refinement algorithm for identifying circulating genetically abnormal cells

Recent studies have suggested that circulating tumor cells with abnormalities in gene copy numbers in mononuclear cell-enriched peripheral blood samples, such as circulating genetically abnormal cells (CACs), can be used as a non-invasive tool to detect patients with benign pulmonary nodules. These cells are identified through fluorescence signals counting by using 4-color fluorescence in situ hybridization (FISH) technology that exhibits high stability, sensitivity, and specificity. When FISH data are analyzed, the overlapping cells and fluorescence noise is a great challenge for identifying of CACs, thereby seriously affecting the efficiency of clinical diagnosis. To address this problem, in this study, we proposed an end-to-end FISH-based method (CACNET) for CAC identification. CACNET achieved nuclear segmentation and counted 4-color staining signals through improved Mask region-based convolutional neural network (R-CNN), followed by cell category (normal cell, deletion cell, gain cell, or CAC) according to pathological criteria. Firstly, the segmentation accuracy of overlapping nuclei was improved by adding an edge constraint head during training. Then, the interference of fluorescence noise was reduced by fusing non-local module to reconstruct the feature extraction network of Mask R-CNN. We trained and tested the proposed model on a dataset comprising 700 frames with 58,083 nuclei. The Accuracy, Sensitivity, and Specificity (overall performance metric for the algorithm) of CAC identification with CACNET were 94.06%, 92.1%, and 99.8%, respectively. Moreover, the developed method exhibited approximately identification speed of approximately 0.22 s per frames. The results showed that the proposed method outperformed the existing CAC identification methods, making it a promising approach for early screening of lung cancer.

The Value of Circulating Tumor Cells and Tumor Markers Detection in Lung Cancer Diagnosis

OBJECTIVE

Circulating tumor cells are complete tumor cells with multi-scale analysis values that present a high potential for lung cancer diagnosis. To enhance the accuracy of lung cancer diagnosis, we detected circulating tumor cells by the innovated conical micro filter integrated microfluidic system.

METHODS

We recruited 45 subjects of study, including 22 lung cancer patients, 2 precancerous patients, the control group including 14 healthy participants, and 7 patients with lung benign lesions in this prospective study. We calculated the area under the receiver operating characteristic curve of circulating tumor cells, cytokeratin19 fragment, carcinoma embryonic antigen, squamous cell carcinoma, neuron-specific enolase, and their combination, respectively, while compared the circulating tumor cells levels between vein blood and arterial blood. A conical shape filter embedded in a microfluidic chip was used to improve the detection capability of circulating tumor cells.

RESULTS

The study indicated that the sensitivity, specificity, positive predictive value, and negative predictive value of circulating tumor cells detection were 81.8%, 90.5%, 90.0%, and 82.6%, respectively. The circulating tumor cells level of lung cancer patient was significantly higher than that of the control group (P < .05). The area under the curve of circulating tumor cells, cytokeratin19 fragment, carcinoma embryonic antigen, squamous cell carcinoma, and neuron-specific enolase alone was 0.838, 0.760, 0.705, 0.614, and 0.636, respectively. The combination area under the curve of the 4 tumor markers (cytokeratin19 fragment, carcinoma embryonic antigen, squamous cell carcinoma, and neuron-specific enolase) was 0.805 less than that of circulating tumor cells alone. Together, the total area under the curve of circulating tumor cell and the 4 tumor markers were 0.847, showing the highest area under the curve value among all biomarkers. In addition, this study found that there was no significant difference in positive rate of circulating tumor cell between arterial and venous blood samples.

CONCLUSION

The circulating tumor cells detection technology by conical micro filter integrated microfluidic could be used for lung cancer diagnosis with high sensitivity and specificity. Complementary combination of circulating tumor cells and conventional 4 lung cancer markers could enhance the clinical application accuracy. Venous blood should be used as a routine sample for circulating tumor cells detections.

An efficient fluorescence in situ hybridization (FISH)-based circulating genetically abnormal cells (CACs) identification method based on Multi-scale MobileNet-YOLO-V4

BACKGROUND

Circulating tumor cells (CTCs) acting as "liquid biopsy" of cancer are cells that have been shed from the primary tumor, which cause the development of a secondary tumor in a distant organ site, leading to cancer metastasis. Recent research suggests that CTCs with abnormalities in gene copy numbers in mononuclear cell-enriched peripheral blood samples, namely circulating genetically abnormal cells (CACs), could be used as a non-invasive decision tool to detect patients with benign pulmonary nodules. Such cells are identified by counting the fluorescence signals of fluorescence in situ hybridization (FISH). However, owing to the rarity of CACs in the blood, identification of CACs using this technique is time-consuming and is a drawback of this method.

METHODS

This study has proposed an efficient and automatic FISH-based CACs identification approach which is based on a combination of the high accuracy of You Only Look Once (YOLO)-V4 and the lightweight and rapidness of MobileNet-V3. The backbone of YOLO-V4 was replaced with MobileNet-V3 to improve the detection efficiency and prevent overfitting, and the architecture of YOLO-V4 was optimized by utilizing a new feature map with a larger scale to enable the enhanced detection ability for small targets.

RESULTS

We trained and tested the proposed model using a dataset containing more than 7,000 cells based on five-fold cross-validation. All the images in the dataset were 2,448×2,048 (pixels) in size. The number of cells in each image was >70. The accuracy of four-color fluorescence signals detection for our proposed model were all approximately 98%, and the mean average precision (mAP) were close to 100%. The final outcome of the developed method was the type of cells, i.e., normal cells, CACs, gaining cells or deletion cells. The method had a CACs identification accuracy of 93.86% (similar to an expert pathologist), and a detection speed that was about 500 times greater than that of a pathologist.

CONCLUSIONS

The developed method could greatly increase the review accuracy, enhance the efficiency of reviewers, and reduce the review turnaround time during CACs identification.

Circulating Tumor-Macrophage Fusion Cells and Circulating Tumor Cells Complement Non-Small-Cell Lung Cancer Screening in Patients With Suspicious Lung-RADS 4 Nodules

PURPOSE

Low-dose computed tomography (LDCT) screening of high-risk patients decreases lung cancer-related mortality. However, high false-positive rates associated with LDCT result in unnecessary interventions. To distinguish non-small-cell lung cancer (NSCLC) from benign nodules, in the present study, we integrated cellular liquid biomarkers in patients with suspicious lung nodules (lung cancer screening reporting and data system [Lung-RADS] 4).

METHODS

Prospectively, 7.5 mL of blood was collected from 221 individuals (training set: 90 nonscreened NSCLC patients, 74 high-risk screening patients with no/benign nodules [Lung-RADS 1-3], and 20 never smokers; validation set: 37 patients with suspicious nodules [Lung-RADS 4]). Circulating tumor cells (CTCs), CTC clusters, and tumor-macrophage fusion (TMF) cells were identified by blinded analyses. Screening patients underwent a median of two LDCTs (range, 1-4) with a median surveillance time of 30 (range, 11-50) months.

RESULTS

In the validation set of 37 Lung-RADS 4 patients, all circulating cellular biomarker counts (P < .005; Wilcoxon test) and positivity rates were significantly higher in 23 biopsy-proven NSCLC patients (CTCs: 23 of 23 [100%], CTC clusters: 6 of 23 [26.1%], and TMF cells: 15 of 23 [65.2%]) than in 14 patients with biopsy-proven benign nodules (6 of 14 [42.9%], 0 of 14 [0%], and 2 of 14 [14.3%]). On the basis of cutoff values from the training set, logistic regression with receiver operating characteristic and area under the curve analyses demonstrated that CTCs (sensitivity: 0.870, specificity: 1.0, and area under the curve: 0.989) and TMF cells (0.652; 0.880; 0.790) complement LDCT in diagnosing NSCLC in Lung-RADS 4 patients.

CONCLUSION

Cellular liquid biomarkers have a potential to complement LDCT interpretation of suspicious Lung-RADS 4 nodules to distinguish NSCLC from benign lung nodules. A future prospective, large-scale, multicenter clinical trial should validate the role of cellular liquid biomarkers in improving diagnostic accuracy in high-risk patients with Lung-RADS 4 nodules.

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.

Clinical Applications of Circulating Tumour Cells and Circulating Tumour DNA in Non-Small Cell Lung Cancer-An Update

Despite efforts to improve earlier diagnosis of non-small cell lung cancer (NSCLC), most patients present with advanced stage disease, which is often associated with poor survival outcomes with only 15% surviving for 5 years from their diagnosis. Tumour tissue biopsy is the current mainstream for cancer diagnosis and prognosis in many parts of the world. However, due to tumour heterogeneity and accessibility issues, liquid biopsy is emerging as a game changer for both cancer diagnosis and prognosis. Liquid biopsy is the analysis of tumour-derived biomarkers in body fluids, which has remarkable advantages over the use of traditional tumour biopsy. Circulating tumour cells (CTCs) and circulating tumour DNA (ctDNA) are two main derivatives of liquid biopsy. CTC enumeration and molecular analysis enable monitoring of cancer progression, recurrence, and treatment response earlier than traditional biopsy through a minimally invasive liquid biopsy approach. CTC-derived ex-vivo cultures are essential to understanding CTC biology and their role in metastasis, provide a means for personalized drug testing, and guide treatment selection. Just like CTCs, ctDNA provides opportunity for screening, monitoring, treatment evaluation, and disease surveillance. We present an updated review highlighting the prognostic and therapeutic significance of CTCs and ctDNA in NSCLC.

Biomarkers and Lung Cancer Early Detection: State of the Art

Simple Summary

Lung cancer is the leading cause of cancer death worldwide. Detecting lung malignancies promptly is essential for any anticancer treatment to reduce mortality and morbidity, especially in high-risk individuals. The use of liquid biopsy to detect circulating biomarkers such as RNA, microRNA, DNA, proteins, autoantibodies in the blood, as well as circulating tumor cells (CTCs), can substantially change the way we manage lung cancer patients by improving disease stratification using intrinsic molecular characteristics, identification of therapeutic targets and monitoring molecular residual disease. Here, we made an update on recent developments in liquid biopsy-based biomarkers for lung cancer early diagnosis, and we propose guidelines for an accurate study design, execution, and data interpretation for biomarker development.

Abstract

Lung cancer burden is increasing, with 2 million deaths/year worldwide. Current limitations in early detection impede lung cancer diagnosis when the disease is still localized and thus more curable by surgery or multimodality treatment. Liquid biopsy is emerging as an important tool for lung cancer early detection and for monitoring therapy response. Here, we reviewed recent advances in liquid biopsy for early diagnosis of lung cancer. We summarized DNA- or RNA-based biomarkers, proteins, autoantibodies circulating in the blood, as well as circulating tumor cells (CTCs), and compared the most promising studies in terms of biomarkers prediction performance. While we observed an overall good performance for the proposed biomarkers, we noticed some critical aspects which may complicate the successful translation of these biomarkers into the clinical setting. We, therefore, proposed a roadmap for successful development of lung cancer biomarkers during the discovery, prioritization, and clinical validation phase. The integration of innovative minimally invasive biomarkers in screening programs is highly demanded to augment lung cancer early detection.

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

PURPOSE

Available 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.

METHODS

This 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.

RESULTS

A 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).

CONCLUSION

CACs 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.

Application of circulating genetically abnormal cells in the diagnosis of early-stage lung cancer

PURPOSE

Lung cancer is the leading cause of cancer-related death worldwide. The early detection of lung cancer is crucial for the diagnosis of this disease. Therefore, an effective and noninvasive method for the early diagnosis of lung cancer is urgently needed.

METHODS

To evaluate the diagnostic performance of circulating genetically abnormal cells (CACs) in early lung cancer, a total of 63 participants who completed CAC detection by Zhuhai SanMed Biotech Inc. and obtained pathological results from January to December 2020 were included in our study; 50 patients had lung cancer and 13 patients had benign lung disease. The levels of lung cancer-related markers in peripheral blood and the chest computed tomography (CT) imaging characteristics of these patients were collected before pathological acquisition.

RESULTS

The positive rate of CAC was 90.0% in the lung cancer group and 23.1% in the benign lung disease group, and the difference was statistically significant (P < 0.01). The area under the receiver operating characteristic (ROC) curve of CAC was 0.837, the sensitivity was 90%, and the specificity was 76.9%. The area under the ROC curve and sensitivity were both higher than those of the combined or single serum tumor marker test.

CONCLUSIONS

This study preliminarily concludes that the CAC test, as a noninvasive test, has high sensitivity and specificity for the early diagnosis of lung cancer. This test is expected to help with the early detection of disease in lung cancer patients.

Circulating Genetically Abnormal Cells Add Non-Invasive Diagnosis Value to Discriminate Lung Cancer in Patients With Pulmonary Nodules ≤10 mm

Background

Lung 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.

Methods

In 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.

Results

Overall, 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).

Conclusion

CACs presented a significant diagnostic value in detecting lung cancer for patients with pulmonary nodules ≤10 mm.

The value of circulating tumor cells with positive centromere probe 8 in the diagnosis of small pulmonary nodules

Circulating cancer cells (CTCs) can serve as a non-invasive liquid biopsy and provide opportunities for early cancer diagnosis and evaluation. However, the value of CTCs for diagnosis or prognosis of small pulmonary nodules (SPNs) is unclear. Fifty-three patients diagnosed with SPNs with a diameter less than 30 mm by CT examination were enrolled in the study. The CTC numbers, CT examination features, serum tumor marker concentrations, and histopathological characteristics were analyzed. Centromere probe 8 (CEP8) was used as a marker for CTC identification. The CTC numbers were significantly different in patients with malignant and benign SPNs and with early (0/Ⅰa) and advanced (Ⅰb/Ⅱ/Ⅲ) lung cancer stages. ROC analysis showed that the CTC numbers was effective on malignant SNP diagnosis. The combined use of CTCs and the density features of the nodules determined by CT further improved the overall screening, the diagnostic effectiveness for malignant SNPs, and determination of the pTNM (≤Ia vs.>Ia) stage. The CT morphology revealed that large, single, and solid SPNs were associated with significant CTC numbers and the CTC numbers were correlated with malignant histopathology. Using CEP8 as a marker resulted in detection of more CTC numbers in 22 patient samples triple stained for CEP8, EpCAM, and CKs. The CTCs determined by CEP8-positive staining could serve as potential screening and diagnostic markers for malignant SPNs.

Immunomagnetic Capture and Multiplexed Surface Marker Detection of Circulating Tumor Cells with Magnetic Multicolor Surface-Enhanced Raman Scattering Nanotags

Circulating tumor cells (CTCs) have substantial clinical implications in cancer diagnosis and monitoring. Although significant progress has been made in developing technologies for CTC detection and counting, the ability to quantitatively detect multiple surface protein markers on individual tumor cells remains very limited. In this work, we report a multiplexed method that uses magnetic multicolor surface-enhanced Raman scattering (SERS) nanotags in conjunction with a chip-based immunomagnetic separation to quantitatively and simultaneously detect four surface protein markers on individual tumor cells in whole blood. Four-color SERS nanotags were prepared using magnetic-optical iron oxide-gold core-shell nanoparticles with different Raman reporters to recognize four different cancer markers with respective antibodies. A microfluidic device was fabricated to magnetically capture the nanoparticle-bound tumor cells and to perform online negative staining and single-cell optical detection. The level of each targeted protein was obtained by signal deconvolution of the mixed SERS signals from individual tumor cells using the classic least squares regression method. The method was tested with spiked tumor cells in human whole blood with three different breast cancer cell lines and compared with the results of purified cancer cells suspended in a phosphate buffer solution. The method, with either spiked cancer cells in blood or purified cancer cells, showed a strong correlation with purified cancer cells by enzyme-linked immunosorbent assay, suggesting the potential of our method for the reliable detection of multiple surface markers on CTCs. Combining immunomagnetic enrichment with high specificity, multiplexed targeting for the capture of CTC subpopulations, multicolor SERS detection with high sensitivity and specificity, microfluidics for handling rare cells and magnetic-plasmonic nanoparticles for dual enrichment and detection, our method provides an integrated, yet a simple and an efficient platform that has the potential to more sensitively detect and monitor cancer metastasis.

Diagnostic and prognostic role of liquid biopsy in non-small cell lung cancer: evaluation of circulating biomarkers

Lung cancer is still one of the main causes of cancer-related death, together with prostate and colorectal cancers in males and breast and colorectal cancers in females. The prognosis for non-small cell lung cancer (NSCLC) is strictly dependent on feasibility of a complete surgical resection of the tumor at diagnosis. Since surgery is indicated only in early stages tumors, it is necessary to anticipate the timing of diagnosis in clinical practice. In the diagnostic and therapeutic pathway for NSCLC, sampling of neoplastic tissue is usually obtained using invasive methods that are not free from disadvantages and complications. A valid alternative to the standard biopsy is the liquid biopsy (LB), that is, the analysis of samples from peripheral blood, urine, and other biological fluids, with a simple and non-invasive collection. In particular, it is possible to detect in the blood different tumor derivatives, such as cell-free DNA (cfDNA) with its subtype circulating tumor DNA (ctDNA), cell-free RNA (cfRNA), and circulating tumor cells (CTCs). Plasma-based testing seems to have several advantages over tumor tissue biopsy; firstly, it reduces medical costs, risk of complications related to invasive procedures, and turnaround times; moreover, the analysis of genes alteration, such as EGFR, ALK, ROS1, and BRAF is faster and safer with this method, compared to tissue biopsy. Despite all these advantages, the evidences in literatures indicate that assays performed on liquid biopsies have a low sensitivity, making them unsuitable for screening in lung cancer at the current state. This is caused by lack of standardization in sampling and preparation of specimen and by the low concentration of biomarkers in the bloodstream. Instead, routinely use of LB should be preferred in revaluation of patients with advanced NSCLC resistant to chemotherapy, due to onset of new mutations.

Liquid biopsy for lung cancer screening: Usefulness of circulating tumor cells and other circulating blood biomarkers

Screening for lung cancer has become a reality in certain countries, most notably the United States, but its implementation currently is under discussion and not established in many nations, including France. Screening for lung cancer currently is proposed using a low-dose computed tomography scanner. However, this approach lacks sensitivity and specificity, and could be improved when combined with a blood test (so-called "liquid biopsy"). Such tests attempt to detect biomarker (s) of early cancer development. Thus, numerous studies performed within the last few years have examined different blood components including circulating tumor cells and free DNA and other circulating elements such as microRNAs, exosomes, antibodies, and proteins. Recent studies have highlighted the value of seeking a signature for the methylation of circulating free DNA, which can be specific for certain solid tumors, including lung carcinoma. The current study describes some recent developments in the use of liquid biopsies for the detection of early-stage lung cancers, even those that are not yet visible using a low-dose computed tomography scanner.