BACKGROUNDCurrent clinical management of patients with pulmonary nodules involves either repeated low-dose CT (LDCT)/CT scans or invasive procedures, yet causes significant patient misclassification. An accurate noninvasive test is needed to identify malignant nodules and reduce unnecessary invasive tests.METHODWe developed a diagnostic model based on targeted DNA methylation sequencing of 389 pulmonary nodule patients' plasma samples and then validation in 140 plasma samples independently. We tested the model in different stages and subtypes of pulmonary nodules.RESULTSA 100-feature model was developed and validated for pulmonary nodule diagnosis; the model achieved a receiver operating characteristic curve-AUC (ROC-AUC) of 0.843 on 140 independent validation samples, with an accuracy of 0.800. The performance was well maintained in (a) a 6 to 20 mm size subgroup (n = 100), with a sensitivity of 1.000 and adjusted negative predictive value (NPV) of 1.000 at 10% prevalence; (b) stage I malignancy (n = 90), with a sensitivity of 0.971; (c) different nodule types: solid nodules (n = 78) with a sensitivity of 1.000 and adjusted NPV of 1.000, part-solid nodules (n = 75) with a sensitivity of 0.947 and adjusted NPV of 0.983, and ground-glass nodules (n = 67) with a sensitivity of 0.964 and adjusted NPV of 0.989 at 10% prevalence. This methylation test, called PulmoSeek, outperformed PET-CT and 2 clinical prediction models (Mayo Clinic and Veterans Affairs) in discriminating malignant pulmonary nodules from benign ones.CONCLUSIONThis study suggests that the blood-based DNA methylation model may provide a better test for classifying pulmonary nodules, which could help facilitate the accurate diagnosis of early stage lung cancer from pulmonary nodule patients and guide clinical decisions.FUNDINGThe National Key Research and Development Program of China; Science and Technology Planning Project of Guangdong Province; The National Natural Science Foundation of China National.

/pdf/accurate-diagnosis-of-pulmonary-nodules-using-a-noninvasive-4dp6gq1jr4.pdf

Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test

Simple Summary ctDNA is a small DNA fragment derived from tumor cells, which contains tumor-related genomic information, such as mutation, methylation, microsatellite instability, etc. It is an ideal biomarker for real-time monitoring of tumor development. This work mainly reviews the different sources of ctDNA, such as blood, urine, uncommon cerebrospinal fluid, ascites, etc. The most frequent mutation and methylation detection methods in ctDNA, such as the most commonly used high-throughput sequencing and innovative methods, combined with new materials in recent years, such as CRISPR-Cas system, graphene, etc. Finally, it is concluded that ctDNA has a comprehensive and accurate application value in all stages of tumor development (early screening, diagnosis, molecular typing, guiding medication, prognosis, and recurrence monitoring). Abstract Cancer is the second leading cause of death in the world and seriously affects the quality of life of patients. The diagnostic techniques for tumors mainly include tumor biomarker detection, instrumental examination, and tissue biopsy. In recent years, liquid technology represented by circulating tumor DNA (ctDNA) has gradually replaced traditional technology with its advantages of being non-invasive and accurate, its high specificity, and its high sensitivity. ctDNA may carry throughout the circulatory system through tumor cell necrosis, apoptosis, circulating exosome secretion, etc., carrying the characteristic changes in tumors, such as mutation, methylation, microsatellite instability, gene rearrangement, etc. In this paper, ctDNA mutation and methylation, as the objects to describe the preparation process before ctDNA analysis, and the detection methods of two gene-level changes, including a series of enrichment detection techniques derived from PCR, sequencing-based detection techniques, and comprehensive detection techniques, are combined with new materials. In addition, the role of ctDNA in various stages of cancer development is summarized, such as early screening, diagnosis, molecular typing, prognosis prediction, recurrence monitoring, and drug guidance. In summary, ctDNA is an ideal biomarker involved in the whole process of tumor development.

/pdf/circulating-tumor-dna-a-novel-biomarker-of-tumor-progression-1teuooeq.pdf

Circulating Tumor DNA—A Novel Biomarker of Tumor Progression and Its Favorable Detection Techniques

BACKGROUNDEarly diagnosis and treatment are key to the long-term survival of lung cancer patients. Although CT has significantly contributed to the early diagnosis of lung cancer, there are still consequences of excessive or delayed treatment. By improving the sensitivity and specificity of circulating tumor cell (CTC) detection, a solution was proposed for differentiating benign from malignant pulmonary nodules.METHODSIn this study, we used telomerase reverse transcriptase-based (TERT-based) CTC detection (TBCD) to distinguish benign from malignant pulmonary nodules < 2 cm and compared this method with the pathological diagnosis as the gold standard. FlowSight and FISH were used to confirm the CTCs detected by TBCD.RESULTSOur results suggest that CTCs based on TBCD can be used as independent biomarkers to distinguish benign from malignant nodules and are significantly superior to serum tumor markers. When the detection threshold was 1, the detection sensitivity and specificity of CTC diagnosis were 0.854 and 0.839, respectively. For pulmonary nodules ≤ 1 cm and 1-2 cm, the sensitivity and specificity of CTCs were both higher than 77%. Additionally, the diagnostic ability of CTC-assisted CT was compared by CT detection. The results show that CT combined with CTCs could significantly improve the differentiation ability of benign and malignant nodules in lung nodules < 2 cm and that the sensitivity and specificity could reach 0.899 and 0.839, respectively.CONCLUSIONTBCD can effectively diagnose pulmonary nodules and be used as an effective auxiliary diagnostic scheme for CT diagnosis.FUNDINGNational Key Research and Development Project grant nos. 2019YFC1315700 and 2017YFC1308702, CAMS Initiative for Innovative Medicine grant no. 2017-I2M-1-005, and National Natural Science Foundation of China grant no. 81472013.

/pdf/combination-of-ct-and-telomerase-positive-circulating-tumor-48h5sgfwr0.pdf

Combination of CT and telomerase-positive circulating tumor cells improves diagnosis of small pulmonary nodules

BackgroundThere is an unmet clinical need for accurate non-invasive tests to facilitate the early diagnosis of lung cancer. We propose a combined model of clinical, imaging, and cell-free DNA methylation biomarkers that aims to improve the classification of pulmonary nodules.MethodsWe conducted a prospective specimen collection and retrospective masked evaluation study. We recruited participants with a solitary pulmonary nodule sized 5–30 mm from 24 hospitals across 20 cities in China. Participants who were aged 18 years or older and had been referred with 5–30 mm non-calcified and solitary pulmonary nodules, including solid nodules, part solid nodules, and pure ground-glass nodules, were included. We developed a combined clinical and imaging biomarkers (CIBM) model by machine learning for the classification of malignant and benign pulmonary nodules in a cohort (n=839) and validated it in two cohorts (n=258 in the first cohort and n=283 in the second cohort). We then integrated the CIBM model with our previously established circulating tumour DNA methylation model (PulmoSeek) to create a new combined model, PulmoSeek Plus (n=258), and verified it in an independent cohort (n=283). The clinical utility of the models was evaluated using decision curve analysis. A low cutoff (0·65) for high sensitivity and a high cutoff (0·89) for high specificity were applied simultaneously to stratify pulmonary nodules into low-risk, medium-risk, and high-risk groups. The primary outcome was the diagnostic performance of the CIBM, PulmoSeek, and PulmoSeek Plus models. Participants in this study were drawn from two prospective clinical studies that were registered (NCT03181490 and NCT03651986), the first of which was completed, and the second of which is ongoing because 25% of participants have not yet finished the required 3-year follow-up.FindingsWe recruited a total of 1380 participants. 1097 participants were enrolled from July 7, 2017, to Feb 12, 2019; 839 participants were used for the CIBM model training set, and the rest (n=258) for the first CIBM validation set and the PulmoSeek Plus training set. 283 participants were enrolled from Oct 26, 2018, to March 20, 2020, as an independent validation set for the PulmoSeek Plus model and the second validation set for the CIBM model. The CIBM model validation cohorts had area under the curves (AUCs) of 0·85 (95% CI 0·80–0·89) and 0·85 (0·81–0·89). The PulmoSeek Plus model had better discrimination capacity compared with the CIBM and PulmoSeek models with an increase of 0·05 in AUC (PulmoSeek Plus vs CIBM, 95% CI 0·022–0·087, p=0·001; and PulmoSeek Plus vs PulmoSeek, 0·018–0·083, p=0·002). The overall sensitivity of the PulmoSeek Plus model was 0·98 (0·97–0·99) at a fixed specificity of 0·50 for ruling out lung cancer. A high sensitivity of 0·98 (0·96–0·99) was maintained in early-stage lung cancer (stages 0 and I) and 0·99 (0·96–1·00) in 5–10 mm nodules. The decision curve showed that if an invasive intervention, such as surgical resection or biopsy, was deemed necessary at more than the risk threshold score of 0·54, the PulmoSeek Plus model would provide a standardised net benefit of 82·38% (76·06–86·79%), equivalent to correctly identifying approximately 83 of 100 people with lung cancer. Using the PulmoSeek Plus model to classify pulmonary nodules with two cutoffs (0·65 and 0·89) would have reduced 89% (105/118) of unnecessary surgeries and 73% (308/423) of delayed treatments.InterpretationThe PulmoSeek Plus Model combining clinical, imaging, and cell-free DNA methylation biomarkers aids the early diagnosis of pulmonary nodules, with potential application in clinical decision making for the management of pulmonary nodules.FundingThe China National Science Foundation, the Key Project of Guangzhou Scientific Research Project, the High-Level University Construction Project of Guangzhou Medical University, the National Key Research & Development Programme, the Guangdong High Level Hospital Construction “Reaching Peak” Plan, the Guangdong Basic and Applied Basic Research Foundation, the National Natural Science Foundation of China, The Leading Projects of Guangzhou Municipal Health Sciences Foundation, the Key Research and Development Plan of Shaanxi Province of China, the Scheme of Guangzhou Economic and Technological Development District for Leading Talents in Innovation and Entrepreneurship, the Scheme of Guangzhou for Leading Talents in Innovation and Entrepreneurship, the Scheme of Guangzhou for Leading Team in Innovation, the Guangzhou Development Zone International Science and Technology Cooperation Project, and the Science and Technology Planning Project of Guangzhou. 

Accurate classification of pulmonary nodules by a combined model of clinical, imaging, and cell-free DNA methylation biomarkers: a model development and external validation study.

Background Lung nodules are a diagnostic challenge. Current clinical management of lung nodule patients is inefficient and therefore causes patient misclassification, which increases healthcare expenses. However, a precise and robust lung nodule classifier to minimize discomfort for patients and healthcare costs is still lacking. The aim of the present protocol is to evaluate the effectiveness of using a liquid biopsy classifier to diagnose nodules compared to physician estimates and whether the classifier can reduce the number of unnecessary biopsies in benign cases. Methods A prospective cohort of 10,560 patients enrolled at 23 clinical centers in China with non-calcified pulmonary nodules, ranging from 0.5 to 3 cm in diameter, indicated by LDCT or CT will be included. After signed consent forms, the participants' pulmonary nodules will be assessed using three evaluation tools: (I) physician cancer probability estimates (II) validated lung nodule risk models, including Mayo Clinic and Veteran's Affairs models (III) ctDNA methylation classifier previously established. Each patient will undergo LDCT/CT follow-ups for 2 to 3 years and their information and one blood sample will be collected at baseline, 3, 6, 12, 24 and 36 months. The primary study outcomes will be the diagnostic accuracy of the methylation classifier in the cohort. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) will be used to compare the diagnostic value of each testing tool in differentiating benign and malignant pulmonary nodules. Discussion We are conducting an observational study to explore the accuracy of using a ctDNA methylation classifier for incidental lung nodules diagnosis. Trial registration Clinicaltrials.gov NCT03651986.

/pdf/evaluating-the-diagnostic-accuracy-of-a-ctdna-methylation-1dt0qstq9l.pdf

Evaluating the diagnostic accuracy of a ctDNA methylation classifier for incidental lung nodules: protocol for a prospective, observational, and multicenter clinical trial of 10,560 cases

The classification of inhibitory control and the relationship between the subcomponents of inhibitory control have been the focus of many studies. This study mainly explored the influence of response inhibition training on interference control through event-related potential data. Forty college students were randomly divided into a training group and a control group. Two response inhibition tasks were used as training tasks and the Stroop and go/no-go tasks were used with electroencephalogram monitoring to evaluate students’ abilities in the two kinds of inhibitory control. The results showed that the conflict effect of the training group significantly improved after training compared with those of the control group. In the training group, the N2 effect was enhanced not only in the no-go stimulation in the training task but also in the incongruent stimulation in the untrained Stroop task and there was a correlation in the enhancement of the N2 effect between the two tasks. To some extent, this study provided neuroscientific evidence that response inhibition training can transfer to interference control.

An event-related potential (ERP) study of the transfer of response inhibition training to interference control.

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Evaluating the diagnostic accuracy of a ctDNA methylation classifier for incidental lung nodules: protocol for a prospective, observational, and multicenter clinical trial of 10,560 cases

An event-related potential (ERP) study of the transfer of response inhibition training to interference control.