Evaluating Molecular Biomarkers for the Early Detection of Lung Cancer: When Is a Biomarker Ready for Clinical Use? An Official American Thoracic Society Policy Statement

Polygenic Risk Score Is Associated with Developing and Dying from Lung Cancer in the National Lung Screening Trial

Background: Epidemiological studies suggest lung cancer results from the combined effects of smoking and genetic susceptibility. The clinical application of polygenic risk scores (PRSs), derived from combining the results from multiple germline genetic variants, have not yet been explored in a lung cancer screening cohort. Methods: This was a post hoc analysis of 9191 non-Hispanic white subjects from the National Lung Screening Trial (NLST), a sub-study of high-risk smokers randomised to annual computed tomography (CT) or chest X-ray (CXR) and followed for 6.4 years (mean). This study's primary aim was to examine the relationship between a composite polygenic risk score (PRS) calculated from 12 validated risk genotypes and developing or dying from lung cancer during screening. Validation was undertaken in the UK Biobank of unscreened ever-smokers (N = 167,796) followed for 10 years (median). Results: In this prospective study, we found our PRS correlated with lung cancer incidence (p < 0.0001) and mortality (p = 0.004). In an adjusted multivariable logistic regression analysis, PRS was independently associated with lung cancer death (p = 0.0027). Screening participants with intermediate and high PRS scores had a higher lung cancer mortality, relative to those with a low PRS score (rate ratios = 1.73 (95%CI 1.14-2.64, p = 0.010) and 1.89 (95%CI 1.28-2.78, p = 0.009), respectively). This was despite comparable baseline demographics (including lung function) and comparable lung cancer characteristics. The PRS's association with lung cancer mortality was validated in an unscreened cohort from the UK Biobank (p = 0.002). Conclusions: In this biomarker-based cohort study, an elevated PRS was independently associated with dying from lung cancer in both screening and non-screening cohorts.

Current Evidence Supporting the Role of miRNA as a Biomarker for Lung Cancer Diagnosis Through Exhaled Breath Condensate Collection: A Narrative Review

Lung cancer, the leading cause of cancer-related mortality, has brought exhaled breath condensate (EBC) into focus as a promising non-invasive sample for detecting molecular biomarkers, particularly microRNAs, which regulate gene expression and contribute to tumorigenesis. Ten key studies encompassing approximately 866 subjects consistently demonstrated distinct patterns of miRNA dysregulation in lung cancer. Notably, several reported panels achieved diagnostic sensitivity and specificity exceeding 75% through the identification of distinct miRNA signatures in EBC, with oncogenic miRNAs (e.g., miR-21) upregulated and tumor-suppressor miRNAs (e.g., miR-486) downregulated in lung cancer patients. Analytical advancements, including next-generation sequencing (NGS), have improved miRNA detection sensitivity and specificity, addressing prior limitations of low yield and variability. NGS enabled the identification of novel miRNAs and proved especially effective in overcoming the low RNA yield associated with EBC samples. However, challenges persist regarding standardization of collection, sample dilution, and potential contamination. Moreover, the reproducibility of miRNA signatures across diverse patient populations remains a critical issue. Large-scale, multicenter validation studies are needed to establish robust diagnostic algorithms integrating EBC-derived miRNAs with existing clinical tools. The potential of EBC miRNA profiling to support current screening strategies could significantly improve early lung cancer detection and patient outcomes. Nevertheless, its clinical transition requires further methodological optimization and biomarker validation. This review critically evaluates current evidence on miRNA detection in EBC for lung cancer diagnosis.

A multivariate cell-based assay for blood-based diagnostics enhances lung cancer risk stratification

The indicator cell assay platform (iCAP) is a tool for blood-based diagnostics that addresses the low signal-to-noise ratio of blood biomarkers by using cells as biosensors. The assay exposes small volumes of patient serum to standardized cells in culture and classifies disease by machine learning analysis of the gene expression readout from the cells. We developed the lung cancer iCAP (LC-iCAP) as a rule-out test for nodule management in computed tomography (CT)-based lung-cancer screening. We performed analytical optimization, rigorous reproducibility testing, and assessed performance in a study with prospective-specimen-collection, retrospective-blinded-evaluation (PRoBE) design. LC-iCAP achieved an AUC of 0.64 (95% CI, 0.51-0.76) on the ROC curve in validation. Post-validation integration of the assay readout with CT-based features showed improved clinical utility compared to the Mayo Clinic model, with 90% sensitivity, 64% specificity, and 95% negative predictive value at 25% prevalence. The lung-cancer specific readout was enriched for hypoxia-responsive genes and was reproducible across different indicator cell lineages. This is the first validation study of an iCAP and the first application for early cancer detection. The LC-iCAP uses immortalized cells, is scalable and cost-effective and has a multivariate readout. This study supports its potential as a next-generation multivalent platform for precision medicine applications in multi-cancer screening and drug development.

Key Points

We developed the LC-iCAP, novel approach for liquid biopsies that uses cultured cells as biosensors. The cells detect cancer signals in serum and transduce them into standardized gene expression profiles, which are analyzed by machine learning for disease classification. The assay is inexpensive and scalable and has a multivariate readout with potential utility for precision medicine and multi-cancer early detection.A LC-iCAP-based lung cancer risk classifier demonstrated improved specificity compared to existing tests, suggesting meaningful clinical utility for managing indeterminate pulmonary nodules.We identified a lung-cancer specific transcriptional response to hypoxia in the assay readout, implicating HIF1A and HIF2A activity in the response consistent with known lung cancer biology and highlighting the platform's mechanistic relevance.Standardized controls and validation studies demonstrated assay reproducibility, lineage stability, and detection of technical errors-supporting the platform's readiness for clinical deployment.

Validation of a High-Specificity Blood Autoantibody Test to Detect Lung Cancer in Pulmonary Nodules

BACKGROUND

Pulmonary nodules (PNs) are frequently detected by chest CT scan, which is increasingly used in clinical practice. Accurately identifying malignant nodules can pose a diagnostic challenge; therefore, a high-specificity biomarker could help clinicians identify malignant nodules and ideally lead to the earlier diagnosis of lung cancer.

RESEARCH QUESTION

What are the performance characteristics of a blood-based biomarker for identifying malignancy in patients with a CT-detected PN?

STUDY DESIGN AND METHODS

Banked plasma samples from 2 independent prospective observational cohorts of patients presenting with benign or malignant PNs 8 to 30 mm in size were tested using a 7-autoantibody panel. Sensitivity, specificity, and positive predictive value of the autoantibody test (AAT) to identify cancer were calculated for the individual and combined cohorts.

RESULTS

Overall, 447 patients (263 and 184 from each cohort) were included in the analysis with a prevalence of malignancy of 55%. The performance of the AAT between the 2 cohorts was similar. The AAT demonstrated a specificity of 90% (95% CI, 85%-93%), a positive predictive value of 66% (95% CI, 52%-77%), sensitivity of 16% (95% CI, 12%-22%), and false-positive rate of 10% in the combined cohort. Using a pretest probability of cancer cutoff of 20% improved the positive predictive value to 76% (95% CI, 61%-88%) and resulted in a 52% decrease in the number of false-positive test results. In the subset of patients who had 18F-fluorodeoxyglucose PET imaging performed for clinical purposes (n = 222), specificity of the AAT was higher (93% vs 58%, P < .001), but the sensitivity was lower than 18F-fluorodeoxyglucose PET scan (17% vs 75%, P < .001).

INTERPRETATION

This study validates the specificity of a blood-based autoantibody biomarker for identifying malignancy in patients with indeterminate PNs. This rule-in biomarker may help to expedite workup of malignant nodules.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov; No.: NCT01752114; URL: www.clinicaltrials.gov CHEST Pulmonary 2025; 3(1):100130.

Biomarkers in lung cancer diagnosis and bronchoscopy: Current landscape and future directions

Lung cancer is the leading cause of cancer death world-wide. Along the entire timeline of lung cancer identification, diagnosis and treatment, clinicians and patients face challenges in clinical decision-making that could be aided by useful biomarkers. In this review, we discuss the development of biomarkers and qualities that are ideal in a biomarker candidate, types of biospecimens that can be utilized for biomarker development in lung cancer, and how biomarkers could be clinically useful at various points along lung cancer timeline. We then review biomarkers that have been validated and are clinically available to assist with the management of lung nodules and diagnosis of lung cancer, which includes blood-based biomarkers to assist with decision-making prior to an invasive diagnostic procedure, as well as specimens obtained during a bronchoscopy and applied in cases of an inconclusive biopsy result. Finally, we discuss challenges in biomarker application and recent publications relevant to future lung cancer biomarker development.

Assessing the 9G Technology Blood Test for Predicting Lung Cancer in Patients with CT-Detected Lung Nodules: A Multicenter Clinical Trial

Background and Objectives: Lung nodules detected by chest computed tomography (CT) often require invasive biopsies for definitive diagnosis, leading to unnecessary procedures for benign lesions. A blood-based biomarker test that predicts lung cancer risk in CT-detected nodules could help stratify patients and direct invasive diagnostics toward high-risk individuals. Methods: In this multicenter, single-blinded clinical trial, we evaluated a test measuring plasma levels of p53, anti-p53 autoantibodies, CYFRA 21-1, and anti-CYFRA 21-1 autoantibodies in patients with CT-detected lung nodules. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated, and subgroup analyses by gender, age, and smoking status were performed. A total of 1132 patients who had CT-detected lung nodules, including 885 lung cancer cases and 247 benign lesions, were enrolled from two academic hospitals in South Korea. Results: The test demonstrated a sensitivity of 78.4% (95% CI: 75.7-81.1) and specificity of 93.1% (95% CI: 90.0-96.3) in predicting lung cancer in CT-detected nodules. The PPV was 97.6%, and the NPV was 54.6%. Performance was consistent across gender (sensitivity 79.3% in men and 76.8% in women) and age groups, with a specificity of 93.4% in men and 92.7% in women. Stage I lung cancer was detected with a sensitivity of 80.6%. Conclusions: The Lung Cancer test based on 9G technology presented here offers a non-invasive method for stratifying lung cancer risk in patients with CT-detected nodules. Its integration into clinical practice could reduce unnecessary interventions and foster earlier detection.

Pulmonary nodules and the psychological harm they can cause: A scoping review

More than 1.6 million pulmonary nodules are diagnosed in the United States each year. Although the majority of nodules are found to be benign, nodule detection and the process of ruling out malignancy can cause patients psychological harm to varying degrees. The present study undertakes a scoping review of the literature investigating pulmonary nodule-related psychological harm as a primary or secondary outcome. Online databases were systematically searched to identify papers published through June 30, 2023, from which 19 publications were reviewed. We examined prevalence by type, measurement, associated factors, and behavioral or clinical consequences. Of the 19 studies reviewed, 11 studies investigated distress, anxiety (n = 6), and anxiety and depression (n = 4). Prevalence of distress was 24.0 %-56.7 %; anxiety 9.9 %-42.1 %, and 14.6 %-27.0 % for depression. A wide range of demographic and social characteristics and clinical factors were associated with nodule-related psychological harm. Outcomes of nodule-related harms included experiencing conflict when deciding about treatment or surveillance, decreased adherence to surveillance, adoption of more aggressive treatment, and lower health-related quality of life. Our scoping review demonstrates that nodule-related psychological harm is common. Findings provide evidence that nodule-related psychological harm can influence clinical decisions and adherence to treatment recommendations. Future research should focus on discerning between nodule-related distress and anxiety; identifying patients at risk; ascertaining the extent of psychological harm on patient behavior and clinical decisions; and developing interventions to assist patients in managing psychological harm for better health-related quality of life and treatment outcomes.

Protein Biomarkers in Lung Cancer Screening: Technical Considerations and Feasibility Assessment

Lung cancer remains the leading cause of cancer-related deaths worldwide, mainly due to late diagnosis and the presence of metastases. Several countries around the world have adopted nation-wide LDCT-based lung cancer screening that will benefit patients, shifting the stage at diagnosis to earlier stages with more therapeutic options. Biomarkers can help to optimize the screening process, as well as refine the TNM stratification of lung cancer patients, providing information regarding prognostics and recommending management strategies. Moreover, novel adjuvant strategies will clearly benefit from previous knowledge of the potential aggressiveness and biological traits of a given early-stage surgically resected tumor. This review focuses on proteins as promising biomarkers in the context of lung cancer screening. Despite great efforts, there are still no successful examples of biomarkers in lung cancer that have reached the clinics to be used in early detection and early management. Thus, the field of biomarkers in early lung cancer remains an evident unmet need. A more specific objective of this review is to present an up-to-date technical assessment of the potential use of protein biomarkers in early lung cancer detection and management. We provide an overview regarding the benefits, challenges, pitfalls and constraints in the development process of protein-based biomarkers. Additionally, we examine how a number of emerging protein analytical technologies may contribute to the optimization of novel robust biomarkers for screening and effective management of lung cancer.

Clinical utility of an artificial intelligence radiomics-based tool for risk stratification of pulmonary nodules

BACKGROUND

Clinical utility data on pulmonary nodule (PN) risk stratification biomarkers are lacking. We aimed to determine the incremental predictive value and clinical utility of using an artificial intelligence (AI) radiomics-based computer-aided diagnosis (CAD) tool in addition to routine clinical information to risk stratify PNs among real-world patients.

METHODS

We performed a retrospective cohort study of patients with PNs who underwent lung biopsy. We collected clinical data and used a commercially available AI radiomics-based CAD tool to calculate a Lung Cancer Prediction (LCP) score. We developed logistic regression models to evaluate a well-validated clinical risk prediction model (the Mayo Clinic model) with and without the LCP score (Mayo vs Mayo + LCP) using area under the curve (AUC), risk stratification table, and standardized net benefit analyses.

RESULTS

Among the 134 patients undergoing PN biopsy, cancer prevalence was 61%. Addition of the radiomics-based LCP score to the Mayo model was associated with increased predictive accuracy (likelihood ratio test, P = .012). The AUCs for the Mayo and Mayo + LCP models were 0.58 (95% CI = 0.48 to 0.69) and 0.65 (95% CI = 0.56 to 0.75), respectively. At the 65% risk threshold, the Mayo + LCP model was associated with increased sensitivity (56% vs 38%; P = .019), similar false positive rate (33% vs 35%; P = .8), and increased standardized net benefit (18% vs -3.3%) compared with the Mayo model.

CONCLUSIONS

Use of a commercially available AI radiomics-based CAD tool as a supplement to clinical information improved PN cancer risk prediction and may result in clinically meaningful changes in risk stratification.

Premalignant Progression in the Lung: Knowledge Gaps and Novel Opportunities for Interception of Non-Small Cell Lung Cancer. An Official American Thoracic Society Research Statement

Rationale: Despite significant advances in precision treatments and immunotherapy, lung cancer is the most common cause of cancer death worldwide. To reduce incidence and improve survival rates, a deeper understanding of lung premalignancy and the multistep process of tumorigenesis is essential, allowing timely and effective intervention before cancer development. Objectives: To summarize existing information, identify knowledge gaps, formulate research questions, prioritize potential research topics, and propose strategies for future investigations into the premalignant progression in the lung. Methods: An international multidisciplinary team of basic, translational, and clinical scientists reviewed available data to develop and refine research questions pertaining to the transformation of premalignant lung lesions to advanced lung cancer. Results: This research statement identifies significant gaps in knowledge and proposes potential research questions aimed at expanding our understanding of the mechanisms underlying the progression of premalignant lung lesions to lung cancer in an effort to explore potential innovative modalities to intercept lung cancer at its nascent stages. Conclusions: The identified gaps in knowledge about the biological mechanisms of premalignant progression in the lung, together with ongoing challenges in screening, detection, and early intervention, highlight the critical need to prioritize research in this domain. Such focused investigations are essential to devise effective preventive strategies that may ultimately decrease lung cancer incidence and improve patient outcomes.

Innovations in Early Lung Cancer Detection: Tracing the Evolution and Advancements in Screening

Lung cancer mortality rates, particularly non-small cell lung cancer (NSCLC), continue to present a significant global health challenge, and the adoption of lung cancer screening remains limited, often influenced by inequities in access to healthcare. Despite clinical evidence demonstrating the efficacy of annual screening with low-dose computed tomography (LDCT) and recommendations from medical organizations including the U.S. Preventive Services Task Force (USPSTF), the national lung cancer screening uptake remains around 5% among eligible individuals. Advancements in the clinical management of NSCLC have recently become more personalized with the implementation of blood-based biomarker testing. Extensive research into tumor-derived cell-free DNA (cfDNA) through fragmentation offers a novel method for improving early lung cancer detection. This review assesses the screening landscape, explores obstacles to lung cancer screening, and discusses how a plasma whole genome fragmentome test (pWGFrag-Lung) can improve lung cancer screening participation and adherence.

A Nasal Swab Classifier to Evaluate the Probability of Lung Cancer in Patients With Pulmonary Nodules

BACKGROUND

Accurate assessment of the probability of lung cancer (pCA) is critical in patients with pulmonary nodules (PNs) to help guide decision-making. We sought to validate a clinical-genomic classifier developed using whole-transcriptome sequencing of nasal epithelial cells from patients with a PN ≤ 30 mm who smoke or have previously smoked.

RESEARCH QUESTION

Can the pCA in individuals with a PN and a history of smoking be predicted by a classifier that uses clinical factors and genomic data from nasal epithelial cells obtained by cytologic brushing?

STUDY DESIGN AND METHODS

Machine learning was used to train a classifier using genomic and clinical features on 1,120 patients with PNs labeled as benign or malignant established by a final diagnosis or a minimum of 12 months of radiographic surveillance. The classifier was designed to yield low-, intermediate-, and high-risk categories. The classifier was validated in an independent set of 312 patients, including 63 patients with a prior history of cancer (other than lung cancer), comparing the classifier prediction with the known clinical outcome.

RESULTS

In the primary validation set, sensitivity and specificity for low-risk classification were 96% and 42%, whereas sensitivity and specificity for high-risk classification was 58% and 90%, respectively. Sensitivity was similar across stages of non-small cell lung cancer, independent of subtype. Performance compared favorably with clinical-only risk models. Analysis of 63 patients with prior cancer showed similar performance as did subanalyses of patients with light vs heavy smoking burden and those eligible for lung cancer screening vs those who were not.

INTERPRETATION

The nasal classifier provides an accurate assessment of pCA in individuals with a PN ≤ 30 mm who smoke or have previously smoked. Classifier-guided decision-making could lead to fewer diagnostic procedures in patients without cancer and more timely treatment in patients with lung cancer.

Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis

Lung cancer (LC) causes few symptoms in the earliest stages, leading to one of the highest mortality rates among cancers. Low-dose computerised tomography (LDCT) is used to screen high-risk individuals, reducing the mortality rate by 20%. However, LDCT results in a high number of false positives and is associated with unnecessary follow-up and cost. Biomarkers with high sensitivities and specificities could assist in the early detection of LC, especially in patients with high-risk features. Carcinoembryonic antigen (CEA), cytokeratin 19 fragments and cancer antigen 125 have been found to be highly expressed during the later stages of LC but have low sensitivity in the earliest stages. We determined the best biomarkers for the early diagnosis of LC, using a systematic review of eight databases. We identified 98 articles that focussed on the identification and assessment of diagnostic biomarkers and achieved a pooled area under curve of 0.85 (95% CI 0.82-0.088), indicating that the diagnostic performance of these biomarkers when combined was excellent. Of the studies, 30 focussed on single/antigen panels, 22 on autoantibodies, 31 on miRNA and RNA panels, and 15 suggested the use of circulating DNA combined with CEA or neuron-specific enolase (NSE) for early LC detection. Verification of blood biomarkers with high sensitivities (Ciz1, exoGCC2, ITGA2B), high specificities (CYFR21-1, antiHE4, OPNV) or both (HSP90α, CEA) along with miR-15b and miR-27b/miR-21 from sputum may improve early LC detection. Further assessment is needed using appropriate sample sizes, control groups that include patients with non-malignant conditions, and standardised cut-off levels for each biomarker.

Systems Biology in Cancer Diagnosis Integrating Omics Technologies and Artificial Intelligence to Support Physician Decision Making

Cancer is the second major cause of disease-related death worldwide, and its accurate early diagnosis and therapeutic intervention are fundamental for saving the patient's life. Cancer, as a complex and heterogeneous disorder, results from the disruption and alteration of a wide variety of biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites, that eventually emerge as clinical symptoms. Traditionally, diagnosis is based on clinical examination, blood tests for biomarkers, the histopathology of a biopsy, and imaging (MRI, CT, PET, and US). Additionally, omics biotechnologies help to further characterize the genome, metabolome, microbiome traits of the patient that could have an impact on the prognosis and patient's response to the therapy. The integration of all these data relies on gathering of several experts and may require considerable time, and, unfortunately, it is not without the risk of error in the interpretation and therefore in the decision. Systems biology algorithms exploit Artificial Intelligence (AI) combined with omics technologies to perform a rapid and accurate analysis and integration of patient's big data, and support the physician in making diagnosis and tailoring the most appropriate therapeutic intervention. However, AI is not free from possible diagnostic and prognostic errors in the interpretation of images or biochemical-clinical data. Here, we first describe the methods used by systems biology for combining AI with omics and then discuss the potential, challenges, limitations, and critical issues in using AI in cancer research.

Prevalence of oncogenic driver mutations in Hispanics/Latin patients with lung cancer. A systematic review and meta-analysis

INTRODUCTION

The frequency of actionable mutations varies between races, and Hispanic/Latino (H/L) people are a population with different proportions of ancestry. Our purpose was to establish prevalence of actionable mutations in the H/L population with NSCLC.

METHODS

EMBASE, LILACS, MEDLINE, and Virtual Health Library were searched for studies published up to April 2023 that evaluated the prevalence of ALK, BRAF, EGFR, HER-2, KRAS, MET, NTRK, RET, ROS1 in H/L patients. Meta-analyses were done to determine prevalence using a random effects model.

RESULTS

Fifty-five articles were included. EGFR and KRAS were the most prevalent genes with high heterogeneity across the countries. The overall mutation frequency for EGFR was 22%. The most frequent mutations in the EGFR gene were del19 (10%) and L858R (7%). The mean of KRAS mutation was a 14% prevalence. KRASG12C was the most frequent mutation with a 7% prevalence in an entire population. The overall frequency of ALK rearrangement was 5%. The mean frequency of ROS-1 rearrangement was 2%, and the frequencies of HER-2, MET, BRAF, RET, NTRK molecular alterations were 4%, 3%, 2%, 2%, and 1% respectively. Almost half of the cases were male, and 65.8% had a history of tobacco exposure. The most common clinical stage was IV.

CONCLUSIONS

The prevalence of driver mutations such as EGFR and KRAS in LA populations differs from what is reported in Asians and Europeans. In the present article, countries with a high proportion of Amerindian ancestry show a greater prevalence of EGFR in contrast to countries with a high proportion of Caucasians. Lack of information on some countries or studies with a small sample size affects the real prevalence data for the region.

Therapeutic effects of medicinal plants and their constituents on lung cancer, in vitro, in vivo and clinical evidence

The most common type of cancer in the world is lung cancer. Traditional treatments have an important role in cancer therapy. In the present review, the most recent findings on the effects of medicinal plants and their constituents or natural products (NP) in treating lung cancer are discussed. Empirical studies until the end of March 2022 were searched using the appropriate keywords through the databases PubMed, Science Direct and Scopus. The extracts and essential oils tested were all shown to effect lung cancer by several mechanisms including decreased tumour weight and volume, cell viability and modulation of cytokine. Some plant constituents increased expression of apoptotic proteins, the proportion of cells in the G2/M phase and subG0/G1 phase, and Cyt c levels. Also, natural products (NP) activate apoptotic pathways in lung cancer cell including p-JNK, Akt/mTOR, PI3/ AKT\ and Bax, Bcl2, but suppressed AXL phosphorylation. Plant-derived substances altered the cell morphology, reduced cell migration and metastasis, oxidative marker production, p-eIF2α and GRP78, IgG, IgM levels and reduced leukocyte counts, LDH, GGT, 5'NT and carcinoembryonic antigen (CEA). Therefore, medicinal plant extracts and their constituents could have promising therapeutic value for lung cancer, especially if used in combination with ordinary anti-cancer drugs.

Update on Biomarkers for the Stratification of Indeterminate Pulmonary Nodules

Lung cancer is the leading cause of cancer-related deaths. Early detection and diagnosis are critical, as survival decreases with advanced stages. Approximately 1.6 million nodules are incidentally detected every year on chest CT scan images in the United States. This number of nodules identified is likely much larger after accounting for screening-detected nodules. Most of these nodules, whether incidentally or screening detected, are benign. Despite this, many patients undergo unnecessary invasive procedures to rule out cancer because our current stratification approaches are suboptimal, particularly for intermediate probability nodules. Thus, noninvasive strategies are urgently needed. Biomarkers have been developed to assist through the continuum of lung cancer care and include blood protein-based biomarkers, liquid biopsies, quantitative imaging analysis (radiomics), exhaled volatile organic compounds, and bronchial or nasal epithelium genomic classifiers, among others. Although many biomarkers have been developed, few have been integrated into clinical practice as they lack clinical utility studies showing improved patient-centered outcomes. Rapid technologic advances and large network collaborative efforts will continue to drive the discovery and validation of many novel biomarkers. Ultimately, however, randomized clinical utility studies showing improved patient outcomes will be required to bring biomarkers into clinical practice.

Improving lung cancer diagnosis with cancer, fungal, and imaging biomarkers

OBJECTIVE

Indeterminate pulmonary nodules (IPNs) represent a significant diagnostic burden in health care. We aimed to compare a combination clinical prediction model (Mayo Clinic model), fungal (histoplasmosis serology), imaging (computed tomography [CT] radiomics), and cancer (high-sensitivity cytokeratin fraction 21; hsCYFRA 21-1) biomarker approach to a validated prediction model in diagnosing lung cancer.

METHODS

A prospective specimen collection, retrospective blinded evaluation study was performed in 3 independent cohorts with 6- to 30-mm IPNs (n = 281). Serum histoplasmosis immunoglobulin G and immunoglobulin M antibodies and hsCYFRA 21-1 levels were measured and a validated CT radiomic score was calculated. Multivariable logistic regression models were estimated with Mayo Clinic model variables, histoplasmosis antibody levels, CT radiomic score, and hsCYFRA 21-1. Diagnostic performance of the combination model was compared with that of the Mayo Clinic model. Bias-corrected clinical net reclassification index (cNRI) was used to estimate the clinical utility of a combination biomarker approach.

RESULTS

A total of 281 patients were included (111 from a histoplasmosis-endemic region). The combination biomarker model including the Mayo Clinic model score, histoplasmosis antibody levels, radiomics, and hsCYFRA 21-1 level showed improved diagnostic accuracy for IPNs compared with the Mayo Clinic model alone with an area under the receiver operating characteristics curve of 0.80 (95% CI, 0.76-0.84) versus 0.72 (95% CI, 0.66-0.78). Use of this combination model correctly reclassified intermediate risk IPNs into low- or high-risk category (cNRI benign = 0.11 and cNRI malignant = 0.16).

CONCLUSIONS

The addition of cancer, fungal, and imaging biomarkers improves the diagnostic accuracy for IPNs. Integrating a combination biomarker approach into the diagnostic algorithm of IPNs might decrease unnecessary invasive testing of benign nodules and reduce time to diagnosis for cancer.

Impact of an integrated classifier using biomarkers, clinical and imaging factors on clinical decisions making for lung nodules

BACKGROUND

An integrated classifier that utilizes plasma proteomic biomarker along with five clinical and imaging factors was previously shown to be potentially useful in lung nodule evaluation. This study evaluated the impact of the integrated proteomic classifier on management decisions in patients with a pretest probability of cancer (pCA) ≤50% in "real-world" clinical setting.

METHODS

Retrospective study examining patients with lung nodules who were evaluated using the integrated classifier as compared to standard clinical care during the same period, with at least 1-year follow-up.

RESULTS

A total of 995 patients were evaluated for lung nodules over 1 year following the implementation of the integrated classifier with 17.3% prevalence of lung cancer. 231 patients met the study eligibility criteria; 102 (44.2%) were tested with the integrated classifier, while 129 (55.8%) did not. The median number of chest imaging studies was 2 [interquartile range (IQR), 1-2] in the integrated classifier arm and 2 [IQR, 1-3] in the non-integrated classifier arm (P=0.09). The median outpatient clinic visit was 2.00 (IQR, 1.00-3.00) in the integrated classifier arm and 2.00 (IQR, 2.00-3.00) in the non-integrated classifier (P=0.004). Fewer invasive procedures were pursued in the integrated classifier arm as compared to non-integrated classifier respectively (26.5% vs. 79.1%, P<0.001). All patients in the integrated classifier arm with post-pCA (likely benign n=39) had designated benign diagnosis at 1-year follow-up.

CONCLUSIONS

In patients with lung nodules with a pCA ≤50%, use of the integrated classifier was associated with fewer invasive procedures and clinic visits without misclassifying patients with likely benign lung nodules results at 1-year follow-up.

Assessing a biomarker's ability to reduce invasive procedures in patients with benign lung nodules: Results from the ORACLE study

A blood-based integrated classifier (IC) has been clinically validated to improve accuracy in assessing probability of cancer risk (pCA) for pulmonary nodules (PN). This study evaluated the clinical utility of this biomarker for its ability to reduce invasive procedures in patients with pre-test pCA ≤ 50%. This was a propensity score matching (PSM) cohort study comparing patients in the ORACLE prospective, multicenter, observational registry to control patients treated with usual care. This study enrolled patients meeting the intended use criteria for IC testing: pCA ≤ 50%, age ≥40 years, nodule diameter 8-30 mm, and no history of lung cancer and/or active cancer (except for non-melanomatous skin cancer) within 5 years. The primary aim of this study was to evaluate invasive procedure use on benign PNs of registry patients as compared to control patients. A total of 280 IC tested, and 278 control patients met eligibility and analysis criteria and 197 were in each group after PSM (IC and control groups). Patients in the IC group were 74% less likely to undergo an invasive procedure as compared to the control group (absolute difference 14%, p <0.001) indicating that for every 7 patients tested, one unnecessary invasive procedure was avoided. Invasive procedure reduction corresponded to a reduction in risk classification, with 71 patients (36%) in the IC group classified as low risk (pCA < 5%). The proportion of IC group patients with malignant PNs sent to surveillance were not statistically different than the control group, 7.5% vs 3.5% for the IC vs. control groups, respectively (absolute difference 3.91%, p 0.075). The IC for patients with a newly discovered PN has demonstrated valuable clinical utility in a real-world setting. Use of this biomarker can change physicians' practice and reduce invasive procedures in patients with benign pulmonary nodules. Trial registration: Clinical trial registration: ClinicalTrials.gov NCT03766958.

The MAGE A1-A10 Expression associated with Histopathological Findings of Malignant or Non-Malignant Cells in Peripheral Lung Tumors

OBJECTIVE

The objective was to evaluate the expression of melanoma antigen (MAGE) A from A1 to 10 (A1-10) and the individual MAGE A family in the peripheral lung tumors and to analyze its association with histopathological findings.

METHODS

A cross-sectional study was conducted on 67 samples of peripheral lung tumor obtained by core biopsies from patients with clinical diagnoses such as lung and mediastinal tumors. The specimens were divided into two, one to perform histopathological diagnosis and the last for mRNA MAGE A examination. A Nested polymerase chain reaction (PCR) was performed using universal primer, MF10/MR10 and MF10/MR12. The collected data were analyzed by appropriate statistical techniques.

RESULT

The histopathological finding showed 41 (61.2 %) of specimens as malignant cells and 26 (38.8 %) of specimens as non-malignant cells. MAGE A1-10 was expressed at 47 (70.1 %) and MAGE A1-6 was expressed at 25 (37.3 %) of specimens. In a malignant cell, MAGE A1-10 and MAGE A1-6 were expressed at 33 (80.5 %) and 19 (46.3 %), respectively. In non-malignant cells, MAGE A1-10 and MAGE A1-6 were expressed at 14 (53.9 %) and 6 (23.1 %,) respectively. The MAGE A1-10 and MAGE A8 expressions were significantly associated with histopathological findings of malignant or non-malignant cells. The sensitivity, specificity, and diagnostic accuracy of MAGE A1-10 were 80.5 %, 46.2 %, and 67.2 %, respectively; while for MAGE A8 were 41.5 %, 88.5 %, and 59.7 %, respectively.

CONCLUSION

The MAGE A1-10 expression was the most commonly detected and associated with the histopathological finding. Moreover, it was more sensitive and specific and had higher diagnostic accuracy than others. Therefore, the MAGE A1-10 assay may improve the accuracy of the diagnosis of malignancy in peripheral lung tumors.

Early detection and stratification of lung cancer aided by a cost-effective assay targeting circulating tumor DNA (ctDNA) methylation

BACKGROUND

Detection of lung cancer at earlier stage can greatly improve patient survival. We aim to develop, validate, and implement a cost-effective ctDNA-methylation-based plasma test to aid lung cancer early detection.

METHODS

Case-control studies were designed to select the most relevant markers to lung cancer. Patients with lung cancer or benign lung disease and healthy individuals were recruited from different clinical centers. A multi-locus qPCR assay, LunaCAM, was developed for lung cancer alertness by ctDNA methylation. Two LunaCAM models were built for screening (-S) or diagnostic aid (-D) to favor sensitivity or specificity, respectively. The performance of the models was validated for different intended uses in clinics.

RESULTS

Profiling DNA methylation on 429 plasma samples including 209 lung cancer, 123 benign diseases and 97 healthy participants identified the top markers that detected lung cancer from benign diseases and healthy with an AUC of 0.85 and 0.95, respectively. The most effective methylation markers were verified individually in 40 tissues and 169 plasma samples to develop LunaCAM assay. Two models corresponding to different intended uses were trained with 513 plasma samples, and validated with an independent collection of 172 plasma samples. In validation, LunaCAM-S model achieved an AUC of 0.90 (95% CI: 0.88-0.94) between lung cancer and healthy individuals, whereas LunaCAM-D model stratified lung cancer from benign pulmonary diseases with an AUC of 0.81 (95% CI: 0.78-0.86). When implemented sequentially in the validation set, LunaCAM-S enables to identify 58 patients of lung cancer (90.6% sensitivity), followed by LunaCAM-D to remove 20 patients with no evidence of cancer (83.3% specificity). LunaCAM-D significantly outperformed the blood test of carcinoembryonic antigen (CEA), and the combined model can further improve the predictive power for lung cancer to an overall AUC of 0.86.

CONCLUSIONS

We developed two different models by ctDNA methylation assay to sensitively detect early-stage lung cancer or specifically classify lung benign diseases. Implemented at different clinical settings, LunaCAM models has a potential to provide a facile and inexpensive avenue for early screening and diagnostic aids for lung cancer.

Multi-omics integrated circulating cell-free DNA genomic signatures enhanced the diagnostic performance of early-stage lung cancer and postoperative minimal residual disease

BACKGROUND

Liquid biopsy is a promising non-invasive alternative for cancer screening and minimal residual disease (MRD) detection, although there are some concerns regarding its clinical applications. We aimed to develop an accurate detection platform based on liquid biopsy for both cancer screening and MRD detection in patients with lung cancer (LC), which is also applicable to clinical use.

METHODS

We applied a modified whole-genome sequencing (WGS) -based High-performance Infrastructure For MultIomics (HIFI) method for LC screening and postoperative MRD detection by combining the hyper-co-methylated read approach and the circulating single-molecule amplification and resequencing technology (cSMART2.0).

FINDINGS

For early screening of LC, the LC score model was constructed using the support vector machine, which showed sensitivity (51.8%) at high specificity (96.3%) and achieved an AUC of 0.912 in the validation set prospectively enrolled from multiple centers. The screening model achieved detection efficiency with an AUC of 0.906 in patients with lung adenocarcinoma and outperformed other clinical models in solid nodule cohort. When applied the HIFI model to real social population, a negative predictive value (NPV) of 99.92% was achieved in Chinese population. Additionally, the MRD detection rate improved significantly by combining results from WGS and cSMART2.0, with sensitivity of 73.7% at specificity of 97.3%.

INTERPRETATION

In conclusion, the HIFI method is promising for diagnosis and postoperative monitoring of LC.

FUNDING

This study was supported by CAMS Innovation Fund for Medical Sciences, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, Beijing Natural Science Foundation and Peking University People's Hospital.

Initial development and testing of an exhaled microRNA detection strategy for lung cancer case-control discrimination

For detecting field carcinogenesis non-invasively, early technical development and case-control testing of exhaled breath condensate microRNAs was performed. In design, human lung tissue microRNA-seq discovery was reconciled with TCGA and published tumor-discriminant microRNAs, yielding a panel of 24 upregulated microRNAs. The airway origin of exhaled microRNAs was topographically "fingerprinted", using paired EBC, upper and lower airway donor sample sets. A clinic-based case-control study (166 NSCLC cases, 185 controls) was interrogated with the microRNA panel by qualitative RT-PCR. Data were analyzed by logistic regression (LR), and by random-forest (RF) models. Feasibility testing of exhaled microRNA detection, including optimized whole EBC extraction, and RT and qualitative PCR method evaluation, was performed. For sensitivity in this low template setting, intercalating dye-based URT-PCR was superior to fluorescent probe-based PCR (TaqMan). In application, adjusted logistic regression models identified exhaled miR-21, 33b, 212 as overall case-control discriminant. RF analysis of combined clinical + microRNA models showed modest added discrimination capacity (1.1-2.5%) beyond clinical models alone: all subjects 1.1% (p = 8.7e-04)); former smokers 2.5% (p = 3.6e-05); early stage 1.2% (p = 9.0e-03), yielding combined ROC AUC ranging from 0.74 to 0.83. We conclude that exhaled microRNAs are qualitatively measureable, reflect in part lower airway signatures; and when further refined/quantitated, can potentially help to improve lung cancer risk assessment.

Development and Validation of a Risk Assessment Model for Pulmonary Nodules Using Plasma Proteins and Clinical Factors

BACKGROUND

Deficiencies in risk assessment for patients with pulmonary nodules (PNs) contribute to unnecessary invasive testing and delays in diagnosis.

RESEARCH QUESTION

What is the accuracy of a novel PN risk model that includes plasma proteins and clinical factors? How does the accuracy compare with that of an established risk model?

STUDY DESIGN AND METHODS

Based on technology using magnetic nanosensors, assays were developed with seven plasma proteins. In a training cohort (n = 429), machine learning approaches were used to identify an optimal algorithm that subsequently was evaluated in a validation cohort (n = 489), and its performance was compared with the Mayo Clinic model.

RESULTS

In the training set, we identified a support vector machine algorithm that included the seven plasma proteins and six clinical factors that demonstrated an area under the receiver operating characteristic curve of 0.87 and met other selection criteria. The resulting risk reclassification model (RRM) was used to recategorize patients with a pretest risk of between 10% and 84%, and its performance was assessed across five risk strata (low, ≤ 10%; moderate, 10%-34%; intermediate, 35%-70%; high, 71%-84%; very high, > 85%). Stratification by the RRM decreased the proportion of intermediate-risk patients from 26.7% to 10.8% (P < .001) and increased the low-risk and high-risk strata from 16.8% to 21.9% (P < .001) and from 3.7% to 12.1% (P < .001), respectively. Among patients classified as low risk by the RRM and Mayo Clinic model, the corresponding true-negative to false-negative ratios were 16.8 and 19.5, respectively. Among patients classified as very high risk by the RRM and Mayo Clinic model, the corresponding true-positive to false-positive ratios were 28.5 and 17.0, respectively. Compared with the Mayo Clinic model, the RRM provided higher specificity at the low-risk threshold and higher sensitivity at the very high-risk threshold.

INTERPRETATION

The RRM accurately reclassified some patients into low-risk and very high-risk categories, suggesting the potential to improve PN risk assessment.

Biomarkers in Lung Cancer Screening: a Narrative Review

Although when used as a lung cancer screening tool low-dose computed tomography (LDCT) has demonstrated a significant reduction in lung cancer related mortality, it is not without pitfalls. The associated high false positive rate, inability to distinguish between benign and malignant nodules, cumulative radiation exposure, and resulting patient anxiety have all demonstrated the need for adjunctive testing in lung cancer screening. Current research focuses on developing liquid biomarkers to complement imaging as non-invasive lung cancer diagnostics. Biomarkers can be useful for both the early detection and diagnosis of disease, thereby decreasing the number of unnecessary radiologic tests performed. Biomarkers can stratify cancer risk to further enrich the screening population and augment existing risk prediction. Finally, biomarkers can be used to distinguish benign from malignant nodules in lung cancer screening. While many biomarkers require further validation studies, several, including autoantibodies and blood protein profiling, are available for clinical use. This paper describes the need for biomarkers as a lung cancer screening tool, both in terms of diagnosis and risk assessment. Additionally, this paper will discuss the goals of biomarker use, describe properties of a good biomarker, and review several of the most promising biomarkers currently being studied including autoantibodies, complement fragments, microRNA, blood proteins, circulating tumor DNA, and DNA methylation. Finally, we will describe future directions in the field of biomarker development.

Immunogenomic Biomarkers and Validation in Lynch Syndrome

Lynch syndrome (LS) is an inherited disorder in which affected individuals have a significantly higher-than-average risk of developing colorectal and non-colorectal cancers, often before the age of 50 years. In LS, mutations in DNA repair genes lead to a dysfunctional post-replication repair system. As a result, the unrepaired errors in coding regions of the genome produce novel proteins, called neoantigens. Neoantigens are recognised by the immune system as foreign and trigger an immune response. Due to the invasive nature of cancer screening tests, universal cancer screening guidelines unique for LS (primarily colonoscopy) are poorly adhered to by LS variant heterozygotes (LSVH). Currently, it is unclear whether immunogenomic components produced as a result of neoantigen formation can be used as novel biomarkers in LS. We hypothesise that: (i) LSVH produce measurable and dynamic immunogenomic components in blood, and (ii) these quantifiable immunogenomic components correlate with cancer onset and stage. Here, we discuss the feasibility to: (a) identify personalised novel immunogenomic biomarkers and (b) validate these biomarkers in various clinical scenarios in LSVH.

SCLC: Epidemiology, Risk Factors, Genetic Susceptibility, Molecular Pathology, Screening, and Early Detection

We review research regarding the epidemiology, risk factors, genetic susceptibility, molecular pathology, and early detection of SCLC, a deadly tumor that accounts for 14% of lung cancers. We first summarize the changing incidences of SCLC globally and in the United States among males and females. We then review the established risk factor (i.e., tobacco smoking) and suspected nonsmoking-related risk factors for SCLC, and emphasize the importance of continued effort in tobacco control worldwide. Review of genetic susceptibility and molecular pathology suggests different molecular pathways in SCLC development compared with other types of lung cancer. Last, we comment on the limited utility of low-dose computed tomography screening in SCLC and on several promising blood-based molecular biomarkers as potential tools in SCLC early detection.

Detection of early-stage lung cancer in sputum using automated flow cytometry and machine learning

BACKGROUND

Low-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.

METHODS

Single 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.

RESULTS

Automated 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.

CONCLUSION

CyPath 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.

Comparing modalities for risk assessment in patients with pulmonary lesions and nondiagnostic bronchoscopy for suspected lung cancer

BACKGROUND

Bronchoscopy is commonly utilized for non-surgical sampling of indeterminant pulmonary lesions, but nondiagnostic procedures are common. Accurate assessment of the risk of malignancy is essential for decision making in these patients, yet we lack tools that perform well across this heterogeneous group of patients. We sought to evaluate the accuracy of three previously validated risk models and physician-assessed risk (PAR) in patients with a newly identified lung lesion undergoing bronchoscopy for suspected lung cancer where the result is nondiagnostic.

METHODS

We performed an analysis of prospective data collected for the Percepta Bronchial Genomic Classifier Multicenter Registry. PAR and three previously validated risk models (Mayo Clinic, Veteran's Affairs, and Brock) were used to determine the probability of lung cancer (low, intermediate, or high) in 375 patients with pulmonary lesions who underwent bronchoscopy for possible lung cancer with nondiagnostic pathology. Results were compared to the actual adjudicated prevalence of malignancy in each pre-test risk group, determined with a minimum of 12 months follow up after bronchoscopy.

RESULTS

PAR and the risk models performed poorly overall in the assessment of risk in this patient population. PAR most closely matched the observed prevalence of malignancy in patients at 12 months after bronchoscopy, but all modalities had a low area under the curve, and in all clinical models more than half of all the lesions labeled as high risk were truly or likely benign. The studied risk model calculators overestimate the risk of malignancy compared to PAR, particularly in the subset in older patients, irregularly bordered nodules, and masses > 3 cm. Overall, the risk models perform only slightly better when confined to lung nodules < 3 cm in this population.

CONCLUSION

The currently available tools for the assessment of risk of malignancy perform suboptimally in patients with nondiagnostic findings following a bronchoscopic evaluation for lung cancer. More accurate and objective tools for risk assessment are needed.

TRIAL REGISTRATION

not applicable.

Assay Performance of a Label-Free, Solution-Phase CYFRA 21-1 Determination

CYFRA 21.1, a cytokeratin fragment of epithelial origin, has long been a valuable blood-based biomarker. As with most biomarkers, the clinical diagnostic value of CYFRA 21.1 is dependent on the quantitative performance of the assay. Looking toward translation, it is shown here that a free-solution assay (FSA) coupled with a compensated interferometric reader (CIR) can be used to provide excellent analytical performance in quantifying CYFRA 21.1 in patient serum samples. This report focuses on the analytical performance of the high-sensitivity (hs)-CYFRA 21.1 assay in the context of quantifying the biomarker in two indeterminate pulmonary nodule (IPN) patient cohorts totaling 179 patients. Each of the ten assay calibrations consisted of 6 concentrations, each run as 7 replicates (e.g., 10 × 6 × 7 data points) and were performed on two different instruments by two different operators. Coefficients of variation (CVs) for the hs-CYFRA 21.1 analytical figures of merit, limit of quantification (LOQ) of ca. 60 pg/mL, B _max, initial slope, probe-target binding affinity, and reproducibility of quantifying an unknown were found to range from 2.5 to 8.3%. Our results demonstrate the excellent performance of our FSA-CIR hs-CYFRA 21-1 assay and a proof of concept for potentially redefining the performance characteristics of this existing important candidate biomarker.

The additional value of lung cancer screening program in identifying unrecognized diseases

Background

A systematic examination of low-dose CT (LDCT) scan, beside lung nodules, may disclose the presence of undiagnosed diseases, improving the efficacy and the cost/efficacy of these programs. The study was aimed at evaluating the association between LDCT scan findings and non-oncologic and oncologic diseases.

Methods

The LDCT scan of participants to the “Un Respiro per la vita”^® lung cancer screening program were checked and abnormal findings, beside lung nodules, recorded. First admission to the acute care because of cardiovascular (CD), respiratory (RD) and oncological diseases (OD) in the following three years were retrieved. The association of LDCT scan abnormal findings with CD, RD and OD was assessed through univariable and multivariable logistic regression models.

Results

Mean age of 746 participants was 62 years (SD:5), 62% were male. 11 (1.5%) received a diagnosis of lung cancer. 16.1% participants were admitted to the acute care in the following three years: 8.6% for CD, 4.3% for RD and 5.2% for OD. Valve calcification (OR 2.02, p:0.02) and mucus plugs (OR 3.37, p:0.04) were positively associated with CD, while sub-pleural fibrosis had a protective role (OR 0.47, p:0.01). Lung nodules > 8 mm (OR 5.54, p: < 0.01), tracheal deviation (OR 6.04, p:0.01) and mucus plugs (OR 4.00, p:0.04) were positively associated with OD admissions. Centrilobular emphysema OR for RD admissions was 1.97 (p:0.06).

Conclusions

The observed association between selected LDCT findings and ensuing CD, RD and OD suggests that the information potential of LCDT goes beyond the screening of lung cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-01826-1.

A Cost-Effective and Non-Invasive pfeRNA-Based Test Differentiates Benign and Suspicious Pulmonary Nodules from Malignant Ones

The ability to differentiate between benign, suspicious, and malignant pulmonary nodules is imperative for definitive intervention in patients with early stage lung cancers. Here, we report that plasma protein functional effector sncRNAs (pfeRNAs) serve as non-invasive biomarkers for determining both the existence and the nature of pulmonary nodules in a three-stage study that included the healthy group, patients with benign pulmonary nodules, patients with suspicious nodules, and patients with malignant nodules. Following the standards required for a clinical laboratory improvement amendments (CLIA)-compliant laboratory-developed test (LDT), we identified a pfeRNA classifier containing 8 pfeRNAs in 108 biospecimens from 60 patients by sncRNA deep sequencing, deduced prediction rules using a separate training cohort of 198 plasma specimens, and then applied the prediction rules to another 230 plasma specimens in an independent validation cohort. The pfeRNA classifier could (1) differentiate patients with or without pulmonary nodules with an average sensitivity and specificity of 96.2% and 97.35% and (2) differentiate malignant versus benign pulmonary nodules with an average sensitivity and specificity of 77.1% and 74.25%. Our biomarkers are cost-effective, non-invasive, sensitive, and specific, and the qPCR-based method provides the possibility for automatic testing of robotic applications.

Tumor-associated autoantibodies in ESCC screening: Detecting prevalent early-stage malignancy or predicting future cancer risk?

BACKGROUND

To assess potential roles for tumor-associated autoantibodies (TAAs) in esophageal squamous cell carcinoma (ESCC) screening: detecting early-stage malignancy, and predicting future cancer risk.

METHOD

Thirteen candidate autoantibodies identified in previous literatures were measured using multiplex serological assays in sera from cases and matched controls nested in two population-level screening cohorts in China. To evaluate the role of TAAs in detecting prevalent esophageal malignant lesions, an identification set (150 cases vs. 560 controls) and an external validation set (34 cases vs. 121 controls) were established with pre-screening sera collected ≤ 12 months prior to screening-related diagnosis. To explore the role of TAAs in predicting future ESCC risk, an exploration set (105 cases vs. 416 controls) with pre-diagnostic sera collected > 12 months before clinical diagnosis was established. Two models, the questionnaire-based model and full model additionally incorporating TAA markers, were constructed. Area under the receiver operating characteristic curve (AUC) and net reclassification improvement (NRI) were calculated to compare the performance of the two models.

FINDINGS

In the identification set, NY-ESO-1 (OR=2·12, 95% CI=1·02-4·40) and STIP1 (OR=1·83, 95% CI=1·10-3·05) were positively associated with higher risk of esophageal malignancy. Elevated MMP-7 was associated with higher risk of malignancy in females (ORfemale=5·07, 95% CI=1·30-19·71). The estimates in validation set were consistent with these results, but were close to null in exploration set. Integration of selected TAAs improved the performance of questionnaire-based models in detecting prevalent esophageal malignancy (female: AUCfull model=0·745, 95% CI=0·675-0·814, AUCquestionnaire-based model=0·658, 95% CI=0·585-0·732, NRI=0·604, P<0·0001; male: AUCfull model=0·662, 95% CI=0·596-0·728, AUCquestionnaire-based model=0·619, 95% CI=0·548-0·690, NRI=0·357, P=0·0028). This improvement was also seen in validation set, but was not similarly effective in distinguishing long-term incident cases from healthy controls.

INTERPRETATION

Serological autoantibodies against NY-ESO-1, STIP1, and MMP-7 perform well in detecting early-stage esophageal malignancy, but are less effective in predicting future ESCC risks.

FUNDING

This work was supported by the National Science & Technology Fundamental Resources Investigation Program of China (2019FY101102), the National Natural Science Foundation of China (82073626), the National Key R&D Program of China (2016YFC0901404), the Beijing-Tianjin-Hebei Basic Research Cooperation Project (J200016), the Digestive Medical Coordinated Development Center of Beijing Hospitals Authority (XXZ0204), and the Natural Science Foundation of Beijing Municipality (7182033).

ATS Core Curriculum 2021. Adult Pulmonary Medicine: Thoracic Oncology

The American Thoracic Society Core Curriculum updates clinicians annually in adult and pediatric pulmonary disease, medical critical care, and sleep medicine at the annual international conference. The 2021 Pulmonary Core Curriculum focuses on lung cancer and include risks and prevention, screening, nodules, therapeutics and associated pulmonary toxicities, and malignant pleural effusions. Although tobacco smoking remains the primary risk factor for developing lung cancer, exposure to other environmental and occupational substances, including asbestos, radon, and burned biomass, contribute to the global burden of disease. Randomized studies have demonstrated that routine screening of high-risk smokers with low-dose chest computed tomography results in detection at an earlier stage and reduction in lung cancer mortality. On the basis of these trials and other lung cancer risk tools, screening recommendations have been developed. When evaluating lung nodules, clinical and radiographic features are used to estimate the probability of cancer. Management guidelines take into account the nodule size and cancer risk estimates to provide recommendations at evaluation. Newer lung cancer therapies, including immune checkpoint inhibitors and molecular therapies, cause pulmonary toxicity more frequently than conventional chemotherapy. Treatment-related toxicity should be suspected in patients receiving these medications who present with respiratory symptoms. Evaluation is aimed at excluding other etiologies, and treatment is based on the severity of symptoms. Malignant pleural effusions can be debilitating. The diagnosis is made by using simple pleural drainage and/or pleural biopsies. Management depends on the clinical scenario and the patient's preferences and includes the use of serial thoracentesis, a tunneled pleural catheter, or pleurodesis.

A simple assessment of lung nodule location for reduction in unnecessary invasive procedures

Background

CT screening for lung cancer results in a significant mortality reduction but is complicated by invasive procedures performed for evaluation of the many detected benign nodules. The purpose of this study was to evaluate measures of nodule location within the lung as predictors of malignancy.

Methods

We analyzed images and data from 3,483 participants in the National Lung Screening Trial (NLST). All nodules (4–20 mm) were characterized by 3D geospatial location using a Cartesian coordinate system and evaluated in logistic regression analysis. Model development and probability cutpoint selection was performed in the NLST testing set. The Geospatial test was then validated in the NLST testing set, and subsequently replicated in a new cohort of 147 participants from The Detection of Early Lung Cancer Among Military Personnel (DECAMP) Consortium.

Results

The Geospatial Test, consisting of the superior-inferior distance (Z distance), nodule diameter, and radial distance (carina to nodule) performed well in both the NLST validation set (AUC 0.85) and the DECAMP replication cohort (AUC 0.75). A negative Geospatial Test resulted in a less than 2% risk of cancer across all nodule diameters. The Geospatial Test correctly reclassified 19.7% of indeterminate nodules with a diameter over 6mm as benign, while only incorrectly classifying 1% of cancerous nodules as benign. In contrast, the parsimonious Brock Model applied to the same group of nodules correctly reclassified 64.5% of indeterminate nodules as benign but resulted in misclassification of a cancer as benign in 18.2% of the cases. Applying the Geospatial test would result in reducing invasive procedures performed for benign lesions by 11.3% with a low rate of misclassification (1.3%). In contrast, the Brock model applied to the same group of patients results in decreasing invasive procedures for benign lesion by 39.0% but misclassifying 21.1% of cancers as benign.

Conclusions

Utilizing information about geospatial location within the lung improves risk assessment for indeterminate lung nodules and may reduce unnecessary procedures.

Trial Registration

NCT00047385, NCT01785342.

Detection and characterization of lung cancer using cell-free DNA fragmentomes

Non-invasive approaches for cell-free DNA (cfDNA) assessment provide an opportunity for cancer detection and intervention. Here, we use a machine learning model for detecting tumor-derived cfDNA through genome-wide analyses of cfDNA fragmentation in a prospective study of 365 individuals at risk for lung cancer. We validate the cancer detection model using an independent cohort of 385 non-cancer individuals and 46 lung cancer patients. Combining fragmentation features, clinical risk factors, and CEA levels, followed by CT imaging, detected 94% of patients with cancer across stages and subtypes, including 91% of stage I/II and 96% of stage III/IV, at 80% specificity. Genome-wide fragmentation profiles across ~13,000 ASCL1 transcription factor binding sites distinguished individuals with small cell lung cancer from those with non-small cell lung cancer with high accuracy (AUC = 0.98). A higher fragmentation score represented an independent prognostic indicator of survival. This approach provides a facile avenue for non-invasive detection of lung cancer.

Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning

The low abundance of circulating tumour DNA (ctDNA) in plasma samples makes the analysis of ctDNA biomarkers for the detection or monitoring of early-stage cancers challenging. Here we show that deep methylation sequencing aided by a machine-learning classifier of methylation patterns enables the detection of tumour-derived signals at dilution factors as low as 1 in 10,000. For a total of 308 patients with surgery-resectable lung cancer and 261 age- and sex-matched non-cancer control individuals recruited from two hospitals, the assay detected 52-81% of the patients at disease stages IA to III with a specificity of 96% (95% confidence interval (CI) 93-98%). In a subgroup of 115 individuals, the assay identified, at 100% specificity (95% CI 91-100%), nearly twice as many patients with cancer as those identified by ultradeep mutation sequencing analysis. The low amounts of ctDNA permitted by machine-learning-aided deep methylation sequencing could provide advantages in cancer screening and the assessment of treatment efficacy.

[Cancer Screening Program in Urban Kunming of Yunnan: Evaluation of Lung Cancer Risk Assessment and Screening]

BACKGROUND

Lung cancer is the most common neoplasmas with a poor prognosis and a low 5-year survival rate. Early screening is an important measure for the prevention and treatment of lung cancer. At present, different countries have issued corresponding lung cancer screening guidelines, but China still lacks guidelines based on Chinese population research. Therefore, the National Cancer Center launched a Multi-center Cancer Screening Program in Urban China. This study analyzed the evaluation of lung cancer risk assessment model and screening effect in urban China of Yunnan, so as to explore the evaluation model of high-risk lung cancer population suitable for China's national conditions and develop lung cancer screening guidelines for Chinese.

METHODS

A questionnaire survey and lung cancer risk assessment were conducted on 165,337 people in 36 street offices in 4 main urban areas of Kunming, Yunnan Province, using cluster sampling method from January 2015 to December 2019. People with high-risk of lung cancer conducted low-dose computed tomography (LDCT) screening of chest. What's more, all participants were followed up by active or passive follow-up.

RESULTS

There were 264 patients were diagnosed lung cancer by pathology, and the overall incidence of lung cancer was 0.16% (264/165,337). The high-risk group (0.31%, 116/37,914) was higher than the non-high-risk group (0.12%, 148/127,423), and the difference was statistically significant (P<0.001). The incidence of lung cancer in the high-risk group was higher than the non-high-risk group among the male, female, and lower 50-year-old or more than 50-year-old subgroups, with statistical differences (P<0.001), but there was no statistical difference in the group without LDCT screening (P=0.73). The sensitivity of the lung cancer high-risk population assessment model was 43.94% (116/264) and the specificity was 77.10% (127,275/165,073). The early diagnosis rate of the screening group was 72.97% (54/74), which was significantly higher than that of the non-screening group [28.48% (43/151)].

CONCLUSIONS

The lung cancer high-risk population assessment model of National Key Public Health Program: Cancer Screening Program in Urban China can detect high-risk populations and improve the early diagnosis rate of lung cancer effectively.

Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test

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.

Exhaled volatile organic compounds analysis in clinical pediatrics: a systematic review

BACKGROUND

Measured exhaled volatile organic compounds (VOCs) in breath also referred to as exhaled volatilome have been long claimed as a potential source of non-invasive and clinically applicable biomarkers. However, the feasibility of using exhaled volatilome in clinical practice remains to be demonstrated, particularly in pediatrics where the need for improved non-invasive diagnostic and monitoring methods is most urgent. This work presents the first formal evidence-based judgment of the clinical potential of breath volatilome in the pediatric population.

METHODS

A rigorous systematic review across Web of Science, SCOPUS, and PubMed databases following the PRISMA statement guidelines. A narrative synthesis of the evidence was conducted and QUADAS-2 was used to assess the quality of selected studies.

RESULTS

Two independent reviewers deemed 22 out of the 229 records initially found to satisfy inclusion criteria. A summary of breath VOCs found to be relevant for several respiratory, infectious, and metabolic pathologies was conducted. In addition, we assessed their associated metabolism coverage through a functional characterization analysis.

CONCLUSION

Our results indicate that current research remains stagnant in a preclinical exploratory setting. Designing exploratory experiments in compliance with metabolomics practice should drive forward the clinical translation of VOCs breath analysis.

IMPACT

What is the key message of your article? Metabolomics practice could help to achieve the clinical utility of exhaled volatilome analysis. What does it add to the existing literature? This work is the first systematic review focused on disease status discrimination using analysis of exhaled breath in the pediatric population. A summary of the reported exhaled volatile organic compounds is conducted together with a functional characterization analysis. What is the impact? Having noted challenges preventing the clinical translation, we summary metabolomics practices and the experimental designs that are closer to clinical practice to create a framework to guide future trials.

Serum Exosomes and Their miRNA Load-A Potential Biomarker of Lung Cancer

Early detection of lung cancer in screening programs is a rational way to reduce mortality associated with this malignancy. Low-dose computed tomography, a diagnostic tool used in lung cancer screening, generates a relatively large number of false-positive results, and its complementation with molecular biomarkers would greatly improve the effectiveness of such programs. Several biomarkers of lung cancer based on different components of blood, including miRNA signatures, were proposed. However, only a few of them have been positively validated in the context of early cancer detection yet, which imposes a constant need for new biomarker candidates. An emerging source of cancer biomarkers are exosomes and other types of extracellular vesicles circulating in body fluids. Hence, different molecular components of serum/plasma-derived exosomes were tested and showed different levels in lung cancer patients and healthy individuals. Several studies focused on the miRNA component of these vesicles. Proposed signatures of exosome miRNA had promising diagnostic value, though none of them have yet been clinically validated. These signatures involved a few dozen miRNA species overall, including a few species that recurred in different signatures. It is worth noting that all these miRNA species have cancer-related functions and have been associated with lung cancer progression. Moreover, a few of them, including known oncomirs miR-17, miR-19, miR-21, and miR-221, appeared in multiple miRNA signatures of lung cancer based on both the whole serum/plasma and serum/plasma-derived exosomes.