Cost-effectiveness of an autoantibody test (EarlyCDT-Lung) as an aid to early diagnosis of lung cancer in patients with incidentally detected pulmonary nodules

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.

Human Autoantigen Atlas: Searching for the Hallmarks of Autoantigens

Understanding autoimmunity to endogenous proteins is crucial in diagnosing and treating autoimmune diseases. In this work, we developed a user-friendly AAgAtlas portal (http://biokb.ncpsb.org.cn/aagatlas_portal/index.php#), which can be used to search for 8045 non-redundant autoantigens (AAgs) and 47 post-translationally modified AAgs against 1073 human diseases that are prioritized by a credential score developed by multisource evidence. Using AAgAtlas, the immunogenic properties of human AAgs was systematically elucidated according to their genetic, biophysical, cytological, expression profile, and evolutionary characteristics. The results indicated that human AAgs are evolutionally conserved in protein sequence and enriched in three hydrophilic and polar amino acid residues (K, D, and E) that are located at the protein surface. AAgs are enriched in proteins that are involved in nucleic acid binding, transferase, and the cytoskeleton. Genome, transcriptome, and proteome analyses further indicated that AAb production is associated with gene variance and abnormal protein expression related to the pathological activities of different tumors. Collectively, our data outlines the hallmarks of human AAgs that facilitate the understanding of humoral autoimmunity and the identification of biomarkers of human diseases.

Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening-Can an Integrated Approach Overcome Current Challenges?

As most lung cancer (LC) cases are still detected at advanced and incurable stages, there are increasing efforts to foster detection at earlier stages by low dose computed tomography (LDCT) based LC screening. In this scoping review, we describe current advances in candidate selection for screening (selection phase), technical aspects (screening), and probability evaluation of malignancy of CT-detected pulmonary nodules (PN management). Literature was non-systematically assessed and reviewed for suitability by the authors. For the selection phase, we describe current eligibility criteria for screening, along with their limitations and potential refinements through advanced clinical scores and biomarker assessments. For LC screening, we discuss how the accuracy of computerized tomography (CT) scan reading might be augmented by IT tools, helping radiologists to cope with increasing workloads. For PN management, we evaluate the precision of follow-up scans by semi-automatic volume measurements of CT-detected PN. Moreover, we present an integrative approach to evaluate the probability of PN malignancy to enable safe decisions on further management. As a clear limitation, additional validation studies are required for most innovative diagnostic approaches presented in this article, but the integration of clinical risk models, current imaging techniques, and advancing biomarker research has the potential to improve the LC screening performance generally.

EarlyCDT Lung blood test for risk classification of solid pulmonary nodules: systematic review and economic evaluation

BACKGROUND

EarlyCDT Lung (Oncimmune Holdings plc, Nottingham, UK) is a blood test to assess malignancy risk in people with solid pulmonary nodules. It measures the presence of seven lung cancer-associated autoantibodies. Elevated levels of these autoantibodies may indicate malignant disease. The results of the test might be used to modify the risk of malignancy estimated by existing risk calculators, including the Brock and Herder models.

OBJECTIVES

The objectives were to determine the diagnostic accuracy, clinical effectiveness and cost-effectiveness of EarlyCDT Lung; and to develop a conceptual model and identify evidence requirements for a robust cost-effectiveness analysis.

DATA SOURCES

MEDLINE (including Epub Ahead of Print, In-Process & Other Non-Indexed Citations, Ovid MEDLINE Daily and Ovid MEDLINE), EMBASE, Cochrane Central Register of Controlled Trials, Science Citation Index, EconLit, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, Health Technology Assessment database, NHS Economic Evaluation Database ( NHS EED ) and the international Health Technology Assessment database were searched on 8 March 2021.

REVIEW METHODS

A systematic review was performed of evidence on EarlyCDT Lung, including diagnostic accuracy, clinical effectiveness and cost-effectiveness. Study quality was assessed with the quality assessment of diagnostic accuracy studies-2 tool. Evidence on other components of the pulmonary nodule diagnostic pathway (computerised tomography surveillance, Brock risk, Herder risk, positron emission tomography-computerised tomography and biopsy) was also reviewed. When feasible, bivariate meta-analyses of diagnostic accuracy were performed. Clinical outcomes were synthesised narratively. A simulation study investigated the clinical impact of using EarlyCDT Lung. Additional reviews of cost-effectiveness studies evaluated (1) other diagnostic strategies for lung cancer and (2) screening approaches for lung cancer. A conceptual model was developed.

RESULTS

A total of 47 clinical publications on EarlyCDT Lung were identified, but only five cohorts (695 patients) reported diagnostic accuracy data on patients with pulmonary nodules. All cohorts were small or at high risk of bias. EarlyCDT Lung on its own was found to have poor diagnostic accuracy, with a summary sensitivity of 20.2% (95% confidence interval 10.5% to 35.5%) and specificity of 92.2% (95% confidence interval 86.2% to 95.8%). This sensitivity was substantially lower than that estimated by the manufacturer (41.3%). No evidence on the clinical impact of EarlyCDT Lung was identified. The simulation study suggested that EarlyCDT Lung might potentially have some benefit when considering intermediate risk nodules (10-70% risk) after Herder risk analysis. Two cost-effectiveness studies on EarlyCDT Lung for pulmonary nodules were identified; none was considered suitable to inform the current decision problem. The conceptualisation process identified three core components for a future cost-effectiveness assessment of EarlyCDT Lung: (1) the features of the subpopulations and relevant heterogeneity, (2) the way EarlyCDT Lung test results affect subsequent clinical management decisions and (3) how changes in these decisions can affect outcomes. All reviewed studies linked earlier diagnosis to stage progression and stage shift to final outcomes, but evidence on these components was sparse.

LIMITATIONS

The evidence on EarlyCDT Lung among patients with pulmonary nodules was very limited, preventing meta-analyses and economic analyses.

CONCLUSIONS

The evidence on EarlyCDT Lung among patients with pulmonary nodules is insufficient to draw any firm conclusions as to its diagnostic accuracy or clinical or economic value.

FUTURE WORK

Prospective cohort studies, in which EarlyCDT Lung is used among patients with identified pulmonary nodules, are required to support a future assessment of the clinical and economic value of this test. Studies should investigate the diagnostic accuracy and clinical impact of EarlyCDT Lung in combination with Brock and Herder risk assessments. A well-designed cost-effectiveness study is also required, integrating emerging relevant evidence with the recommendations in this report.

STUDY REGISTRATION

This study is registered as PROSPERO CRD42021242248.

FUNDING

This project was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 26, No. 49. See the NIHR Journals Library website for further project information.

Evaluating treatment strategies for non-small cell lung cancer during COVID-19: A propensity score matching analysis

We employed pandemic treatment strategies that we developed at the beginning of the coronavirus disease 2019 (COVID-19) pandemic, and it was not clear whether any adverse results were associated with our strategies. Therefore, we carried out a retrospective study to compare our pandemic treatment strategies with prepandemic protocols to determine whether the strategies used during the high-risk period of COVID-19 were appropriate. The observation period was September 2019 to February 2020. Patients hospitalized from December 2019 to February 2020 were included as an experimental group, and individuals hospitalized from September 2019 to November 2019 were included as a control group. All non-small cell lung cancer patients hospitalized during the observation period were included except for pediatric and obstetric patients, patients younger than 18 years old, and patients admitted only for routine follow-up examinations. Treatment strategies were evaluated based on the prognosis of the different treatment methods, including surgical and nonsurgical treatments and discontinuation of therapy. Survival curves were analyzed using the Kaplan-Meier method. Cox regression analysis was used for multivariate analysis of risk factors for progress-free survival. Propensity score matching was used for clinical characteristics to adjust for selection bias. Therapy discontinuation in the experimental group was significantly higher than in the control group (P < .001). The differences in cancer progression and the number of deaths between the 2 groups were not significant (P = .38 and .13, respectively). For late-stage patients, there were significant differences in nonsurgical treatment and discontinued therapy (P < .001 and < .001, respectively) between the 2 groups, while the cancer progression and death toll differences were not significant (P = .20 and .20, respectively). For early-stage patients, the differences in surgical treatment, discontinued therapy, cancer progression, and death toll were not significant (P = .24, 0.24, 0.61, and 0.49, respectively) between the 2 groups. Multivariate analysis revealed that temporary discontinuation of therapy did not predict poor progress-free survival independently (hazard ratio = 1.007, 95% confidence interval: 0.653-1.552, P = .98). For patients in geographical regions with a high risk for COVID-19 infections, temporarily suspending treatment for late-stage non-small cell lung cancer patients is not likely to significantly impact their prognosis if they can return to treatment within 3 months of discontinuation.

Biomarkers and the microbiome in the detection and treatment of early-stage non-small cell lung cancer

Lung cancer is one of the most common and deadly cancers in the world. However, over the last several years, research into lung cancer screening and novel therapeutic approaches have provided promise that earlier detection combined with new treatment strategies may result in significantly improved outcomes. Biomarkers will most certainly play a major role in identifying those who may benefit from, and how to apply, these new treatment strategies. Here we discuss potential biomarkers, including the microbiome, in both detection and treatment strategies for early stage lung cancer.

The applications of plasma cell-free DNA in cancer detection: Implications in the management of breast cancer patients

Liquid biopsy probes DNA, RNA, and proteins in body fluids for cancer detection and is one of the most rapidly developing areas in oncology. Tumor-derived DNA (circulating tumor DNA, ctDNA) in the context of cell-free DNA (cfDNA) in blood has been the main target for its potential utilities in cancer detection. Liquid biopsy can report tumor burden in real-time without invasive interventions, and would be feasible for screening tumor types that lack standard-of-care screening approaches. Two major approaches to interrogating ctDNA are genetic mutation and DNA methylation profiling. Mutation profiling can identify tumor driver mutations and guide precision therapy. Targeted genomic profiling of DNA methylation has become the main approach for cancer screening in the general population. Here we review the recent technological development and ongoing efforts in clinical applications. For clinical applications, we focus on breast cancer, in which subtype-specific biology demarcates the applications of ctDNA.

The Autoantibodies against Tumor-Associated Antigens as Potential Blood-Based Biomarkers in Thyroid Neoplasia: Rationales, Opportunities and Challenges

The Autoantibodies targeting Tumor-Associated Antigens (TAA-AAbs) emerge as a result of a variety of tumor-related immunogenic stimuli and may be regarded as the eyewitnesses to the anti-tumor immune response. TAA-AAbs may be readily detected in peripheral blood to unveil the presence of a particular TAA-expressing tumor, and a fair number of TAAs eliciting the tumor-associated autoantibody response have been identified. The potential of TAA-AAbs as tumor biomarkers has been extensively studied in many human malignancies with a major influence on public health; however, tumors of the endocrine system, and, in particular, the well-differentiated follicular cell-derived thyroid neoplasms, remain understudied in this context. This review provides a detailed perspective on and legitimate rationales for the potential use of TAA-AAbs in thyroid neoplasia, with particular reference to the already established diagnostic implications of the TAA-AAbs in human cancer, to the windows for improvement and diagnostic niches in the current workup strategies in nodular thyroid disease and differentiated thyroid cancer that TAA-AAbs may successfully occupy, as well as to the proof-of-concept studies demonstrating the usefulness of TAA-AAbs in thyroid oncology, particularly for the pre-surgical discrimination between tumors of different malignant potential in the context of the indeterminate results of the fine-needle aspiration cytology.

Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2-4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P < 2 × 10-16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

Analysis of the Repertoires of Circulating Autoantibodies' Specificities as a Tool for Identification of the Tumor-Associated Antigens: Current Problems and Solutions

Circulating autoantibodies against tumor-associated autoantigens (TAA) may serve as valuable biomarkers for a wide range of diagnostic purposes. Modern immunology offers a large variety of methods for in-depth comparative analysis of the repertoires of circulating antibodies' antigenic specificities in health and disease. Nevertheless, this research field so far has met somewhat limited clinical success, while numerous data on the repertoires of circulating autoantibodies' specificities in cancer patients are poorly integrated into the contemporary picture of the immunological and molecular landscapes of human tumors. This review is an attempt to identify and systematize the key and essentially universal conceptual and methodological limitations of analyses of the repertoires of circulating antibodies' antigenic specificities in cancer (expression bias, redundancy of TAA repertoires, identification of natural IgG, the absence of the pathogenetically relevant context in the experimental systems used to detect TAA), as well as to discuss potential and already known methodological improvements that may significantly increase the detectability of the pathogenetically relevant and diagnostically significant bona fide TAA.

Liquid biopsies to distinguish malignant from benign pulmonary nodules

Over the past decades, low-dose computed tomography (LD-CT) screening has been widely used for the early detection of lung cancer. Increasing numbers of indeterminate pulmonary nodules are now being discovered. However, it remains challenging to distinguish malignant from benign pulmonary nodules, especially those considered to be small or ground-glass (GGN) nodules. Liquid biopsies have been successfully applied in the diagnosis of advanced lung cancer, and the potential value for early detection of lung cancer has made great progress. Recent studies have demonstrated the value of various blood-based tumor biomarkers in determining the nature of pulmonary nodules, including cell-free DNA (cfDNA), microRNAs (miRNAs), circulating tumor cells (CTCs) and tumor-associated autoantibodies (AAbs). In this review, we summarize the latest progress of liquid biopsies, and their potential applications and challenges in the diagnosis of malignant pulmonary nodules.

Anti-Cancer Auto-Antibodies: Roles, Applications and Open Issues

Auto-antibodies are classically associated with autoimmune diseases, where they are an integral part of diagnostic panels. However, recent evidence is accumulating on the presence of auto-antibodies against single or selected panels of auto-antigens in many types of cancer. Auto-antibodies might initially represent an epiphenomenon derived from the inflammatory environment induced by the tumor. However, their effect on tumor evolution can be crucial, as is discussed in this paper. It has been demonstrated that some of these auto-antibodies can be used for early detection and cancer staging, as well as for monitoring of cancer regression during treatment and follow up. Interestingly, certain auto-antibodies were found to promote cancer progression and metastasis, while others contribute to the body's defense against it. Moreover, auto-antibodies are of a polyclonal nature, which means that often several antibodies are involved in the response to a single tumor antigen. Dissection of these antibody specificities is now possible, allowing their identification at the genetic, structural, and epitope levels. In this review, we report the evidence available on the presence of auto-antibodies in the main cancer types and discuss some of the open issues that still need to be addressed by the research community.

Molecular biomarkers in early stage lung cancer

Low dose computed tomography (LDCT) screening, together with the recent advances in targeted and immunotherapies, have shown to improve non-small cell lung cancer (NSCLC) survival. Furthermore, screening has increased the number of early stage-detected tumors, allowing for surgical resection and multimodality treatments when needed. The need for improved sensitivity and specificity of NSCLC screening has led to increased interest in combining clinical and radiological data with molecular data. The development of biomarkers is poised to refine inclusion criteria for LDCT screening programs. Biomarkers may also be useful to better characterize the risk of indeterminate nodules found in the course of screening or to refine prognosis and help in the management of screening detected tumors. The clinical implications of these biomarkers are still being investigated and whether or not biomarkers will be included in further decision-making algorithms in the context of screening and early lung cancer management still needs to be determined. However, it seems clear that there is much room for improvement even in early stage lung cancer disease-free survival (DFS) rates; thus, biomarkers may be the key to refine risk-stratification and treatment of these patients. Clinicians’ capacity to register, integrate, and analyze all the available data in both high risk individuals and early stage NSCLC patients will lead to a better understanding of the disease’s mechanisms, and will have a direct impact in diagnosis, treatment, and follow up of these patients. In this review, we aim to summarize all the available data regarding the role of biomarkers in LDCT screening and early stage NSCLC from a multidisciplinary perspective. We have highlighted clinical implications, the need to combine risk stratification, clinical data, radiomics, molecular information and artificial intelligence in order to improve clinical decision-making, especially regarding early diagnostics and adjuvant therapy. We also discuss current and future perspectives for biomarker implementation in routine clinical practice.

Earlier diagnosis of lung cancer in a randomised trial of an autoantibody blood test followed by imaging

The EarlyCDT-Lung test is a high-specificity blood-based autoantibody biomarker that could contribute to predicting lung cancer risk. We report on the results of a phase IV biomarker evaluation of whether using the EarlyCDT-Lung test and any subsequent computed tomography (CT) scanning to identify those at high risk of lung cancer reduces the incidence of patients with stage III/IV/unspecified lung cancer at diagnosis compared with the standard clinical practice at the time the study began.The Early Diagnosis of Lung Cancer Scotland (ECLS) trial was a randomised controlled trial of 12 208 participants at risk of developing lung cancer in Scotland in the UK. The intervention arm received the EarlyCDT-Lung test and, if test-positive, low-dose CT scanning 6-monthly for up to 2 years. EarlyCDT-Lung test-negative and control arm participants received standard clinical care. Outcomes were assessed at 2 years post-randomisation using validated data on cancer occurrence, cancer staging, mortality and comorbidities.At 2 years, 127 lung cancers were detected in the study population (1.0%). In the intervention arm, 33 out of 56 (58.9%) lung cancers were diagnosed at stage III/IV compared with 52 out of 71 (73.2%) in the control arm. The hazard ratio for stage III/IV presentation was 0.64 (95% CI 0.41-0.99). There were nonsignificant differences in lung cancer and all-cause mortality after 2 years.ECLS compared EarlyCDT-Lung plus CT screening to standard clinical care (symptomatic presentation) and was not designed to assess the incremental contribution of the EarlyCDT-Lung test. The observation of a stage shift towards earlier-stage lung cancer diagnosis merits further investigations to evaluate whether the EarlyCDT-Lung test adds anything to the emerging standard of low-dose CT.

Biomarkers for Lung Cancer Screening and Detection

Lung cancer is the leading worldwide cause of cancer mortality, as it is often detected at an advanced stage. Since 2011, low-dose CT scan-based screening has promised a 20% reduction in lung cancer mortality. However, effectiveness of screening has been limited by eligibility only for a high-risk population of heavy smokers and a large number of false positives generated by CT. Biomarkers have tremendous potential to improve early detection of lung cancer by refining lung cancer risk, stratifying positive CT scans, and categorizing intermediate-risk pulmonary nodules. Three biomarker tests (Early CDT-Lung, Nodify XL2, Percepta) have undergone extensive validation and are available to the clinician. The authors discuss these tests, with their clinical applicability and limitations, current ongoing evaluation, and future directions for biomarkers in lung cancer screening and detection.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Modeling the Impact of Delaying the Diagnosis of Non-Small Cell Lung Cancer During COVID-19

BACKGROUND

The novel coronavirus (COVID-19) pandemic has led surgical societies to recommend delaying diagnosis and treatment of suspected lung cancer for lesions less than 2 cm. Delaying diagnosis can lead to disease progression, but the impact of this delay on mortality is unknown. The COVID-19 infection rate at which immediate operative risk exceeds benefit is unknown. We sought to model immediate versus delayed surgical resection in a suspicious lung nodule less than 2 cm.

METHODS

A decision analysis model was developed, and sensitivity analyses performed. The base case was a 65-year-old male smoker with chronic obstructive pulmonary disease presenting for surgical biopsy of a 1.5 to 2 cm lung nodule highly suspicious for cancer during the COVID-19 pandemic. We compared immediate surgical resection to delayed resection after 3 months. The likelihood of key outcomes was derived from the literature where available. The outcome was 5-year overall survival.

RESULTS

Immediate surgical resection resulted in a similar but slightly higher 5-year overall survival when compared with delayed resection (0.77 versus 0.74) owing to the risk of disease progression. However, if the probability of acquired COVID-19 infection is greater than 13%, delayed resection is favorable (0.74 vs 0.73).

CONCLUSIONS

Immediate surgical biopsy of lung nodules suspicious for cancer in hospitals with low COVID-19 prevalence likely results in improved 5-year survival. However, as the risk of perioperative COVID-19 infection increases above 13%, a delayed approach has similar or improved survival. This balance should be frequently reexamined at each health care facility throughout the curve of the pandemic.

Autoantibodies to tumor-associated antigens in lung cancer diagnosis

Lung cancer (LC) accounts for the majority of cancer-related deaths worldwide. Although screening the high-risk population by low-dose CT (LDCT) has reduced mortality, the cost and high false positivity rate has prevented its general diagnostic use. As such, better and more specific minimally invasive biomarkers are needed in general and for early LC detection, specifically. Autoantibodies produced by humoral immune response to tumor-associated antigens (TAA) are emerging as a promising noninvasive biomarker for LC. Given the low sensitivity of any one single autoantibody, a panel approach could provide a more robust and promising strategy to detect early stage LC. In this review, we summarize the background of TAA autoantibodies (TAAb) and the techniques currently used for identifying TAA, as well as recent findings of LC specific antigens and TAAb. This review provides guidance toward the development of accurate and reliable TAAb as immunodiagnostic biomarkers in the early detection of LC.

Cost-effectiveness of a new autoantibody test added to Computed Tomography (CT) compared to CT surveillance alone in the diagnosis of lung cancer amongst patients with indeterminate pulmonary nodules

Oncimmune's EarlyCDT®-Lung is a simple ELISA blood test that measures seven lung cancer specific autoantibodies and is used in the assessment of malignancy risk in patients with indeterminate pulmonary nodules (IPNs). The objective of this study was to examine the cost-effectiveness of EarlyCDT-Lung in the diagnosis of lung cancer amongst patients with IPNs in addition to CT surveillance, compared to CT surveillance alone which is the current recommendation by the British Thoracic Society guidelines. A model consisting of a combination of a decision tree and Markov model was developed using the outcome measure of the quality adjusted life year (QALY). A life-time time horizon was adopted. The model was parameterized using a range of secondary sources. At £70 per test, EarlyCDT-Lung and CT surveillance was found to be cost-effective compared to CT surveillance alone with an incremental cost-effectiveness ratio (ICER) of less than £2,500 depending on the test accuracy parameters used. It was also found that EarlyCDT-Lung can be priced up to £1,177 and still be cost-effective based on cost-effectiveness acceptance threshold of £20,000 / QALY. Further research to resolve parameter uncertainty, was not found to be of value. The results here demonstrate that at £70 per test the EarlyCDT-Lung will have a positive impact on patient outcomes and coupled with CT surveillance is a cost-effective approach to the management of patients with IPNs. The conclusions drawn from this analysis are robust to realistic variation in the parameters used in the model.

Biomarkers in Early Diagnosis and Early Stage Lung Cancer: The Clinician's Point of View

Starting from the work of Ulivi and colleagues, we aim to summarize the research area of biomarkers for early diagnosis and early stage lung cancer.

Noninvasive biomarkers for lung cancer diagnosis, where do we stand?

The 2010's saw demonstration of the power of lung cancer screening to reduce mortality. However, with implementation of lung cancer screening comes the challenge of diagnosing millions of lung nodules every year. When compared to other cancers with widespread screening strategies (breast, colorectal, cervical, prostate, and skin), obtaining a lung nodule tissue biopsy to confirm a positive screening test remains associated with higher morbidity and cost. Therefore, non-invasive diagnostic biomarkers may have a unique opportunity in lung cancer to greatly improve the management of patients at risk. This review covers recent advances in the field of liquid biomarkers and computed tomographic imaging features, with special attention to new methods for combination of biomarkers as well as the use of artificial intelligence for the discrimination of benign from malignant nodules.

Multiplexed monitoring of a novel autoantibody diagnostic signature of colorectal cancer using HaloTag technology-based electrochemical immunosensing platform

Background and Purpose: The humoral immune response in cancer patients can be used for early detection of the disease. Autoantibodies raised against tumor-associated antigens (TAAs) are promising clinical biomarkers for reliable cancer diagnosis, prognosis, and therapy monitoring. In this study, an electrochemical disposable multiplexed immunosensing platform able to integrate difficult- and easy-to-express colorectal cancer (CRC) TAAs is reported for the sensitive determination of eight CRC-specific autoantibodies. Methods: The electrochemical immunosensing approach involves the use of magnetic microcarriers (MBs) as solid supports modified with covalently immobilized HaloTag fusion proteins for the selective capture of specific autoantibodies. After magnetic capture of the modified MBs onto screen-printed carbon working electrodes, the amperometric responses measured using the hydroquinone (HQ)/H2O2 system were related to the levels of autoantibodies in plasma. Results: The biosensing platform was applied to the analysis of autoantibodies against 8 TAAs described for the first time in this work in plasma samples from healthy asymptomatic individuals (n=3), and patients with high-risk of developing CRC (n=3), and from patients already diagnosed with colorectal (n=3), lung (n=2) or breast (n=2) cancer. The developed bioplatform demonstrated an improved discrimination between CRC patients and controls (asymptomatic healthy individuals and breast and lung cancer patients) compared to an ELISA-like luminescence test. Conclusions: The proposed methodology uses a just-in-time produced protein in a simpler protocol, with low sample volume, and involves cost-effective instrumentation, which could be used in a high-throughput manner for reliable population screening to facilitate the detection of early CRC patients at affordable cost.

A panel of autoantibodies against tumor-associated antigens in the early immunodiagnosis of lung cancer

OBJECTIVE

Lung cancer (LC) is one of the most common malignant tumors worldwide with low five-year survival rate due to lack of effective diagnosis. This study aims to find an optimal combination of autoantibodies for detecting of early-stage LC.

METHODS

Nine relatively novel autoantibodies against tumor-associated (TAAs) (PSIP1, TOP2A, ACTR3, RPS6KA5, HMGB3, MMP12, GREM1, ZWINT and NUSAP1) were detected by using ELISA. Diagnostic models were developed by using the training set (n = 644) and further validated in another independent set (n = 248). We also evaluated the diagnostic accuracy of the model to detect benign lung diseases (BLD) from the early-stage lung cancer.

RESULTS

The areas under the receiver operating characteristic curve (AUC) for the model with three TAAs panel (GREM1, HMGB3 and PSIP1) was 0.711(95% CI 0.674-0.746) in the training set and 0.858 (95% CI 0.808-0.899) in the validation set, which demonstrated a higher diagnostic capability. The AUC of this three TAAs model was 0.833 (95%CI 0.780-0.878) in discriminating LC from BLD. This model could identify early-stage LC patients from normal control (NC) individuals, with AUC of 0.687(95% CI 0.634-0.736) in training set and AUC of 0.920(95% CI 0.860-0.960) in validation set, and the overall AUC for early-stage LC was 0.779(95% CI 0.739-0.816) when the training set and validation set were combined.

CONCLUSIONS

The model with three TAAs panel would detect LC with higher effectiveness, and might be potential screening method for the early LC.

High-throughput autoantibody analysis in malignant pleural effusion and tuberculosis pleural effusion

BACKGROUND

Malignant pleural effusion (MPE) and tuberculosis pleural effusion (TPE) are 2 kinds of common pleural diseases. Finding efficient and accurate biomarkers to distinguish the 2 is of benefit to basic and clinical research. In the present study, we carried out the first high-throughput autoantibody chip to screen the beneficial biomarker with samples of MPE and TPE and the corresponding serum.

METHODS

We collected pleural effusion and serum of patients with MPE (n = 10) and TPE (n = 10) who had been in Beijing Chao-Yang hospital from June 2013 to August 2014. Using RayBio Human Protein Array-G2 to measure the concentration of 487 defined autoantibodies.

RESULTS

Fold changes of Bcl-2-like protein 11 (BIM) autoantibody in MPE-serum/TPE-serum and MPE/TPE groups were 10 (P = .019) and 6 (P = .001); for decorin autoantibody, MPE-serum/TPE-serum ratio was 0.6 (P = .029), and MPE/TPE ratio was 0.3 (P < .001).

CONCLUSION

BIM autoantibody is a promising MPE biomarker by high-throughput autoantibody analysis in MPE and TPE.

Serum anti-EIF3A autoantibody as a potential diagnostic marker for hepatocellular carcinoma

Tumor-associated autoantibodies are promising diagnostic biomarkers for early detection of tumors. We have screened a novel tumor-associated autoantibody in hepatocellular carcinoma (HCC) model mice. Its target antigen was identified as eukaryotic translation initiation factor 3 subunit A (EIF3A) by proteomic analysis, and the elevated expression of EIF3A in HCC tissues of tumor model mice as well as human patients was shown. Also, its existence in tumor-derived exosomes was revealed, which seem to be the cause of tumor-associated autoantibody production. To use serum anti-EIF3A autoantibody as biomarker, ELISA detecting anti-EIF3A autoantibody in human serum was performed using autoantibody-specific epitope. For the sensitive detection of serum autoantibodies its specific conformational epitopes were screened from the random cyclic peptide library, and a streptavidin antigen displaying anti-EIF3A autoantibody-specific epitope, XC90p2(-CPVRSGFPC-), was used as capture antigen. It distinguished patients with HCC (n = 102) from healthy controls (n = 0285) with a sensitivity of 79.4% and specificity of 83.5% (AUC = 0.87). Also, by simultaneously detecting with other HCC biomarkers, including alpha-fetoprotein, HCC diagnostic sensitivity improved from 79.4% to 85%. Collectively, we suggest that serum anti-EIF3A autoantibody is a useful biomarker for the diagnosis of HCC and the combinational detection of related biomarkers can enhance the accuracy of the cancer diagnosis.

Combined detection of serum autoantibodies as diagnostic biomarkers in esophagogastric junction adenocarcinoma

BACKGROUND

We previously found that autoantibodies against a panel of six tumor-associated antigens (p53, NY-ESO-1, MMP-7, Hsp70, PRDX6 and Bmi-1) may aid in early detection of esophageal squamous cell carcinoma. Here we aimed to evaluate the diagnostic value of this autoantibody panel in esophagogastric junction adenocarcinoma (EJA) patients.

METHODS

Serum autoantibody levels were measured by enzyme-linked immunosorbent assay in a training cohort and a validation cohort. We used receiver-operating characteristics (ROC) to calculate diagnostic accuracy.

RESULTS

We recruited 169 normal controls and 122 EJA patients to the training cohort, and 80 normal controls and 70 EJA patients to the validation cohort. Detection of the autoantibody panel demonstrated an area under the curve (AUC) of 0.818, sensitivity 59.0% and specificity 90.5% in training cohort, and AUC 0.815, sensitivity 61.4% and specificity 90.0% in validation cohort in the diagnosis of EJA. Measurement of the autoantibody panel could distinguish early stage EJA patients from normal controls (AUC 0.786 and 0.786, sensitivity 50.0% and 56.0%, and specificity 90.5% and 90.0%, for training and validation cohorts, respectively). Moreover, a restricted panel consisting of autoantibodies against p53, NY-ESO-1 and Bmi-1 exhibited similar diagnostic performance for EJA (AUC 0.814 and 0.823, sensitivity 53.5% and 60.0%, and specificity 90.5% and 93.7%, for training and validation cohorts, respectively) and early stage EJA (AUC 0.744 and 0.773, sensitivity 55.6% and 52.0%, and specificity 90.5% and 93.7%, for training and validation cohorts, respectively).

CONCLUSIONS

Autoantibodies against an optimized TAA panel as serum biomarkers appear to help identify the present of early stage EJA.

Biomarkers in Lung Cancer Screening: Achievements, Promises, and Challenges

The present review is an update of the research and development efforts regarding the use of molecular biomarkers in the lung cancer screening setting. The two main unmet clinical needs, namely, the refinement of risk to improve the selection of individuals undergoing screening and the characterization of undetermined nodules found during the computed tomography-based screening process are the object of the biomarkers described in the present review. We first propose some principles to optimize lung cancer biomarker discovery projects. Then, we summarize the discovery and developmental status of currently promising molecular candidates, such as autoantibodies, complement fragments, microRNAs, circulating tumor DNA, DNA methylation, blood protein profiling, or RNA airway or nasal signatures. We also mention other emerging biomarkers or new technologies to follow, such as exhaled breath biomarkers, metabolomics, sputum cell imaging, genetic predisposition studies, and the integration of next-generation sequencing into study of circulating DNA. We also underline the importance of integrating different molecular technologies together with imaging, radiomics, and artificial intelligence. We list a number of completed, ongoing, or planned trials to show the clinical utility of molecular biomarkers. Finally, we comment on future research challenges in the field of biomarkers in the context of lung cancer screening and propose a design of a trial to test the clinical utility of one or several candidate biomarkers.