The Airway Transcriptome as a Biomarker for Early Lung Cancer Detection

Exploring the Utility of Long Non-Coding RNAs for Assessing the Health Consequences of Vaping

Electronic cigarette (e-cig) use, otherwise known as "vaping", is widespread among adolescent never-smokers and adult smokers seeking a less-harmful alternative to combustible tobacco products. To date, however, the long-term health consequences of vaping are largely unknown. Many toxicants and carcinogens present in e-cig vapor and tobacco smoke exert their biological effects through epigenetic changes that can cause dysregulation of disease-related genes. Long non-coding RNAs (lncRNAs) have emerged as prime regulators of gene expression in health and disease states. A large body of research has shown that lncRNAs regulate genes involved in the pathogenesis of smoking-associated diseases; however, the utility of lncRNAs for assessing the disease-causing potential of vaping remains to be fully determined. A limited but growing number of studies has shown that lncRNAs mediate dysregulation of disease-related genes in cells and tissues of vapers as well as cells treated in vitro with e-cig aerosol extract. This review article provides an overview of the evolution of e-cig technology, trends in use, and controversies on the safety, efficacy, and health risks or potential benefits of vaping relative to smoking. While highlighting the importance of lncRNAs in cell biology and disease, it summarizes the current and ongoing research on the modulatory effects of lncRNAs on gene regulation and disease pathogenesis in e-cig users and in vitro experimental settings. The gaps in knowledge are identified, priorities for future research are highlighted, and the importance of empirical data for tobacco products regulation and public health is underscored.

MicroRNAs and the Mediterranean diet: a nutri-omics perspective for lung cancer

Lung cancer is the deadliest cancer type worldwide with ~ 1.8 million deaths per-year. Smoking accounts for ~ 85% of all cases, with a described joint effect with unhealthy diet in lung cancer risk increase. Public health policies to prevent carcinogens exposure, promote smoking cessation and advocacy for healthy nutrition, are therefore highly recommended. Here we have examined the benefits of the Mediterranean Diet (MedDiet) in protecting against some non-communicable diseases including lung cancer, highlighting the epidemiological and biomolecular aspects of MedDiet anti-inflammatory effect and its interaction with smoking habits closely linked to risk of lung cancer. Considering the high incidence and mortality rates of lung cancer, we discussed also about the global impact that a Planeterranean extension of the benefits of MedDiet could have on controlling lung cancer risk. We also debated the impact of personalized nutrition on lung cancer prevention, considering individual heterogeneity in response to diet plans as well as recent advancements on nutri-omics in lung cancer research, with a specific focus on the role of microRNAs (miRNAs) as a promising nutritional molecular hub for lung cancer prevention. We strongly believe that a deep understanding of the molecular link between food components and genetic/epigenetics factors can expand effective intervention strategies.

Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma

Approximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up. Through histologic, mutational, and transcriptomic profiling of tumor and adjacent-normal tissue, we identified an inflammatory gene signature in tumor-adjacent tissue as the strongest clinical predictor of disease progression. Single-cell transcriptomic analysis demonstrated the progression-associated inflammatory signature was expressed in both immune and non-immune cells, and cell type-specific profiling in monocytes further improved outcome predictions. Additional analyses of tumor-adjacent transcriptomic data from The Cancer Genome Atlas validated the association of the inflammatory signature with worse outcomes across cancers. Collectively, our study suggests that molecular profiling of tumor-adjacent tissue can identify patients at high risk for disease progression.

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.

Role of untargeted omics biomarkers of exposure and effect for tobacco research

Tobacco research remains a clear priority to improve individual and population health, and has recently become more complex with emerging combustible and noncombustible tobacco products. The use of omics methods in prevention and cessation studies are intended to identify new biomarkers for risk, compared risks related to other products and never use, and compliance for cessation and reinitation. to assess the relative effects of tobacco products to each other. They are important for the prediction of reinitiation of tobacco use and relapse prevention. In the research setting, both technical and clinical validation is required, which presents a number of complexities in the omics methodologies from biospecimen collection and sample preparation to data collection and analysis. When the results identify differences in omics features, networks or pathways, it is unclear if the results are toxic effects, a healthy response to a toxic exposure or neither. The use of surrogate biospecimens (e.g., urine, blood, sputum or nasal) may or may not reflect target organs such as the lung or bladder. This review describes the approaches for the use of omics in tobacco research and provides examples of prior studies, along with the strengths and limitations of the various methods. To date, there is little consistency in results, likely due to small number of studies, limitations in study size, the variability in the analytic platforms and bioinformatic pipelines, differences in biospecimen collection and/or human subject study design. Given the demonstrated value for the use of omics in clinical medicine, it is anticipated that the use in tobacco research will be similarly productive.

Establishing a genomic radiation-age association for space exploration supplements lung disease differentiation

Purpose

One possible way to quantify each individual's response or damage from ionizing radiation is to estimate their accelerated biological age following exposure. Since there is currently no definitive way to know if biological age estimations are accurate, we aim to establish a rad-age association using genomics as its foundation.

Methods

Two datasets were combined and used to empirically find the age cutoff between young and old patients. With age as both a categorical and continuous variable, two other datasets that included radiation exposure are used to test the interaction between radiation and age. The gene lists are oriented in preranked lists for both pathway and diseases analysis. Finally, these genes are used to evaluate another dataset on the clinical relevance in differentiating lung disease given ethnicity and sex using both pairwise t-tests and linear models.

Results

Using 12 well-known genes associated with aging, a threshold of 29-years-old was found to be the difference between young and old patients. The two interaction tests yielded 234 unique genes such that pathway analysis flagged IL-1 signaling and PRPP biosynthesis as significant with high cell proliferation diseases and carcinomas being a common trend. LAPTM4B was the only gene with significant interaction among lung disease, ethnicity, and sex, with fold change greater than two.

Conclusion

The results corroborate an initial association between radiation and age, given inflammation and metabolic pathways and multiple genes emphasizing mitochondrial function, oxidation, and histone modification. Being able to tie rad-age genes to lung disease supplements future work for risk assessment following radiation exposure.

Non-Invasive Biomarkers for Early Lung Cancer Detection

Worldwide, lung cancer (LC) is the most common cause of cancer death, and any delay in the detection of new and relapsed disease serves as a major factor for a significant proportion of LC morbidity and mortality. Though invasive methods such as tissue biopsy are considered the gold standard for diagnosis and disease monitoring, they have several limitations. Therefore, there is an urgent need to identify and validate non-invasive biomarkers for the early diagnosis, prognosis, and treatment of lung cancer for improved patient management. Despite recent progress in the identification of non-invasive biomarkers, currently, there is a shortage of reliable and accessible biomarkers demonstrating high sensitivity and specificity for LC detection. In this review, we aim to cover the latest developments in the field, including the utility of biomarkers that are currently used in LC screening and diagnosis. We comment on their limitations and summarise the findings and developmental stages of potential molecular contenders such as microRNAs, circulating tumour DNA, and methylation markers. Furthermore, we summarise research challenges in the development of biomarkers used for screening purposes and the potential clinical applications of newly discovered biomarkers.

Role of biomarkers in lung nodule evaluation

PURPOSE OF REVIEW

Worldwide, lung cancer is the leading cause of cancer mortality. Much of this mortality is thought to be secondary to detection in later stages, where treatment options and survivability are limited. The goals of lung nodule evaluation are to expedite the diagnosis and treatment of patients with malignant nodules and to minimize unnecessary diagnostic procedures in those with benign nodules. However, the differentiation between benign and malignant has been challenging and is further complicated by the benefits of early diagnosis competing with potential morbidity of invasive diagnostic procedures.

RECENT FINDINGS

Biomarkers have the potential to improve estimates of pretest probability of malignancy in pulmonary nodules, especially in the intermediate-risk subgroup. Four biomarkers have undergone extensive validation and are available for clinical use, and we will discuss each in this review.

SUMMARY

The application of biomarkers to lung cancer risk assessment has the potential to improve cancer probability assessments, which in turn can reduce unnecessary invasive testing and/or reduce delays in diagnosis and treatment.

An Update on the Use of Exhaled Breath Analysis for the Early Detection of Lung Cancer

Lung cancer has historically been the main responsible for cancer associated deaths. Owing to this is our current inability to screen for and diagnose early pathological findings, preventing us from a timely intervention when cure is still achievable. Over the last decade, together with the extraordinary progress in therapeutical alternatives in the field, there has been an ongoing search for a biomarker that would allow for this. Numerous technologies have been developed but their clinical application is yet to come. In this review, we provide an update on volatile organic compounds, a non-invasive method that can hold the key for detecting early metabolic pathway changes in carcinogenesis. For its compilation, web-based search engines of scientific literature such as PubMed were explored and reviewed, using articles, research, and papers deemed meaningful by authors discretion. After a brief description, we depict how this technique can complement current methods and present the value of electronic noses in the identification of the “breathprint”. Lastly, we bring some of the latest updates in the field together with the current limitations and final remarks.

A Soft Robot for Peripheral Lung Cancer Diagnosis and Therapy

Lung cancer is one of the deadliest forms of cancers and is often diagnosed by performing biopsies with the use of a bronchoscope. However, this diagnostic procedure is limited in ability to explore deep into the periphery of the lung where cancer can remain undetected. In this study, we present design, modeling, fabrication, and testing of a one degree of freedom soft robot with integrated diagnostic and interventional capabilities as well as vision sensing. The robot can be deployed through the working channel of commercial bronchoscopes or used as a stand-alone system as it integrates a micro camera to provide vision sensing and controls to the periphery of the lung. The small diameter (2.4 mm) of the device allows navigation in branches deeper in the lung, where current devices have limited reachability. We have performed mechanical characterizations of the robotic platform, including blocked force, maximum bending angle, maximum angular velocity, and workspace, and assessed its performance in in vitro and ex vivo experiments. We have developed a computer vision algorithm, and validated it in in vitro conditions, to autonomously align the robot to a selected branch of the lung and aid the clinician (by means of a graphical user interface) during navigation tasks and to perform robot-assisted stabilization in front of a lesion, with automated tracking and alignment.

Biomarkers and Lung Cancer Early Detection: State of the Art

Simple Summary

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

Abstract

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

Immunosurveillance and Regression in the Context of Squamous Pulmonary Premalignancy

In this issue of Cancer Discovery, Pennycuick and colleagues comprehensively evaluate the immune contexture of progressive and regressive lesions in squamous pulmonary premalignancy. The authors dissect the molecular features of these lesions and the potential pathways of immune escape operative in progression to invasive cancer.See related article by Pennycuick et al., p. 1489.

Autofluorescence Bronchoscopy Video Analysis for Lesion Frame Detection

Because of the significance of bronchial lesions as indicators of early lung cancer and squamous cell carcinoma, a critical need exists for early detection of bronchial lesions. Autofluorescence bronchoscopy (AFB) is a primary modality used for bronchial lesion detection, as it shows high sensitivity to suspicious lesions. The physician, however, must interactively browse a long video stream to locate lesions, making the search exceedingly tedious and error prone. Unfortunately, limited research has explored the use of automated AFB video analysis for efficient lesion detection. We propose a robust automatic AFB analysis approach that distinguishes informative and uninformative AFB video frames in a video. In addition, for the informative frames, we determine the frames containing potential lesions and delineate candidate lesion regions. Our approach draws upon a combination of computer-based image analysis, machine learning, and deep learning. Thus, the analysis of an AFB video stream becomes more tractable. Using patient AFB video, 99.5%/90.2% of test frames were correctly labeled as informative/uninformative by our method versus 99.2%/47.6% by ResNet. In addition, ≥97% of lesion frames were correctly identified, with false positive and false negative rates ≤3%.Clinical relevance-The method makes AFB-based bronchial lesion analysis more efficient, thereby helping to advance the goal of better early lung cancer detection.

scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data

Single-cell RNA sequencing has enabled the characterization of highly specific cell types in many tissues, as well as both primary and stem cell-derived cell lines. An important facet of these studies is the ability to identify the transcriptional signatures that define a cell type or state. In theory, this information can be used to classify an individual cell based on its transcriptional profile. Here, we present scPred, a new generalizable method that is able to provide highly accurate classification of single cells, using a combination of unbiased feature selection from a reduced-dimension space, and machine-learning probability-based prediction method. We apply scPred to scRNA-seq data from pancreatic tissue, mononuclear cells, colorectal tumor biopsies, and circulating dendritic cells and show that scPred is able to classify individual cells with high accuracy. The generalized method is available at https://github.com/powellgenomicslab/scPred/.

Feature selection may improve deep neural networks for the bioinformatics problems

Motivation

Deep neural network (DNN) algorithms were utilized in predicting various biomedical phenotypes recently, and demonstrated very good prediction performances without selecting features. This study proposed a hypothesis that the DNN models may be further improved by feature selection algorithms.

Results

A comprehensive comparative study was carried out by evaluating 11 feature selection algorithms on three conventional DNN algorithms, i.e. convolution neural network (CNN), deep belief network (DBN) and recurrent neural network (RNN), and three recent DNNs, i.e. MobilenetV2, ShufflenetV2 and Squeezenet. Five binary classification methylomic datasets were chosen to calculate the prediction performances of CNN/DBN/RNN models using feature selected by the 11 feature selection algorithms. Seventeen binary classification transcriptome and two multi-class transcriptome datasets were also utilized to evaluate how the hypothesis may generalize to different data types. The experimental data supported our hypothesis that feature selection algorithms may improve DNN models, and the DBN models using features selected by SVM-RFE usually achieved the best prediction accuracies on the five methylomic datasets.

Availability and implementation

All the algorithms were implemented and tested under the programming environment Python version 3.6.6.

Supplementary information

Supplementary data are available at Bioinformatics online.

Current omics-based biomarkers for cholangiocarcinoma

Introduction: Cholangiocarcinoma (CCA) is a malignancy of the biliary tract. CCA generally has a low incidence worldwide but incidence is typically high in Southeast Asian countries, particularly in northeastern Thailand, where small liver-fluke (Opisthorchis viverrini) infection is endemic. CCA has a poor prognosis as most CCA patients present with advanced stages. Poor prognosis and worse outcomes are due to the lack of specific and early-stage CCA biomarkers. Areas covered: In this review, we discuss the use of CCA tissues, serum and bile samples as sources of diagnostic and prognostic markers by using -omics approaches, including genomics, epigenomics, transcriptomics and proteomics. The current state of the discovery of molecular candidates and their potential to be used as diagnostic and prognostic biomarkers for CCA are summarized and discussed. Expert opinion: Various potential molecules have been discovered, some of which have been verified as diagnostic biomarkers for CCA. However, most identified molecules require much further evaluation to help us find markers with high specificity, low cost and ease-of-use in routine diagnostic laboratories.

Optimal route planning for image-guided EBUS bronchoscopy

The staging of the central-chest lymph nodes is a major lung-cancer management procedure. To perform a staging procedure, the physician first uses a patient's 3D X-ray computed-tomography (CT) chest scan to interactively plan airway routes leading to selected target lymph nodes. Next, using an integrated EBUS bronchoscope (EBUS = endobronchial ultrasound), the physician uses videobronchoscopy to navigate through the airways toward a target node's general vicinity and then invokes EBUS to localize the node for biopsy. Unfortunately, during the procedure, the physician has difficulty in translating the preplanned airway routes into safe, effective biopsy sites. We propose an automatic route-planning method for EBUS bronchoscopy that gives optimal localization of safe, effective nodal biopsy sites. To run the method, a 3D chest model is first computed from a patient's chest CT scan. Next, an optimization method derives feasible airway routes that enables maximal tissue sampling of target lymph nodes while safely avoiding major blood vessels. In a lung-cancer patient study entailing 31 nodes (long axis range: [9.0 mm, 44.5 mm]), 25/31 nodes yielded safe airway routes having an optimal tissue sample size = 8.4 mm (range: [1.0 mm, 18.6 mm]) and sample adequacy = 0.42 (range: [0.05, 0.93]). Quantitative results indicate that the method potentially enables successful biopsies in essentially 100% of selected lymph nodes versus the 70-94% success rate of other approaches. The method also potentially facilitates adequate tissue biopsies for nearly 100% of selected nodes, as opposed to the 55-77% tissue adequacy rates of standard methods. The remaining nodes did not yield a safe route within the preset safety-margin constraints, with 3 nodes never yielding a route even under the most lenient safety-margin conditions. Thus, the method not only helps determine effective airway routes and expected sample quality for nodal biopsy, but it also helps point out situations where biopsy may not be advisable. We also demonstrate the methodology in an image-guided EBUS bronchoscopy system, used successfully in live lung-cancer patient studies. During a live procedure, the method provides dynamic real-time sample size visualization in an enhanced virtual bronchoscopy viewer. In this way, the physician vividly sees the most promising biopsy sites along the airway walls as the bronchoscope moves through the airways.

A promising role of interferon regulatory factor 5 as an early warning biomarker for the development of human non-small cell lung cancer

OBJECTIVES

Non-small cell lung cancer (NSCLC) accounts for 85%-90% of lung cancer cases and is a covert disease lacking early symptoms. Since cancer is recognised as an inflammation-associated condition, we analysed the relationship between the expression of interferon regulatory factor 5 (IRF5), a key transcription factor controlling inflammatory responses, and NSCLC development with the aim of identifying a warning biomarker for early diagnosis of the disease.

MATERIALS AND METHODS

The expression of IRF5 and its associated inflammatory factors IL-6, IL-10, IP-10, and TNF-α in the peripheral blood of NSCLC patients (n = 66) and healthy controls (n = 42) was analysed by quantitative RT-PCR, flow cytometry, and a cytometric bead array. IRF5 protein expression in NSCLC tissues (n = 102) was detected by Western blotting. The diagnostic value of IRF5 expression was determined by a receiver-operating characteristic (ROC) curve analysis.

RESULTS

The protein levels of IRF5, IL-6, and IP-10 were significantly higher in the peripheral blood of NSCLC patients than in that of healthy controls. IP-10 levels in plasma and IL-10 mRNA expression in white blood cells (WBCs) were significantly upregulated in early-stage NSCLC, whereas plasma IL-6 and IL-10 were elevated in the progressive stage. IRF5 protein levels in WBCs were positively correlated with plasma IP-10 but negatively correlated with plasma IL-10. Furthermore, the mRNA and protein levels of IRF5 in WBCs were significantly elevated in patients with early stage NSCLC compared to those in the progressive stage. Additionally, IRF5 protein levels were significantly lower in NSCLC tumour tissues than those in normal lung tissues.

CONCLUSIONS

IRF5 levels in WBCs can be significantly upregulated in early stage NSCLC and were shown to have diagnostic value as an early warning biomarker of NSCLC development.

Cancer overdiagnosis: a biological challenge and clinical dilemma

For cancer screening to be successful, it should primarily detect cancers with lethal potential or their precursors early, leading to therapy that reduces mortality and morbidity. Screening programmes have been successful for colon and cervical cancers, where subsequent surgical removal of precursor lesions has resulted in a reduction in cancer incidence and mortality. However, many types of cancer exhibit a range of heterogeneous behaviours and variable likelihoods of progression and death. Consequently, screening for some cancers may have minimal impact on mortality and may do more harm than good. Since the implementation of screening tests for certain cancers (for example, breast and prostate cancers), a spike in incidence of in situ and early-stage cancers has been observed, but a link to reduction in cancer-specific mortality has not been as clear. It is difficult to determine how many of these mortality reductions are due to screening and how many are due to improved treatments of tumours. In cancers with lower incidence but high mortality (for example, pancreatic cancer), screening has focused on high-risk populations, but challenges similar to those for general population screening remain, particularly with regard to finding lesions with difficult-to-characterize malignant potential (for example, intraductal papillary mucinous neoplasms). More sensitive screening methods are detecting smaller and smaller lesions, but this has not been accompanied by a comparable reduction in the incidence of invasive cancers. In this Opinion article, we focus on the contribution of screening in general and high-risk populations to overdiagnosis, the effects of overdiagnosis on patients and emerging strategies to reduce overdiagnosis of indolent cancers through an understanding of tumour heterogeneity, the biology of how cancers evolve and progress, the molecular and cellular features of early neoplasia and the dynamics of the interactions of early lesions with their surrounding tissue microenvironment.

Dual-biomarker-triggered fluorescence probes for differentiating cancer cells and revealing synergistic antioxidant effects under oxidative stress

Hydrogen sulfide (H2S) and human NAD(P)H:quinine oxidoreductase 1 (hNQO1) are potential cancer biomarkers and also vital participants in cellular redox homeostasis. Simultaneous detection of these two biomarkers would benefit the diagnostic precision of related cancers and could also help to investigate their crosstalk in response to oxidative stress. Despite this importance, fluorescent probes that can be activated by the dual action of H2S detection and hNQO1 activity have not been investigated. To this end, dual-biomarker-triggered fluorescent probes 1 and 2 were rationally constructed by installing two chemoselective triggering groups into one fluorophore. Probe 1 provides a small turn-on fluorescence response toward H2S but a much larger response to both H2S and hNQO1 in tandem. By contrast, fluorescence probe 2 is activated only in the presence of both H2S and hNQO1. Probe 2 exhibits a large fluorescence turn-on (>400 fold), high sensitivity, excellent selectivity as well as good biocompatibility, enabling the detection of both endogenous H2S and hNQO1 activity in living cells. Bioimaging results indicated that probe 2 could differentiate HT29 and HepG2 cancer cells from HCT116, FHC and HeLa cells owing to the existence of relatively high endogenous levels of both biomarkers. Expanded investigations using 2 revealed that cells could generate more endogenous H2S and hNQO1 upon exposure to exogenous hydrogen peroxide (H2O2), implying the synergistic antioxidant effects under conditions of cellular oxidative stress.

Lung Cancer Screening, Towards a Multidimensional Approach: Why and How?

Early-stage treatment improves prognosis of lung cancer and two large randomized controlled trials have shown that early detection with low-dose computed tomography (LDCT) reduces mortality. Despite this, lung cancer screening (LCS) remains challenging. In the context of a global shortage of radiologists, the high rate of false-positive LDCT results in overloading of existing lung cancer clinics and multidisciplinary teams. Thus, to provide patients with earlier access to life-saving surgical interventions, there is an urgent need to improve LDCT-based LCS and especially to reduce the false-positive rate that plagues the current detection technology. In this context, LCS can be improved in three ways: (1) by refining selection criteria (risk factor assessment), (2) by using Computer Aided Diagnosis (CAD) to make it easier to interpret chest CTs, and (3) by using biological blood signatures for early cancer detection, to both spot the optimal target population and help classify lung nodules. These three main ways of improving LCS are discussed in this review.

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.