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

Open ArchivePublished:December 04, 2018DOI:https://doi.org/10.1016/j.jtho.2018.11.023

      Abstract

      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.

      Keywords

      Lung Cancer Screening Challenges: Is Imaging Sufficient for Successful Screening? The Unmet Needs

      Current evidence supports lung cancer screening of subjects who fit the National Cancer Institute–sponsored National Lung Screening Trial (NLST) criteria. The NLST compared annual low-dose computed tomography (LDCT) screening with conventional chest radiography and found that LDCT screening achieved a 20% reduction in lung cancer mortality after only three rounds of screening.
      • Aberle D.R.
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      Reduced lung-cancer mortality with low-dose computed tomographic screening.
      Prospective data accrued by the International Early Lung Cancer Action Program from thousands of screening rounds also support annual screening with LDCT,
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      CT screening for lung cancer: significance of diagnoses in its baseline cycle.
      whereas data from the European NELSON trial, a Dutch-Belgian initiative, are also favorable toward screening.
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      • Van Der Aalst C.
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      • et al.
      Final screening round of the NELSON lung cancer screening trial: the effect of a 2.5-year screening interval.
      Results from the NELSON trial that were recently reported at the International Association for the Study of Lung Cancer meeting confirm the findings from the landmark NLST, showing that use of LDCT as a screening tool is effective, leading to a 26% (95% confidence interval: 9%–41%) reduction in lung cancer deaths in asymptomatic men at high risk for lung cancer after 10 years of follow-up. The reduction in mortality was even more impressive in women (OR for death = 0.39–0.61). The overall rate of adherence in that study, which enrolled almost 16,000 individuals, was 86%.

      De Koning H, Van Der Aalst C, ten Haaf K, Oudkerk M. Effects of volume CT lung cancer screening: mortality results of the NELSON randomized-controlled population trial. Paper presented at: 19th World Conference on Lung Cancer. September 23–26, 2018; Toronto, Canada.

      Consequently, recommendations from prominent scientific societies support screening with LDCT despite concerns regarding false-positive findings, risk of overdiagnosis, logistical challenges, and differences in selection criteria.
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      European position statement on lung cancer screening.
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      Screening for lung cancer: U.S. preventive services task force recommendation statement.
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      Screening high-risk populations for lung cancer: guideline recommendations.
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      Screening for lung cancer: CHEST guideline and expert panel report.
      Chief among these concerns is the widespread adoption of the NLST age and tobacco exposure (≥30 pack-years) inclusion criteria by screening guidelines. Current evidence suggests that such criteria may preclude screening of many individuals who are at risk.
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      Improving selection criteria for lung cancer screening: the potential role of emphysema.
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      Applying the National Lung Screening Trial eligibility criteria to the US population: what percent of the population and of incident lung cancers would be covered?.
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      Selection criteria for lung-cancer screening.
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      • Bade B.C.
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      • Silvestri G.A.
      Assessing the generalizability of the National Lung Screening Trial: comparison of patients with stage 1 disease.
      • Wood D.E.
      POINT: should lung cancer screening be expanded to persons who don’t currently meet accepted criteria set forth by the CHEST guidelines on lung cancer screening? Yes.
      It is becoming clearer that a more sophisticated risk-based strategy, taking emphysema into account, for example, may be better than the current NLST criteria.
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      • Wilson D.O.
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      Improving selection criteria for lung cancer screening: the potential role of emphysema.
      • Tammemagi C.M.
      • Pinsky P.F.
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      Lung cancer risk prediction: prostate, lung, colorectal and ovarian cancer screening trial models and validation.
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      Risk prediction models for selection of lung cancer screening candidates: a retrospective validation study.
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      • Yuan S.
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      Genetic predisposition to lung cancer: comprehensive literature integration, meta-analysis, and multiple evidence assessment of candidate-gene association studies.
      • De-Torres J.P.
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      • et al.
      Lung cancer in patients with chronic obstructive pulmonary disease: development and validation of the COPD lung cancer screening score.
      • Van Riel S.J.
      • Ciompi F.
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      • et al.
      Malignancy risk estimation of pulmonary nodules in screening CTs: comparison between a computer model and human observers.
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      • Schmidt H.
      • Martel S.
      • et al.
      Participant selection for lung cancer screening by risk modelling (the Pan-Canadian Early Detection of Lung Cancer [PanCan] study): a single-arm, prospective study.
      The advantage of the LDCT-based protocol is its simplicity and its high sensitivity. Refined criteria defining positive findings that are largely based on nodule size and/or volume reduce false-positive rates. That notwithstanding, there is a need for evidence-based biomarkers to support pretest and posttest (LDCT) risk assessment.
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      • Ilouze M.
      Screening for lung cancer: what comes next?.
      Ideally, robust biomarkers would optimize image-based screening in two ways. First, they would allow further refinement of screening selection criteria, independent of age and tobacco exposure, to limit the costs of lung cancer screening. This risk management biomarker strategy would be a welcome addition to current screening practice. For example, a number of single-nucleotide polymorphisms have been proposed in this regard as potential biomarkers of constitutive genomic risk for a given individual (see the section Genetic Predisposition to Lung Cancer). Such biomarkers are the focus of ongoing research when integrated with current clinicoepidemiological risk models for lung cancer.
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      The OncoArray Consortium: a network for understanding the genetic architecture of common cancers.
      Second, a validated panel of biomarkers may provide a posttest risk assessment capable of informing clinical decision making in the management of indeterminate pulmonary nodules (IPNs). Current management of IPNs is largely based on watchful waiting and may imply a risk of dissemination. Nodules found on annual screening, which are often so small that they are out of reach of current biopsy techniques, may benefit from a biomarker-based risk assessment. In particular, biomarkers may be helpful in the case of patients with nodules that need closer surveillance or a decision regarding biopsy. Patients with multiple nodules or those subject to frequent interval scans during screening might also benefit. Finally, biomarkers might also inform decisions regarding screening intervals, personalized follow-up of survivors of screen-detected early-stage lung cancer, outcome prediction, or response to adjuvant therapy for those at high-risk of recurrence. In the present review we will refer only to the biomarkers intended for the first two unmet needs (risk management and IPN characterization). Other recent articles have dealt with early prognostic biomarkers of lung cancer.
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      Prognostic and predictive biomarkers post curative intent therapy.
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      • et al.
      A novel protein-based prognostic signature improves risk stratification to guide clinical management in early lung adenocarcinoma patients.
      In the current review, we will focus initially on biomarkers that are noninvasive, reproducible, and validated and conclude with other promising technologies that are being developed in the context of early detection.

      What Is a Good Biomarker?

      The National Institutes of Health define a biomarker as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention.”
      • Atkinson A.J.
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      Biomarkers and surrogate endpoints: preferred definitions and conceptual framework.
      A useful biomarker must influence clinical decision making in a manner that leads to improved patient care. The benefit of clinical decisions based on true test results must outweigh the harm of decisions based on false positives or false negatives. In the risk management setting, a biomarker should minimize harm and expense without leading to an increase in lung cancer deaths. When used for IPN characterization, a biomarker should anticipate the diagnosis of malignancy without substantially increasing the number of diagnostic procedures performed for benign nodules or delaying therapeutic procedures for malignant nodules.
      • Mazzone P.J.
      • Sears C.R.
      • Arenberg D.A.
      • et al.
      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.

      Optimizing Discovery of Lung Cancer Biomarkers

      We believe that validation of a clinically useful biomarker should adhere to the following principles, which are summarized in Table 1.
      Table 1Principles to Optimize the Research on Development of Lung Cancer Biomarkers
      • Principle 1: Select the molecular approach guided by the intended use
      • Principle 2: Look for stable analytes that are minimally dependent on storage time, temperature, pH, and enzymatic or oxidative stress
      • Principle 3: The analyte should be measured with accuracy, precision, and robustness. Thoroughly test for reproducibility across sources of variability, laboratories, conditions, etc.
      • Principle 4: Test the biomarker in multiple cohorts in the clinical context in which it will be considered for use (screening, nodule management). Case-control studies are discouraged, whereas prospective cohort studies and, eventually, observational registries are favored though less convenient
      • Principle 5: Conduct the tests in larger cohorts to demonstrate superiority over standard of care, reduction of cost, and reduction of false-positive and false-negative rates

      Study Design

      Much is gained from the careful selection of the molecular approach chosen and should be guided by the intended use, where the biomarker would find potential clinical utility.
      • Pepe M.S.
      • Etzioni R.
      • Feng Z.
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      Phases of biomarker development for early detection of cancer.
      All too often investigators focus on versatility, seeking biomarkers that address multiple clinical needs such as risk assessment, diagnosis, or response to therapy. Much merit lies in limiting scope by addressing specific clinical needs.

      Biomarker Stability

      Information about the stability of the analyte over time, including changes in temperature, pH, and enzymatic or oxidative stress is critical.
      • Atwater T.
      • Cook C.
      • Massion P.
      The pursuit of noninvasive diagnosis of lung cancer.

      Analytical Validation

      Biomarker measurements should follow a well-defined strategy, and they should be accurate, precise, and robust. Validation should include testing of reproducibility against larger sources of variability such as biospecimen collection (e.g., sample processing, freeze-thaw cycles, duration of storage, etc.), operator characteristics, laboratory environment, and quality control (standard curves, standard operating procedures). Some variability is inherent to the technology itself (energy source, enzymatic activity, and temperature control).
      • Rodriguez H.
      • Težak Ž.
      • Mesri M.
      • et al.
      Analytical validation of protein-based multiplex assays: a workshop report by the NCI-FDA interagency oncology task force on molecular diagnostics.
      Metrics of success include coefficient of variance, z statistic, limits of detection, and quantitation.

      Clinical Validation

      The ideal diagnostic biomarker is both sensitive and specific, with diagnostic likelihood ratios independent of known predictors of the disease (e.g., age, smoking history, or chronic obstructive pulmonary disease [COPD]). Validation should be performed in the clinical context of intended use. Case-control studies are discouraged; prospective cohort studies and observational registries are preferable. The biomarker will be tested in multiple cohorts with similar prevalence of disease. Biomarkers rarely perform well across a large range of disease prevalences, and their performance characteristics are often susceptible to changes in simple variables such as age or disease stage.

      Clinical Utility

      The biomarker should be tested for clinical utility in larger studies in a pragmatic setting that does not disrupt the clinical workflow. The goal of any biomarker is to achieve superior performance compared with standard of care–based approaches and eventually reduce the cost and harm of testing while limiting false-negative rates.
      • Mazzone P.J.
      • Sears C.R.
      • Arenberg D.A.
      • et al.
      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.
      Study design is challenging owing to randomization and the need to affect clinical management (see in the section Future Research Challenges for a potential trial design). Ultimately, implementation of the biomarker in routine practice will determine its true value for clinical decision making.

      The Metrics of Success

      Biomarker performance and accuracy are dependent on the intended use and current alternatives. A successful biomarker must supersede the current standard of care,
      • Atwater T.
      • Cook C.
      • Massion P.
      The pursuit of noninvasive diagnosis of lung cancer.
      • Pecot C.V.
      • Li M.
      • Zhang X.J.
      • et al.
      Added value of a serum proteomic signature in the diagnostic evaluation of lung nodules.
      • Silvestri G.A.
      • Vachani A.
      • Whitney D.
      • et al.
      A bronchial genomic classifier for the diagnostic evaluation of lung cancer.
      be cost-effective, be welcomed by the community, and eventually demonstrate cancer control if early detection is the goal (Fig. 1) or promote personalized medicine by identifying candidates for targeted therapies. Understanding traditional metrics of success in this context is key. Sensitivity and specificity, for example, are often unstable over multiple variables such as age or disease stage. Positive predictive values and negative predictive values (NPVs) are dependent on the prevalence of disease. Receiver operating characteristic curves (i.e., true positive rates versus false-positive rates) are helpful, but they complicate decision making because of the need for dichotomous biomarker cutoff values. Reclassification indices have a role in testing a biomarker’s ability to accurately reclassify cases and controls and therefore influence clinical decision making.
      Figure thumbnail gr1
      Figure 1Criteria for clinical use of biomarkers.

      Current Promising Molecular Biomarkers

      Molecular Biomarkers for Lung Cancer Screening

      Blood is an obvious first choice as the source of biomarker candidates for lung cancer screening. Blood-based biomarkers provide an overview of the whole patient body, including the primary tumor, metastatic disease, immune response, and peritumoral stroma. However, sputum, bronchial lavage or aspirate samples, exhaled breath (EB), and airway epithelium sampling are unique to lung and other respiratory tract cancers as potential sources of alternative biomarkers. These may provide information regarding molecular changes that may be anatomically closer to the tumor cells and their microenvironment and therefore potentially more relevant to clinical decision making in screened patients with early-stage disease (Fig. 2). Urine and saliva have also been collected as potential sources of biomarkers. The former is particularly useful in a metabolomics-based approach.
      Figure thumbnail gr2
      Figure 2Currently explored biomarker candidates. miRNA, microRNA.
      A concise review of the most prominent molecular biomarkers for lung cancer screening includes examples of molecular candidates for both risk management and IPN characterization in diverse stages of validation. We have included those that we consider the most promising. We are well aware of the risk of omitting potential candidates. Figure 3 includes a list of biomarkers that have reached different levels of validation.
      Figure thumbnail gr3
      Figure 3Candidate biomarkers for lung cancer early detection and phase of development. AAb, autoantibody; CA-125, cancer antigen 125; CAGE, cancer-associated antigen gene; CEA, carcinoembryonic antigen; COPD, chronic obstructive pulmonary disease; CT, computed tomography; CYFRA, cytokeratin fragment; DIPN, diagnosis of indeterminate pulmonary nodules; FISH, fluorescence in situ hybridization; HGF, hepatocyte growth factor; HUD Hu antigen D, embryonic lLethal, abnormal vision, drosophila-like protein 4; LG3BP, galectin-3 recombinant protein; MAGE, melanoma-associated antigen; MIRNA, microRNA; MPO, myeloperoxidase; NBT, nanoparticle biometric tagging; NGS, next-generation sequencing; NSE, neuron-specific enolase; NY-ESO-1, New York esophageal squamous cell carcinoma 1; OPN, osteopontin; PRL, prolactin; PROGRP, progastrin-releasing peptide; PTGER4, prostaglandin E receptor 4; RASSF1A, an isoform of Ras association domain family member 1; RIMS, risk management screening context; SHOX2, short stature homeobox 2; SNP, single-nucleotide polymorphism; SOX2, SRY-box 2; TEC-Seq, targeted error correction sequencing; TIMP1, TIMP metallopeptidase inhibitor 1; VOC, volatile organic compound.
      • Liu L.
      • Teng J.
      • Zhang L.
      • et al.
      The combination of the tumor markers suggests the histological diagnosis of lung cancer.
      • Boeri M.
      • Verri C.
      • Conte D.
      • et al.
      MicroRNA signatures in tissues and plasma predict development and prognosis of computed tomography detected lung cancer.
      • Bianchi F.
      • Nicassio F.
      • Marzi M.
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      A serum circulating miRNA diagnostic test to identify asymptomatic high-risk individuals with early stage lung cancer.
      • Maisonneuve P.
      • Bagnardi V.
      • Bellomi M.
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      Lung cancer risk prediction to select smokers for screening CT–a model based on the Italian COSMOS trial.
      • Yanaihara N.
      • Caplen N.
      • Bowman E.
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      Unique microRNA molecular profiles in lung cancer diagnosis and prognosis.
      • Weiss G.
      • Schlegel A.
      • Kottwitz D.
      • König T.
      • Tetzner R.
      Validation of the SHOX2/PTGER4 DNA methylation marker panel for plasma-based discrimination between patients with malignant and nonmalignant lung disease.
      • Newman A.M.
      • Bratman S.V.
      • To J.
      • et al.
      An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage.
      • Guo N.
      • Lou F.
      • Ma Y.
      • et al.
      Circulating tumor DNA detection in lung cancer patients before and after surgery.
      • Phallen J.
      • Sausen M.
      • Adleff V.
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      Direct detection of early-stage cancers using circulating tumor DNA.
      • Showe M.K.
      • Vachani A.
      • Kossenkov A.V.
      • et al.
      Gene expression profiles in peripheral blood mononuclear cells can distinguish patients with non-small cell lung cancer from patients with nonmalignant lung disease.
      • Halling K.C.
      • Rickman O.B.
      • Kipp B.R.
      • Harwood A.R.
      • Doerr C.H.
      • Jett J.R.
      A comparison of cytology and fluorescence in situ hybridization for the detection of lung cancer in bronchoscopic specimens.
      • Nichols J.A.A.
      • Grob P.
      • de Lusignan S.
      • Kite W.
      • Williams P.
      Genetic test to stop smoking (GeTSS) trial protocol: randomised controlled trial of a genetic test (Respiragene) and Auckland formula to assess lung cancer risk.
      • Zhang C.
      • Yu W.
      • Wang L.
      • et al.
      DNA methylation analysis of the SHOX2 and RASSF1A panel in bronchoalveolar lavage fluid for lung cancer diagnosis.
      • Xing L.
      • Su J.
      • Guarnera M.A.
      • et al.
      Sputum microRNA biomarkers for identifying lung cancer in indeterminate solitary pulmonary nodules.
      • Subramanian H.
      • Viswanathan P.
      • Cherkezyan L.
      • et al.
      Procedures for risk-stratification of lung cancer using buccal nanocytology.
      • Patriquin L.
      • Merrick D.T.
      • Hill D.
      • et al.
      Early detection of lung cancer with meso tetra (4-carboxyphenyl) porphyrin-labeled sputum.

      AAbs

      Autoantibodies (AAbs) develop in response to an abnormal tumor antigen in some patients with lung cancer, often in the preclinical phase well before symptoms appear or imaging-based detection is possible. AAbs have been identified in all histological types and stages of lung cancer. They are usually absent or found in low titers not only in those without cancer but also in many patients with the disease. AAb panels are therefore likely to be specific but not sensitive. Use of a well-validated AAb panel has been studied in different screening cohorts as an approach to management of lung cancer risk.
      • Lam S.
      • Boyle P.
      • Healey G.F.
      • et al.
      EarlyCDT-Lung: an immunobiomarker test as an aid to early detection of lung cancer.
      • Macdonald I.K.
      • Murray A.
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      • et al.
      Application of a high throughput method of biomarker discovery to improvement of the EarlyCDT®-Lung test.
      • Chapman C.J.
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      • Murray A.
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      EarlyCDT®-Lung test: improved clinical utility through additional autoantibody assays.
      • Healey G.F.
      • Lam S.
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      • Hamilton-Fairley G.
      • Peek L.J.
      • Robertson J.F.R.
      Signal stratification of autoantibody levels in serum samples and its application to the early detection of lung cancer.
      • Jett J.R.
      • Peek L.J.
      • Fredericks L.
      • Jewell W.
      • Pingleton W.W.
      • Robertson J.F.R.
      Audit of the autoantibody test, EarlyCDT®-Lung, in 1600 patients: an evaluation of its performance in routine clinical practice.
      • Massion P.P.
      • Healey G.F.
      • Peek L.J.
      • et al.
      Autoantibody signature enhances the positive predictive power of computed tomography and nodule-based risk models for detection of lung cancer.
      • Sullivan F.M.
      • Farmer E.
      • Mair F.S.
      • et al.
      Detection in blood of autoantibodies to tumour antigens as a case-finding method in lung cancer using the EarlyCDT®-Lung Test (ECLS): study protocol for a randomized controlled trial.
      • Edelsberg J.
      • Weycker D.
      • Atwood M.
      • Hamilton-fairley G.
      • Jett R.
      Cost-effectiveness of an autoantibody test ( Early CDT-Lung ) as an aid to early diagnosis of lung cancer in patients with incidentally detected pulmonary nodules.
      In a clinical validation study including all lung cancer histological types and stages the panel performed well, with 93% specificity but only 40% sensitivity.
      • Boyle P.
      • Chapman C.J.
      • Holdenrieder S.
      • et al.
      Clinical validation of an autoantibody test for lung cancer.
      Similarly, a practice audit of 1699 patients (61 with lung cancer and one-third in stage I) found that the panel had high specificity (91%) but low sensitivity (37%).
      • Jett J.R.
      • Peek L.J.
      • Fredericks L.
      • Jewell W.
      • Pingleton W.W.
      • Robertson J.F.R.
      Audit of the autoantibody test, EarlyCDT®-Lung, in 1600 patients: an evaluation of its performance in routine clinical practice.
      AAbs may find a place in clinical practice by improving the overall test accuracy of hybrid panels featuring diverse biomarkers.
      • Doseeva V.
      • Colpitts T.
      • Gao G.
      • Woodcock J.
      • Knezevic V.
      Performance of a multiplexed dual analyte immunoassay for the early detection of non-small cell lung cancer.

      Complement Fragments

      Lung cancer can activate the complement cascade by the classical complement pathway.
      • Ajona D.
      • Pajares M.J.
      • Corrales L.
      • et al.
      Investigation of complement activation product C4d as a diagnostic and prognostic biomarker for lung cancer.
      Concentrations of a downstream split product of this pathway, C4d, are increased in biological fluids from patients with lung cancer. Plasma C4d levels have been linked to increased lung cancer risk in a cohort of 190 asymptomatic individuals, including 32 patients with screening-detected cancer who were enrolled in the International Early Lung Cancer Action Program cohort (OR = 4.38; 95% CI = 1.61–11.93). In that study, potential confounders such as emphysema and COPD did not appear to affect C4d plasma levels.
      • Ajona D.
      • Pajares M.J.
      • Corrales L.
      • et al.
      Investigation of complement activation product C4d as a diagnostic and prognostic biomarker for lung cancer.
      Unfortunately, its use as a marker for the selection of high-risk individuals could not be validated by using samples from the MILD computed tomography (CT) screening trial.
      • Ajona D.
      • Okrój M.
      • Pajares M.J.
      • et al.
      Complement C4d-specific antibodies for the diagnosis of lung cancer.
      Nevertheless, the results on its use for the management of IPNs are more promising. Plasma samples from patients from two independent cohorts with malignant nodules presented significantly higher levels of C4d than did those from patients with benign nodules. In selected patients with intermediate-sized pulmonary nodules (8–30 mm), C4d plasma levels identified benign lung nodules with an 84% NPV and a 54% positive predictive value. Once again, the test enjoyed high specificity (89%) at the expense of low sensitivity (44%).
      • Ajona D.
      • Okrój M.
      • Pajares M.J.
      • et al.
      Complement C4d-specific antibodies for the diagnosis of lung cancer.

      miRNAs

      Circulating microRNAs (miRNAs) reflecting tumor-host interactions have emerged as potential biomarkers for cancer diagnosis and prognosis irrespective of tumor stage and mutational bourden.
      • Verri C.
      • Borzi C.
      • Holscher T.
      • et al.
      Mutational profile from targeted NGS predicts survival in LDCT screening–detected lung cancers.
      The role of miRNA-based liquid biopsies has been assessed in the context of screening with LDCT in two large Italian retrospective validation studies.
      • Sozzi G.
      • Boeri M.
      • Rossi M.
      • et al.
      Clinical utility of a plasma-based miRNA signature classifier within computed tomography lung cancer screening: a correlative MILD trial study.
      • Montani F.
      • Marzi M.J.
      • Dezi F.
      • et al.
      MiR-test: a blood test for lung cancer early detection.
      Use of the miRNA signature classifier (MSC) and the miR-Test resulted in fivefold and fourfold reductions in the LDCT false-positive rate, respectively, with comparable specificity (81%–75%) and sensitivity (87%–78%). In postsurgical plasma samples, the MSC showed good performance in monitoring disease relapse.
      • Sestini S.
      • Boeri M.
      • Marchiano A.
      • et al.
      Circulating microRNA signature as liquid-biopsy to monitor lung cancer in low-dose computed tomography screening.
      The two tests are now undergoing prospective validation in three independent screening trials enrolling a total of 16,000 high-risk subjects.

      ctDNA

      The value of circulating tumor DNA (ctDNA) as a biomarker in advanced tumor stages is well established.
      • Jenkins S.
      • Yang J.C.H.
      • Ramalingam S.S.
      • et al.
      Plasma ctDNA analysis for detection of the EGFR T790M mutation in patients with advanced non–small cell lung cancer.
      • Giroux Leprieur E.
      • Herbretau G.
      • Dumenil C.
      • et al.
      Circulating tumor DNA evaluated by next-generation sequencing is predictive of tumor response and prolonged clinical benefit with nivolumab in advanced non-small cell lung cancer.
      However, its role in early lung cancer detection is still uncertain.
      • Merker J.D.
      • Oxnard G.R.
      • Compton C.
      • et al.
      Circulating tumor DNA analysis in patients with cancer: American Society of Clinical Oncology and College of American Pathologists joint review.
      Abbosh et al. reported 48% overall sensitivity, setting a threshold of two single-nucleotide variants in 96 patients with stage I to III NSCLC. Sensitivity ranged from 15% for stage I adenocarcinomas to 100% for stage II or III squamous cell carcinomas.
      • Abbosh C.
      • Birkbak N.J.
      • Wilson G.A.
      • et al.
      Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution.
      Current efforts to develop next-generation sequencing (NGS) technologies to study ctDNA in the context of early detection may improve sensitivity in this context. A recently reported test for pan-cancer early detection combined the NGS analysis of ctDNA in blood with a large panel of protein biomarkers in 1005 patients with stage I to III pan-cancer and 812 cancer-free controls.
      • Cohen J.D.
      • Li L.
      • Wang Y.
      • et al.
      Detection and localization of surgically resectable cancers with a multi-analyte blood test.
      Although specificity was higher than 99%, sensitivity ranged from 33% for breast cancer to 98% for ovarian cancer. The sensitivity for lung cancer was 59% in 104 patients.

      DNA Methylation

      Tumor tissue is characterized by a global DNA hypomethylation status together with hypermethylation of specific CpG islands in the promoter region of tumor suppressor genes.
      • Ehrlich M.
      DNA hypomethylation in cancer cells.
      Hypermethylation of at least one of four studied genes was detected 20 years ago in 15 of 22 NSCLC tumors (68%) but not in any paired normal lung tissue sample. In these primary tumors with methylation, 11 of 15 samples (73%) also had abnormal methylated DNA in the matched serum sample.
      • Esteller M.
      • Sanchez-Cespedes M.
      • Resell R.
      • Sidransky D.
      • Baylin S.B.
      • Herman J.G.
      Detection of aberrant promoter hypermethylation of tumor suppressor genes in serum DNA from non-small cell lung cancer patients.
      More recently, a 64-gene panel quantitative polymerase chain reaction assay was used to study 204 serum samples from 33 patients with lung cancer, 68 patients with fibrotic interstitial lung disease, 42 patients with COPD, and 61 healthy controls. The test had 88% sensitivity and 90% specificity when compared with controls, and 88% specificity when compared with patients with COPD and interstitial lung disease.
      • Wielscher M.
      • Vierlinger K.
      • Kegler U.
      • Ziesche R.
      • Gsur A.
      • Weinhäusel A.
      Diagnostic performance of plasma DNA methylation profiles in lung cancer, pulmonary fibrosis and COPD.
      In 2017, Ooki et al. reported that a six-gene panel correctly classified 43 stage IA and 42 control subjects with 72% sensitivity and 71% specificity.
      • Ooki A.
      • Maleki Z.
      • Tsay J.C.J.
      • et al.
      A panel of novel detection and prognostic methylated DNA markers in primary non–small cell lung cancer and serum DNA.
      Hulbert et al. recently described a three-gene model discriminating subjects with suspicious nodules on CT imaging (150 subjects with stage I or II NSCLC and 60 controls) with 98% sensitivity and 71% specificity in sputum and 93% sensitivity and 62% specificity in plasma samples.
      • Hulbert A.
      • Jusue Torres I.
      • Stark A.
      • et al.
      Early detection of lung cancer using DNA promoter hypermethylation in plasma and sputum.

      Blood Protein Profiling

      Many studies have identified measurable serum antigens in patients with lung cancer. Panels of serum cancer antigens have been developed to improve diagnostic accuracy. One panel of three serum proteins (carcinoembryonic antigen, cancer antigen 125, cytokeratin fragment 21-1) and an AAb (New York esophageal squamous cell carcinoma 1) performed well in a high-risk cohort, with 71% sensitivity and 88% specificity for lung cancer.
      • Doseeva V.
      • Colpitts T.
      • Gao G.
      • Woodcock J.
      • Knezevic V.
      Performance of a multiplexed dual analyte immunoassay for the early detection of non-small cell lung cancer.
      Clinical validation was performed in a separate high-risk cohort (based on age and smoking history) with lower sensitivity (49%) but higher specificity (96%). The incorporation of clinical variables improved accuracy.
      • Mazzone P.J.
      • Wang X.
      • Han X.
      • et al.
      Evaluation of a serum lung cancer biomarker panel.
      A different panel of cancer antigens (carcinoembryonic antigen, cancer antigen 15.3, squamous cell carcinoma, cytokeratin fragment 21-1, neuron-specific enolase, and progastrin-releasing peptide) increased the area under the curve of a clinical prediction model based on nodule size, age, and smoking status from 0.85 to 0.93 in a series of 3144 symptomatic individuals, including 1828 with lung cancer (52% stage IV).
      • Molina R.
      • Marrades R.M.
      • Augé J.M.
      • et al.
      Assessment of a combined panel of six serum tumor markers for lung cancer.
      A two-protein biomarker ratio combined with a lung nodule clinical risk predictor had a sensitivity of 97% and a specificity of 44% for malignant disease in a series of 178 patients with suspicious lung nodules. The biomarker’s performance was evaluated for nodules that had a calculated pretest risk of malignancy of 50% or less. This integrated classifier could have led to a 40% relative reduction in invasive testing for patients with benign nodules (10% absolute risk reduction) while potentially delaying the management of 3% of malignant nodules.
      • Silvestri G.A.
      • Tanner N.T.
      • Kearney P.
      • et al.
      Assessment of plasma proteomics biomarker’s ability to distinguish benign from malignant lung nodules: results of the PANOPTIC (PulmonAry NOdule Plasma proTeomIc Classifier) trial.

      RNA Airway and Nasal Signature

      On the basis of the “field of injury” paradigm,
      • Billatos E.
      • Vick J.L.
      • Lenburg M.E.
      • Spira A.E.
      The airway transcriptome as a biomarker for early lung cancer detection.
      airway epithelial gene expression has been developed as a diagnostic biomarker for lung cancer. The initial studies focused on bronchial airway epithelial cells obtained by endobronchial brushings of the mainstem bronchus.
      • Spira A.
      • Beane J.E.
      • Shah V.
      • et al.
      Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer.
      • Blomquist T.
      • Crawford E.L.
      • Mullins D.
      • et al.
      Pattern of antioxidant and DNA repair gene expression in normal airway epithelium associated with lung cancer diagnosis.
      A 23-gene biomarker measured in bronchial epithelial cells has been tested as an adjuvant diagnostic biomarker for patients undergoing bronchoscopy for suspected lung cancer.
      • Whitney D.H.
      • Elashoff M.R.
      • Porta-Smith K.
      • et al.
      Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy.
      This biomarker underwent clinical validation in two independent prospective cohorts, demonstrating a sensitivity of 88% to 89% and a specificity of 48%. The biomarker was particularly helpful in patients with an intermediate (10%–60%) pretest risk of lung cancer (91% NPV). Patients with inconclusive bronchoscopy results could have benefited from the biomarker’s negative predictive value by avoiding further invasive testing, suggesting such patients could be followed safely with serial imaging studies.
      • Silvestri G.A.
      • Vachani A.
      • Whitney D.
      • et al.
      A bronchial genomic classifier for the diagnostic evaluation of lung cancer.
      After analytic validation
      • Hu Z.
      • Whitney D.
      • Anderson J.R.
      • et al.
      Analytical performance of a bronchial genomic classifier.
      and other clinical studies,
      • Vachani A.
      • Whitney D.H.
      • Parsons E.C.
      • et al.
      Clinical utility of a bronchial genomic classifier in patients with suspected lung cancer.

      Hogarth DK, Dotson TL, Lee HL, Whitten PE, Smith K, Lenburg ME. The Percepta® Registry: a prospective registry to evaluate percepta bronchial genomic classifier patient data. Paper presented at: CHEST Annual Meeting. October 22–26, 2016; Los Angeles, CA.

      the test received a favorable Medicare coverage decision in 2017.
      The same field of injury concept may be useful in samples of nasal epithelial cells. This approach has obvious advantages as a minimally invasive diagnostic alternative for those not undergoing bronchoscopy as part of their clinical work-up. A 30-gene nasal expression panel has been developed for diagnosing lung cancer among ever-smokers with suspected disease, demonstrating improvement in area under the curve, sensitivity, and NPV when combined with clinical risk models.
      • Perez-Rogers J.F.
      • Gerrein J.
      • et al.
      AEGIS Study Team
      Shared gene expression alterations in nasal and bronchial epithelium for lung cancer detection.
      This is preliminary evidence in need of supporting studies.

      Current Trials in Which Biomarkers Are Considered or Included

      Clinical validation study results have been published for a handful of biomarkers. Other biomarkers linger at various stages of development, whereas a few have entered formal clinical testing. The aforementioned panel of AAbs
      • Lam S.
      • Boyle P.
      • Healey G.F.
      • et al.
      EarlyCDT-Lung: an immunobiomarker test as an aid to early detection of lung cancer.
      is currently being assessed as part of a Scottish National Health Service–funded randomized controlled screening study enrolling 12,000 subjects (the ECLS study [NCT01925625]). A bronchial gene expression classifier that could improve the diagnostic performance of bronchoscopy is being tested in a large registry. The combination of the plasma MSC with LDCT results informs screening intervals in 4119 at-risk subjects in the bioMILD screening trial (NCT02247453).
      • Boeri M.
      • Sestini S.
      • Fortunato O.
      • et al.
      Recent advances of microRNA-based molecular diagnostics to reduce false-positive lung cancer imaging.
      Plasma samples that were prospectively collected during the COSMOS II screening trial have been profiled to set up and validate the clinical utility of the microRNA-signature based miR-Test.
      • Filippo L.
      • Principe R.
      • Cesario A.
      • et al.
      Smoking cessation intervention within the framework of a lung cancer screening program: preliminary results and clinical perspectives from the “Cosmos-II” Trial.
      The DECAMP-1 and DECAMP-2 prospective observational trials (NCT01785342 and NCT02504697) have been designed to examine a variety of existing biomarkers for lung cancer diagnosis as well as new biomarkers discovered specifically in this clinical setting. DECAMP-1 seeks to improve follow-up of patients with IPNs by determining whether analyzed biomarkers are able to distinguish incidentally detected malignant from benign pulmonary nodules in high-risk smokers, whereas DECAMP-2 will test biomarkers of risk in screened asymptomatic high-risk individuals.
      An exciting amount of high-quality discovery and clinical validation work is ongoing. Some companies are in the process of planning true clinical utility studies for lung nodule management. The lack of an established trade-off regarding the consequences of true and false biomarker results is a challenge that every biomarker developer will face. It would behoove the clinical community to provide guidance regarding acceptable trade-offs in both the screening and lung nodule management settings.
      • Mazzone P.J.
      • Sears C.R.
      • Arenberg D.A.
      • et al.
      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.

      Emerging Biomarkers, New Technologies to Follow, and Future Directions. The Power of Integration

      The aforementioned biomarkers have been the object of intense research, and a number of them are being assessed in a risk management strategy to recommend screening or aiming to characterize IPNs. We will now discuss promising new technologies with potential, including integrated approaches to biomarker development in lung cancer screening.

      EB Biomarkers

      There is growing evidence to support the use of EB, including EB condensate (EBC) for cancer detection. The use of EBC, which includes cells and DNA fragments, may even support detection of EGFR-resistant clones.
      • Smyth R.J.
      • Toomey S.M.
      • Sartori A.
      • et al.
      Brief report on the detection of the EGFR-T790M mutation in exhaled breath condensate from lung cancer patients.
      The volatile fragments of EB are sensitive biomarkers of lung cancer. Volatile organic compounds can be captured and analyzed by a wide range of technologies, including gas chromatography/mass spectrometry, nanosensors, colorimetric sensors, and other methods.
      • Mazzone P.J.
      • Wang X.F.
      • Lim S.
      • et al.
      Progress in the development of volatile exhaled breath signatures of lung cancer.
      • Nakhleh M.K.
      • Amal H.
      • Jeries R.
      • et al.
      Diagnosis and classification of 17 diseases from 1404 subjects via pattern analysis of exhaled molecules.
      An artificially intelligent nanoarray sensor has been used in the diagnosis and classification of 17 different diseases from breath samples of 1404 subjects, providing 86% accuracy. Some studies suggest that such an array may discriminate benign from malignant pulmonary nodules
      • Peled N.
      • Hakim M.
      • Bunn P.A.
      • et al.
      Non-invasive breath analysis of pulmonary nodules.
      or predict response to therapy and recurrence.
      • Nardi-Agmon I.
      • Abud-Hawa M.
      • Liran O.
      • et al.
      Exhaled breath analysis for monitoring response to treatment in advanced lung cancer.
      It may also distinguish histological type
      • Hakim M.
      • Broza Y.Y.
      • Barash O.
      • et al.
      Volatile organic compounds of lung cancer and possible biochemical pathways.
      or predict molecular analysis results.
      • Peled N.
      • Barash O.
      • Tisch U.
      • et al.
      Volatile fingerprints of cancer specific genetic mutations.
      Interestingly, it apparently may also discriminate between different types of cancer (lung, breast, colorectal, and prostate).
      • Peng G.
      • Hakim M.
      • Broza Y.Y.
      • et al.
      Detection of lung, breast, colorectal, and prostate cancers from exhaled breath using a single array of nanosensors.

      Sputum Cell–Based Image Analysis

      Although cytologic examination of sputum has traditionally failed to yield either adequate or useful samples for lung cancer screening, the advent of enhanced cytology, in which sophisticated image analysis algorithms are combined with artificial intelligence, may yet prove sputum useful in this context. A newly developed test can identify abnormal cells in sputum samples of screened patients.
      • Meyer M.G.
      • Hayenga J.W.
      • Neumann T.
      • et al.
      The Cell-CT 3-dimensional cell imaging technology platform enables the detection of lung cancer using the noninvasive LuCED sputum test.
      This test may be used as a primary screening modality with a reported sensitivity of 90% when at least 800 bronchial cells are available for analysis, or it may be integrated with LDCT in the context of a conventional CT-based screening program for IPN characterization.
      • Nelson A.
      • Meyer M.
      • Katdare R.
      • et al.
      Early detection of lung cancer based on three-dimensional, morphometric analysis of cells from sputum [abstract].
      In the latter case, fewer cells may be needed because the clinician can integrate clinical, molecular, or conventional sputum cytologic data with imaging results for greater diagnostic accuracy.

      Metabolomics

      A range of different analytical platforms and methodologies have been applied to identify metabolic biomarkers of lung cancer.
      • Yu L.
      • Li K.
      • Zhang X.
      Next-generation metabolomics in lung cancer diagnosis, treatment and precision medicine: mini review.
      Metabolomics provides a direct functional readout of the phenotypic changes associated with the development of lung tumors and their microenvironment. Metabolomics has several advantages when compared with other "omics," including a reduced number of metabolites and a wide range of biological samples that can be tested. Changes in lung cancer metabolites include those involved in glycolysis, the citric acid cycle, amino acid metabolism, and cell membrane synthesis.
      • Yu L.
      • Li K.
      • Zhang X.
      Next-generation metabolomics in lung cancer diagnosis, treatment and precision medicine: mini review.
      • Puchades-Carrasco L.
      • Jantus-Lewintre E.
      • Pérez-Rambla C.
      • et al.
      Serum metabolomic profiling facilitates the non-invasive identification of metabolic biomarkers associated with the onset and progression of non-small cell lung cancer.
      Metabolomics can differentiate between histological subtypes or genetic backgrounds.
      • Rezola A.
      • Pey J.
      • Rubio Á.
      • Planes F.J.
      In-silico prediction of key metabolic differences between two non-small cell lung cancer subtypes.
      • Caiola E.
      • Brunelli L.
      • Marabese M.
      • Broggini M.
      • Lupi M.
      • Pastorelli R.
      Different metabolic responses to PI3K inhibition in NSCLC cells harboring wild-type and G12C mutant KRAS.
      A panel of metabolites excreted in the urine, namely, creatine riboside and N-acetyl neuraminic acid, have been associated with lung cancer risk before clinically detectable disease.
      • Mathé E.A.
      • Patterson A.D.
      • Haznadar M.
      • et al.
      Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer.
      • Haznadar M.
      • Cai Q.
      • Krausz K.W.
      • et al.
      Urinary metabolite risk biomarkers of lung cancer: a prospective cohort study.
      Panels as well as individual markers in blood, sputum, or EBC have also been proposed to identify high-risk candidates for screening or to discriminate between benign and malignant IPNs.
      • Roś-Mazurczyk M.
      • Wojakowska A.
      • Marczak Ł.
      • et al.
      Panel of serum metabolites discriminates cancer patients and healthy participants of lung cancer screening - a pilot study.
      • Wikoff W.R.
      • Hanash S.
      • DeFelice B.
      • et al.
      Diacetylspermine is a novel prediagnostic serum biomarker for non-small-cell lung cancer and has additive performance with pro-surfactant protein B.
      • Wen C.P.
      • Zhang F.
      • Liang D.
      • et al.
      The ability of bilirubin in identifying smokers with higher risk of lung cancer: a large cohort study in conjunction with global metabolomic profiling.
      • Fahrmann J.F.
      • Grapov D.
      • Defelice B.C.
      • et al.
      Serum phosphatidylethanolamine levels distinguish benign from malignant solitary pulmonary nodules and represent a potential diagnostic biomarker for lung cancer.
      • Cameron S.J.S.
      • Lewis K.E.
      • Beckmann M.
      • et al.
      The metabolomic detection of lung cancer biomarkers in sputum.
      • Peralbo-Molina A.
      • Calderón-Santiago M.
      • Priego-Capote F.
      • Jurado-Gámez B.
      • Luque De Castro M.D.
      Identification of metabolomics panels for potential lung cancer screening by analysis of exhaled breath condensate.
      Finally, other omics, such as microbiomics, are providing us with novel diagnostic markers that merit a closer look.
      • Lee S.H.
      • Sung J.Y.
      • Yong D.
      • et al.
      Characterization of microbiome in bronchoalveolar lavage fluid of patients with lung cancer comparing with benign mass like lesions.

      Genetic Predisposition to Lung Cancer

      The advent of genomewide association studies may provide the lung cancer community with strong evidence of genetic susceptibility genes, which may be included in lung cancer risk prediction models.
      • Tammemagi C.M.
      • Pinsky P.F.
      • Caporaso N.E.
      • et al.
      Lung cancer risk prediction: prostate, lung, colorectal and ovarian cancer screening trial models and validation.
      • ten Haaf K.
      • Jeon J.
      • Tammemägi M.C.
      • et al.
      Risk prediction models for selection of lung cancer screening candidates: a retrospective validation study.
      • Cassidy A.
      • Myles J.P.
      • Van Tongeren M.
      • et al.
      The LLP risk model: an individual risk prediction model for lung cancer.
      Current evidence from a major review in 2017 of more than 1000 candidate association studies identified 22 variants in 21 genes that had strong cumulative epidemiological evidence of significant associations with lung cancer risk.
      • Wang J.
      • Liu Q.
      • Yuan S.
      • et al.
      Genetic predisposition to lung cancer: comprehensive literature integration, meta-analysis, and multiple evidence assessment of candidate-gene association studies.
      The OncoArray Consortium research program
      • Amos C.I.
      • Dennis J.
      • Wang Z.
      • et al.
      The OncoArray Consortium: a network for understanding the genetic architecture of common cancers.
      has provided recent new insights and a new set of susceptibility genes.
      • McKay J.D.
      • Hung R.J.
      • Han Y.
      • et al.
      Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes.
      However, the potential contribution of this new set of genes to risk prediction models used in lung cancer screening trials has yet to be demonstrated.
      • Field J.K.
      • Duffy S.W.
      • Baldwin D.R.
      • et al.
      The UK Lung Cancer Screening Trial: a pilot randomised controlled trial of low-dose computed tomography screening for the early detection of lung cancer.
      Clinical applications aside, these susceptibility genes do provide an insight into the biological process and association with specific diseases, which are relevant to lung cancer etiology.
      • Ji X.
      • Bossé Y.
      • Landi M.T.
      • et al.
      Identification of susceptibility pathways for the role of chromosome 15q25.1 in modifying lung cancer risk.

      Fusco JP, Pita G, Pajares MJ, et al. Genomic characterization of individuals presenting extreme phenotypes of high and low risk to develop tobacco-induced lung cancer [e-pub ahead of print]. Cancer Med. https://doi.org/10.1002/cam4.1500, accessed

      Clearly, there is a wealth of information captured within the current genome-wide association studies' data sets. Taking advantage of it will depend on state-of-the-art mathematical and statistical approaches capable of incorporating large numbers of single-nucleotide polymorphisms into risk models, artificial intelligence, and supervised machine learning approaches.
      • Mieth B.
      • Kloft M.
      • Rodríguez J.A.
      • et al.
      Combining multiple hypothesis testing with machine learning increases the statistical power of genome-wide association studies.
      • Schrider D.R.
      • Kern A.D.
      Supervised machine learning for population genetics: a new paradigm.

      Integration of Molecular Biomarkers with Radiomics and Artificial Intelligence

      The current scientific field of radiomics, a term first used by Dutch researcher Philippe Lambin in 2012, is a newcomer in search of biomarkers among the seemingly limitless supply of data related to lung cancer imaging–based phenotypes and tumor microenvironment.
      • Lambin P.
      • Rios-Velazquez E.
      • Leijenaar R.
      • et al.
      Radiomics: Extracting more information from medical images using advanced feature analysis.
      • Chen B.
      • Zhang R.
      • Gan Y.
      • Yang L.
      • Li W.
      Development and clinical application of radiomics in lung cancer.
      The accumulation of detailed imaging data in the current era of artificial intelligence has set the stage for much progress in this field. Deep learning architectures, for example, can be useful in characterization of lung nodules.
      • Shen W.
      • Zhou M.
      • Yang F.
      • Yang C.
      • Tian J.
      Multi-scale convolutional neural networks for lung nodule classification.
      • Arindra A.
      • Setio A.
      • Ciompi F.
      • et al.
      Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks.
      Current research in the field is centered on robust identification of the region of interest in time; direct spatiotemporal phenotypic characterization of tumor microenvironments; integration of multiscale information at the local (nodule), regional (lobe), and organ levels; and integration of imaging, clinical, and omics data in end-to-end learning architectures.
      The combination of imaging-based deep learning with molecular biomarkers may be very powerful in the characterization of IPNs. Radiomics can identify EGFR- and KRAS-mutated tumors.
      • Liu Y.
      • Kim J.
      • Balagurunathan Y.
      • et al.
      Radiomic features are associated with egfr mutation status in lung adenocarcinomas.
      • Rios Velazquez E.
      • Parmar C.
      • Liu Y.
      • et al.
      Somatic mutations drive distinct imaging phenotypes in lung cancer.
      Imaging signatures based on quantitative analysis of imaging data can also predict survival.
      • Lee J.
      • Li B.
      • Cui Y.
      • et al.
      A quantitative CT imaging signature predicts survival and complements established prognosticators in stage I non-small cell lung cancer.
      Some studies have shown that the integration of plasma biomarkers and radiological characteristics is a better predictor of lung cancer in patients with IPNs.
      • Lin Y.
      • Leng Q.
      • Jiang Z.
      • et al.
      A classifier integrating plasma biomarkers and radiological characteristics for distinguishing malignant from benign pulmonary nodules.
      Prediction models integrating serum biomarkers with clinical characteristics and radiographic features of suspicious nodules correctly identified malignant nodules in several studies.
      • Pecot C.V.
      • Li M.
      • Zhang X.J.
      • et al.
      Added value of a serum proteomic signature in the diagnostic evaluation of lung nodules.
      • Ma J.
      • Guarnera M.A.
      • Zhou W.
      • Fang H.
      • Jiang F.
      A prediction model based on biomarkers and clinical characteristics for detection of lung cancer in pulmonary nodules.
      The integrated models outperform the use of serum biomarkers alone and overall represent a very promising approach for the future of early lung cancer detection, especially if artificial intelligence is incorporated.
      • Jiang R.
      • Dong X.
      • Zhu W.
      • et al.
      Combining PET/CT with serum tumor markers to improve the evaluation of histological type of suspicious lung cancers.
      • Jiang F.
      • Todd N.W.
      • Qiu Q.
      • Liu Z.
      • Katz R.L.
      • Stass S.A.
      Combined genetic analysis of sputum and computed tomography for noninvasive diagnosis of non-small-cell lung cancer.

      Grossmann P, Stringfield O, El-Hachem N, et al. Defining the biological basis of radiomic phenotypes in lung cancer. Elife. https://doi.org/10.7554/eLife.23421. Accessed June 26, 2018.

      Integration of Multiple NGS Analysis in ctDNA

      We have already alluded to the use of NGS of ctDNA as a promising strategy for early lung cancer detection. The biggest technical challenge is sensitivity. In an attempt to overcome this limitation, a recently reported test for pan-cancer early detection combined the NGS analysis of ctDNA in blood with a large panel of protein biomarkers
      • Cohen J.D.
      • Li L.
      • Wang Y.
      • et al.
      Detection and localization of surgically resectable cancers with a multi-analyte blood test.
      (see the section ctDNA). Although promising, the study had some important limitations, including the fact that most patients with cancer were symptomatic and the fact that control subjects had no comorbidities that could have acted as confounding variables.
      The scientific community is also awaiting results of the Circulating Cell-free Genome Atlas Study for early cancer detection, enrolling 15,000 participants (including cancer-free controls) in the United States and Canada. Plasma samples collected at baseline and during 5 years of follow-up will be analyzed by whole genome sequencing (WGS) for copy number variation (CNV), targeted DNA sequencing (a 507-gene panel), and whole genome methylome profiling. Preliminary results in an observational case-control setting include 95% specificity, high sensitivity for advanced lung cancer in 54 patients (85% for targeted sequencing, 91% for CNV WGS, and 93% for methylome profiling), and modest sensitivity for 63 patients with stage I to III lung cancer (48% for targeted NGS, 54% for CNV WGS, and 56% for methylome profiling).

      GR. Oxnard T. Maddala E. Hubbell et al. Genome-wide sequencing for early stage lung cancer detection from plasma cell-free DNA (cfDNA): the Circulating Cancer Genome Atlas (CCGA) study. Paper presented at: 2018 American Society of Clinical Oncology Annual Meeting. June 1–5, 2018; Chicago, IL.

      The generalizability of these findings to the screening setting is uncertain. A recent review by Aravanis et al.
      • Aravanis A.M.
      • Lee M.
      • Klausner R.D.
      Next-generation sequencing of circulating tumor dna for early cancer detection.
      addressed the challenges that NGS faces in early cancer detection. The authors suggested that a successful pan-cancer screening NGS-based blood test would have to test up to 1000 genes, and the ctDNA limit of detection would have to improve 10-fold from the current 0.1% to less than 0.01%. More importantly, a validation trial would have to enroll between 10,000 and 100,000 individuals. Despite these seemingly long odds, an observational trial (NCT02889978) investigating the discriminating power of the Circulating Cell-free Genome Atlas Study test is already under way.

      Future Research Challenges

      Table 2 summarizes the research challenges faced by biomarker development in the context of lung cancer screening. The interaction between genetics and environment is multidimensional and hard to control. Samples need to be carefully collected, processed by using standard operating procedures, and annotated by using clinical variables reliably collected from patients and/or electronic medical records.
      • Tuck M.K.
      • Chan D.W.
      • Chia D.
      • et al.
      Standard operating procedures for serum and plasma collection: early detection research network consensus statement standard operating procedure integration working group.
      Informed consent is essential to preserve certificate of confidentiality. Researchers with a certificate of confidentiality may only disclose identifiable, sensitive information if the subject consents, whereas anyone conducting research as a subawardee or receiving a copy of identifiable sensitive information must also comply with and understand disclosure restrictions. Even though samples may be anonymized, genetic fingerprints may reveal a subject’s identity, rendering us vulnerable to the misuse of our most personal information. On the other hand, genetic privacy acts can hinder progress in this field.
      • Minari J.
      • Brothers K.B.
      • Morrison M.
      Tensions in ethics and policy created by National Precision Medicine Programs.
      Table 2Challenges Faced by Research on Development of Lung Cancer Biomarkers
      • Challenge 1: Need for deeper knowledge of lung carcinogenesis, the tumor molecular and cellular landscape, the gene-environment relationship, etc.
      • Challenge 2: Need for careful sample collection, processing using standard operating procedures, and properly annotated clinical data in the intended use type of patient (screening cohorts)
      • Challenge 3: Need to obtain the samples from individuals after informed consent and comply with all rules, regulations, and policies regarding research involving human subjects
      • Challenge 4: Need to establish robust consensus criteria for the selection of the single or integrated combined biomarkers to be tested
      • Challenge 5: Need to design and approve new mechanisms to show clinical utility of care, reduction of cost, reduction of false-positive and false-negative rates, and acceptable ratios of true and false results
      • Challenge 6: Need to further convince stakeholders and research promoters and funders of the relevance of developing single and integrated biomarkers to optimize the efficacy of current lung cancer screening protocols
      • Challenge 7: Need to analyze, determine the causes of, and try to overcome potentially unnecessary hurdles to approval even after utility testing is complete
      Because so many biomarkers are approaching clinical validation, the field is in great need of standardized metrics of clinical utility. In the context of lung cancer screening, we can envision a study design (Fig. 4) that would test the clinical utility of a biomarker-based risk assessment strategy. Because many patients with lung cancer do not meet NLST or other (PLCO2012, LLPv2) proposed screening criteria, the study would test the value of a candidate biomarker as a predictor of risk independent of age and tobacco exposure and therefore justify annual screening with LDCT for 5 years (the duration of the trial). Conceivably, patients not meeting the U.S. Preventive Services Task Force or other formally accepted screening criteria could be prospectively enrolled on the basis of modeling outcomes. Indeed, the selection criteria could also include those used in other settings, such as the PanCAN and UKLS, which used risk-based prediction models.
      • Amos C.I.
      • Dennis J.
      • Wang Z.
      • et al.
      The OncoArray Consortium: a network for understanding the genetic architecture of common cancers.
      • McKay J.D.
      • Hung R.J.
      • Han Y.
      • et al.
      Large-scale association analysis identifies new lung cancer susceptibility loci and heterogeneity in genetic susceptibility across histological subtypes.
      The study would perform biomarker testing by using a clinically validated biomarker(s) and assign patients to management strategies based on the results of combined testing. Those identified by the biomarker as having a lung cancer risk akin to those meeting U.S. Preventive Services Task Force, PLCO2012, LLPv2, or other accepted criteria would be offered LDCT. Those identified as having a lower risk profile would be followed without LDCT. All subjects would sign an informed consent and undergo biomarker testing (or a series of tests). The primary outcome of this hypothetical trial would be risk prediction accuracy. Nodule management would follow current clinical guidelines. Biomarker test results would be shared with the patient and his or her provider, who would in turn decide in light of the results whether LDCT is warranted. We would recommend testing patient-reported outcomes based on expected risks and benefits of getting tested, the way in which the test results are communicated, anxiety related to test results, smoking habits, and willingness to undergo further testing based on biomarker results. We would also recommend determining the accuracy of the risk assessment before and after biomarker testing as well as outcome values. The best sequence (annual versus biannual) and combination of tests to offer should also be tested. Such a study would pave the way for a lung cancer screening strategy driven or at least reinforced by biomarkers of risk. An alternative trial designed to validate the clinical utility of diagnostic biomarkers in the context of IPNs that are found incidentally could also be undertaken.
      Figure thumbnail gr4
      Figure 4Design of a trial to test the clinical utility of one or several candidate biomarkers. CT, computed tomography; LDCT, low-dose computed tomography; SOC, standard of care; USPSTF, U.S. Preventive Services Task Force; PRO, patient-reported outcomes.

      The Future of Molecular Biomarkers in the Context of Lung Cancer Screening

      Despite the vast potential of existing candidates and methodologies, no single molecular biomarker of lung cancer is currently being used in routine clinical practice. The clinical validation and utility steps are critical, but much more demanding, resource intensive, and time-consuming than the initial discovery and retrospective validation. That notwithstanding, the unmet clinical needs remain. Individual risk needs to be refined, and screening criteria need to be modified to have an impact on lung cancer–related mortality. Orphan images of IPNs stand to improve our success differentiating benign from malignant with a robust biomarker at our disposal. There is also a clear unmet need for prognostic molecular and clinical markers for patients with screening-detected early-stage tumors. Although some believe that testing a new biomarker would be comparable to the gargantuan effort embodied by the NLST, we believe that less complex and more affordable validation is possible in the setting of established lung cancer screening programs.
      There is plenty of room for improvement. We need to promote studies integrating promising candidate biomarkers, including molecular and image-based biomarkers, and the use of artificial intelligence technologies to help in selection of the most appropriate combinations. Head-to-head comparisons of biomarkers in specific clinical scenarios would also be welcome. Deep mining of the troves of data provided by ongoing screening efforts with new mathematical and computational models based on machine learning will surely help. This will require a systematic collection of patient samples in the context of screening. Finally, ways to prove the cost-effectiveness of the new tests, as well as to overcome the potential hurdles to get the approval by regulatory agencies, need to be considered in the list of challenges that we face ahead in the development of molecular biomarkers in screening. Although the logistics and expense of such an effort may seem daunting at first, we believe that the long-term outcome may prove highly efficient.

      Acknowledgments

      The work of Dr. Montuenga and Dr. Pio work is funded by Foundation for Medical Research (FIMA), the Spanish Ministry of Economy and Innovation, and Health Research Fund (FIS)/European Regional Development Fund (FEDER) (FIS projects PI16/01821 , PI17/00411 , and CIBERONC), Spanish Association Against Cancer (AECC) Scientific Foundation ( GCB14-2170 and PROYE16091 ), and the Juan Serrano and Ramon Areces Foundation. Dr. Sozzi's work is funded by investigator grant 18812 and 5×1000 grant 12162 from the Italian Association for Cancer Research . Dr. Boeri was supported by a Fondazione Pezcoller Fellowship. Dr. Massion's work was supported by National Cancer Institute grant A152662 . We thank Heber Longas (a medical and scientific illustrator from Fundamentium) for his contribution to the graphics of the present review, in particular Figure 2.

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