The State of Molecular Biomarkers for the Early Detection ... · rate for lung cancer exceeds the annual rate for breast, prostate, and colon cancer combined, all of which have successful

Review

The State of Molecular Biomarkers for the Early Detectionof Lung Cancer

Mohamed Hassanein1, J. Clay Callison1, Carol Callaway-Lane4, Melinda C. Aldrich2,3,Eric L. Grogan2,4, and Pierre P. Massion1,4

AbstractUsing biomarkers to select themost at-risk population, to detect the disease whilemeasurable and yet not

clinically apparent has been the goal of many investigations. Recent advances in molecular strategies and

analytic platforms, including genomics, epigenomics, proteomics, and metabolomics, have identified

increasing numbers of potential biomarkers in the blood, urine, exhaled breath condensate, bronchial

specimens, saliva, and sputum, but none have yet moved to the clinical setting. Therefore, there is a

recognized gap between the promise and the product delivery in the cancer biomarker field. In this review,

we define clinical contexts where risk and diagnostic biomarkers may have use in the management of lung

cancer, identify the most relevant candidate biomarkers of early detection, provide their state of develop-

ment, and finally discuss critical aspects of study design in molecular biomarkers for early detection of lung

cancer. Cancer Prev Res; 5(8); 992–1006. �2012 AACR.

IntroductionLung cancer is the leading cause of cancer-related death in

the United States (1). More than 60% of patients arediagnosed at advanced stages when a cure is unlikely (2).Five-year survival rate for patients with advanced disease isless than 10%, whereas 5-year survival rate in patients withstage I disease is greater than 70% (3). The annualmortalityrate for lung cancer exceeds the annual rate for breast,prostate, and colon cancer combined, all of which havesuccessful clinical screening tools for the detection of early-stage disease (4). For this reason, the search for diagnosticstrategies for early lung cancer detection has intensified.

Until recently, the case for early detection in lung cancerwas extrapolated from other cancers such as colon andbreast. Clinicians and scientists continued to hypothesizethat the earlier lung cancer is diagnosed, the opportunity forimproved survival increases. Historically, lung cancer hasbeen difficult to detect early and thus survival advantageswere difficult to ascertain. In the 1970s and 1980s, chest x-ray and sputum cytology were tested in screening trials forlung cancer. Although this approach increases the number

of lung cancers diagnosed, it did not improve lung cancer–specific mortality (5, 6). The Early Lung Cancer ActionProgram (ELCAP) was a large lung cancer screening trialstarted in the 1990s using chest computed tomographic(CT) imaging (7). It showed an improved detection rate andsurvival of early-stage lung cancers, which prompted thedesign of a large randomized National Lung Screening Trial(NLST). Exciting results of the recently completed studyshowed a 20% reduction in lung cancer–specific mortalityusing low-doseCT screening for patients at high risk for lungcancer after amedian follow up of 6.5 years, compared withchest x-ray (8, 9). This is the first large randomized screeningstudy of lung cancer by low-dose chest CT to show animprovement in overall survival, thus giving new hope inthe survival for this cancer. Extrapolating from the NLSTresults, a screening method that reduces lung cancer–spe-cific mortality by 20% could save an estimated 11,074 livesannually in the United States., which is far greater than2,303, the number currently estimated to be saved withadjuvant chemotherapy (see Supplementary Data), there-fore providing a strong rationale to pursue efforts in earlydetection.

How do we define early detection?Early detection involves a high-risk population, a screen-

ing test, and a testing schedule. Within this context, onemust distinguish populations of individuals at-risk beforeor after the disease becomes measurable (Fig 1).

What clinical endpoints do the biomarker candidatesof early detection address?

A distinction is made between risk biomarkers to assessthe risk of developing lung cancer (individuals at risk butwith no measurable disease) and diagnostic biomarkers to

Authors' Affiliations: 1Division of Allergy, Pulmonary and Critical CareMedicine, Vanderbilt-Ingram Cancer Center; 2Department of ThoracicSurgery, 3Division of Epidemiology, Vanderbilt University Medical Center;and 4Veterans Affairs, Tennessee Valley Healthcare System, Nashville,Tennessee

Note:Supplementary data for this article are available atCancer PreventionResearch Online (http://cancerprevres.aacrjournals.org/).

Corresponding Author: Pierre P. Massion, Vanderbilt Ingram CancerCenter, PRB 640, 2220 Pierce Avenue, Nashville TN 37232. Phone: 615-936-2256; Fax: 615-936-1790; E-mail: [email protected]

doi: 10.1158/1940-6207.CAPR-11-0441

�2012 American Association for Cancer Research.

CancerPreventionResearch

Cancer Prev Res; 5(8) August 2012992

Research. on June 16, 2020. © 2012 American Association for Cancercancerpreventionresearch.aacrjournals.org Downloaded from

Published OnlineFirst June 11, 2012; DOI: 10.1158/1940-6207.CAPR-11-0441

http://cancerpreventionresearch.aacrjournals.org/

determine whether cancer is present (individuals at riskwith measurable asymptomatic disease such as lungnodules). Prognostic biomarkers in patients with early-stage disease can identify individuals with an aggressivephenotype and shorter survival regardless of the type oftreatment provided and may help select populations whomay benefit from adjuvant therapy. Biomarkers of riskof developing lung cancer in the absence of measurabledisease are only discussed when originally developed asdiagnostic biomarkers. The literature on biomarkers of riskof developing lung cancer based on proteins or single-nucleotide polymorphism (SNP) and including genome-wide association studies (GWAS) is beyond the scope ofthis review. Likewise, prognostic biomarkers will not bediscussed further.

What are the benchmarks for clinical utility?To be useful in the clinical setting, biomarkers go

through careful phases of development as discussedbelow and should respond to specific criteria. The bio-markers should (i) be quantifiable and reproducible, (ii)have good testing performance [with good positive pre-dictive value (PPV) and negative predictive value (NPV)],(iii) be measurable in accessible material, in smallamounts and with little preparation, (iv) indicate a dis-ease state, (v) have proven clinical use, (vi) be adopted bythe community-at-large to take advantages of the benefitstesting affords, (vii) be cost-effective; and (viii) be reim-bursed by health insurers.

The Clinical Context of Early DetectionTo be successful at improving lung cancer detection,

biomarkers must address a specific clinical question. Twopressing clinical needs are identified, biomarkers that willaddress the risk of developing lung cancer and others thatare diagnostic innature andwill distinguishmalignant frombenign nodules.

The risk of developing lung cancerBiomarkers of risk for lung cancer have the potential to

improve early detection beyond the use of CT scans thatsuffer from lack of sensitivity (particularly among never-smokers), specificity (high false-positive rate), and fromhigh cost. Several published models exist that predict anindividual’s risk of developing lung cancer (10–15). Thesemodels were developed to select patients who may benefitfrom additional radiographic screening. Identifying a riskbiomarker for developing lung cancer would further definethe at-risk population, decrease the overall number ofscreening CTs conducted, and ultimately limit the down-stream consequences of discovering these "false positive"nodules.

Distinguishing benign from malignant lung nodulesDiagnostic biomarkers that may assist in distinguishing a

benign nodule from a malignant one would be invaluable.Depending on geographic location, up to 30% of indeter-minate pulmonary nodules are ultimately found to havebenign pathology when surgically resected (16). In theNLST, 24% of patients who underwent a diagnostic oper-ation (mediastinoscopy, thoracoscopy, or thoracotomy)had benign disease (17). Thus, additional testing with abiomarker could decrease the number of surgical resectionsfor ultimately benign disease. Current guidelines recom-mend that providers use models to assist with determiningthe likelihood that a particular nodule identified by CT scanis malignant and thus should be resected (18). However, asthe use of low-dose CT for lung cancer screening evolves,better predictivemodels that incorporate biomarkerswouldassist the clinical provider in determining which patientshave lung cancer. We recently validated a blood-basedproteomic signature for lung cancer diagnosis and showedthat it may provide added value to the clinical and imagingassessment of indeterminate lung nodules (19). Hopefullyas biomarkers are developed, they will assist in identifyingnot only those individuals without malignancy but also

Chemoprevention

Early detection screening

Disease nonmeasurable

Risk assessment Early diagnosisDetection ofrecurrence

Clin

ical

dia

gnos

isDisease measurable

Figure 1. Clinical contexts for biomarker development in early detection of lung cancer. This diagram illustrates 4 clinical contexts within 4 windows oftime. The period during which lung cancer is nonmeasurable and precedes the diagnosis characterizes the context of risk assessment. It represents along window of time during which the disease develops and corresponds to an opportunity for chemoprevention. When the disease becomesmeasurable but remains asymptomatic, we enter the context of early diagnosis. Two other clinical contexts relate to clinical diagnosis, that is, when thedisease is measurable and patients symptomatic, and to detection of recurrence. These windows of time correspond to the different contexts for whichdifferent biomarker targets can be developed (109). Reprinted with permission of the American Thoracic Society. Copyright � 2012 American ThoracicSociety.

Lung Cancer Molecular Biomarker Candidates

www.aacrjournals.org Cancer Prev Res; 5(8) August 2012 993




help indetermining thosewhoaremalignant and amenableto surgical resection.

Current Status of Early Detection Biomarkers forLung Cancer

We will review the most recent advances made to date inthe field of molecular biomarkers for risk assessment anddiagnosis of lung cancer, as well as discuss the clinical useand limitations of different approaches.

In an effort to identify the most relevant lung cancerbiomarkers of early detection, we selected published reportsfromPubMedon the basis of the keywords biomarkers, risk,diagnosis, early detection, and lung cancer. To narrow oursearch, we further applied the following 2 filters. First, theproposed marker, or panel of markers, must be quantita-tively measurable and its performance tested in at least onesample set of clinically relevant specimens. Second, thereport adhered to the PRoBE biomarker validation guide-lines discussed later. As a result, we have selected originalreports summarized in Tables 1 to 3. We recognize thelimitation of selection, outcome reporting, and publicationbiases.

We have organized our report on the basis of specimentypes, either tissue-based (Table 1) or biofluids-based mar-kers. We have further subcategorized biofluids-based bio-markers into blood-based (Table 2), sputum, white bloodcells, and peripheral blood cells (Table 3). The phases ofbiomarker development are assessed following the EarlyDetection Research Network (EDRN) classification (20).This classification was designed for biomarkers of earlydetection in the context of screening and therefore may notdirectly address phases of development of diagnostic bio-markers. These tables also include efforts from investigatorsto integrate biomarkers in models of risk prediction ordiagnosis. The validation sets reported in the tables corre-spond to an attempt to test the biomarker (or signature) in atrue independent population, also described as clinicalvalidation (4), to evaluate the performance of the test.

Tissue-based candidate biomarkersNumerous studies have adapted large-scale analytic

approaches to profile the full spectrum ofmolecular aberra-tions associated with lung cancer malignancy in tumors.These studies have yielded valuable information that hasunraveled several key molecular events of lung cancertumorigenesis, including mapping the genomic loci asso-ciated with high risk of developing lung cancer, hyper-methylation of a number of tumor suppressor genes (21–25), regions of chromosomal amplification (26, 27),mRNAexpression variation (28–31), the differential expression ofseveral microRNAs (32), and the proteomic signatureof invasive (33, 34) and preinvasive lesions in lung tissues(35, 36).

Several research groups, including ours, have been testinggenetic and proteomic alterations in surrogate tissues suchas bronchial brushings and biopsies to determine the prob-ability of having lung cancer (25–27, 36, 37), see Table 1.

While this approach requires bronchoscopy, molecularmarkers obtained from the airways may pair with therecently proven CT screening to provide additional benefitwhen evaluating individuals at high risk for lung cancer.Although much of the early biomarker discovery effortshave used fresh-frozen samples as a primary source, acquir-ing these specimens is costly and laborious. Because surgicalpathology specimens stored as formalin-fixed, paraffin-embedded (FFPE) blocks are widely available, manyresearchers are attempting to profile genomic and proteo-mic aberrations in such specimens. Some of these aberra-tions include hypermethylation of genes (38) and micro-RNA (miRNA) expression (39), which can be successfullyextracted as candidate biomarkers.

miRNAs are a class of small noncodingRNAgenes that arethought to regulate gene expression. They are abnormallyexpressed in several types of cancer (40, 41) and involved ina variety of biologic and pathologic processes with tissuespecificity (41, 42)with thepotential for clinical application(43). Another advantage of miRNA is that it is well pre-served in formalin-fixed tissue, making it ideal for use inroutinely processed material (44). Previous studies haveidentified differences in miRNA expression between squa-mous cell carcinoma and adenocarcinoma in lung cancer(32), as well as in other cancers (42, 43, 45). The currenttrend toward using FFPE samples will allow for a greaternumber of available samples and thus will increase statis-tical power and generalization of results.

Tissue-based biomarkers that reflect the molecularchanges associated with specific histologic subtypes ofnon–small cell lung cancer (NSCLC) may provide themeans to differentiate tumors originating in the lung frommetastases from other organ sites. Furthermore, usingimmunohistochemical profiling of lung cancer tissue mar-kers in conjunction with well-established histologic exam-ination can provide more accurate subclassification of lungmalignancies and thus may directly impact the clinicaldecision making of antitumor therapy. The molecularchanges associated with progression from normal to malig-nant tissue may lead to the discovery of novel markers thatcan be detected in circulation or other biofluids. Limitedaccess to early-stage tumor tissue samples, tumor hetero-geneity combined with the complexity of the genome andthe proteome, and the low abundance of potential biomar-kers represent some of the challenges that translationalresearchers face when attempting to bring these biomarkercandidates from the bench to the bedside.

Therefore, the potential use of tissue-based biomarkers ishighly dependent on the accessibility of the specimens andthe robustness of the assay offered. FFPE samples are pre-ferred by scientists because of their availability, but theirmolecular analyses remain more challenging. Althoughnoninvasive diagnostic approaches are also preferred, itmay take additional time to refine an airway epithelium–based biomarker versus one that can derive the same infor-mation from a less invasive sample. For example, develop-ing surrogate biomarkers of early-stage disease from tissuesin the field of cancerization (bronchial brushings or

Hassanein et al.

Cancer Prev Res; 5(8) August 2012 Cancer Prevention Research994




biopsies) may require testing in more proximal and lessinvasive samples (e.g., nasal epithelium). This problemmaybe less acute for prognostic biomarkers and biomarkerspredictive of response to therapy because tumor sampleswill be generally available for analysis. Although molecularanalysis of lung tumor tissues holds great promise to rev-olutionize our understanding of the disease developmentand progression, tissue-based biomarkers from the bron-chial airway have significant limitations related to tissueacquisition that may be overcome by the translation of thatknowledge tomore accessible specimens andby guiding thedevelopment of biofluids-based early detection strategies.

Biofluids-based markersThe underlying premise of biofluids-based biomarker

research is that molecular alterations of tumor cells leadto the synthesis of distinct molecular species that can bedetected in biofluids. Biofluids-based detection strategiesare an attractive approach for screening, namely due to theirease of acquisition. Biofluids including peripheral bloodand its components (circulating cells, plasma, and serum),exhaled breath condensate (EBC), urine, and sputum offernoninvasive access to large quantities of samples availablefor analysis. These alterations can lead to the generation ofdisease-specific molecular species such as altered or meth-ylatedDNA, overexpressedmRNA,miRNA, or proteins thatcan potentially be released into the extracellular microen-vironment. Therefore, molecular analyses of early-stagelung cancer-related biofluids represent an attractive choicefor the discovery and validation of diagnostic biomarkers(46, 47).Blood-based markers. Blood is a complex and dynamic

mediumwhose components can reflect various physiologicor pathologic states such as the presence of some cancers.Detectablemoieties of the blood are currently the subject ofmany investigations and include cellular elements such ascirculating tumor cells (CTC), cell-free DNA and RNA,proteins, peptides, and metabolites. Changes of the cell-free genomic components of the blood, including DNAmethylation (48, 49), DNA amplification, and gene expres-sion (50), have been reported in the circulation of patientswith lung cancer. These candidates are reported in Table 2.microRNAs. More recently, miRNAs have also been

identified in the blood of patients with lung cancer (51,52). In an effort to test the validity of miRNA as biomarkersable to predict lung tumor development, diagnosis, andprognosis, an extensive miRNA profiling was conducted inpaired lung tumor and normal lung tissue and in plasmacollected at the time of diagnosis by spiral CT. A signature of15 miRNAs present in the blood was able to identifysubjects at high risk of developing lung cancer in 2 inde-pendent cohorts of patient with 80% sensitivity and 90%specificity (53). These results suggest that miRNA expres-sion ratios may be molecular predictors of lung cancerdevelopment and aggressiveness and may have clinicalimplication for lung cancer management in the future. Ina separate study, a test included 34 serum miRNAs thatcould identify patients with early-stage NSCLCs in a pop-

ulation of asymptomatic high-risk individuals with 80%accuracy (54). These provocative results will have to bevalidated in independent cohorts.

Proteomic profiles. Recent proteomic studies havefocused on rapid proteomic profiling of blood with min-imal sample preparation. One of these approaches usesmatrix-assisted laser desorption/ionization—time-of-flight/mass spectrometric (MALDI-TOF/MS) patterns ofabundant proteins or peptide fragments that correlate withearly disease stage. Several other studies usedMALDI/MS toidentify proteins and peptides in serum. For example, Patzand colleagues were able to identify 4 differentiallyexpressed serum proteins (transferrin, retinol-binding pro-tein, antitrypsin, and haptoglobin) that discriminatebetween NSCLCs and controls (55). Using the sameMALDI/MS approaches, several other groups includingours(56) have reported serum protein expression profiles thatdistinguish patients with various cancers from control sub-jects (57). Recently, we validated the proteomic signature in2 prospective cohorts of patients with lung nodules andshowed that it may provide added value to the clinical andimaging assessment of indeterminate lung nodules (19).

Autoantibodies. Other promising development in theblood biomarkers field is the discovery of autoantibodiesdirected against tumor proteins. Alterations of the proteinproduction in cancer cells by overexpression, mutations,misfolding, truncation, or proteolysis break immunologictolerance and generate tumor-specific antigens which inturn elicit a host immune response (58). Autoantibodiesgenerated against these tumor-associated antigens (TAA)during the course of disease progression can be furtheramplified by the immune network, making them attractivecandidates for the early detection and diagnosis of cancer.Many TAA targets have been identified in patient sera inseveral immunologic diseases andmalignancies using high-throughput screening platforms, such as cDNA expressionlibraries, phage display, and protein microarrays (59). Forexample, 2 separate groups identified several potentialimmunoreactive peptides for autoantibodies using a T-7cDNA-based phage library to screen the sera of patients withNSCLCs (60, 61). Using similar techniques, Chen andcolleagues also identified and validated ubiquilin-1 pep-tides as a potential autoantibody target in lung adenocar-cinoma from sera of patients with early-stage lung cancer(62). More recently, Wu and colleagues reported the iden-tification of 6 peptide clones discriminatory of NSCLCsusing phage display techniques, but only one protein hasbeen confirmed (63). Recent improvements in blood frac-tionation techniques and liquid chromatography led to theidentification of several other autoantibodies (47). Auto-antibodies against known lung cancer–associated proteinssuch as autoantibodies against p53, c-Myc, HER2, MUC1,CAGE, GBU4-5, NY-ESO-1 (64) or annexin I, PGP9.5, and14-3-3 theta, LAMAR1 (65), or IMPDH, PGAM1 andANXA2 (66) have also been reported as independent sig-natures. These recent autoantibody studies are particularlyprovocative because some allow the detection of cancer-specific markers in the preclinical phase of lung cancer






Tab

le1.

Cha

racteristic

san

dperform

ance

ofmos

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nttis

sue-bas

edca

ndidatebiomarke

rsfortheea

rlydetec

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Referen

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sTyp

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marke

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Clin

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samples

BM

dev

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seTraining

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Validation

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Sen

si-

tivity

Spec

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AUC

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(26)

Bronc

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spec

imen

sDNA

5p15

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12(EGFR

),8q

24(C-M

YC),

CEP6

Diagn

osis

4NSCLC

FISH

þcy

tology

n/a

In/a

137

61–75

a83

–

100a

n/a

Mas

sion

(27)

Bronc

hial

biopsies

DNA

TP63

,MYC,

CEP3,

CEP6þ

sputum

cytology

þdem

ographics

Diagn

osis

4NSCLC

FISH

n/a

IIn/a

70n/a

n/a

92.6

Feng

(38)

Tumorsan

dno

rmal

tissu

es

DNA methy

la-

tion

RARB,BVES,

CDKN2A

,KCNH5,

RASSF1

,CDH13

,RUNX,C

DH1

Diagn

osis

8NSCLC

Methy

latio

narray

n/a

I49

n/a

n/a

n/a

n/a

Ang

lim(22)

Tumorsan

dno

rmal

tissu

es

DNA methy

la-

tion

GDNF,

MTH

FR,

OPCML,

TNFR

SF2

5,TC

F21,

PAX8,

PTP

RN2,

andPITX2

Diagn

osis

8SCC

Methy

latio

narray

n/a

I43

n/a

95.6

a95

.6a

n/a

Sch

midt

(25)

Bronc

hial

aspira

tes

DNA methy

la-

tion

SHOX2

Diagn

osis

1NSCLC

PCR

n/a

IIn/a

523

6895

86

Richa

rds

(24)

Tumorsan

dno

rmal

tissu

es

DNA methy

la-

tion

TCF2

1Diagn

osis

1NSCLC

PCR

n/a

II42

6376

98a

n/a

Spira

(37)

Airw

ayep

ithelium

mRNA

Gen

eex

press

ion

sign

ature

Diagn

osis

80NSCLC

Affyarray

n/a

II77

5280

84n/a

Bea

ne(28)

Airw

ayep

ithelium

mRNA

Gen

eex

press

ion

sign

atureþ

clinical

factors

Diagn

osis

80NSCLC

and

SCLC

Affyarray

n/a

II76

6210

091

97

(Con

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don

thefollo

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Hassanein et al.





Tab

le1.

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Sen

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ALD

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NC,

LOC14

7

Diagn

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7NSCLC

qRT-PCR

n/a

II32

36b

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n/a

n/a

Blomquist

(30)

Tumors

and

norm

altis

sues

mRNA

CAT,

CEBPG,

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1,ERCC4,

ERCC5,

GPX1,

GPX3,

GSTM

3,GSTP

1,GSTT

1,GSTZ

1,MGST1

,SOD1,

XRCC1

Diagn

osis

14NSCLC

RT-PCR

n/a

IIn/a

49,4

0n/a

n/a

82–87

Rah

man

(36)

Bronc

hial

biopsies

MALD

Isign

ature

TMLS

4,ACBP,

CSTA

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C,M

IF,

ubiquitin

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Diagn

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9NSCLC

MALD

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n/a

II51

6066

8877

NOTE

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.Abbreviations

:AUC,areaun

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Tab

le2.

Cha

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san

dperform

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ofmos

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a91

a99

a

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Serum

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Diagn

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n/a

Qiu

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Serum

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Diagn

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3NSCLC

Protein

array

170

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8273

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Serum

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6NSCLC

ELISA

n/a

II20

180

9292

96

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Diagn

osis

6NSCLC

ELISA

n/a

II19

6n/a

94.8

a91

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96.4

a

Boy

le(113

)Serum

AutoA

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Ann

exin

1,an

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Diagn

osis

6NSCLC

ELISA

n/a

II24

125

532

9164

Green

berg(48)

Serum

DNA methy

latio

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den

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thionine

Diagn

osis

1Lu

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n/a

I68

n/a

92–10

0a91

–97

a94

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a

Beg

um(49)

Serum

DNA methy

latio

nAPC,C

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MGMT,

DCC,

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Diagn

osis

6NSCLC

qPCR

n/a

II32

–63

910

684

57n/a

Che

n(114

)Serum

miRNA

miRNAsign

ature

Diagn

osis

10NSCLC

qRT-PCR

n/a

II31

031

093

9097

Bianc

hi(54)

Serum

miRNA

miRNAsign

ature

Diagn

osis

34NSCLC

qRT-PCR

n/a

II64

6471

9089

Kulpa(115

)Serum

Protein

CEA,C

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A21

-1,

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Diagn

osis

4SCC

ELISA

n/a

II42

020

–62

9571

–90

Patz(55)

Serum

Protein

CEA,R

BP4,

hAAT,

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Diagn

osis

4Lu

ng canc

erELISA

n/a

II10

097

7875

n/a

Taka

no(116

)Serum

Protein

Nec

tin-4

Diagn

osis

1NSCLC

ELISA

n/a

II29

554

98n/a

Yild

iz(56)

Serum

Protein

MALD

I/MS

sign

ature

Diagn

osis

7NSCLC

MALD

I/MS

n/a

II18

510

658

85.7

82

Pec

ot(19)

Serum

Protein

Mod

el:MALD

I/MS

sign

atureþ

clinical

and

imag

ingdata

Diagn

osis

7Indeterm.

lung

nodule

MALD

I/MS

n/a

II10

0n/a

n/a

72

Diaman

dis

(117

)Serum

Protein

Pen

atraxin-3

Diagn

osis

1Lu

ng canc

erELISA

n/a

I42

637

–48

80–90

60–74

(Con

tinue

don

thefollo

wingpag

e)

Hassanein et al.





Tab

le2.

Cha

racteristic

san

dperform

ance

ofmos

trece

ntblood

-bas

edca

ndidatebiomarke

rsfortheea

rlydetec

tionof

lung

canc

er(Con

t'd)

Referen

ces

Spec

imen

sTyp

eof

marke

rAna

lyte

Clin

ical

purpose

No.o

fmarke

rsPatho

logic

subtype

Ass

ayplatform

Preclinical

samples

BM

dev

.pha

seTraining

set

Valida-

tion

set

Sen

si-

tivity

Spec

i-ficity

AUC

Ostroff(118

)Serum

Aptamers

cadhe

rin-1,CD30

ligan

d,

endos

tatin

,HSP90

a,LR

IG3,

MIP-4,

pleiotrop

hin,

PRKCI,RGM-C

,SCF-sR

,sL

-se

lectin,and

YES

Diagn

osis

6NSCLC

Aptamers

n/a

II98

534

189

8390

Zho

ng(119

)Plasm

aAutoA

BTA

Asign

ature

Diagn

osis

5NSCLC

Protein

microarray

5I

81n/a

90a

95a

n/a

Kne

ip(120

)Plasm

aDNA methy

latio

nSHOX2

Diagn

osis

1NSCLC

qPCR

n/a

II40

371

6090

78

She

n(121

)Plasm

amiRNA

miRNA-21,

-126

,-210

,-48

6-5p

Diagn

osis

4NSCLC

qRT-PCR

n/a

II28

8786

9793

Wei

(122

)Plasm

amiRNA

miR-21

Diagn

osis

1NSCLC

qRT-PCR

n/a

I93

n/a

76a

70a

77.5

a

Tagu

chi(12

3)Plasm

aProtein,2

pan

els

EGFR

,SFT

PB,

WFD

C2,

ANGPTL

3,ANXA1,YWHAQ,

Lmr1

Diagn

osis

7NSLC

LELISA

52III

n/a

n/a

n/a

89

Boe

ri(53)

Plasm

a/tis

sues

miRNA

miRNAsign

ature

Diagn

osis

13Lu

ng canc

ermiRNAarray

and RT-PCR

n/a

II19

2275

100

88

Boe

ri(53)

Plasm

a/tis

sues

miRNA

miRNAsign

ature

Diagn

osis

15Lu

ng canc

ermiRNA

arrayan

dRT-PCR

25III

2025

8090

85

NOTE

:Dataorga

nize

dbyye

arof

pub

lication,

spec

imen

type,

andtypeof

marke

rco

nsidered

.Abbreviations

:ADC,ad

enoc

arcino

ma;

AUC,area

under

thecu

rve;

AutoA

B,au

toan

tibod

y;BM

dev

.pha

se,biomarke

rdev

elop

men

tph

ase;

HPLC

,high

-perform

ance

liquid

chromatog

raphy

;n/a,n

otav

ailable;q

PCR,q

uantita

tivereal-tim

ePCR;R

T-PCR,rev

erse

tran

scrip

tase

PCR;S

CC,s

qua

mou

sce

llca

rcinom

a.aValue

sderived

from

training

seton

ly.






Tab

le3.

Cha

racteristic

san

dperform

ance

ofmos

trec

ents

putum

,EBC,a

ndperiphe

ralblood

cells

cand

idatebiomarke

rsforthe

early

detec

tion

oflung

canc

er

Referen

ces

Spec

imen

sTyp

eof

marke

rAna

lyte

Clin

ical

purpose

No.o

fmarke

rsPatho

logic

subtype

Ass

ayplatform

Preclinical

samples

BM

dev

.pha

seTraining

set

Valida-

tion

set

Sen

si-

tivity

Spec

i-ficity

AUC

Palmisan

o(124

)Sputum

DNA methy

latio

np16

,MGMT

Diagn

osis

2SCC

PCR

11III

144

n/a

n/a

n/a

n/a

Belinsk

y(91)

Sputum

DNA methy

latio

np16

,MGMT,

DAP,

RASSFIA

Diagn

osis

4NSCLC

PCR

n/a

I14

1n/a

n/a

n/a

n/a

Varella-

Garcia

(125

)

Sputum

DNA

Chrom

osom

alan

euso

myþ

cytology

Diagn

osis

4Lu

ngca

ncer

FISH

þcy

tology

36III

66n/a

83a

80a

n/a

Li(87)

Sputum

DNA

HYAL2

,FHIT,

SFT

PC

Diagn

osis

3NSCLC

FISH

n/a

I10

2n/a

76a

92a

n/a

Yu(92)

Sputum

miRNA

miR-21,

miR-

486,

miR-

375,

miR-

200b

Diagn

osis

4NSCLC

qRT-PCR

n/a

II72

122

7080

84

Sho

we

(50)

Periphe

ral

blood

cells

mRNA

Gen

eex

press

ion

sign

ature

Diagn

osis

29NSCLC

cDNAarray

n/a

II22

855

7682

n/a

Tana

ka(126

)Periphe

ral

blood

cells

CTC

CTC

sDiagn

osis

1NSCLC

Cells

earch

system

n/a

I15

0n/a

30a

88a

60a

Phillips

(77)

EBC

VOCs

VOCs

sign

ature

Diagn

osis

22NSCLC

GC-M

Sn/a

I10

8n/a

100a

81a

n/a

Bajtarevic

(79)

EBC

VOCs

VOCs

sign

ature

Diagn

osis

50Lu

ngca

ncer

GC-M

Sn/a

I96

52–80

a10

0an/a

Ges

sner

(84)

EBC

Protein

VEGF,

bFG

F,ANG,TN

F-a,

IL-8

Diagn

osis

5NSCLC

ELISA

n/a

I74

n/a

n/a

99–10

0

NOTE

:Dataorga

nize

dby

year

ofpub

lication,

spec

imen

type

,and

type

ofmarke

rco

nsidered

.Abbreviations

:ADC,a

den

ocarcino

ma;

AUC,a

reaun

der

thecu

rve;

AutoA

B,a

utoa

ntibod

y;BM

dev

.pha

se,b

iomarke

rdev

elop

men

tpha

se;n

/a,n

otav

ailable;q

PCR,q

uantita

tive

real-tim

ePCR;S

CC,s

quam

ousce

llca

rcinom

a;SCLC

,smallc

elllun

gca

ncer.

aValue

sde

rived

from

training

seton

ly.

Hassanein et al.





progression. Similar to other circulating protein markers,the low abundance of autoantibodies and the complexity ofthe blood proteome are still substantial challenges facingthese discovery efforts.Circulating tumor cells. The ability to capture and study

CTCs is an emerging and interesting development in thefield that carries the potential to become a noninvasive toolfor early detection and diagnosis of cancer, measuringresponse to therapy, as well as for understanding the basicbiologyof cancer progression andmetastasis (67–71).CTCsare rare cells that originate from amalignancy and circulatefreely in the peripheral blood. CTCs are usually captured byimmobilized antiepithelial cell adhesion molecule(EpCAM, also known as TACSTD1) antibodies either inchip or bead platforms (72, 73). The technology of rareCTCs capture is still in the early phase of development andrequires more specific surface markers to increase its spec-ificity for circulating lung cancer cells.In summary, peripheral blood is a rich medium for

cancer-specific markers from small molecules such as miR-NAs to whole cells, all of which represent a great opportu-nity for developing a minimally invasive diagnostic test oflung cancer. Significant challenges are still preventing theclinical success of blood markers, including the extremecomplexity of the blood matrix, the scarce quantity of anygiven marker, and the lack of sensitive, reproducible, andhigh-throughput verification modalities, in particular inproteomics research. New and innovative fractionationtechniques, more sensitive and specific detection reagents,and well-validated assays will increase our chances of cap-turing blood-based biomarkers.Exhaled breath condensate. The analysis of EBC repre-

sents another noninvasive method of diagnosing lung can-cer. The analysis of volatile organic compounds (VOC) thatare linked to cancer is likely to provide a novel opportunityfor the identification of diagnostic cancer biomarkersbecause such a large volume of sample can be collectedeasily and inexpensively (74–76). The underlying rationaleof this approach is based on the observation that tumor cellgrowth is accompanied by the alteration of protein expres-sion pattern that may lead to peroxidation of the cellmembrane and thus to the emission of VOCs (76). Severalrecent studies have used gas chromatography combinedwithmass spectrometric analysis (GC-MS) of VOCs as bothdiscovery and validation platforms (77–81). Other groupshaveused the analytic power ofGC-MSand the sensitivity ofcustom-designed nanosensors in which changes in electri-cal resistance from organic compounds contained inexhaled breath of patients can be detected by these sensorsand recorded. For example, in a study by Peng and collea-gues a VOC signature that distinguished patients with lung,colorectal, and breast cancers from healthy individuals wasrecently identified from exhaled alveolar breath (82). Thesecandidates are reported in Table 3. Other studies attemptedto identify volatile proteins andpeptides present in EBC andused them as potential markers for the early detection oflung cancer (83–85). The results of these studies provideevidence for feasibility of this strategy to isolate and identify

proteins useful for early detection of lung cancer. Furtherstudies are still needed to standardize a collection device, tofurther show specificity of any test, and to determine the useof this approach in clinical practice.

Sputum and urine. Cigarette smoking leads to theincreased productionof sputumwith glycoproteins, inflam-matory cells, and exfoliated cells from the bronchial tree.Because sputum is so readily available, particularly in cur-rent and ex-smokers, that its molecular analysis has been anactive area of research for lung cancer biomarkers (21).Although detecting lung cancer using sputum cytologyalone has low sensitivity (86), several studies showed thatcombining cytology with analysis of genetic abnormalitiesimproves diagnosis accuracy. Several types of genetic abnor-malities have been detected in the sputum of patients withlung cancer, such as deletions of HYAL2, FHIT, and SFTPC(87), chromosomal aneusomy (88, 89), DNA methylation(90, 91), and miRNA (92, 93). These candidates arereported in Table 3. Also recently, measurements of geno-mic aneuploidy when combined with pulmonary functioncan significantly improve lung cancer risk prediction (94).The performance ofmost of these potential markers has notbeen tested in large-scale validation studies, and whetherthese markers will add value to standardized sputum cytol-ogy remains to be seen.

Urine, much like blood, EBC, and sputum, is anothereasily accessible biofluid that could be an important sourceof cancer-specific markers. Some recent proof-of-principlestudies have attempted to profile the molecular changes ofurine using mass spectrometric analysis. The molecularspecies that were detected in urine include VOCs previouslyidentified in an animalmodel of lung cancer (95), and theirinvestigation in patients with lung cancer is just beginning(96).

Study Design for Early Detection BiomarkersValidation

Appropriate study design is crucial for the successfulvalidation of a promising biomarker for clinical use. Vali-dation of a biomarker useful for lung cancer screeningshould be conducted using a nested case–control studydesign within a prospective longitudinal cohort followingthe PRoBE design (97). Specifically, random sampling ofcases and controls identified from within a well-definedcohort population allows both cases and controls to besampled from the same source population, thus providingvalidity to the case–control design. Matching strategies maybe considered, such as using incidence density sampling tosample controls at the same time each case occurs, so thatcases and controls are matched on time. While there areadvantages to matching, the potential pitfalls of matchingshould be carefully considered before implementation(97, 98).

Generalizability of biomarkers to the appropriate clinicalsetting and populations is requisite for a clinically usefulbiomarker. The prospective cohort from whom the casesand controls are sampled must be representative of the






targeted clinical population to which the biomarker will beapplied. Thus, the cohort study population should com-prise individuals with conditions found in the target pop-ulation, such as inflammatory disease, granulomas, orbenign tumors, so that false-positives can be minimizedand individuals developing lung cancer can be differenti-ated from those not developing the disease (99).

Biospecimens necessary for biomarker developmentshould be collected at the initiation of the prospectivecohort study, before ascertainment of lung cancer status(97), and potentially over multiple time points if thebiomarker changeswith age andwith progression to disease(99). These biospecimens are then evaluated in patientswho develop biopsy-proven cancer (cases) and those whodonot (controls) to develop a biomarker for clinical use as acost-efficient approach. Importantly, the outcome shouldbe clearly defined (100), and the biomarker assay develop-ment should be blinded to case–control status to avoidinformation bias (97). To validate the usefulness of abiomarker for early detection of lung cancer, diagnosticvalidation of the biomarker should be conducted in adifferent population than the one in which the biomarkerwas developed. Finally, this should be followed by earlydiagnosis validation using a screening trial with lung cancermortality as the endpoint (101).

Assessing whether a biomarker has clinical validityrequires estimation of sensitivity and specificity, which canbe summarized with the receiver operator characteristic(ROC) curve (102). Two additional clinically relevant mea-sures that can be measured by ROC include NPV and PPV,which are estimated using sensitivity and specificity. Theseclinically important indices describe the probability ofdeveloping or having disease given a positive test. Estimatesof PPV and NPV are influenced by the prevalence of thedisease and consequently will vary by patient age, targetpopulation, and disease stage. Merely targeting the screen-ing to a high-risk population based on demographic factorscan alter the screening test performance characteristics(103). Thus, for a biomarker to be clinically valid andgeneralizable, the biomarker validation process must beapplied to multiple populations having different demo-graphic characteristics for determining the clinical validityand use of a biomarker.

The use of lung cancer diagnosis prediction models willgrow as the models accuracy improves. When a patientpresents in the clinic and undergoes imaging, for example,CT resulting in a detected pulmonary nodule, current pre-dictivemodels for assessing lung cancermalignancy includethose developed by Cummings and colleagues (104), Gur-ney and colleagues (105), Swensen and colleagues (106),and Gould and colleagues (18). However, these modelssuffer from relatively poor accuracy in particular for inde-terminate pulmonary nodules and do not provide accurateprediction of malignancy among patients referred for sur-gery (16). While these models may include predictors suchas patient age, smoking status, duration and intensity ofsmoking, cancer history, gender, race, asbestos exposure,chronic obstructive pulmonary disease (COPD)/emphyse-

ma, and pulmonary nodule characteristics by CT (size,shape, density, and location), the addition of biomarkersmayprovide additional classification accuracy to the currentlung cancer malignancy prediction models. Recent interesthas focused on the potential for molecular tools to improvemodels predicting lung cancer diagnosis, yet most studieshave shown little improvement with added gene expressionprofile in cytologically normal large airway epitheliumobtained via bronchoscopic brushings (28) or a serumproteomic profile in patients presenting with pulmonarynodules (19). While molecular markers are not yet fullyincorporated into lung cancer malignancy prediction mod-els, it is likely that a profile of molecular markers will benecessary to be clinically useful as biomarkers for earlydetection of lung cancer (107). Future development ofpredictive models should incorporate previously identifiedpredictors and newly identified biomarkers (100).

Current Challenges in Lung Cancer BiomarkerDevelopment and Implementation

Oneof themain objectives ofmolecularmedicine in lungcancer is to identify biomarkers that discriminate betweenlow- versus high-risk individuals and between benign andmalignant lung tumors. Ultimately, these biomarkers canpotentially be translated to noninvasive, simple, and reli-able diagnostic tests for early detection of the disease. Theunderlying assumption behind these efforts is that tumor-specific or overexpressed proteins can be detected simplyand accurately in complex clinical samples such as surrogatetissues and biofluids. The intensive research in genomicsand proteomics aimed at identifying these biomarkers hasyielded a large number of potential diagnostic biomarkers,although few have progressed to the level of U.S. Food andDrug Administration (FDA) approval for diagnostics (108).

This disappointingly slowpace of lung cancer biomarkersdiscovery and validation is attributed to a host of techno-logic and methodologic factors. The gap between promiseand product can partially be explained by the fact that thecurrent discovery methods are neither reliable nor efficient.One reason is that the current analytic technologies stillsuffer from the limited power to detect low-abundantcancer markers against a high background of high-abun-dance molecular species such as proteins in very complexmatrices such as plasma or serum. These low-abundancemarkers in biofluids may be the most promising cancerbiomarkers. Consequently, many of the best candidatesmay thus be missed during the discovery phase.

Another quandary is the limited capacity to verify andvalidate analytically existing candidate markers in a high-throughput manner. This is particularly true in proteomicsresearch. The lack of available quality reagents such asantibodies or methodologies to translate the discovery ofcandidates in tissue specimens and measure their concen-tration in the circulation remains an enormous challenge.Therefore, it is possible that biomarkers have already been"discovered" but not yet validated. Furthermore, once along list of candidate biomarkers is compiled, no current

Hassanein et al.





standardizedmethod exists for selecting those that aremostpromising for systematic validation. In addition, the repro-ducibility of biomarker data has been flawed because of thepoor design [e.g., underrepresentation of studies using anested case–control design (ref. 97)],model overfitting, andthe lack of cross-validation and independent validation.Changing technology, low concentration of signals com-binedwith very fewprospective studies, and a low incidencedisease make the area of biomarker research challenging.

Conclusions and Future Clinical ImplicationsThe molecular analysis of a variety of biospecimens has

allowed the discovery of relevant candidate biomarkers andconsequently the identification of novel proteins that mayhave a role in the development of lung cancer. A highvolume of data from multiple high-throughput biochem-ical analyses of clinical material from "-omics" sources hasbeen accumulating at an exponential rate in the last fewyears, generating large number of biomarker candidates.None of the published candidate biomarkers of risk or oflung cancer diagnosis are ready for clinical use, and fewhavemoved to phase III of biomarker development. Lung canceris recognized as a complex and heterogeneous disease, notonly at the biochemical level (genes, proteins, metabolites)but also at the tissue, organism, and population level. There

is a need for incorporating findings frommultiple discoveryplatforms into amathematical framework that can improveour level of understanding of the disease process. A bio-fluids-based molecular test may improve the selection ofhigh-risk individuals for CT screening, distinguish thosewith malignant nodules from benign lesions, and identifypatients with particularly aggressive cancer. Clinical benefitcould include further reductions in mortality and thusprovide significant cost-savings to the health care system.

Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.

AcknowledgmentsThe authors thank Mr. William Alborn for his insightful comments and

suggestions and Judith Roberts from the Vanderbilt Tumor Registry for herhelp in providing data to support the estimates of lives saved from adjuvantchemotherapy in the United States.

Grant SupportThe authors thank the following funding source for support of this work:

NIH (U01CA152662 and RO1 CA102353) awarded to P.P. Massion, Veter-ans Health Administration (VA-CDA2) awarded to E.L. Grogan, and AVanderbilt Clinical and Translational Research Scholars award to M.C.Aldrich.

Received September 20, 2011; revisedMay7, 2012; acceptedMay17, 2012;published OnlineFirst June 11, 2012.

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2012;5:992-1006. Published OnlineFirst June 11, 2012.Cancer Prev Res Mohamed Hassanein, J. Clay Callison, Carol Callaway-Lane, et al. CancerThe State of Molecular Biomarkers for the Early Detection of Lung

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