Due to unfavorable lifestyle habits (unhealthy diet and tobacco abuse) the incidence of gastroesophageal reflux disease (GERD) in western countries is increasing. The GERD-Barrett-Adenocarcinoma sequence currently lacks well-defined diagnostic, progressive, predictive, and prognostic biomarkers (i) providing an appropriate screening method identifying the presence of the disease, (ii) estimating the risk of evolving cancer, that is, the progression from Barrett’s esophagus (BE) to esophageal adenocarcinoma (EAC), (iii) predicting the response to therapy, and (iv) indicating an overall survival—prognosis for EAC patients. Based on histomorphological findings, detailed screening and therapeutic guidelines have been elaborated, although epidemiological studies could not support the postulated increasing progression rates of GERD to BE and EAC. Additionally, proposed predictive and prognostic markers are rather heterogeneous by nature, lack substantial proofs, and currently do not allow stratification of GERD patients for progression, outcome, and therapeutic effectiveness in clinical practice. The aim of this paper is to discuss the current knowledge regarding the GERD-BE-EAC sequence mainly focusing on the disputable and ambiguous status of proposed biomarkers to identify promising and reliable markers in order to provide more detailed insights into pathophysiological mechanisms and thus to improve prognostic and predictive therapeutic approaches.
In western countries, the particular importance of gastroesophageal reflux disease (GERD) as a main risk factor for Barrett’s esophagus (BE) and esophageal adenocarcinoma (EAC) promoted by obesity, hiatus hernia, and tobacco use has increased constantly [
Although no increase of EAC incidence was postulated in epidemiologic studies, about 5% of patients with GERD and 0.5% with BE developed EAC [
As dysplasia and adenocarcinoma are diagnosed by pathologists routineously (based on Haematoxylin-Eosin-stained biopsies), the question arises how the “risk progression” of GERD to BE and further to dysplasia and EAC can be evaluated and predicted by prognostic molecular markers and ideally may predict therapeutic success. In this paper, we try to refer to these FAQs and to provide a panel of diagnostic and predictive markers.
(i) GERD describes the chronic reflux of gastric acid or bile fluid to the esophagus resulting in metaplastic changes of the normal squamous esophageal tissue to columnar epithelium (BE) (for review, see [
Whereas the detection of intestinal goblet cells in BE samples is already established by using histochemical staining like Alcian-PAS, the diagnosis of dysplasia in BE remains a great challenge due to inter- and intraobserver variation in histology grading (discussed later); therefore, the incidence of dysplasia inside BE varies from 5, to 10% according to national screening efforts and surveillance programs [
Moreover, diagnosis of the progression from BE with dysplasia to invasive EAC becomes sometimes impracticable when biopsies are small and criteria of invasiveness are mimicked by distorted rearrangement of glandular structures caused by ulceration and inflammation. At present, using the grade of dysplasia in BE represents the best biomarker in predicting the progression probability for nondysplastic BE (about 0.5%), low-grade dysplasia in BE (13%), and up to 40% in high-grade dysplasia in BE [
(ii) Complexity factor “diagnosis”: Several issues in BE as well as in EAC detection are still unsolved. The majority of patients with BE remain undiagnosed [
(iii) Definition of predictive and prognostic factors (for reviews, see [
As recommend by Pepe et al. [
During carcinogenesis of BE to EAC, heterogeneous hallmarks of molecular changes are described in the literature [
(a) Genetic abnormalities of BE include loss of genetic information (especially loss of 9p21, 5q, 13q, 17p, and 18q), whereas for progressive disease, a more extensive imbalance including gain of genetic information (especially gain of 2p, 8q, and 20q) is observed. Finally, enhanced chromosomal instability could be found in the progressive lesions of EAC.
(b) These genetic abnormalities cause consecutive deregulations of their products like tumor suppressor genes (p53 (loss of 17p), p16 (loss of 9p21), fragile histidine triad protein (FHIT), adenomatous polyposis coli (APC) (loss of 5q), retinoblastoma (Rb) (loss of 13q)), cell cycle regulatory factors (cyclin D1 and MDM2 (mouse double minute 2 homolog)), growth factor receptors (EGFR (epidermal growth factor receptor), TGF-
(c) Distinct changes in expression pattern of various miRNAs (microRNA) have been demonstrated in BE or EAC. miRNAs are small regulative noncoding RNA molecules (18–22mer) which inhibit the expression of their target genes on posttranscriptional levels; about 30% of human genes are estimated to be regulated by miRNAs [
Using global miRNA expression profiling or
Recently, a link between EMT and miRNA expression in BE or EAC was established in both: Barrett’s epithelia and EAC displayed a reduced expression of miRNA-200 family members [
Taken together, the relevance of miRNA for prognosis and progression of BE and EAC is being unveiled in current research. Final statements require additional studies using independent patient cohort—also with higher case-load—accompanied by functional verification [
Previous reviews already discussed the importance of biomarkers in this area and proclaimed further investigations thereof in gastroenterological oncology (for review, see Ong et al. [
Based on studies regarding potential predictive and prognostic markers within the GERD-BE-EAC sequence, we classified them into four groups (Table
Summary of investigated and published biomarkers in the GERD-BE-EAC axis. The categorization is based on four groups according to their potential usage as A = Diagnostic Biomarker indicates the presence of disease, B = Progression Biomarker indicates the risk of developing cancer—progression in BE to EAC, C = Predictive Biomarker predicts response to therapy (CTX, RTX, photodynamic therapy), or D = Prognostic Biomarker indicates overall survival—prognostic in EAC (survival, recurrence).
Biomarker | Method | Remarks/findings | OR/RR/ |
Refs | |
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A = Diagnostic Biomarker | TFF3 | novel nonendoscopic screening modality in a prospective cohort study |
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[ | |
TFF3 | IHC, esophageal cytosponge samples for BE combined with IHC for TFF3 | biomarker to screen asymptomatic patients for BE; |
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[ | |
Chromosomes 7 and 17 (copy number changes) | ICDA & FISH | chromosomal gains in early stages of BE; |
IND/LGD: 75% sensitivity, (76% specificity) |
[ | |
8q24 ( |
FISH | chromosomal gains in early stages of BE; |
LGD (50% sensitivity) |
[ | |
17q11.2 ( |
Southern blotting, microarray analysis | amplified copies of the |
10-fold amplification in 3 of 25 (12%) tumors | [ | |
Serum proteomic pattern analysis | mass spectrometry | several limitations due to applied technology | identified 10 of 11 normal’s; and 42 of 43 EAC’s correctly | [ | |
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B = Progression Biomarkers | P53 positivity | IHC | limited efficacy as a single progression biomarker | OR 11.7 (95% CI: 1.93–71.4) | [ |
P53 positivity | IHC | positive in 4/31 that regressed, 3/12 that persisted, and 3/5 that progressed to HGD or EAC | RR not available | [ | |
DNA content abnormalities | flow cytometry | higher relative risk for EAC in patients with tetraploidy (4N) or aneuploidy (>6%) | tetraploidy: RR 7.5 (95% CI: 4–14) ( |
[ | |
4N fraction cut point of 6% for cancer risk | RR 11.7 ( 95% CI: 6.2–22) | ||||
aneuploid DNA contents of 2.7N were predictive of higher cancer risk | RR 9.5 (95% CI: 4.9–18) | ||||
DNA content abnormalities | flow cytometry | presence of both 4N fraction of 6% and aneuploid DNA content of 2.7N is highly predictive for progression | RR 23 (95% CI: 10–50) |
[ | |
17p(p53) LOH associated with higher risk of progression to HGD + EAC | HGD: RR 3.6 ( |
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flow cytometry, PCR | EAC: RR 16 ( |
[ | |||
combined LOH of 17p and 9p and DNA content abnormalities can best predict progression to EAC | RR 38.7 (95% CI: 10.8–138.5) not clinical applicable | ||||
LOH of 157p and 9p and DNA content abnormalities | LOH of 17p alone | RR 10.6 (95% CI: 5.2–21.3) | |||
flow cytometry, PCR | LOH of 9p alone | RR 2.6 (95% CI: 1.1–6.0) | |||
Aneuploidy alone | RR 8.5 (95% CI: 4.3–17.0) |
[ | |||
Tetraploidy alone | RR 8.8 (95% CI: 4.3–17.7) | ||||
mutations of |
flow cytometry, PCR | significant predictors for EAC progression, not clinical applicable |
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[ | |
EGFR | IHC | overexpression in HGD/EAC | 35% of HGD/80% of EAC specimens | [ | |
MCM2 | IHC | correlation between degree of dysplasia and level of ectopic luminal surface MCM2 expression | MCM2-positive |
[ | |
Cyclin A | IHC | surface expression of cyclin A in BE samples correlates with the degree of dysplasia | OR 7.5 (95% CI: 1.8–30.7) ( |
[ | |
Cyclin D1 | IHC | association with increased risk of EAC | OR 6.85 (95% CI: 1.57–29.91) | [ | |
hypermethylation of |
association with increased risk of progression to HGD/EAC | OR 1.74 (95% CI: 1.33–2.2) | |||
hypermethylation of |
association with increased risk of progression to HGD/EAC | OR 1.80 (95% CI: 1.08–2.81) | |||
hypermethylation of HPP1 | RT-PCR | association with increased risk of progression to HGD/EAC | OR 1.77 (95% CI: 1.06–2.81) |
[ | |
hypermethylation of |
PCR | predictor of progression to HGD/EAC | OR 14.97 (95% CI: 1.73–inf.) | [ | |
8 gene methylation panel | RT-PCR | age dependent; predicts 60.7% of progression to HGD/EAC within 2 yrs | RR not available (90% specificity) | [ | |
Gene expression profile | microarray analysis | 64 genes up regulated |
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[ | |
Cathepsin D, AKR1B10, and AKR1C2 mRNA levels | Western blotting, qRT-PCR | dysregulation predicts progression to HGD/EAC | AKR1C2: |
[ | |
ICDA | aneuploidy predicts progression to EAC | 60% with LGD; 73% with HGD, and 100% with EAC (total number of samples = 56) | [ | ||
DNA abnormalities | ACIS | frequency and severity of aneuploidy predicts progression to EAC | unstable aneuploidy in 95% with EAC | [ | |
DICM | relationship between DICM status and progression to HGD/EAC |
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[ | ||
SNP-based genotyping in BE/EAC specimens | flow cytometry, 33K SNP array | copy gains, losses, and LOH increased in frequency and size between early and late stage of disease |
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[ | |
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C = Predictive Biomarkers |
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FISH | decreased response to photodynamic therapy | OR 0.32 (95% CI: 0.10–0.96) | [ |
DNA ploidy abnormalities | ICDA | DNA ploidy as a covariate value for recurrence | HR 6.3 (1.7–23.4) ( |
[ | |
HSP27 | IHC | association between low HSP27 expression and no response to neoadjuvante chemotherapy |
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[ | |
Ephrin B3 receptor | microarray | response prediction in EAC in patients with Ephrin B3 receptor positive versus Ephrin B3 receptor negative | Response rate <50%: 3 (15.8) versus 16 (84.2) ( |
[ | |
Genetic polymorphisms | qRT-PCR | association between individual single nucleotide polymorphisms |
comprehensive panel of genetic polymorphisms on clinical outcomes in 210 esophageal cancer patients | [ | |
P21 | IHC | alteration in expression correlated with better CTX-response |
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[ | |
P53 | IHC | alteration in expression correlated with better CTX-response |
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[ | |
ERCC1 | IHC | ERCC1-positivity predicts CTX-resistance and poor outcome |
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[ | |
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D = Prognostic Biomarkers | DCK |
RT-PCR, |
prognostic 4-gene signature in EAC predicts 5-year survival | 0/4 genes dysregulated: 58% |
[ |
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FISH | association between therapy response status and FISH positivity |
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[ | |
ASS expression | microarrays | low expression correlates with lymph node metastasis |
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[ | |
microRNA expression profiles | miRNA microarray, qRT-PCR | association with prognosis (e.g. low levels of mir-375 in EAC → worse prognosis) | HR = 0.31 (95% CI: 0.15–0.67) ( |
[ | |
Genomic alterations | MLPA | reverse association between survival and DNA copy number alterations (>12 aberrations |
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[ | |
Cyclin D1 | FISH, IHC | 2 of 3 genotypes confers to |
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[ | |
IHC | expression = |
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[ | ||
EGFR | IHC |
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[ | |
Ki-67 | IHC | low levels of staining (<10%) |
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[ | |
Her2/neu | FISH | amplification = |
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[ | |
IHC | low levels = |
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[ | ||
TGF- |
IHC, ISH | high levels = tumor progression and lymph node metastasis |
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[ | |
qRT-PCR | overexpression = |
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[ | ||
TGF- |
ELISA | high plasma levels = |
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[ | |
APC | RT-PCR | high plasma levels of methylation |
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[ | |
Bcl-2 | IHC | expression = |
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[ | |
IHC, RT-PCR |
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[ | ||
IHC | strong staining = |
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[ | ||
COX-2 | IHC | strong staining = |
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[ | |
NF- |
IHC | activated NF- |
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[ | |
Telomerase | Southern blot analysis, RT-PCR | higher telomere-length ratio |
RR of death: 3.4 |
[ | |
expression = |
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CD105 | angiolymphatic invasion |
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IHC |
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VEGF | angiolymphatic invasion |
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Cadherin | IHC |
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[ | |
uPA | ELISA |
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TIMP | IHC, RT-PCR |
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[ | |
Promoter hypermethylation of multiple genes | IHC, methylation specific PCR | if >50% of gene profile methylated |
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[ | |
MGMT hypermethylation | IHC, methylation specific PCR | correlation with higher tumor differentiation |
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[ |
ACIS: automated cellular imaging system; ASS: argininosuccinate synthase; APC: adenomatous polyposis coli; BE: barrett’s esophagus; COX: cyclooxygenase; DCK: deoxycytidine kinase; DICM: digital image cytometry; EAC: esophageal adenocarcinoma; EGFR: epidermal growth factor receptor; ELISA: enzyme-linked immunosorbent assay; FISH: fluorescence
GERD-associated progression for Barrett’s esophagus (BE) to esophageal adenocarcinoma (EAC). A–D refer to biomarkers which could be most relevant at the indicated stages of the disease progression (according to Table
Proposed approach for identification of novel biomarkers for the GERD-BE-AEC sequence. Based on theheterogeneous and patient-specific progression sequence from BE to EAC, the figure indicates the disease stages and mandatory (histology, IHC) and supplementary potential methods for investigation of putative biomarkers for progression, prediction, and prognosis. These data possibly result in an evidence-based stratification of patients for various available therapies (X–Z) based on a rational selection and evaluation of specific biomarkers. Abbreviations. Esophageal adenocarcinoma: AEC; dysplasia: Dys; fluorescence
The conventional approach for detection and diagnosis is the histochemical analysis of endoscopically derived biopsies of the gastro-esophageal junction, albeit the proposed importance of histological subtypes, the gastric fundus, the cardiac subtype, and the metaplastic columnar epithelium with intestinal-type goblet cells remains unclear [
Similar to the situation for diagnostic biomarkers (A), the most frequently applied progression marker for clinicians and pathologists is the degree of dysplasia in obtained biopsies. Although the inter- and intra-observer error [
As displayed in Table
It is not surprising that the majority of biomarkers are listed in the last category—displaying the typical survey of hallmarks of cancer [
To assemble the sometimes confusing data on possible biomarkers (as listed in Table
Synopsis of biomarkers in the GERD-BE-EAC axis. According to Table
Dysplasia | P53 | P16 | P21 | Growth factors | Cell cycle | |
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A = Diagnostic Biomarker |
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B = Progression Biomarker |
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C = Predictive Biomarker |
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D = Prognostic Biomarker |
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Finally, two major questions arise and are still unsolved: (i) why are proposed biomarkers not (yet) really embedded in clinical routine, and (ii) what impairs the identification of more reliable and significant biomarkers?
First of all, two major limitations are the technical and financial aspects. Special molecular biological techniques require fresh frozen samples; DNA-, RNA-extraction, and nucleic acid amplification as well as subsequent hybridization or sequencing are time-consuming and need special facilities which are, again, cost intensive. Additionally, validation of specific methods to detect genetic and epigenetic alterations is still not completed. In conclusion, costs and practicability of these biomarkers are the limiting factors until now [
Possible answers to the second question are that more relevant entities like inflammation or epithelial-mesenchymal-transition (EMT), which have yet not been completely considered, should be integrated in the evaluation-process of biomarkers for GERD, BE and EAC.
The potential role of the localized inflammation in disease prediction and prognosis is currently rather underestimated in experimental and clinical investigations. Generally, it has been shown that inflammation influences cancerogenesis by key mediators including reactive oxygen species (ROS), NF-
Additionally, the process of EMT with its key players Snail, Twist, and ZEB and their repressed target protein E-Cadherin is essentially linked to development, regeneration, inflammation, and cancerogenesis [
The probability to find one single specific biomarker providing all diagnostic, predictive, and prognostic significance in GERD, BE, and/or EAC is rather utopian, and a panel of biomarkers maybe will solve this problem [
As depicted in Figure
Automated cellular imaging system
Argininosuccinate synthase
Adenomatous polyposis coli
Barret’s esophagus
Cyclooxygenase
Deoxycytidine kinase
Digital image cytometry
Esophageal adenocarcinoma
Epidermal growth factor receptor
Enzyme linked immunosorbent assay
Epithelial-mesenchymal-transition
Fragile histidine triad protein
Fluorescence
Gastro-esophageal reflux disease
Image cytometric DNA analysis
Heat-shock protein 27
Immunohistochemistry
Loss of heterozygosity
3′-phosphoadenosine 5′-phosphosulfate synthase 2
Polymerase chain reaction
Quantitative reverse transcriptase
Mouse double minute 2 homolog
Multiplex ligation dependent probe amplification
Nuclear factor kappa B
Retinoblastoma
Sirtuin 2
Single nucleotide polymorphism
Trefoil factor 3,
Transforming growth factor
Tissue inhibitors of metalloproteinases
Tripartite motif-containing 44
Urokinase-type plasminogen activator
Vascular endothelial growth factor.
The authors thank Professor Dr. Otto Dietze for his crucial suggestions regarding paper preparation. They apologize to colleagues whose work could only be cited indirectly. This work was supported by the Wissenschaftlicher Verein der Pathologie Salzburg/Austria.