Abstract
Barrett's intestinal metaplasia (BIM) may harbor genomic mutations before the histologic appearance of dysplasia and cancer and requires frequent surveillance. We explored next-generation sequencing to detect mutations with the analytical sensitivity required to predict concurrent high-grade dysplasia (HGD) and esophageal adenocarcinoma (EAC) in patients with Barrett's esophagus by testing nonneoplastic BIM. Formalin-fixed, paraffin-embedded (FFPE) routine biopsy or endoscopic mucosal resection samples from 32 patients were tested: nonprogressors to HGD or EAC (BIM-NP) with BIM, who never had a diagnosis of dysplasia or EAC (N = 13); progressors to HGD or EAC (BIM-P) with BIM and a worse diagnosis of HGD or EAC (N = 15); and four BIM-negative samples. No mutations were detected in the BIM-NP (0 of 13) or BIM-negative samples, whereas the BIM-P samples had mutations in 6 (75%) of 8 cases in TP53, APC, and CDKN2A (P = 0.0005), detected in samples with as low as 20% BIM. We found that next-generation sequencing from routine FFPE nonneoplastic Barrett's esophagus samples can detect multiple mutations in minute areas of BIM with high analytical sensitivity. Next-generation sequencing panels for detection of TP53 and possibly combined mutations in other genes, such as APC and CDKN2A, may be useful in the clinical setting to improve dysplasia and cancer surveillance in patients with Barrett's esophagus.
CME Accreditation Statement: This activity (“JMD 2015 CME Program in Molecular Diagnostics”) has been planned and implemented in accordance with the Essential Areas and policies of the Accreditation Council for Continuing Medical Education (ACCME) through the joint providership of the American Society for Clinical Pathology (ASCP) and the American Society for Investigative Pathology (ASIP). ASCP is accredited by the ACCME to provide continuing medical education for physicians.
The ASCP designates this journal-based CME activity (“JMD 2015 CME Program in Molecular Diagnostics”) for a maximum of 36 AMA PRA Category 1 Credit(s)™. Physicians should only claim credit commensurate with the extent of their participation in the activity.
CME Disclosures: The authors of this article and the planning committee members and staff have no relevant financial relationships with commercial interests to disclose.
Esophageal adenocarcinoma (EAC) most frequently develops in patients with Barrett's esophagus (BE), estimated to affect 3.3 million adults in the United States.1 BE results from injury of the esophageal mucosa associated with gastroesophageal reflux, which leads to esophagitis and eventually BE. The incidence of EAC has increased greater than fivefold during the past 4 decades in the United States, paralleling the increase in detection of esophageal reflux and diagnosis of BE.2 Barrett’s intestinal metaplasia (BIM) is characterized by the replacement of normal squamous esophageal mucosa by columnar epithelium with intestinal metaplasia, often occurring in the background of patches of cardiac, oxyntic, or cardiooxyntic mucosa along the length of the BE. Patients with BE may sequentially progress to low-grade dysplasia, high-grade dysplasia (HGD), and eventually EAC. Patients with BE without dysplasia have a lower EAC risk (0.1% to 0.5% per patient-year) than those with high-grade dysplasia (6% to 19% per patient-year).3,4 Current guidelines for the prevention of EAC require repeat surveillance endoscopies with biopsies of the Barrett's mucosa followed by pathological examination to detect BIM and dysplasia.4–6 Unfortunately, the detection of dysplasia is hampered by sampling errors and high interobserver diagnostic variability.7–10
Known risk factors associated with EAC include male sex, older age, white race, hiatal hernia size, length of Barrett's epithelium, smoking, and high body mass index.6 Recently, it was reported that persistence of BE negative for dysplasia over several endoscopic examinations identifies patients who are at low risk for development of EAC.11
It has been hoped that surveillance could potentially be improved by the implementation of risk stratification protocols using both clinical and biological markers because management of BE is costly and inefficient because only a small percentage of patients with BE progress to HGD or EAC.12 Furthermore, recent data revealed that endoscopic surveillance of patients with BE was not associated with a substantially decreased risk of death from EAC.13
Biomarkers that can be assessed in random biopsy specimens from Barrett's mucosa negative for dysplasia with the ability to predict development or concurrent (co-existing) dysplasia elsewhere in the esophagus are warranted to improve surveillance approaches in the BE population. Previously evaluated testing approaches for EAC risk stratification in BE using esophageal biopsy samples include nuclear DNA content abnormalities, such as aneuploidy and tetraploidy; gene copy number alterations, such as loss of heterozygosity of p16 (CDKN2A) and p53 (TP53)14,15; somatic gene mutations; and hypermethylation of a number of genes.14,16,17 However, published studies used fresh or frozen tissue or special sampling procedures, which limits their clinical implementation, and data from studies using routine formalin-fixed, paraffin-embedded (FFPE) clinical endoscopic samples replicating the clinical setting of BE patients undergoing endoscopic surveillance have not been reported.6
Next-generation sequencing (NGS) approaches using nucleic acids obtained from routinely processed FFPE tissues to detect mutations in cancer samples, enabling high analytical sensitivity and detection of low-frequency mutational events, are well established. However, NGS testing of preneoplastic tissues, such as BE, have not been evaluated in routine FFPE clinical samples, and there is no information regarding the minimal percentage of intestinal metaplasis in the tested mucosal tissue that may be considered for mutation testing. Therefore, we hypothesized that targeted NGS may be ideally suited for clinical application in the BE surveillance setting because it can be performed with minimal amounts of DNA from routine FFPE tissue samples, permits biomarker multiplexing, and can reach high sensitivity to detect low-frequency mutational events in heterogeneous BE tissues, where the foci of intestinal metaplasia may be small. Sensitivity of mutation detection by NGS can be 0.5% or lower due to the high level of sequencing coverage, reaching several thousand reads per amplicon when targeted sequencing is used. Therefore, targeted NGS of BE FFPE samples enables the characterization of the mutational status of hundreds to thousands of target functional sites in oncogenes and tumor suppressor genes that may be critical in BE, dysplastic precursor lesions, and EAC in individual biopsy samples collected in the clinical diagnostic setting.
We used two NGS-targeted amplicon sequencing technology platforms, the Illumina (San Diego, CA) TruSeq Cancer Panel for Illumina MiSeq platform and the Ion Torrent Ion AmpliSeq Cancer Panel (Life Technologies, Carlsbad, CA), to determine whether mutations of genes known to undergo mutagenesis in esophageal dysplasia or cancer arising in BE could predict the presence of HGD or EAC through testing random nondysplastic or noncancer BE mucosa with intestinal metaplasia using endoscopic FFPE samples.
Materials and Methods
Patients and Tissue Samples
Patients with esophageal biopsies or endoscopic mucosal resections (EMRs) with the pathological diagnosis of intestinal metaplasia, HGD, or EAC were searched in the pathology records from the University of Pennsylvania and Columbia University. The EAC lesions were intramucosal or superficial submucosal adenocarcinomas. Two patient groups were selected: nonprogressors to HGD or EAC (BIM-NP) are patients with a histologic diagnosis of intestinal metaplasia in at least two biopsies within ≥2 consecutive years and who never had a diagnosis of dysplasia (indefinite, low grade, or high grade) or EAC in any esophageal biopsy or resection, and progressors to HDG or EAC (BIM-P) are patients with a histologic diagnosis of intestinal metaplasia with a concurrent worse diagnosis of HGD or EAC. BIM-NP patients had a mean of 61.8 months (range, 25 to 108 months) of follow-up with all biopsies with a diagnosis of BIM negative for dysplasia.
Histologic review and assessment were performed by a gastrointestinal pathologist (A.R.S.). The BIM samples used for DNA extraction had 10% to 80% intestinal metaplasia involving the overall mucosal area in the guiding hematoxylin and eosin–stained sections, and the mucosa background consisted of cardiac, cardiooxyntic, and squamous mucosa, as is usual in endoscopic biopsy samples of BE patients. Furthermore, the BIM samples from each patient included a range of 2 to 8 (mean, 2.5) separate biopsy fragments, with multifocal areas of intestinal metaplasia in some cases, and DNA was obtained from the pooled biopsy fragments of each patient. Control cardiac or cardiooxyntic mucosa samples from esophageal biopsies of two BIM-NP and two BIM-P patients were also tested. Overall, we tested 40 tissue samples from 32 patients: 13 samples from BIM-NP and 23 from BIM-P patients, two samples with cardiac mucosa from BIM-NP patients, and two with cardiac mucosa from BIM-P patients with HGD or EAC elsewhere in the esophagus. There were 24 males and 8 females, consistent with the epidemiology of BE, and the mean (SEM) age was 66.5 (1.5) years, with no significant difference in age between nonprogressors [mean (SEM) age, 64.4 (2.6) years] and progressors [mean (SEM) age, 66.9 (2.1) years; P = 0.51] (Table 1). For the 13 BIM-NP patients, DNA was obtained from 12 biopsies and 1 EMR. For the eight BIM-P patients with DNA from BIM tissue tested, there were five biopsies and three EMRs (Table 1). The DNA yield ranged from 108 to 2903 ng per sample (Table 1).
Table 1.
Patients and Sample Characteristics for BIM and HGD or EAC Tissues Tested by NGS
| Group | Patient no. (N = 28) | Age, years | Biopsy or EMR |
BIM and HGD or EAC, % area∗ | DNA, ng | |
|---|---|---|---|---|---|---|
| BIM | HGD or EAC | |||||
| BIM-NP (n = 13) | 1 | 63 | Biopsy | NA | 50 | 275 (BIM) |
| 2 | 62 | Biopsy | NA | 15 | 1188 (BIM) | |
| 3 | 51 | Biopsy | NA | 20 | 866 (BIM) | |
| 4 | 63 | Biopsy | NA | 10 | 282 (BIM) | |
| 5 | 64 | EMR | NA | 80 | 628 (BIM) | |
| 6 | 80 | Biopsy | NA | 80 | 108 (BIM) | |
| 7 | 54 | Biopsy | NA | 50 | 257 (BIM) | |
| 21 | 55 | Biopsy | NA | 40 | 341 (BIM) | |
| 22 | 54 | Biopsy | NA | 50 | 283 (BIM) | |
| 23 | 56 | Biopsy | NA | 50 | 157 (BIM) | |
| 24 | 71 | Biopsy | NA | 20 | 911 (BIM) | |
| 25 | 73 | Biopsy | NA | 20 | 1241 (BIM) | |
| 26 | 76 | Biopsy | NA | 20 | 497 (BIM) | |
| BIM-P (n = 15) | 8 | 64 | EMR | EMR | 20, 70 | 485 (BIM), 808 (HGD) |
| 9 | 68 | Biopsy | Biopsy | 30, 50 | 328 (BIM), 310 (HGD) | |
| 10 | 69 | EMR | EMR | 80, 80 | 220 (BIM), 311 (HGD) | |
| 27 | 70 | Biopsy | EMR | 30, 80 | 1126 (BIM), 2138 (HGD) | |
| 28 | 61 | Biopsy | Biopsy | 30, 30 | 321 (BIM), 246 (HGD) | |
| 11 | 70 | EMR | ND | 30 | 2903 (BIM) | |
| 12 | 57 | Biopsy | ND | 20 | 592 (BIM) | |
| 13 | 67 | Biopsy | ND | 50 | 149 (BIM) | |
| 14 | 64 | ND | EMR | 50 | 2052 (HGD) | |
| 15 | 63 | ND | EMR | 50 | 2169 (EAC) | |
| 16 | 60 | ND | Biopsy | 60 | 593 (EAC) | |
| 17∗ | 70 | ND | EMR | 80 | 288 (HGD), 274 (EAC)† | |
| 18 | 68 | ND | EMR | 80 | 1377 (HGD) | |
| 19 | 83 | ND | EMR | 70 | 495 (HGD) | |
| 20 | 47 | ND | EMR | 50 | 1701 (HGD) | |
The age of patients at the time of biopsy used for NGS is recorded. For BIM-P patients, the BIM and HGD or EAC samples tested were from the same procedure.
BIM, Barrett’s intestinal metaplasia; BIM-NP, BIM nonprogressors; BIM-P, BIM progressors to HGD or EAC; EAC, esophageal adenocarcinoma; EMR, endoscopic mucosal resection; HGD, high-grade dysplasia; NA, not applicable; ND, not done due to insufficient sample or lesion; NGS, next-generation sequencing.
Percentage of BIM and HGD or EAC area in the tissue samples tested by NGS.
In this case, HGD and EAC samples were tested separately.
Tissue Microdissection, DNA Extraction, and Quantitation
The selected tissue blocks were sectioned at 5-μm thick, and 15 unstained sections and hematoxylin and eosin–stained sections from the top and bottom tissue profiles were obtained to assess for presence, extent, and grade of lesions. Lesional areas for microdissection were marked on the guiding hematoxylin and eosin–stained slides to obtain at least 10% BIM or HGD or EAC in the tissue sections. Genomic DNA was extracted with the QIAamp DNA FFPE Tissue Kit (Qiagen, Germantown, MD), as recommended by the manufacturer. DNA was quantitated by fluorometry with the Invitrogen Qubit fluorometer and the Invitrogen Quant-iT double-strand DNA BR Assay Kit (Life Technologies), as recommended by the manufacturer.
NGS
Ion Torrent
The AmpliSeq Cancer Hotspot panel version 2 (Life Technologies) used in the study includes 207 target regions for amplification ranging in size from 111 to 187 bp, covering 50 oncogenes and tumor suppressor genes previously implicated in cancer, and >2800 sequence variants described in the COSMIC database of cancer mutations. This gene panel covers the following cancer genes: ABL1, AKT1, ALK, APC, ATM, BRAF, CDH1, CDKN2A, CSF1R, CTNNB1, EGFR, ERBB2, ERBB4, EZH2, FBXW7, FGFR1, FGFR2, FGFR3, FLT3, GNA11, GNAS, GNAQ, HNF1A, HRAS, JAK2, JAK3, IDH1, IDH2, KDR/VEGFR2, KIT, KRAS, MET, MLH1, MPL, NOTCH1, NPM1, NRAS, PDGFRA, PIK3CA, PTEN, PTPN11, RB1, RET, SMAD4, SMARCB1, SMO, SRC, STK11, TP53, and VHL.
Library preparation was performed with 10 to 15 ng of DNA. Briefly, the primer pool was mixed with the DNA and the AmpliSeq HiFi master mix (Life Technologies), and amplification was performed following the manufacturer's instructions. After partial digestion of the primers with FuPa, Ion Xpress Barcode adapters were ligated to the amplicons, and the library was purified using AmPure beads from Agencourt (Beckman and Coulter, Brea, CA). For quality assessment, the bar-coded libraries were quantified with a Picogreen assay on a Qubit fluometer (Life Technologies) and were run on a BioAnalyzer (Agilent Technologies, Santa Clara, CA) after loading on a DNA high-sensitivity chip. After equimolar pooling of six libraries, the pooled libraries were diluted to 20 pM and were further amplified on Ion Sphere Particles using the Ion OneTouch 200 system. After enrichment to eliminate null beads, the sequencing was performed on a 318 chip in the Ion PGM sequencer (Life Technologies).
MiSeq
The TruSeq Amplicon Cancer Panel was used, and sequencing was performed with the MiSeq reagent kit version 2 (Illumina), according to the manufacturer’s instructions. Sequencing reactions used 250 ng of genomic DNA obtained from the FFPE tissue and were run on a paired end 2 × 150-bp cycler run.
The TruSeq Amplicon Cancer Panel targets mutational hotspots in 48 genes implicated in cancer in a single reaction generating 212 different amplicons. The TruSeq gene panel covers all but the EZH2 and IDH2 genes compared with the AmpliSeq Cancer Panel. Samples from patients 1 to 17, 19, and 20 were sequenced with Ion Torrent. Samples from 10 patients were sequenced in the Ion Torrent and MiSeq platforms in parallel: 3, 4, 8, 10, 12, 14, 15, 18, 19, and 20. Samples from patients 18 and 21 to 28 were analyzed only by the MiSeq platform.
Statistical Analysis
Ion Torrent sequencing data were analyzed with the Ion Torrent Suite version 3.4 (Life Technologies). Briefly, the software deconvoluted the bar-coded reads and aligned the reads to the human reference genome build 37 (hg19) using the torrent mapping alignment program. The resulting .bam files were analyzed to detect single-nucleotide variants and indels using Ion Torrent's Variant Caller. The resulting variant files (.vcf) were then annotated using the Annovar tool (http://www.openbioinformatics.org/annovar; last accessed January 27, 2015).
The MiSeq sequencing data files were analyzed with the standard somatic variant caller built into the MiSeq Reporter software version 1.3+ (Illumina). The output variant files contain the identified single-nucleotide polymorphisms and associated quality (Q) scores. Base calls >Q20 and depth of coverage >10 were required for single-nucleotide polymorphism calling.
Nonsynonymous variants were inspected using the Integrated Genome Viewer software version 2.3.7.18 Variants within <5 bp of the amplicon edge or in regions of homopolymers were rejected if the overall variant frequency was <20% or the strand bias was >−3.0. The remaining variants were accepted if allele frequencies were ≥5%.
Fisher’s exact tests19 were used to identify mutations more frequent in one group than the other and to determine differences in the frequency of biopsies and EMR in BIM-P versus BIM-NP patients. Two-tailed t-tests were used to determine whether there were significant age and area of lesional tissue differences in BIM-P versus BIM-NP patients. SigmaPlot software version 11.2 (Systat Software, Point Richmond, CA) was used. P < 0.05 was considered significant.
Results
Genomic Mutations in BIM and in Paired HGD or EAC Lesions
Samples from 13 BIM-NP patients and 15 BIM-P patients were tested in the Ion Torrent platform with the AmpliSeq Cancer Panel and/or with the Illumina MiSeq TruSeq Cancer Panel. Samples of background BIM were available for 21 patients (13 BIM-NP patients and 8 BIM-P patients), whereas seven BIM-P patients only had available tissue from the HGD or EAC lesional areas (Table 1). In the BIM-P group, the tissue samples from BIM patients were obtained during the same endoscopic procedure, separate from areas of HGD or EAC. The areas of intestinal metaplasia ranged from 10% to 80% in BIM samples (Table 1). HGD tissues were tested in 10 patients, and EAC tissues were tested in three patients (Table 1).
Only nonsynonymous pathogenic mutations were cataloged (Table 2). The most frequent mutations in HGD or EAC were detected in TP53 spanning codons 175 to 280 in seven HGD and three EAC patients of 13 tested patients with HGD or EAC lesions (69% of the patients with HGD or EAC), followed by mutations in CDKN2A, which were detected in two HGD patients and one EAC (25%) patient. Mutations in APC were present in one HGD and one EAC patient (17%), and PDGFRA, FGFR, and KRAS genes were each present in one HGD patient (8%).
Table 2.
Mutations Detected by NGS in BIM or HGD or EAC Samples
| Group | Patient no. (N = 28) | BIM mutation (% reads, total no. of reads) | HGD or EAC mutation (% reads, total no. of reads) |
|---|---|---|---|
| BIM-NP (n = 13) | 1–7, 21–26 | No mutations found | NA |
| BIM-P (n = 15) | 8 | TP53 c.G839A:p.R280K (15%; 4757) | TP53 c.G839A:p.R280K (38%; 3145)† |
| 9 |
TP53 c.C577T:p.H193Y (35%; 4764) CDKN2A c.G293A:p.R98Q (53%; 975) |
TP53 c.C577T:p.H193Y (13%; 4741)† CDKN2A c.G293A:p.R98Q (37%; 1359) |
|
| 10 |
TP53 c.C466T:p.R156C (5%; 14,589) CDKN2A c.C238T:p.R80∗ (46%; 4220) |
TP53 c.A536G:p.H179R (16%; 1316)† CDKN2A c.C238T:p.R80∗ (31%; 742) FGFR3 c.G2374A:p.D792N (5%; 977) |
|
| 27 | APC c.G3349C E1299Q (22%, 15,854) | APC c.G3349C:p.E1299Q (22%, 20,176)† | |
| 28 | TP53 c.G839A R213∗ (7%, 3350) | TP53 c.G839A:p.R213∗ (18%, 1357)† | |
| 11 | TP53 c.G878A:p.G293E (6%; 780) | ND | |
| 12 | No mutations found | ND | |
| 13 | No mutations found | ND | |
| 14 | ND | TP53 c.C742T:p.R248W (29%; 12,745)† | |
| 15 | ND | TP53 c.A659G:p.Y220C (50%; 3787)‡ | |
| 16 | ND | TP53 c.G524A:p.R175H (44%; 2787)‡ | |
| 17 | ND |
TP53 c.G743A:p.R248Q (48%; 2712)† TP53 c.G743A:p.R248Q (45%; 3325)‡ APC c.C4321G:p.P1441A (8%; 372)‡ APC c.C4324A:p.P1442T (15%; 371)‡ APC c.C4343G:p.T1448S (23%; 451)‡ |
|
| 18 | ND | TP53 c.C817G; pR273 G (41%; 8415)† | |
| 19 | ND | PDGFRA c.C2515A:p.L839M (28%; 1325)† | |
| 20 | ND |
CDKN2A c.C172T:p.R58∗ (58%; 1682)† KRAS c.G436A:p.A146T (9%; 8785) |
Both the HGD and EAC lesional tissues were tested for patient 17. The highest number of sequence reads is reported here when samples were tested with both the Ion Torrent and MiSeq platforms.
BIM, Barrett's intestinal metaplasia; BIM-NP, BIM nonprogressors; BIM-P, BIM progressors to HGD or EAC; EAC, esophageal adenocarcinoma; HGD, high-grade dysplasia; NA, not applicable; ND, not done due to insufficient sample or lesion; NGS, next-generation sequencing.
Stop codon.
HGD sample.
EAC sample.
When both BIM and HGD or EAC samples were available from the same patient, in cases 8 to 10, 27, and 28, at least one identical mutation was detected in both samples (Table 2). For example, the BIM sample from patient 8 had a G to A TP53 mutation at cDNA position 839 in 15% of the sequence reads, resulting in a previously characterized R280K amino acid substitution. In the HGD sample from the same patient, the same G to A mutation was detected in 38% of the reads. Patients 9 and 10 had the same combination of mutated genes in both BIM and HGD samples, carrying mutations in CDKN2A and TP53. In addition, patient 10 had 2 different genes mutated in the BIM sample (CDKN2A and TP53) and an additional gene (FGFR) with a mutation in the HGD sample. Interestingly, in patient 10, the TP53 mutation in the BIM sample (C466T) differed from the HGD sample (A536G). CDKN2A mutations were detected in matched BIM and HGD of patients 9 and 10. The CDKN2A R80* mutation was detected in the BIM and HGD samples of case 10, and the R98Q mutation was detected in the BIM and HGD of patient 9. The APC mutation E1299Q was present at similar levels (22%) in the HGD and matched BIM-P samples of patient 27.
Frequency of Mutations in BIM-P and BIM-NP Patients
No nonsynonymous mutations were detected in any of the samples from 13 BIM-NP patients (Table 2) or in the four cardiac mucosa samples. In eight BIM-P patients (patients 8 to 13, 27, and 28), we analyzed the mutation status of BIM samples without dysplasia and detected mutations in TP53 in five patients (63%). Two BIM samples from BIM-P patients had a mutation in CDKN2A (25%), and these cases were also positive for TP53 mutations. One case of HGD had an APC mutation, which was also present in the concurrent intestinal metaplasia. The difference in the overall number of mutations in BIM-NP (0 of 13) and BIM-P (6 of 8 carrying mutations in TP53 or APC) was statistically significant (P = 0.0005). The area of BIM in histologic sections from BIM-P and BIM-NP patients was not significantly different (P = 0.97). Furthermore, the number of biopsies versus EMR samples tested was not significantly different between BIM-P and BIM-NP patients (P = 0.3).
Among all genes analyzed, TP53 was the most commonly mutated gene in the BE progressors in BIM and HGD or EAC lesions. TP53 nonsynonymous mutations were detected in BIM tissue in 5 (63%) of the 8 BIM-P cases, and none of the BIM-NP patients had any detectable mutations in their BIM tissue (Table 2). BIM-P patients 9 and 10 had CDKN2A mutations detected in their BIM tissue; however, they also carried TP53 mutations. The frequency of mutations in BIM samples from BIM-P patients was 75% when combining APC mutations together with TP53. Interestingly, the CDKN2A mutations in the BIM tissues of patients 9 and 10 had higher frequency of mutated sequences than the TP53 mutations. Patient 9 had 35% mutated sequences in TP53 and 53% in CDKN2A, and patient 10 had 5% mutated sequences in TP53 and 46% in CDKN2A, suggesting that CDKN2A may precede TP53 mutation in the stepwise progression from intestinal metaplasia to dysplasia and cancer in these cases.
Comparison of the Ion Torrent and MiSeq Platforms for Mutational Analysis
Genomic DNA from lesional tissue from a subset of nine samples was amplified and sequenced using both the Ion Torrent Ampliseq Cancer Hotspot Panel and the TruSeq Amplicon Cancer Panel, targeting 50 and 48 cancer-related genes, respectively. Both platforms detected the same mutations for each sample and with comparable frequencies when the mutation site was present in both amplicons (Table 3). For example, for patient 19, Ion Torrent detected the PDGFRA mutation L839M in 26% of the reads from 2364 total reads, whereas MiSeq detected the same mutation in 28% of the reads from 6328 total reads. The largest absolute difference in mutation frequency between the two platforms was detected in patient 18, with a 22% difference in frequency of CDKN2A R48* (58% versus 36%), in which the MiSeq run only had 122 reads. For all other samples, which had >1000 reads on either platform, the absolute difference in mutation frequency ranged from 0% to 9%. For patient 10, the MiSeq platform missed a CDKN2A CZ83T mutation, which is not covered by the TrueSeq amplicons.
Table 3.
BIM and HGD or EAC Samples Tested with Both the Ion Torrent and MiSeq Platforms
| Patient no. | Gene mutation; amino acid change | Ion Torrent, % mutation; total no. of reads |
MiSeq, % mutation; total no. of reads |
||||
|---|---|---|---|---|---|---|---|
| BIM | HGD | EAC | BIM | HGD | EAC | ||
| 8 | TP53; R280K | 15%; 4757 | 10%; 6302 | ||||
| 10 | TP53; R156C | 7%; 9294 | 5%; 14,589 | ||||
| 14 | TP53; R248W | 20%; 2062 | 29%; 12,745 | ||||
| 15 | TP53; Y220C | 50%; 3787 | 50%; 29,998 | ||||
| 19 | PDGFRA; L839M | 28%; 2364 | 26%; 6328 | ||||
| 20 | CDKN2A; R58 | 58%; 1682 | 36%; 122 | ||||
| KRAS; A146D | 9%; 8785 | 8%; 8239 | |||||
Samples from nine patients were tested in both platforms. Six samples listed in the table had mutations identified, which were detected by both platforms. BIM-NP samples from three patients (patients 3, 4, and 5) had no mutations detected by either platform (not shown), whereas the BIM-P samples tested from patients 8 and 10 had the same mutations detected by both platforms.
BIM, Barrett's intestinal metaplasia; BIM-NP, BIM nonprogressors; BIM-P, BIM progressors to HGD or EAC; EAC, esophageal adenocarcinoma; HGD, high-grade dysplasia.
Discussion
BIM is the major risk factor for the development of HGD or EAC. Endoscopic surveillance is recommended for patients with a diagnosis of BIM; however, because the absolute risk of EAC in patients with BIM negative for dysplasia is overall low, it is important to identify additional biomarkers that will identify patients who are at a significant risk of EAC so that they may be subjected to more intensive surveillance.6,14 We tested FFPE routine biopsies or EMR samples with BIM from BIM-P and BIM-NP patients using NGS and detected no mutations in the BIM-NP (0 of 13), whereas BIM samples from BIM-P patients had mutations in 6 (75%) of 8 cases in TP53, APC, and CDKN2A (P = 0.0005) in samples with an area of IM as low as 20%.
Molecular biomarkers of risk of HGD or EAC in BIM patients have been investigated in previous studies, including ploidy analyses, gene copy number, mutational analyses, and epigenetic alterations.14,16 Recently, Agrawal et al20 performed exome sequencing from frozen tissues, including two samples of BE adjacent to cancer, finding that most mutations in EAC were already present in the nondysplastic matched BE, supporting the notion of a molecular field defect in BIM. However, this study did not address whether the same mutations might be present in BE tissues from individuals who have not progressed to EAC. Another study by Weaver et al17 screened for recurrently mutated genes in a cohort of individuals with BE who had never developed dysplasia and a cohort of individuals with BE with HGD, using whole-genome mutational analyses. They identified numerous mutations in patients with BE without dysplasia and found that the most prevalent gene mutations in EAC were also present at similar frequencies in samples from patients with BE and HGD without dysplasia, but in their study only TP53 and SMAD4 mutations occurred in a stage-specific manner, confined to HGD and EAC, respectively.17
What makes our study unique in the BE and HGD or EAC field is that first we used NGS to test FFPE endoscopic samples of preneoplastic BIM, whereas other studies used frozen samples of BIM, either from resections20 or from endoscopic samples.17 Because the standard of practice for BE surveillance is based on FFPE endoscopic samples, our study is the first to uniquely provide detailed performance information of NGS directly applicable to the BE clinical setting. Second, we report the extension of intestinal metaplasia with detectable mutations not previously characterized. Specifically, we report that even an area as low as 20% of sample containing intestinal metaplasia was sufficient for detection of mutations in patients who progressed to HGD or EAC. Third, although recent studies have used NGS to detect mutations in cancers from FFPE and the platforms used in our study have been validated for their application in cancer tissues, the performance of NGS from FFPE has not been reported in precancer tissues, namely, in BE tissues (BIM) obtained from routine FFPE endoscopic samples, as we report. Fourth, we used high-depth sequence reading (several hundred to thousands of reads per amplicon) to achieve high analytical sensitivity, which is necessary for mutation detection in nondysplastic BIM-P patients. In contrast, in previous NGS studies of BIM,17,20 whole-exome analysis was performed at typical lower depth of reading, which may not detect low-frequency mutational events.
We demonstrate that NGS of DNA from routine FFPE endoscopic biopsy or EMR samples can be used to detect genomic mutations in nonneoplastic BIM with high analytical sensitivity. The ability to detect mutations in nonneoplastic mucosa, quantitatively and with high detection sensitivity, makes it possible to use NGS mutational testing in the early detection and surveillance of patients who develop BE as a tool to detect mutations in the field defect that is associated with increased risk of dysplasia and cancer development. We demonstrate that targeted NGS can detect mutations in cancer-related genes with a minimum of 20% of nonneoplastic BIM lesional tissue from endoscopic biopsies of routinely processed FFPE samples, reliably detecting 5% or higher mutation frequencies in the BIM mucosa. The two panels and corresponding platforms tested, Ion Torrent-AmpliSeq and MiSeq-TruSeq, had comparable performance.
Importantly, no significant mutations were detected in any of the BIM of BIM-NP or cardiac mucosa samples, whereas nonsynonymous mutations in TP53, APC, and CDKN2A were detected in the BIM of BIM-P patients. Therefore, our study suggests that TP53 and possibly other mutations, such as in APC, are sensitive and highly specific for predicting concurrent HGD or EAC through testing random endoscopic BIM samples negative for dysplasia.
Our findings are consistent with previous genomic analyses that have found significant abnormalities in BIM, dysplasia, and EAC.14,17,20–22 TP53 mutations and 17p allelic losses precede the development of HGD and EAC.17,23–25 Earlier studies used p53 accumulation by immunohistochemistry demonstrating p53 as a marker of malignant potential in BIM.26 Current guidelines do not recommend the use of specific biomarkers for risk stratification of dysplasia and cancer during surveillance of patients with BE.6 Recently, the 2014 British Society of Gastroenterology guidelines for diagnosis and management of BE recommended immunostaining for p53 as an adjunct to histologic diagnosis of dysplastic BE, reporting that the addition of the p53 immunostain to the histopathologic assessment may improve the diagnostic reproducibility of a diagnosis of dysplasia in BE.27 However, interpretation of p53 immunostains can be problematic and poorly reproducible, subject to differences in methods and interobserver variation.27 In contrast to TP53, CDKN2A mutation has not emerged as a predictor of HGD or EAC. In recent studies, the genomes of nonprogressor BE were characterized by low levels of somatic chromosomal alterations, including CDKN2A, and the mutational frequency was not significantly different among BE, HGD, and EAC.17,22
Recent NGS studies have supported the notion of a field defect in BE. Current guidelines recommend repeat surveillance endoscopies with biopsies every 2 cm at 3- to 5-year intervals in patients without dysplasia, followed by pathological examination to detect intestinal metaplasia and dysplasia.28 This approach, however, has not led to a significant decrease in esophageal cancer–associated mortality and is fraught with sampling errors and high interobserver diagnostic variability of dysplasia.7–10,13 Therefore, biomarkers for early detection of dysplasia in BE patients that can be feasibly tested in routine clinical samples are needed.
Our results reveal that when mutations in TP53 or APC were detected in the background nonneoplastic BIM, those patients had HGD or EAC lesions at the time of endoscopy, suggesting that the high analytical sensitivity of NGS genomic mutational testing may be helpful in combination with the current biopsy mapping approaches and pathological examination to improve detection rates of dysplasia. However, larger studies are warranted. Importantly, in contrast to the NGS approaches used in our study, other mutational testing methods may require special sample handling or have insufficient analytical sensitivity, limiting their potential clinical applications.29,30
In summary, our data indicate that DNA from routine endoscopic FFPE samples of nondysplastic BIM can be efficiently used to simultaneously detect multiple mutations by NGS with high analytical sensitivity, enabling the application of genomic testing of BE patients for improved HGD and EAC surveillance in clinical practice.
Footnotes
Supported in part by a Clinical and Translational Research Institute-Pilots Across the Spirit Program (CTSI-PATS) pilot grant from the Institute for Translational Medicine and Therapeutics (University of Pennsylvania, Philadelphia, PA; A.R.S. and G.W.F.); the Department of Pathology and Cell Biology (Columbia University, New York City, NY); pilot award 5U54CA163004 from the BETRNET consortium (A.R.S.); and NIH grants UL1TR000005 (Y.L. and R.B.), R01EB016657 (Y.L.), and R01CA185363 (Y.L.).
Disclosures: None declared.
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