Open Access

Comparison between two amplicon-based sequencing panels of different scales in the detection of somatic mutations associated with gastric cancer

  • Yosuke Hirotsu1Email author,
  • Yuichiro Kojima2,
  • Kenichiro Okimoto1, 3,
  • Kenji Amemiya1,
  • Hitoshi Mochizuki1, 2 and
  • Masao Omata1, 2, 4
BMC Genomics201617:833

https://doi.org/10.1186/s12864-016-3166-4

Received: 7 June 2016

Accepted: 15 October 2016

Published: 26 October 2016

Abstract

Background

Sequencing data from The Cancer Genome Atlas (TGCA), the International Cancer Genome Consortium and other research institutes have revealed the presence of genetic alterations in several tumor types, including gastric cancer. These data have been combined into a catalog of significantly mutated genes for each cancer type. However, it is unclear to what extent significantly mutated genes need to be examined for detecting genetic alterations in gastric cancer patients. Here, we constructed two custom-made sequencing panels of different scales, the Selective hotspot Panel and the Comprehensive Panel, to analyze genetic alterations in 21 resected specimens endoscopically obtained from 20 gastric cancer patients, and we assessed how many mutations were detectable using these different panels.

Results

A total of 21 somatic mutations were identified by the Selective hotspot Panel and 70 mutations were detected by the Comprehensive Panel. All mutations identified by the Selective hotspot Panel were detected by the Comprehensive Panel, with high concordant values of the variant allelic fraction of each mutation (correlation coefficient, R = 0.92). At least one mutation was identified in 13 patients (65 %) by the Selective hotspot Panel, whereas the Comprehensive Panel detected mutations in 19 (95 %) patients. Library preparation and sequencing costs were comparable between the two panels.

Conclusions

Our results indicate the utility of comprehensive panel-based targeted sequencing in gastric cancer.

Keywords

Endoscopic submucosal dissection Endoscopy Gastric cancer Ion PGM Ion Proton Mutation Next-generation sequencing Targeted sequencing Tumor

Background

Gastric cancer is the third- and fifth-highest cause of cancer mortality in men and women, respectively, and accounts for 8 % of total cancer cases and 10 % of total cancer-related deaths worldwide [1]. The highest incidence rates of gastric cancer are in Eastern Asia, Eastern Europe, and South America, while the lowest rates are in North America and most parts of Africa [1]. Major risk factors include Helicobacter pylori and Epstein–Barr virus infection, as well as dietary factors such as excessive salt intake [2, 3].

Gastric cancer develops in a step-wise manner, involving chronic gastritis, atrophy, intestinal metaplasia, and dysplasia [4]. Early gastric cancer presents as a malignant tumor confined to the mucosa or submucosa, regardless of the presence of regional lymph node metastasis [5, 6]. The detection of early gastric cancer has recently improved, following the development of endoscopic techniques [7, 8]. In particular, endoscopic submucosal dissection (ESD) has enabled a high en bloc resection rate for small and large lesions, as well as in patients with scarring. Moreover, the specimens obtained by ESD can be used for a histological assessment of curability [9]. Endoscopic resection is now widely accepted as a low invasive method for the local resection of early gastric cancer with a negligible risk of lymph node metastasis [10, 11]. Endoscopically-resected early gastric cancer also provides suitable material for genomic analysis to better understand the molecular and genetic features of the initial event leading to cancer development [12].

Next-generation sequencing (NGS) technology enables us to determine the sequence of the genome at a range of different scales, including whole genome, whole exome, and the targeted sequencing of multiple regions of interest. Whereas large-scale analyses are essential for discovery projects, targeted sequencing can focus on genes associated with disease and may lead to advances in the molecular diagnostics of cancer [13]. As an example, NGS has identified a subset of driver and tumor suppressor genes associated with several cancer types [14]. It can also produce thousands to millions of short sequence reads that are massively parallel, and offers a cost-effective approach for detecting genetic alterations.

Large amounts of sequencing data have been disclosed from The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC) and other research institutes. Analyses of these data identified significantly mutated genes (SMGs) in several cancer types [15, 16]. Although SMGs have been revealed by whole exome and whole genome sequencing data, it is unclear to what extent SMGs need to be examined for detecting genetic alterations in gastric cancer. In the present study, we used gastric cancer-associated SMGs to construct two sequencing panels of different scales [1723]. We performed targeted sequencing and analyzed genetic alterations in gastric tumors at an early phase and assessed how many mutations were detectable using these different panels.

Methods

Patients and sample preparation

This study included 20 patients who were diagnosed with gastric cancer (16 males and four females; age 60–87 years) at our hospital (Yamanashi, Japan), one of whom had two tumors. Informed consent was obtained from all subjects. This study was approved by the Institutional Review Board at our hospital and complied with Declaration of Helsinki principles. Peripheral blood samples were obtained from gastric cancer patients and DNA extraction was performed as previously described [24]. Briefly, peripheral blood samples were centrifuged at 820 × g at 25 °C for 10 min, and buffy coats were isolated and stored at −80 °C until required for DNA extraction. Buffy coat DNA was extracted using the QIAamp DNA Blood Mini QIAcube Kit (Qiagen, Hilden, Germany) with the QIAcube (Qiagen). The concentration of DNA was determined using the Nano Drop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA).

Laser capture microdissection and histology

Tumor samples were fixed using 10 % buffered formalin. Serial sections of 10-μm-thick, formalin-fixed, paraffin-embedded (FFPE) tissue were stained with hematoxylin and eosin, and then microdissected using an ArcturusXT laser capture microdissection system (Thermo Fisher Scientific) using ESD-resected specimens. Tumor cells from endoscopic biopsy samples were obtained from 25 serial sections because of the high tumor content. Tumor DNA was extracted using the QIAamp DNA FFPE Tissue Kit (Qiagen).

DNA quality analysis

The integrity of purified DNA from FFPE samples was assessed using the TaqMan RNase P Detection Reagents kit and the FFPE DNA QC Assay v2 on the ViiA 7 Real-Time PCR System (Thermo Fisher Scientific). Human control genomic DNA included in the TaqMan RNase P Detection Reagents Kit was diluted to create a five-point serial dilution for a standard curve, and absolute DNA concentrations were determined. DNA fragmentation was estimated as the ratio of DNA (relative quantification; RQ) obtained for the long amplicon to the short amplicon. High RQ values indicated that the genomic DNA was intact and high quality.

Selecting genes and primer design

We searched the literature and selected genes based on the following criteria (Additional file 1: Table S1): (a) SMGs relative to the background mutation rates analyzed by MutSigCV analysis tool [17]; (b) genes involved in signaling pathways and potential therapeutic targets in gastric cancer; and (c) known drivers of gastric carcinogenesis reported by TCGA [17] and other projects [1822]. We examined the hotspot mutation site of each gene in gastric cancer from the COSMIC database (http://cancer.sanger.ac.uk/cancergenome/projects/cosmic).

We selected 20 genes for the Selective hotspot Panel, which comprises a subset of SMGs and genes related to receptor tyrosine kinases (RTKs) and RAS signaling pathway based on the TCGA project [17]. To expand and cover more SMGs, we selected 58 genes (which include the 20 genes in the Selective hotspot Panel) based on published data from TCGA and another research institute [1723]. Ion AmpliSeq designer software (Thermo Fisher Scientific) was used to design two custom sequencing panels: the Selective hotspot Panel targeting 20 genes in gastric cancer and the Comprehensive Panel targeting 58 genes [1723] (Table 1). A total of 376 and 3515 primer pairs were contained within the Selective hotspot Panel (covering 38.01 kb) and the Comprehensive Panel (covering 351.05 kb), respectively.
Table 1

Targeted sequencing panels and the analyzed genes associated with gastric cancer

Panel name

Targets size

No. of Amplicons

No. of genes

Covered rate

Gene list

Selective hotspot Panel

38.01 kb

376

20

99.99 %

APC*, ARID1A, BCOR, CDH1*, CTNNB1*, EGFR*, ERBB2*, ERBB3, FGFR2*, JAK2*, KRAS*, MET*, NRAS*, PIK3CA*, PTEN*, RASA1, RHOA*, RNF43, SMAD4*, TP53*

Comprehensive Panel

351.05 kb

3515

58

96.86 %

ABCA10, ACVR2A, AKAP13, APC , ARHGAP5, ARID1A , BCOR , BNC2, CD274, CDH1 , CNGA4, CTNNA1, CTNNA2, CTNNB1* , DLC1, DNAH7, EGFR , EIF2C4, ELF3, ERBB2 , ERBB3 , EYA4, FAM46D, FAT4, FGFR1, FGFR2 , GLI3, JAK2 , KIF2B, KMT2A, KMT2C, KRAS* , LDOC1, MACF1, MET , MUC6, NRAS* , PCDH9, PDCD1LG2, PIK3CA , PIK3R1, PKHD1, PLB1, PTEN , PTPRC, RASA1 , RGNEF, RHOA* , RNF43 , SMAD2, SMAD4 , SOHLH2, SYNE1, TGFBR2, TMPRSS2, TP53 , VEGFA, ZIC4

Genes shown in bold font represent the 20 identified by the Selective hotspot Panel

*Genes targeting hotspot regions

Targeted sequencing

Targeted sequencing was performed as previously described [25]. Multiplex polymerase chain reaction (PCR) of these panels was performed using the Ion AmpliSeq Library Kit 2.0 (Thermo Fisher Scientific). Primer sequences were digested with FuPa reagent (Thermo Fisher Scientific), and then barcoded using Ion Xpress Barcode Adapters (Thermo Fisher Scientific). Purification was carried out by Agencourt AMPure XP reagents (Beckman Coulter, Brea, CA). The library concentration was determined using an Ion Library Quantitation Kit (Thermo Fisher Scientific); each library was diluted to 10 pM, and the same amount of libraries was pooled for one sequence reaction. Emulsion PCR was carried out using the Ion OneTouch System and Ion PGM Template OT2 200 kit or Ion PI Template OT2 200 Kit v3 (Thermo Fisher Scientific). Template-positive Ion Sphere Particles were then enriched using the Ion OneTouch ES system (Thermo Fisher Scientific), and purified Ion Sphere particles were loaded on an Ion 318 Chip v2 or PI Chip (Thermo Fisher Scientific). Massively parallel sequencing was carried out on Ion PGM or Ion Proton systems (Thermo Fisher Scientific).

Data analysis

Sequence data were processed using standard Ion Torrent Suite Software running on the Torrent Server. Raw signal data were analyzed using Torrent Suite version 4.4. The data processing pipeline involved signaling processing, base calling, quality score assignment, adapter trimming, PCR duplicate removal, read alignment to the human genome 19 reference (hg19), quality control of mapping quality, coverage analysis, and variant calling. Following data analysis, the annotation of single nucleotide variants, insertions, and deletions was performed by the Ion Reporter Server System (Thermo Fisher Scientific), and peripheral blood DNA was used as a control to detect variants in tumors (Tumor–Normal pairs). We used the following filtering parameters for variant calling: the minimum number of variant allele reads was ≥5, the coverage depth was ≥10, and the variant allele fraction was ≥10 %. If somatic mutations were called using either the Selective hotspot Panel or Comprehensive Panel, sequence data were visually confirmed with the Integrative Genomics Viewer and any sequence, alignment, or variant call error artifacts were discarded.

Results

Quality assessment of extracted FFPE DNA

We examined 21 FFPE tumor samples collected from 20 patients (early stage, 19 patients; advanced stage, one patient) who had not previously undergone chemotherapy or radiotherapy. Matched peripheral blood lymphocytes were included as a control. Of the 21 FFPE tumor samples, 19 tumors had been resected by ESD and two by endoscopic biopsy. ESD-resected tumor tissue was dissected by laser capture microdissection with an average cutting area of 29.4 mm2 (range, 12.4–51.5 mm2) (Fig. 1 and Additional file 1: Table S2). Endoscopic biopsy samples were not microdissected because of the high tumor content.
Fig. 1

Representative image of microdissected specimen. Tumor cells were obtained from ESD-resected specimens using laser capture microdissection (LCM). Left image (Pre-LCM) is before microdissection; right image is after microdissection (Post-LCM). Cyan circles indicate the cutting area

To assess the extent of DNA degradation, we performed quantitative real-time PCR using two primer pairs (short amplicon, 87 bp; long amplicon, 268 bp) flanking the human RNase P locus [26, 27]. Short and long DNA fragment yields were estimated as 14.4 ng/μL (range, 0.6–65.0 ng/μL) and 8.0 ng/μL (range, 0.2–35.8 ng/μL), respectively (Additional file 1: Table S3). An estimate of FFPE-derived genomic DNA fragmentation using the RQ gave an average value of 0.49 (range, 0.14–0.73) (Additional file 1: Table S3), indicating that DNA of high quality had been extracted from FFPE specimens.

Targeted sequencing analysis

To identify genetic alternations in gastric cancer, we reviewed cancer genome sequences from TCGA, ICGC, and COSMIC databases, and selected all SMGs associated with gastric cancer. We constructed two custom-made gastric cancer panels. The Selective hotspot Panel spans 38,010 nucleotides, covers 20 SMGs, and mainly targets hotspot regions (Table 1). The Comprehensive Panel spans 354,050 nucleotides, and 58 of the genes contained within this panel overlapped with the Selective hotspot Panel (Table 1).

We performed targeted sequencing using the two panels with a next-generation sequencer (Ion Proton or Ion PGM, Thermo Fisher Scientific). The percentage of mapped reads aligned to target regions was 98.7 % (97.6–99.3 %) in the Selective hotspot Panel and 97.0 % (95.0–98.6 %) in the Comprehensive Panel, suggesting that all FFPE-derived DNAs had been successfully subjected to library preparation following sequencing analysis (Table 2).
Table 2

Coverage depth of the data from the two panels

  

Selective hotspot Panel

Comprehensive Cancer Panel

Case

Sample

Mapped Reads

On Target

Mean Depth

Uniformity

Mapped Reads

On Target

Mean Depth

Uniformity

Case 1

Buffy coat

484124

98.0 %

1402

94.0 %

1097404

96.4 %

307

96.0 %

Case 2

Buffy coat

426768

98.1 %

1233

94.0 %

1267404

96.3 %

355

96.1 %

Case 3

Buffy coat

518868

98.2 %

1507

93.8 %

810553

96.3 %

221

95.0 %

Case 4

Buffy coat

542769

98.4 %

1576

93.9 %

379011

96.1 %

106

94.5 %

Case 5

Buffy coat

624920

97.9 %

1796

94.3 %

11170406

96.8 %

3112

95.1 %

Case 6

Buffy coat

164634

98.9 %

477

93.5 %

2069779

97.9 %

611

90.6 %

Case 7

Buffy coat

126183

99.0 %

363

91.0 %

2256973

97.8 %

666

91.0 %

Case 8

Buffy coat

108641

99.1 %

315

90.5 %

2027465

97.7 %

601

92.0 %

Case 9

Buffy coat

166684

99.0 %

487

94.7 %

1857285

97.8 %

551

92.2 %

Case 10

Buffy coat

107898

99.0 %

314

90.7 %

2013379

98.0 %

593

89.0 %

Case 11

Buffy coat

79551

99.2 %

231

87.1 %

2106246

98.1 %

624

88.7 %

Case 12

Buffy coat

67486

99.2 %

197

88.3 %

1859173

98.3 %

542

82.5 %

Case 13

Buffy coat

60485

99.2 %

175

91.2 %

1918204

98.3 %

548

78.6 %

Case 14

Buffy coat

260250

98.8 %

759

92.4 %

1295372

96.7 %

360

94.9 %

Case 15

Buffy coat

235410

98.7 %

680

94.2 %

1053705

96.9 %

293

94.6 %

Case 16

Buffy coat

246227

99.0 %

715

93.7 %

845571

97.0 %

234

94.7 %

Case 17

Buffy coat

268465

98.8 %

779

94.4 %

1223358

96.8 %

341

95.2 %

Case 18

Buffy coat

280097

99.1 %

811

93.9 %

3130126

97.0 %

902

93.6 %

Case 19

Buffy coat

256281

98.9 %

744

94.5 %

3423132

97.0 %

987

94.4 %

Case 20

Buffy coat

207402

98.9 %

598

92.4 %

2913580

97.0 %

830

93.6 %

 

Mean ± SD

261657 ± 170658

98.7 ± 0.42 %

758 ± 493

92.6 ± 2.1 %

2235906 ± 2247475

97.2 ± 0.71 %

639 ± 627

92.1 ± 4.6 %

Case 1

Tumor

403252

98.5 %

1143

87.8 %

2025838

97.7 %

522

71.0 %

Case 2

Tumor

426999

98.4 %

1222

91.6 %

999656

96.9 %

276

94.1 %

Case 3

Tumor

524706

98.5 %

1502

91.8 %

1032722

96.3 %

281

94.6 %

Case 4

Tumor

467625

98.0 %

1337

91.0 %

789820

95.8 %

210

95.2 %

Case 5

Tumor

412941

98.5 %

1186

90.7 %

829933

96.0 %

223

94.9 %

Case 6

Tumor

72752

99.3 %

207

84.8 %

1949114

98.6 %

577

83.2 %

Case 7

Tumor

119809

99.3 %

340

85.8 %

2511041

97.6 %

718

91.2 %

Case 8

Tumor

85768

99.2 %

246

88.2 %

1521154

98.6 %

437

82.4 %

Case 9

Tumor

182739

99.2 %

523

89.0 %

2048473

98.2 %

583

88.3 %

Case 10

Tumor

110281

98.0 %

311

88.5 %

2702014

97.3 %

762

87.2 %

Case 11

Tumor

96338

99.2 %

273

89.8 %

2668972

98.0 %

750

86.1 %

Case 12

Tumor

106265

99.2 %

303

87.7 %

2382665

97.8 %

673

89.0 %

Case 13

Tumor site1

87099

98.8 %

250

92.4 %

2861114

97.5 %

819

91.1 %

Case 13

Tumor site2

138279

99.3 %

395

88.9 %

2705723

97.9 %

777

91.5 %

Case 14

Tumor

230616

98.9 %

662

92.6 %

781688

96.5 %

211

95.1 %

Case 15

Tumor

262428

99.1 %

742

65.8 %

631147

96.6 %

171

95.2 %

Case 16

Tumor

179627

99.1 %

512

87.8 %

3189103

95.9 %

929

95.8 %

Case 17

Tumor

153577

99.0 %

434

85.7 %

539462

96.5 %

145

95.0 %

Case 18

Tumor

145284

98.4 %

411

94.7 %

2514172

96.4 %

697

91.0 %

Case 19

Tumor

184785

97.6 %

520

94.0 %

2333968

95.0 %

637

91.4 %

Case 20

Tumor

105210

97.8 %

294

95.0 %

1663278

96.6 %

436

80.3 %

 

Mean ± SD

214113 ± 143314

98.7 ± 0.54 %

610 ± 411

88.7 ± 6.0 %

1841955 ± 850360

97.0 ± 1.0 %

516 ± 247

89.7 ± 6.3 %

The mean coverage depth of tumors was 610× (range, 207–1502) by the Selective hotspot Panel, and 516× (range, 145–923) by the Comprehensive Panel (Table 2). The two approaches identified a total of 21 and 70 somatic mutations in tumors, respectively (Fig. 2a and Table 3). All 21 mutations identified by the Selective hotspot Panel were also confirmed by the Comprehensive Panel (Fig. 2a). The variant allelic fraction values were significantly correlated between the two panels (Fig. 2b). Seventy mutations were detected in the 21 tumors. Overall, an average of 3.2 mutations (range, 0–8) were detected in each early gastric tumor, whereas seven mutations were detected in the advanced tumor. At least one mutation was detected in 13 of the 20 patients (65 %) by the Selective hotspot Panel, and in 19 of the 20 patients (95 %) by the Comprehensive Panel. These results suggest that the Comprehensive Panel covered the genetic alterations of almost all gastric cancer patients.
Fig. 2

Correlation of variant allele fractions detected in the two panels. Panel a: Venn diagram of identified mutations in the two panels. Twenty-one variants identified by the Selective hotspot Panel were also detected by the Comprehensive Panel. Panel b: Comparison of variant allelic fractions (AF) between the two panels. The AF value of 21 variants is plotted. The correlation coefficient (R) is 0.92

Table 3

Somatic mutations identified using the two panels

Case

Specimen

Characteristics

Gene

Mutation

Selective hotspot panel, allelic fraction

Comprehensive panel, allelic fraction

Case 1

ESD

Early

MUC6

F1843S

Not included

28 %

MUC6

S1531P

Not included

23 %

PKHD1

F83S

Not included

11 %

Case 2

ESD

Early

ZIC4

R107H

Not included

21 %

RASA1

K825X

Not included

21 %

Case 3

ESD

Early

MACF1

L2900F

Not included

11 %

Case 4

ESD

Early

APC

C207X

Not included

34 %

APC

Q1447X

35 %

31 %

MUC6

A1637Q

Not included

16 %

SYNE1

D5070G

Not included

13 %

PKHD1

Q3467K

Not included

12 %

MUC6

L1836H

Not included

10 %

Case 5

ESD

Early

SYNE1

G474R

Not included

83 %

TP53

G266V

83 %

65 %

PKHD1

R723C

Not included

32 %

FAM46D

S69C

Not included

16 %

Case 6

ESD

Early

MUC6

T2041M

Not included

33 %

SMAD2

A278P

Not included

17 %

MACF1

R3680K

Not included

14 %

Case 7

ESD

Early

TP53

D148fs

67 %

61 %

Case 8

ESD

Early

APC

L1564X

42 %

59 %

SYNE1

D903Y

Not included

43 %

APC

S940X

Not included

42 %

TMPRSS2

L141V

Not included

40 %

AKAP13

A2256V

Not included

28 %

MUC6

T2041M

Not included

25 %

MUC6

P1571T

Not included

21 %

Case 9

ESD

Early

APC

Q1237fs

Not included

38 %

Case 10

ESD

Early

TP53

H193Y

63 %

52 %

APC

S1068X

Not included

42 %

SMAD4

G477X

Not included

35 %

KRAS

G13D

38 %

33 %

RHOA

M1V

13 %

14 %

APC

Q1517fs

5 %

14 %

Case 11

ESD

Early

ELF3

D220N

Not included

42 %

SYNE1

K874N

Not included

40 %

SMAD4

R497H

28 %

39 %

FAT4

K225E

Not included

35 %

KMT2C

Y987H

Not included

33 %

ERBB2

R897Q

Not included

32 %

SYNE1

R7753H

Not included

32 %

MET

N381fs

19 %

13 %

Case 12

ESD

Early

APC

R876X

38 %

38 %

MUC6

T2041M

Not included

28 %

Case 13_site1a

ESD

Early

TP53

R209fs

70 %

67 %

ARHGAP5

L297X

Not included

31 %

PKHD1

I3786M

Not included

12 %

Case 13_site2a

ESD

Early

TP53

E258D

Not included

71 %

MACF1

D968A

Not included

51 %

Case 14

ESD

Early

-

-

Not detected

Not detected

Case 15

ESD

Early

SYNE1

R6836C

Not included

51 %

ACVR2A

R202fs

Not included

39 %

MUC6

S2378fs

Not included

38 %

DLC1

E854K

Not included

17 %

Case 16

ESD

Early

ARID1A

K1072fs

43 %

51 %

RASA1

R512X

44 %

41 %

TP53

G154S

35 %

32 %

RASA1

D380E

18 %

19 %

MUC6

P1724S

Not included

15 %

ARHGAP5

L259S

Not included

10 %

Case 17

ESD

Early

CTNNB1

S45F

40 %

32 %

Case 18

ESD

Early

TP53

N200fs

3 %

16 %

Case 19

Biopsy

Early

TP53

R175H

15 %

6 %

APC

E262X

Not included

12 %

Case 20

Biopsy

Advanced

TGFBR2

S94R

Not included

28 %

CDH1

Splice site

Not included

28 %

MACF1

G5253E

Not included

19 %

TP53

R248Q

17 %

16 %

DNAH7

Y2563N

Not included

13 %

DLC1

W10L

Not included

13 %

CDH1

Splice site (c.1009-2A>C)

13 %

8 %

aCase 13 had two tumors

Running costs

Primer costs for the Comprehensive Panel were higher than those of the Selective hotspot Panel (Comprehensive Panel: $26363 vs. Selective hotspot Panel: $2820). However, the total cost of library preparation, emersion PCR, and massively parallel sequencing was comparable between the two panels at $200–250 per sample. Use of the Barcode Xpress toolkit enabled multiple samples to be simultaneously sequenced in 4–5 h and allowed us to obtain high-depth sequence data using the Ion PGM or Ion Proton system.

Discussion

The identification of oncogenic driver genes has led to the development of potent molecular targeting drugs together with companion diagnostics. The advent of NGS has also resulted in the identification of a subset of cancer-related genes in several tumors [14, 15], including hundreds of genes mainly associated with tumor development [28]. TCGA, ICGC, and other research institutes have revealed a tumor mutational landscape and produced a catalog of somatic mutations associated with tumors. Information from this catalog has enabled the analysis of recurrently mutated genes by targeted sequencing [29]. This is a useful, cost-effective method for identifying variants in dozens to hundreds of genes, and is fairly readily available for routine diagnosis in a clinical setting as well as for research purposes.

In this study, we constructed two amplicon-based targeted panels of different scales to analyze the genetic alterations associated with gastric cancer. In our cohort, 20 out of 21 tumors (95 %) were shown to carry at least one mutation by the Comprehensive Panel. Thus, our panel-based approach enabled us to detect somatic mutations in gastric cancer, suggesting that it has the potential to obtain robust data and to detect genetic events in tumors. Furthermore, two patients (10 %) harbored mutations in potential therapeutic targets such as KRAS (5 %), ERBB2 (5 %) and MET (5 %) [17, 23]. With the increasing numbers of molecular targeting drugs under development or clinical trial, Comprehensive Panels may offer better selection for molecular-targeted therapy for gastric cancer patients. Collectively, this demonstrates the utility of targeted sequencing using a multi-gene panel in cancer genome research and clinical settings.

Progress in endoscopic technology has led to the curative resection of gastric cancer at an early stage. However, although ESD is widely performed to resect early gastric cancer, the genetic alterations occurring in such tumors are not fully understood, even though this would provide us with an insight into the mechanisms of tumorigenesis. Here, we performed targeted sequencing using ESD-resected early gastric cancers, together with endoscopically-resected biopsies of advanced cancer. A total of 70 somatic mutations were identified in 19 patients, and an average 3.2 mutations were found in early gastric cancer. The most recurrent mutation was identified in TP53 gene (43 %, 9/21). In line with this observation, previous studies have shown that TP53 mutations occur in early gastric cancer as well as in high-grade intraepithelial neoplasia [30]. These observations indicate that TP53 is a key molecule for the progression of gastric tumorigenesis.

In this study, somatic mutations in TP53 (43 %), APC (29 %), MUC6 (33 %), and SYNE1 (24 %) were frequently observed (identified in over 20 % of tumors). These frequencies are almost consistent with previous studies that reported mutations in TP53 (36–73 %), APC (5–14 %), MUC6 (6–18 %), and SYNE1 (20 %). Less common mutations were observed in CTNNB1 (5 %) and KRAS (5 %) genes in our study, but these gene mutations (CTNNB1 S45F and KRAS G13D) are well-known hotspot driver mutations [31]. Previous data also showed that CTNNB1 (1–9 %) and KRAS (5–6 %) mutations were relatively uncommon in gastric cancer. These results indicated that our designed panels validated the data of previous reports.

The TCGA project demonstrated there are four major subtypes of gastric cancer based on the genomic analysis, i.e., chromosomal instability (CIN), genomically stable (GS), Epstein-Barr virus-positive and microsatellite instability [17]. According to this molecular classification, TP53 mutation mostly occurs in the CIN category and intestinal histology. Consistent with this, we examined ESD-resected gastric tumors and most were intestinal type gastric cancer (data not shown). Additionally, the GS subtype is classified as diffuse histology and frequently shows CDH1 and RHOA mutations and CLDN18-ARHGAP fusion. Again, in our series, one advanced gastric cancer was diffused type histology and had a CDH1 splice site mutation (Case 20 in Table 3). Collectively, our data reinforced the molecular classifications of gastric cancer.

Analyses that include a large number of SMGs are important for several reasons. First, analyzing additional SMGs will detect more somatic alterations in tumors. In this study, we were unable to identify any mutations in seven patients using the Selective hotspot Panel, compared with only one using the Comprehensive Panel (Table 3). A recent study reported newly identified SMGs including NRG1, ERBB4, XIRP2, NBEA, COL14A1, CNBD1, ITGAV, and AKAP6 [32, 33] that should be included in the mutational spectrum analyzed in all patients with gastric cancer. Second, from a cost perspective, covering more SMGs is beneficial, as shown by the comparable library preparation and sequencing running costs between the two panels used in this study. Third, including more primer pairs in the design of the panel enables more high-resolution copy number data to be examined [34]. Previous bioinformatics analysis combined with variant allelic fraction and copy number alteration data revealed the cellular prevalence of tumor heterogeneity [35]. Together, these findings suggest that SMG-based sequencing analysis is a useful method for further investigating tumor heterogeneity in clinical samples.

Conclusions

In the present study, use of the Comprehensive Panel covering SMGs associated with gastric cancer enabled the analysis of genetic alterations in patients with early gastric cancer.

Abbreviations

ESD: 

Endoscopic submucosal dissection

FFPE: 

Formalin-fixed, paraffin-embedded

ICGC: 

International Cancer Genome Consortium

NGS: 

Next-generation sequencing

SMGs: 

Significantly mutated genes

TCGA: 

The Cancer Genome Atlas

Declarations

Acknowledgments

We thank all medical and ancillary staff of the hospital and the patients for consenting to participate. We also thank Hidetoshi Shigetomo, Yumi Kubota, and Ritsuko Yokouchi for their help.

Funding

This study was supported by a Grant-in-Aid for Genome Research Project from Yamanashi Prefecture (to YH and MO) and a grant from The YASUDA Medical Foundation (to YH).

Availability of data and materials

The datasets supporting the conclusions of this article are included within the article and its additional files.

Authors’ contributions

YH wrote the manuscript. YK performed endoscopic submucosal dissection and tumor biopsies. YH, KO, KA, HM, and MO participated in genomic analyses. MO was involved in the final editing. All authors have read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Informed consent was obtained from all participants, and this study was approved by the Institutional Review Board at our hospital. The study complied with Declaration of Helsinki principles.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Genome Analysis Center, Yamanashi Prefectural Central Hospital
(2)
Department of Gastroenterology, Yamanashi Prefectural Central Hospital
(3)
Department of Gastroenterology and Nephrology, Graduate School of Medicine, Chiba University
(4)
The University of Tokyo

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Copyright

© The Author(s). 2016

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