Genomic profile analysis of diffuse-type gastric cancers
- Yeon-Su Lee1,
- Yun Sung Cho2,
- Geon Kook Lee3,
- Sunghoon Lee2,
- Young-Woo Kim4,
- Sungwoong Jho2,
- Hak-Min Kim2,
- Seung-Hyun Hong1,
- Jung-Ah Hwang1,
- Sook-young Kim1,
- Dongwan Hong1,
- Il Ju Choi4,
- Byung Chul Kim2, 5,
- Byoung-Chul Kim2,
- Chul Hong Kim5,
- Hansol Choi2,
- Youngju Kim3,
- Kyung Wook Kim3,
- Gu Kong6,
- Hyung Lae Kim7,
- Jong Bhak2, 5, 8, 9Email author,
- Seung Hoon Lee10Email author and
- Jin Soo Lee10
© Lee et al.; licensee BioMed Central Ltd. 2014
Received: 23 November 2013
Accepted: 1 April 2014
Published: 1 April 2014
Stomach cancer is the third deadliest among all cancers worldwide. Although incidence of the intestinal-type gastric cancer has decreased, the incidence of diffuse-type is still increasing and its progression is notoriously aggressive. There is insufficient information on genome variations of diffuse-type gastric cancer because its cells are usually mixed with normal cells, and this low cellularity has made it difficult to analyze the genome.
We analyze whole genomes and corresponding exomes of diffuse-type gastric cancer, using matched tumor and normal samples from 14 diffuse-type and five intestinal-type gastric cancer patients. Somatic variations found in the diffuse-type gastric cancer are compared to those of the intestinal-type and to previously reported variants. We determine the average exonic somatic mutation rate of the two types. We find associated candidate driver genes, and identify seven novel somatic mutations in CDH1, which is a well-known gastric cancer-associated gene. Three-dimensional structure analysis of the mutated E-cadherin protein suggests that these new somatic mutations could cause significant functional perturbations of critical calcium-binding sites in the EC1-2 junction. Chromosomal instability analysis shows that the MDM2 gene is amplified. After thorough structural analysis, a novel fusion gene TSC2-RNF216 is identified, which may simultaneously disrupt tumor-suppressive pathways and activate tumorigenesis.
We report the genomic profile of diffuse-type gastric cancers including new somatic variations, a novel fusion gene, and amplification and deletion of certain chromosomal regions that contain oncogenes and tumor suppressors.
Stomach cancer ranks as the third most important cause of global cancer mortality . Histopathologically, gastric cancer (GC) can be classified into two categories based on morphological differences: intestinal-type GC (IGC) and diffuse-type GC (DGC) [2, 3]. IGC is typically associated with Helicobacter pylori infection, and is especially common in Japan and Korea [4–6]. DGC is uniformly distributed geographically, and includes aggressive clinical forms, such as linitis plastica, which have poor prognosis, especially in young patients [7, 8]. Genomic DNA modifications leading to GC can happen as a result of several environmental risk factors such as a high-salt diet and tobacco smoking . Although the incidence of IGC has decreased steadily over several decades (44% reduction from 1978 to 2005), DGC increased rapidly (by 62%) from 1978 up to 2000, before decreasing slightly in 2001–2005 . Despite the cumulative evidence that IGC and DGC develop via different carcinogenic pathways [11, 12], detailed genomic scale data for DGC are lacking because of limited availability of clinical samples and a low level of purity of the cancer cell population.
To date, very few genes associated with GC subtypes have been identified. The CDH1 gene, which encodes the E-cadherin protein, are the best-known genes associated with hereditary DGC (HDGC) [13–16]. Genetic screening for these mutations has been suggested in order to diagnose early-onset GC . E-cadherin dysfunction, caused by mutations, loss of heterozygosity, and promoter hypermethylation, is the most well-established defect in GC initiation and development [18–20]. A genome-wide association study showed that polymorphisms in the prostate stem cell antigen gene (PSCA) are strongly associated with susceptibility to DGC . The microarray-based method, however, is limited to single nucleotide variations, and cannot detect copy-neutral structural variations (SVs). Two recent studies reported on GC exomes, and showed that mutations in the ARID1A gene are frequently detected in GC with microsatellite instability, and in Epstein-Barr virus (EBV)-positive GCs [22, 23]. No analysis of GC subtypes was performed, and the majority of the samples analyzed in the studies were from patients with IGC.
Next-generation sequencing (NGS) has allowed researchers to detect disease-associated variations, and helped uncover the underlying mechanisms of disease development. In particular, whole genome sequencing (WGS) can detect most genomic variations, including SVs, such as intrachromosomal and interchromosomal rearrangements. Alternatively, whole exome sequencing (WES), a captured-target sequencing method, can be used for high-depth sequencing of a large number of samples at a relatively low cost , although only single nucleotide variations (SNVs) and small insertions or deletions (indels) can be identified using this method. WGS and WES each have advantages and disadvantages, and a number of recent studies have used both methods [25–27].
Here we present detailed characterization of DGC genomes from matched tumor and normal samples by generating whole genomic profiles followed by WES. We used blood samples as a normal control, as in previous studies [28–31]. In order to find DGC-specific variations, IGC genomes were also analyzed and compared with variations identified in genomes of DGCs. Three-dimensional protein structure analysis was performed for novel somatic mutations of the CDH1 gene, and this identified critical regions that were functionally altered by the mutations. In addition, we found a novel fusion gene that could be involved in tumorigenesis.
Results and discussion
Whole genome and exome sequencing
Tumor and matched normal (blood) samples from 14 patients with DGC (the clinicopathological characteristics of these patients are shown in Table S1 in Additional file 1), who were all relatively young (median age 38 years) Korean women, were sequenced using an Illumina HiSeq 2000, which produced paired-end, 90-base and 101-base DNA reads. Additionally, five pairs of tumor and matched normal samples from patients with IGC (median age 42 years) were subjected to DNA sequencing; one of these samples was identified later as a case of microsatellite instability (MSI) and hence was excluded from the mutation analysis. None of the samples had any familial history of cancer, and the subtypes were histopathologically confirmed. Only tumor cells were collected by macrodissection after hematoxylin staining.
For the whole genome analysis, on average, 92 gigabases (Gb) per sample were produced at approximately 32 times sequencing depth, reaching 3.5 terabases (Tb) in total, and were mapped to the reference genome (NCBI build 37, hg19) at a mapping rate greater than 94.5% (for sequencing statistics, see Additional file 1: Table S2). Using the final 3.3 Tb of mapped reads, a genomic profile database was constructed for detecting SNVs, copy number variations (CNVs), and SVs. Because the cellular purity of a tumor sample is a critical feature in cancer genome analysis, it was evaluated using an in-house calculation method (see Materials and Methods; see Additional file 1: Table S3 and Figure S1). Although we tried to collect only tumor cells, our samples still showed a high level of stromal admixture. To increase the accuracy of mutation detection in genic regions even in low-purity samples, additional WES was performed at approximately 103 times sequencing depth on average, which produced a total of 17 Gb sequence data. The captured WES covered 93.1% of the genic region at 10 times or greater depth, and this coverage is similar to that of previously reported exome data on GC [22, 23].
Identification of diffuse-type-specific SNVs and indels
In each sample pair, we identified approximately 3.7 million SNVs, which were verified using single nucleotide polymorphism (SNP) chips (average concordance rate: 99.2%; see Additional file 1: Table S4), and approximately 0.69 million indels (for details, see Additional file 1: Table S5 and Table S6). We first assessed mutational frequency of both types of GC at the single nucleotide level (see Additional file 1: Figure S2 a, b). The somatic mutation spectrum was dominated by C > T (G > A) transitions in both the DGC and IGC samples, and there were no significant differences in mutational contexts between the two GC types, in accordance with previous studies of GC [23, 30]. When we analyzed two previously reported exome datasets, we found that the spectrum of the nucleotide substitution ratio was similar to our data (see Additional file 1: Figure S2c, d).
Although the mutation spectrum of DGC is similar to that of IGC, individual mutations in affected genes were different. By subtracting mutations found in normal blood genomes, we identified 922 non-synonymous SNVs (nsSNVs) as somatic mutations in the 18 tumor samples (see Additional file 1: Table S7; see Additional file 2). The average mutation rate of the 18 GCs (1.97 mutations/Mb) was comparable with that reported in other studies on colon, pancreatic, and liver cancers [33–35]. Of 847 mutated genes affected by the 922 nsSNVs, 581 were in 14 DGC cases, 288 were in 4 IGC cases, and 22 (2.6%) were common to both types. The MSI sample, which was excluded from the comparative analysis, showed approximately six times more SNVs and indels than did the other samples; this result is in agreement with a previous report . When we combined the two previously reported exome datasets, we identified 967 and 2,077 somatic nsSNVs in 19 DGCs and 28 IGCs, respectively. The somatic mutation rate of the IGCs (3.71 mutations/Mb in the 28 samples) was higher than that of the DGCs (2.29 mutations/Mb in the 19 samples) (see Additional file 1: Table S8). Previously published research suggests that melanoma and lung cancer have high mutation rates, owing to the involvement of potent mutagens . Likewise, it is possible that IGC has this high mutation rate because its tumorigenic mechanism may be associated more with environmental and/or parasitic mutagens compared with DGC.
Top candidate driver genes in 14 diffuse-type gastric cancers
SNVs in splice site, n
Driver gene score
3.63 × 10−12
4.64 × 10−10
1.86 × 10−07
4.88 × 10−07
5.33 × 10−07
1.53 × 10−06
1.97 × 10−06
2.15 × 10−06
2.27 × 10−06
2.51 × 10−06
3.76 × 10−06
4.59 × 10−06
5.87 × 10−06
6.86 × 10−06
7.65 × 10−06
8.38 × 10−06
9.94 × 10−06
CDH1 alterations in 18 gastric cancers
Exons 1 to 16
Exons 1 to 16
Donor site of Intron 4
Exons 1 to 16
Exons 1 to 16
Exons 1 to 16
Exons 1 to 16
Introns 2 and 10
Introns 2, 5 and 9
Exons 1 to 16
Exons 1 to 16
Introns 10 and 13
Exons 1 to 16
The somatic variations were then mapped onto the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways database. This analysis revealed that the mutated genes of DGCs were significantly associated with the calcium signaling pathway (P = 7.00 × 10−5; see Additional file 1: Table S12 and Table S13). Low calcium intake may contribute to GC development . Calcium is essential for the function of E-cadherin, and a loss of E-cadherin-mediated adhesion is involved in the transition from a benign lesion to invasive metastatic cancer . Furthermore, the somatic mutations were strongly associated with pathways related to small cell lung cancer (P = 1.00 × 10−6 in DGC and P = 4.24 × 10−2 in IGC). In particular, genes involved in focal adhesion pathways, such as ITGA, PIK3CA, and PTEN, were frequently mutated.
SV and CNV analysis
SVs were detected based on discordantly mapped read pairs, and any SVs that were present in the patients’ germline genomes were excluded. On average, we found 552 somatic SVs per DGC sample pair (211 large insertions, 264 large deletions, 27 inversions, 44 intrachromosomal translocations, and 6 interchromosomal translocations). We found 664 somatic SVs in each IGC sample pair (285 large insertions, 283 large deletions, 34 inversions, 38 intrachromosomal translocations, and 24 interchromosomal translocations) (for details for each sample, see Additional file 1: Table S14 and Figure S3). Additionally, we found 2,258 genes to be impaired, and 1,736 of these were found only in the DGC samples (for data for each sample, see Additional file 1: Table S15; and see Additional file 3). Three tumor suppressor genes FHIT, WWOX, and MIPOL1, which were reported in a previous GC study , had impairments due to the SVs (FHIT in 11 samples, WWOX in 5 samples, and MIPOL1 in 3 samples).
In DGCs, chromosomes 16, 17, 19, 20, 21, and 22 contained an increased amount of block deletions, while chromosomes 3, 7, 8, and 13 showed notably increased duplications (Figure 1). Many tumor suppressor genes, such as CDH1, PLA2G2A, RUNX3, SMAD2, and TP53, are located in extensively deleted chromosomal regions. Notably, somatic mutation (nsSNV or splice site mutation) and copy number loss of CDH1 were generally mutually exclusive: four out of five DGC samples with somatic mutation did not have gene copy number losses, and eight out of nine DGC samples with a CDH1 gene copy number loss did not have any somatic mutations in CDH1. Only one sample (1/18, 5.6%) had both alterations (mutation and copy number loss) concomitantly, and this observation coincides with previous studies reporting that concomitant alterations in CDH1 are rare [19, 40, 51, 52]. When we considered SVs in CDH1 together, we found that other three samples had a mutation/copy number loss concomitant with SV. Additionally, the copy numbers of the oncogene MYC were increased in five DGC samples (see Additional file 4), and copy numbers of MET were increased in three DGC samples . The oncogenes MOS and ZHX2 also showed a copy number gain in five and four DGC samples, respectively. More than half of the samples (10 out of 18) showed a copy number reduction of ARID1A, which is a driver gene for ovarian clear cell carcinoma, and a chromatin remodeler in GC [22, 54, 55]. It is known that the majority of GCs with ARID1A mutations show lower protein expression compared with GCs without an ARID1A mutation . If the dosage effect is important in these cancer tissues, copy number reduction of ARID1A could be a possible cancer-associated factor.
3D structural analysis of mutated CDH1
Additionally, we structurally analyzed previously reported 19 missense mutations in CDH1 (see Additional file 1; Table S20), which were found in hereditary DGC [60, 63–65]. We found that the in vitro functional changes by the missense mutations corresponded exactly to the effects on calcium interaction and structural integrity as described above. The somatic mutations that we found were concentrated in the EC1-2 junction region, whereas the 19 germline mutations were scattered throughout the E-cadherin protein (Figure 4c). This finding coincides with previous results that germline CDH1 mutations are not restricted to specific E-cadherin domains, but are distributed throughout all protein functional domains . In this study, we identified four somatic missense mutations in exons 5 and 6, and it is known that somatic CDH1 mutations found in sporadic DGCs cluster in exons 7 to 10 [66, 67]. Exons 5 and 6 encode the structural components of EC1, EC2, and EC1-2 junction, as in the case of exons 7 and 8. Taken together, these results suggest that CDH1 somatic mutations in the EC1-2 junction that disrupt cell adhesion function are prevalent in DGCs, and thus that dysfunction of the EC1-2 junction is specific to DGC.
WGS and WES were used here to identify somatic variations that are characteristic of DGC. The samples contained both DGC and normal cells, such that the sample purity range was as low as 20% according to our genomic profile analyses. Our approach (WGS combined with exome data with sequencing depth of greater than 120 times) resulted in accurate detection of SNVs and indels in genic regions. The efficacy of this approach is evident in the verification data, which showed a positive rate of 96.6% for somatic SNVs and indels. This combination approach also has the benefit of detecting SVs and large-scale abnormalities, whereas WES alone can identify only somatic variations such as SNVs and indels in exonic regions. This strategy may facilitate analysis of heterogeneous cancer cells, an important issue in cancer genomics .
To the best of our knowledge, this is the first extensive genomic analysis of DGC. We identified somatic SNVs and indels in the DGC samples, compared with the IGC samples. We also found SVs and a novel fusion gene in GC samples, although their functional effects need to be validated in further studies. CDH1 mutations are known to be prevalent in several types of cancers: gastric, colorectal, breast, thyroid, and ovarian. E-cadherin dysfunction is the most well-established defect in GC development, and our data support its importance in DGC. The DGC samples showed a high frequency of somatic mutations in CDH1, and protein structural analysis suggested that the mutations influence the interaction between E-cadherin and calcium, and the stability of β-barrel structures of cadherin. These results indicate that CDH1 and the calcium signaling pathway are associated with the pathogenesis of DGC. Our data from GC genomes should improve the understanding of the mechanism via which protein structural perturbations can cause pathological changes and possibly lead to cancer development. This knowledge may help to diagnose and treat GCs in a more individualized manner, taking into account the different subtypes.
Materials and methods
Patients and specimen collection
This study was performed in accordance with the Declaration of Helsinki and was approved by the local ethics committee of the National Cancer Center (IRB No. NCCNCS-10-392). Signed informed consent was obtained from all participants before enrolment.
GC specimens and peripheral blood samples were collected from 18 patients (14 with DGC and 4 with IGC) who had undergone surgical resection at the National Cancer Center, South Korea, between 2005 and 2010 (see Additional file 1: Table S1). Tumor and adjacent normal specimens were examined by pathologists to remove the necrotic region and the intervening tissue, in accordance with the World Health Organization histopathological criteria. After pathological examination, the samples were snap-frozen and stored in liquid nitrogen until genomic DNA extraction.
Nucleic acid preparation
The frozen tumor samples were macro-dissected and lightly stained with hematoxylin to identify regions consisting of 80% or more cancer cells. Genomic DNA was extracted with the MagAttract DNA Blood Midi Kit (Qiagen Inc, Valencia, CA, USA), in accordance with the manufacturer’s protocol. DNA quality was assessed using a Nanodrop spectrometer (Nanodrop Technologies, Wilmington, DE, USA). Control DNA from matched peripheral blood samples was processed in the same manner. The same frozen tumor samples were used for total RNA extraction using a Qiagen RNeasy Mini Kit (Qiagen). Quality of total RNA was assessed with Lab-on-a-Chip on an Agilent 2100 Bioanalyzer (Aglient Technologies, Santa Clara, CA, USA). The total RNA (1 μg) was used in a reverse transcription reaction with poly (dT) primers using the SuperScriptTMIII First-Strand Synthesis system (Invitrogen/Life Technologies, Grand Island, NY, USA), in accordance with the manufacturer’s instructions. DNA and RNA of adjacent normal tissues were obtained using same methods as tumor samples.
Whole genome sequencing
Genomic DNA was sheared using Covaris S series (Covaris, MS, USA). The sheared DNA was end-repaired, A-tailed, and ligated to pair-end adapters, in accordance with the manufacturer’s protocol (Pair End Library Preparation Kit, Illumina, San Diego, CA, USA). Adapter-ligated fragments were purified and dissolved in 30 μl of elution buffer, and 1 μl of the mixture was used as a template for 12 cycles of PCR amplification. The PCR product was gel-purified using the QIAquick Gel Extraction Kit (Qiagen). Library quality and concentration were determined using an Agilent 2100 BioAnalyzer (Agilent). Libraries were quantified using a SYBR green qPCR protocol on a LightCycler 480 (Roche, Indianapolis, IN, USA), in accordance with Illumina’s library quantification protocol. Based on the qPCR quantification, libraries were normalized to 2 nM, and then denatured using 0.1 N NaOH. Cluster amplification of denatured templates was performed in flow cells, in accordance with the manufacturer’s protocol (Illumina). Flow cells were paired-end sequenced on an Illumina HiSeq 2000 using HiSeq Sequencing kits. A base-calling pipeline (Sequencing Control Software (SCS), Illumina) was used to process the raw fluorescent images and the called sequences.
WES was performed using SureSelect Human All Exon 44 Mb (Agilent), following the manufacturer’s standard protocol. Briefly, a paired-end DNA sequencing library was prepared through genomic DNA shearing, end-repair, A-tailing, PE adaptor ligation, and amplification. After hybridization of the library with bait sequences for 24 hours, the captured library was purified and amplified with an index barcode tag, and the library quality and quantity were determined. Sequencing of the exome library was carried out using the 100 bp paired-end mode of the HiSeq SBS kit, in accordance with the manufacturer’s manual.
Read alignment and variation detection
Paired-end sequence reads were aligned to the hg19 human reference genome (NCBI build 37) with the Burrows-Wheeler Aligner (BWA)  (v0.5.9). Two mismatches were permitted in a 45 bp seed sequence. The rmdup command of SAMtools was used to remove PCR duplicates of sequence reads, which can be generated during the library construction process . Aligned reads were realigned at putative indel positions with the Genome Analysis Toolkit (GATK)  IndelRealigner algorithm to enhance mapping quality. Base quality scores were recalibrated using the TableRecalibration algorithm of GATK.
SNP and small insertion/deletion analysis and somatic mutation filtering
Putative SNVs were called and filtered using the UnifiedGenotyper and VariantFiltration commands in GATK. The options used for SNP calling were a read mapping depth of 5 to 200 times with a consensus quality of 20, and a prior likelihood for heterozygosity value of 0.001. To obtain small indels, the UnifiedGenotyper DINDEL mode of GATK was used with default values, including a window size of 300 bp. To identify somatic mutations in cancer genomes, mutations from cancer genomes were filtered using the mutations from blood genomes. The remaining mutations were filtered again using the mapping status of the blood genomes. At each remaining tumor mutation position, if the minimum mapping depth was at least 3 and the mutation ratio of the blood genome was at least 0.2, the tumor SNV was discarded. To remove false-positive reads caused by genomic duplications, the somatic mutations were called from uniquely mapped reads. Additionally, mutations located in duplicated sequences (≥90% identity) were filtered out if the mutations were not detected by both WGS and WES. The indels were called from reads aligned using the Smith-Waterman algorithm . Two additional databases, dbSNP 131, and an internal Korean variation database that contains variations found in 20 healthy Koreans, were used to filter out additional SNVs. All somatic mutations altering amino acid sequences were checked by expert laboratory personnel using the tview command of SAMtools. The same method was applied to call SNVs and small indels from the previously reported exome data, except for the step filtering mutations located in duplicate sequences.
Mutation rate calculation
For the mutation rate calculation, the number of mutations was compared with the total number of bases in sufficiently covered coding DNA sequence (CDS) regions. The mutations consisted of SNVs and small indels. The sufficiently covered CDS region was defined where its read mapping depth was at least five reads.
A total of 94 nsSNVs, indels, and SNVs in splice sites were verified by conventional Sanger sequencing using dye-terminator chemistry and analyzed with an automatic sequencer ABI 3730 (Applied Biosystems). The target regions were amplified by PCR followed by direct sequencing, or cloned into TA vectors. At least 20 TA vector clones were sequenced, because mutations in low purity samples are difficult to detect by Sanger sequencing. Details of the PCR and sequencing primers are given (see Additional file 1: Table S11).
Annotation of variations
Predicted SNVs were compared with NCBI dbSNP (version 131) to annotate known SNP information. Each SNV was mapped on the University of California Santa Cruz (UCSC) gene table by genomic features such as coding region, untranslated region, and intron. Non-synonymous SNV information was extracted by comparing UCSC reference gene information. The KEGG pathway  was used to analyze altered protein sets. Information on cancer-related mutations was obtained from COSMIC (Catalogue of Somatic Mutations In Cancer) .
Driver gene prediction
Driver gene scores were calculated using SNVs as described in a previous report , with an efficiently covered region with a normal sample mapping depth of 4 times or greater and a cancer sample mapping depth of 3 times or greater. In brief, the driver gene score was calculated by comparing the observed number of nsSNVs with the expected number. The expected number of nsSNVs was calculated from the background non-synonymous to synonymous SNV ratio, and the number of observed synonymous SNVs. The P-value for a driver gene score was calculated from the numbers of expected and observed nsSNVs, assuming that the numbers of nsSNVs had a Poisson distribution.
R2n is the ratio of the diploid (2 N) region, and R1n is the ratio of the haploid (1 N) region. When the purity was lower than about 0.5, the peaks were not distinct. To overcome this ambiguity, somatic deletion regions detected by BreakDancer were used as the 1 N depth regions . The false-positive somatic deletion regions were filtered out using the deletion regions detected in the blood genomes. When the average depth ratio of the somatic deletion region was greater than that of the depth of the 2 N region, the deletion was regarded as a false positive.
Identification of copy number variation regions
CNVs based on the differences in sequencing depths between normal and cancerous samples were detected using BIC-seq  v1.1.2 with λ = 100 and bin_size = 1000 bp. Regions with a log2 ratio smaller than −0.2 or larger than 0.2 were defined as deleted or duplicated regions, respectively. The CNV candidates were mapped to COSMIC  data to find cancer-associated genes. For previously reported array CGH data , +0.152173 and −0.135797 were applied as thresholds for gain and loss, respectively. Genes having its corresponding clones were used for CNV analysis.
Identification of structural variants and gene fusions
SVs were scanned using BreakDancer  with score ≥80. A somatic SV was defined as an SV not found in blood samples. We obtained structural variation signals (SVSs), which are clusters composed of more than three uniquely and discordantly mapped read pairs from all SV regions. We used SVSs found only in tumor tissue samples for consecutive analysis. We considered two SVSs as equal, if the breakpoints of the two SVSs were 400 bp or closer to each other. SVSs located in intergenic and intron region were excluded. A gene was determined to have a breakage event when an SVS breakpoint occurred within the gene. Gene fusion was defined as a connection of two genes by a SVS. The final gene fusion candidates were selected when the number of supporting read pairs was above 10, and the only interchromosomal gene fusions were chosen.
Structure prediction of CDH1
Genome-wide SNP analysis
SNP genotyping was performed using an Axiom genotyping solution with an Axiom Genome-Wide ASI 1 Array Plate and a reagent kit, in accordance with the manufacturer’s protocol (Affymetrix). Briefly, total genomic DNA (200 ng) was treated with 20 μl of denaturation buffer and 40 μl of neutralization buffer, followed by amplification for 23 hours using 320 μl of Axiom amplification mix. Amplified DNA was randomly digested into 25 to125 bp fragments with 57 μl of Axiom fragmentation mix at 37°C for 30 minutes, followed by DNA precipitation for DNA purification and recovery. DNA pellets were dried and resuspended in 80 μl of hybridization master mix, and 3 μl of suspended sample was used for sample qualification. A hybridization-ready sample was denaturated by PCR at 95°C for 20 minutes and 48°C for 3 minutes. The denatured DNA was transferred to a hybridization tray, and loaded onto a GeneTitan MC with an Axiom ASI array plate (Affytmerix). Hybridization continued on the GeneTitan for 24 hours, after which ligation, staining, and stabilization reagent trays were sequentially loaded onto the instrument. GeneTitan was controlled by an Affymetrix GeneChip Command Console GeneTitan Control (Affymetrix). The chip image was scanned with the GeneTitan, and the resulting data, a Image data (DAT) file, was automatically converted to a Cell Intensity data (CEL) file. The CEL intensity file was normalized, and genotype calling was performed using Genotyping Console 4.1 with Axiom GT1 algorithms, in accordance with the manufacturer’s manual. The cut-off value for data quality control was a DISHQC of 0.82 or greater for hybridization, and a call rate of 97% or greater.
MDM2gene expression analysis by quantitative real-time PCR
MDM2 mRNA expression was analyzed using a quantitative real-time PCR system, and the MDM2 gene expression was normalized to GAPDH. Primer sequences for MDM2 and GAPDH were as follows. MDM2-RT forward sequence was 5′-GGCCTGCTTTACATGTGCAA-3′, MDM2-RT reverse sequence was 5′-GCACAATCATTTGAATTGGTTGTC-3′, GAPDH forward sequence was 5′-TGCACCACCAACTGCTTA-3′, and GAPDH reverse sequence was 5′- GGATGCAGGGATGATGTTC-3′. Quantitative real-time PCR was performed with SYBR Green I PCR Master Mix (Qiagen) on a LightCycler 480 Real-Time PCR System (Roche). The experiments were performed in triplicate, and the PCR reaction was performed as follows: 5 minutes at 95°C for initial denaturation, then 45 cycles at 95°C for 10 seconds, 58°C for 10 seconds, and 72°C for 10 seconds, followed by melting curve analysis at 95°C for 5 seconds, 65°C for 1 minute, and cooling for 30 seconds at 40°C. For each reaction, 5 ng of cDNA, 500 nM primer (final concentration) and 5 μl of 2X SYBR Green I PCR Master Mix was used in a 10 μl reaction volume.
Fusion gene analysis
Genomic rearrangement of the fusion gene was verified by PCR using a forward primer located in TSC2 (5′-CTCAGGTTCCGAGCCTAACAG-3′) and a reverse primer in RNF216 (5′-GCAAACATAGTGAGACCCCATCT-3′). The PCR reaction was performed as follows: 15 minutes at 94°C for initial denaturation, then 40 cycles at 94°C for 30 seconds, 60°C for 30 seconds, and 72°C for 1 minute, with 5 minutes at 72°C for post-extension. For each reaction, 30 ng/μl gDNA, 100 nM primer, and 0.5 U of Taq polymerase (Qiagen) were used in a 20 μl reaction. The expression of a fusion gene in one patient sample was analyzed by RT-PCR using a forward primer located in TSC2 (5′-GAGCATGGCTCCTACAGGTACAC-3′) and a reverse primer in RNF216 (5′-CTCTTCACAGGTGAGGCCATTAT-3′). The RT-PCR reaction was performed as follows: 5 minutes at 94°C for initial denaturation, then 40 cycles at 94°C for 30 seconds, 60°C for 30 seconds, and 72°C for 1 minute, with 5 minutes at 72°C for post-extension. For each reaction, 10 ng cDNA, 200 nM primer, and 0.5 U of Taq polymerase (Solgent, Korea) were used in each 20 μl reaction. The RT-PCR products were analyzed by Sanger sequencing using an automatic sequencer (ABI3700; Applied Biosystems) to verify their fusion at the sequence level.
The data from this study have been submitted to NCBI Sequence Read Archive (SRA)  under accession number SRA057772 (WGS) and SRA057973 (WES).
comparative genome hybridization
Copy number variation
Insertion or deletion
diffuse-type gastric cancer
Hereditary diffuse-type gastric cancer
Intestinal-type gastric cancer
mammalian target of rapamycin
Single nucleotide polymorphism
Single nucleotide variation
Structural variation signal
Whole exome sequencing
Whole genome sequencing.
This research was approved by National Cancer Center institutional review board with IRB No. NCCNCS-10-392, Cancer Genome study for GC using Next Generation Sequencing methods. This research was supported by National Cancer Center grant numbers 1110520 and 1011680. The bioinformatic work was supported by a grant from the KRIBB Research Initiative Program. We thank Maryana Bhak for editing, and Dr Cheolju Lee in KIST for providing the normal gastric cell line.
- Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F: GLOBOCAN. 2012, http://globocan.iarc.fr, : Estimated Cancer Incidence, Mortality and Prevalence Worldwide in 2012,Google Scholar
- Lauren P: The Two histological main types of gastric carcinoma: diffuse and so-called intestinal-type carcinoma. An attempt at a histo-clinical classification. Acta Pathol Microbiol Scand. 1965, 64: 31-49.PubMedGoogle Scholar
- Crew KD, Neugut AI: Epidemiology of gastric cancer. World J Gastroenterol. 2006, 12: 354-362.View ArticlePubMedPubMed CentralGoogle Scholar
- Parkin DM, Bray F, Ferlay J, Pisani P: Estimating the world cancer burden: Globocan 2000. Int J Cancer. 2001, 94: 153-156. 10.1002/ijc.1440.View ArticlePubMedGoogle Scholar
- Ushijima T, Sasako M: Focus on gastric cancer. Cancer Cell. 2004, 5: 121-125. 10.1016/S1535-6108(04)00033-9.View ArticlePubMedGoogle Scholar
- Vauhkonen M, Vauhkonen H, Sipponen P: Pathology and molecular biology of gastric cancer. Best Pract Res Clin Gastroenterol. 2006, 20: 651-674. 10.1016/j.bpg.2006.03.016.View ArticlePubMedGoogle Scholar
- Rosai J, Ackerman LV: Rosai and Ackerman’s Surgical Pathology. 2004, St. Louis, Mo. London: Mosby, 9Google Scholar
- Henson DE, Dittus C, Younes M, Nguyen H, Albores-Saavedra J: Differential trends in the intestinal and diffuse types of gastric carcinoma in the United States, 1973–2000: increase in the signet ring cell type. Arch Pathol Lab Med. 2004, 128: 765-770.PubMedGoogle Scholar
- Compare D, Rocco A, Nardone G: Risk factors in gastric cancer. Eur Rev Med Pharmacol Sci. 2010, 14: 302-308.PubMedGoogle Scholar
- Wu H, Rusiecki JA, Zhu K, Potter J, Devesa SS: Stomach carcinoma incidence patterns in the United States by histologic type and anatomic site. Cancer Epidemiol Biomarkers Prev. 2009, 18: 1945-1952. 10.1158/1055-9965.EPI-09-0250.View ArticlePubMedPubMed CentralGoogle Scholar
- Tahara E: Genetic pathways of two types of gastric cancer. IARC Sci Publ. 2004, 157: 327-349.PubMedGoogle Scholar
- Nobili S, Bruno L, Landini I, Napoli C, Bechi P, Tonelli F, Rubio CA, Mini E, Nesi G: Genomic and genetic alterations influence the progression of gastric cancer. World J Gastroenterol. 2011, 17: 290-299. 10.3748/wjg.v17.i3.290.View ArticlePubMedPubMed CentralGoogle Scholar
- Guilford P, Humar B, Blair V: Hereditary diffuse gastric cancer: translation of CDH1 germline mutations into clinical practice. Gastric Cancer. 2010, 13: 1-10. 10.1007/s10120-009-0531-x.View ArticlePubMedGoogle Scholar
- Chen Y, Kingham K, Ford JM, Rosing J, Van Dam J, Jeffrey RB, Longacre TA, Chun N, Kurian A, Norton JA: A prospective study of total gastrectomy for CDH1-positive hereditary diffuse gastric cancer. Ann Surg Oncol. 2011, 18: 2594-2598. 10.1245/s10434-011-1648-9.View ArticlePubMedGoogle Scholar
- Suriano G, Oliveira C, Ferreira P, Machado JC, Bordin MC, De Wever O, Bruyneel EA, Moguilevsky N, Grehan N, Porter TR, Richards FM, Hruban RH, Roviello F, Huntsman D, Mareel M, Carneiro F, Caldas C, Seruca R: Identification of CDH1 germline missense mutations associated with functional inactivation of the E-cadherin protein in young gastric cancer probands. Hum Mol Genet. 2003, 12: 575-582. 10.1093/hmg/ddg048.View ArticlePubMedGoogle Scholar
- Oliveira C, Senz J, Kaurah P, Pinheiro H, Sanges R, Haegert A, Corso G, Schouten J, Fitzgerald R, Vogelsang H, Keller G, Dwerryhouse S, Grimmer D, Chin SF, Yang HK, Jackson CE, Seruca R, Roviello F, Stupka E, Caldas C, Huntsman D: Germline CDH1 deletions in hereditary diffuse gastric cancer families. Hum Mol Genet. 2009, 18: 1545-1555. 10.1093/hmg/ddp046.View ArticlePubMedPubMed CentralGoogle Scholar
- Corso G, Pedrazzani C, Pinheiro H, Fernandes E, Marrelli D, Rinnovati A, Pascale V, Seruca R, Oliveira C, Roviello F: E-cadherin genetic screening and clinico-pathologic characteristics of early onset gastric cancer. Eur J Cancer. 2011, 47: 631-639. 10.1016/j.ejca.2010.10.011.View ArticlePubMedGoogle Scholar
- Carneiro P, Figueiredo J, Bordeira-Carriço R, Fernandes MS, Carvalho J, Oliveira C, Seruca R: Therapeutic targets associated to E-cadherin dysfunction in gastric cancer. Expert Opin Ther Targets. 2013, 17: 1187-1201. 10.1517/14728222.2013.827174.View ArticlePubMedGoogle Scholar
- Carvalho J, van Grieken NC, Pereira PM, Sousa S, Tijssen M, Buffart TE, Diosdado B, Grabsch H, Santos MA, Meijer G, Seruca R, Carvalho B, Oliveira C: Lack of microRNA-101 causes E-cadherin functional deregulation through EZH2 up-regulation in intestinal gastric cancer. J Pathol. 2012, 228: 31-44.PubMedGoogle Scholar
- Carneiro F, Oliveira C, Leite M, Seruca R: Molecular targets and biological modifiers in gastric cancer. Semin Diagn Pathol. 2008, 25: 274-287. 10.1053/j.semdp.2008.07.004.View ArticlePubMedGoogle Scholar
- Sakamoto H, Yoshimura K, Saeki N, Katai H, Shimoda T, Matsuno Y, Saito D, Sugimura H, Tanioka F, Kato S, Matsukura N, Matsuda N, Nakamura T, Hyodo I, Nishina T, Yasui W, Hirose H, Hayashi M, Toshiro E, Ohnami S, Sekine A, Sato Y, Totsuka H, Ando M, Takemura R, Takahashi Y, Ohdaira M, Aoki K, Honmyo I, Study Group of Millennium Genome Project for Cancer, et al: Genetic variation in PSCA is associated with susceptibility to diffuse-type gastric cancer. Nat Genet. 2008, 40: 730-740. 10.1038/ng.152.View ArticlePubMedGoogle Scholar
- Wang K, Kan J, Yuen ST, Shi ST, Chu KM, Law S, Chan TL, Kan Z, Chan AS, Tsui WY, Lee SP, Ho SL, Chan AK, Cheng GH, Roberts PC, Rejto PA, Gibson NW, Pocalyko DJ, Mao M, Xu J, Leung SY: Exome sequencing identifies frequent mutation of ARID1A in molecular subtypes of gastric cancer. Nat Genet. 2011, 43: 1219-1223. 10.1038/ng.982.View ArticlePubMedGoogle Scholar
- Zang ZJ, Cutcutache I, Poon SL, Zhang SL, McPherson JR, Tao J, Rajasegaran V, Heng HL, Deng N, Gan A, Lim KH, Ong CK, Huang D, Chin SY, Tan IB, Ng CC, Yu W, Wu Y, Lee M, Wu J, Poh D, Wan WK, Rha SY, So J, Salto-Tellez M, Yeoh KG, Wong WK, Zhu YJ, Futreal PA, Pang B, et al: Exome sequencing of gastric adenocarcinoma identifies recurrent somatic mutations in cell adhesion and chromatin remodeling genes. Nat Genet. 2012, 44: 570-574. 10.1038/ng.2246.View ArticlePubMedGoogle Scholar
- Ng SB, Turner EH, Robertson PD, Flygare SD, Bigham AW, Lee C, Shaffer T, Wong M, Bhattacharjee A, Eichler EE, Bamshad M, Nickerson DA, Shendure J: Targeted capture and massively parallel sequencing of 12 human exomes. Nature. 2009, 461: 272-276. 10.1038/nature08250.View ArticlePubMedPubMed CentralGoogle Scholar
- Imielinski M, Berger AH, Hammerman PS, Hernandez B, Pugh TJ, Hodis E, Cho J, Suh J, Capelletti M, Sivachenko A, Sougnez C, Auclair D, Lawrence MS, Stojanov P, Cibulskis K, Choi K, de Waal L, Sharifnia T, Brooks A, Greulich H, Banerji S, Zander T, Seidel D, Leenders F, Ansén S, Ludwig C, Engel-Riedel W, Stoelben E, Wolf J, Goparju C, et al: Mapping the hallmarks of lung adenocarcinoma with massively parallel sequencing. Cell. 2012, 150: 1107-1120. 10.1016/j.cell.2012.08.029.View ArticlePubMedPubMed CentralGoogle Scholar
- Pugh TJ, Morozova O, Attiyeh EF, Asgharzadeh S, Wei JS, Auclair D, Carter SL, Cibulskis K, Hanna M, Kiezun A, Kim J, Lawrence MS, Lichenstein L, McKenna A, Pedamallu CS, Ramos AH, Shefler E, Sivachenko A, Sougnez C, Stewart C, Ally A, Birol I, Chiu R, Corbett RD, Hirst M, Jackman SD, Kamoh B, Khodabakshi AH, Krzywinski M, Lo A, et al: The genetic landscape of high-risk neuroblastoma. Nat Genet. 2013, 45: 279-284. 10.1038/ng.2529.View ArticlePubMedPubMed CentralGoogle Scholar
- Banerji S, Cibulskis K, Rangel-Escareno C, Brown KK, Carter SL, Frederick AM, Lawrence MS, Sivachenko AY, Sougnez C, Zou L, Cortes ML, Fernandez-Lopez JC, Peng S, Ardlie KG, Auclair D, Bautista-Piña V, Duke F, Francis J, Jung J, Maffuz-Aziz A, Onofrio RC, Parkin M, Pho NH, Quintanar-Jurado V, Ramos AH, Rebollar-Vega R, Rodriguez-Cuevas S, Romero-Cordoba SL, Schumacher SE, Stransky N, et al: Sequence analysis of mutations and translocations across breast cancer subtypes. Nature. 2012, 486: 405-409. 10.1038/nature11154.View ArticlePubMedPubMed CentralGoogle Scholar
- Chang VY, Federman N, Martinez-Agosto J, Tatishchev SF, Nelson SF: Whole exome sequencing of pediatric gastric adenocarcinoma reveals an atypical presentation of Li-Fraumeni syndrome. Pediatr Blood Cancer. 2013, 60: 570-574. 10.1002/pbc.24316.View ArticlePubMedGoogle Scholar
- Berger MF, Lawrence MS, Demichelis F, Drier Y, Cibulskis K, Sivachenko AY, Sboner A, Esgueva R, Pflueger D, Sougnez C, Onofrio R, Carter SL, Park K, Habegger L, Ambrogio L, Fennell T, Parkin M, Saksena G, Voet D, Ramos AH, Pugh TJ, Wilkinson J, Fisher S, Winckler W, Mahan S, Ardlie K, Baldwin J, Simons JW, Kitabayashi N, MacDonald TY, et al: The genomic complexity of primary human prostate cancer. Nature. 2011, 470: 214-220. 10.1038/nature09744.View ArticlePubMedPubMed CentralGoogle Scholar
- Nagarajan N, Bertrand D, Hillmer AM, Zang ZJ, Yao F, Jacques PE, Teo AS, Cutcutache I, Zhang Z, Lee WH, Sia YY, Gao S, Ariyaratne PN, Ho A, Woo XY, Veeravali L, Ong CK, Deng N, Desai KV, Khor CC, Hibberd ML, Shahab A, Rao J, Wu M, Teh M, Zhu F, Chin SY, Pang B, So JB, Bourque G, et al: Whole-genome reconstruction and mutational signatures in gastric cancer. Genome Biol. 2012, 13: R115-10.1186/gb-2012-13-12-r115.View ArticlePubMedPubMed CentralGoogle Scholar
- Lee E, Iskow R, Yang L, Gokcumen O, Haseley P, Luquette LJ, Lohr JG, Harris CC, Ding L, Wilson RK, Wheeler DA, Gibbs RA, Kucherlapati R, Lee C, Kharchenko PV, Park PJ: Cancer Genome Atlas Research Network: landscape of somatic retrotransposition in human cancers. Science. 2012, 337: 967-971. 10.1126/science.1222077.View ArticlePubMedPubMed CentralGoogle Scholar
- Tsukamoto Y, Uchida T, Karnan S, Noguchi T, Nguyen LT, Tanigawa M, Takeuchi I, Matsuura K, Hijiya N, Nakada C, Kishida T, Kawahara K, Ito H, Murakami K, Fujioka T, Seto M, Moriyama M: Genome-wide analysis of DNA copy number alterations and gene expression in gastric cancer. J Pathol. 2008, 216: 471-482. 10.1002/path.2424.View ArticlePubMedGoogle Scholar
- Li M, Zhao H, Zhang X, Wood LD, Anders RA, Choti MA, Pawlik TM, Daniel HD, Kannangai R, Offerhaus GJ, Velculescu VE, Wang L, Zhou S, Vogelstein B, Hruban RH, Papadopoulos N, Cai J, Torbenson MS, Kinzler KW: Inactivating mutations of the chromatin remodeling gene ARID2 in hepatocellular carcinoma. Nat Genet. 2011, 43: 828-829. 10.1038/ng.903.View ArticlePubMedPubMed CentralGoogle Scholar
- Jones S, Zhang X, Parsons DW, Lin JC, Leary RJ, Angenendt P, Mankoo P, Carter H, Kamiyama H, Jimeno A, Hong SM, Fu B, Lin MT, Calhoun ES, Kamiyama M, Walter K, Nikolskaya T, Nikolsky Y, Hartigan J, Smith DR, Hidalgo M, Leach SD, Klein AP, Jaffee EM, Goggins M, Maitra A, Iacobuzio-Donahue C, Eshleman JR, Kern SE, Hruban RH, et al: Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science. 2008, 321: 1801-1806. 10.1126/science.1164368.View ArticlePubMedPubMed CentralGoogle Scholar
- Parsons DW, Li M, Zhang X, Jones S, Leary RJ, Lin JC, Boca SM, Carter H, Samayoa J, Bettegowda C, Gallia GL, Jallo GI, Binder ZA, Nikolsky Y, Hartigan J, Smith DR, Gerhard DS, Fults DW, VandenBerg S, Berger MS, Marie SK, Shinjo SM, Clara C, Phillips PC, Minturn JE, Biegel JA, Judkins AR, Resnick AC, Storm PB, Curran T, et al: The genetic landscape of the childhood cancer medulloblastoma. Science. 2011, 331: 435-439. 10.1126/science.1198056.View ArticlePubMedGoogle Scholar
- Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Kinzler KW: Cancer genome landscapes. Science. 2013, 339: 1546-1558. 10.1126/science.1235122.View ArticlePubMedPubMed CentralGoogle Scholar
- Becker KF, Atkinson MJ, Reich U, Becker I, Nekarda H, Siewert JR, Höfler H: E-cadherin gene mutations provide clues to diffuse type gastric carcinomas. Cancer Res. 1994, 54: 3845-3852.PubMedGoogle Scholar
- Berx G, Becker KF, Höfler H, van Roy F: Mutations of the human E-cadherin (CDH1) gene. Hum Mutat. 1998, 12: 226-237. 10.1002/(SICI)1098-1004(1998)12:4<226::AID-HUMU2>3.0.CO;2-D.View ArticlePubMedGoogle Scholar
- Machado JC, Soares P, Carneiro F, Rocha A, Beck S, Blin N, Berx G, Sobrinho-Simões M: E-cadherin gene mutations provide a genetic basis for the phenotypic divergence of mixed gastric carcinomas. Lab Invest. 1999, 79: 459-465.PubMedGoogle Scholar
- Corso G, Carvalho J, Marrelli D, Vindigni C, Carvalho B, Seruca R, Roviello F, Oliveira C: Somatic mutations and deletions of the E-cadherin gene predict poor survival of patients with gastric cancer. J Clin Oncol. 2013, 31: 868-875. 10.1200/JCO.2012.44.4612.View ArticlePubMedGoogle Scholar
- Carneiro F, Oliveira C, Suriano G, Seruca R: Molecular pathology of familial gastric cancer, with an emphasis on hereditary diffuse gastric cancer. J Clin Pathol. 2008, 61: 25-30.View ArticlePubMedGoogle Scholar
- Oliveira C, Pinheiro H, Figueiredo J, Seruca R, Carneiro F: E-cadherin alterations in hereditary disorders with emphasis on hereditary diffuse gastric cancer. Prog Mol Biol Transl Sci. 2013, 116: 337-359.View ArticlePubMedGoogle Scholar
- Kim HC, Wheeler JM, Kim JC, Ilyas M, Beck NE, Kim BS, Park KC, Bodmer WF: The E-cadherin gene (CDH1) variants T340A and L599V in gastric and colorectal cancer patients in Korea. Gut. 2000, 47: 262-267. 10.1136/gut.47.2.262.View ArticlePubMedPubMed CentralGoogle Scholar
- Barbi S, Cataldo I, De Manzoni G, Bersani S, Lamba S, Mattuzzi S, Bardelli A, Scarpa A: The analysis of PIK3CA mutations in gastric carcinoma and metanalysis of literature suggest that exon-selectivity is a signature of cancer type. J Exp Clin Cancer Res. 2010, 29: 32-10.1186/1756-9966-29-32.View ArticlePubMedPubMed CentralGoogle Scholar
- Ding L, Getz G, Wheeler DA, Mardis ER, McLellan MD, Cibulskis K, Sougnez C, Greulich H, Muzny DM, Morgan MB, Fulton L, Fulton RS, Zhang Q, Wendl MC, Lawrence MS, Larson DE, Chen K, Dooling DJ, Sabo A, Hawes AC, Shen H, Jhangiani SN, Lewis LR, Hall O, Zhu Y, Mathew T, Ren Y, Yao J, Scherer SE, Clerc K, et al: Somatic mutations affect key pathways in lung adenocarcinoma. Nature. 2008, 455: 1069-1075. 10.1038/nature07423.View ArticlePubMedPubMed CentralGoogle Scholar
- Yang CY, Cheng MF, Tsai SS, Hsieh YL: Calcium, magnesium, and nitrate in drinking water and gastric cancer mortality. Jpn J Cancer Res. 1998, 89: 124-130. 10.1111/j.1349-7006.1998.tb00539.x.View ArticlePubMedGoogle Scholar
- Pećina-Slaus N: Tumor suppressor gene E-cadherin and its role in normal and malignant cells. Cancer Cell Int. 2003, 3: 17-View ArticlePubMedPubMed CentralGoogle Scholar
- Ma L, Chen Z, Erdjument-Bromage H, Tempst P, Pandolfi PP: Phosphorylation and functional inactivation of TSC2 by Erk implications for tuberous sclerosis and cancer pathogenesis. Cell. 2005, 121: 179-193. 10.1016/j.cell.2005.02.031.View ArticlePubMedGoogle Scholar
- Kang YJ, Lu MK, Guan KL: The TSC1 and TSC2 tumor suppressors are required for proper ER stress response and protect cells from ER stress-induced apoptosis. Cell Death Differ. 2011, 18: 133-144. 10.1038/cdd.2010.82.View ArticlePubMedGoogle Scholar
- Miah SM, Purdy AK, Rodin NB, MacFarlane AW, Oshinsky J, Alvarez-Arias DA, Campbell KS: Ubiquitylation of an internalized killer cell Ig-like receptor by Triad3A disrupts sustained NF-kappaB signaling. J Immunol. 2011, 186: 2959-2969. 10.4049/jimmunol.1000112.View ArticlePubMedGoogle Scholar
- Tamura G, Yin J, Wang S, Fleisher AS, Zou T, Abraham JM, Kong D, Smolinski KN, Wilson KT, James SP, Silverberg SG, Nishizuka S, Terashima M, Motoyama T, Meltzer SJ: E-Cadherin gene promoter hypermethylation in primary human gastric carcinomas. J Natl Cancer Inst. 2000, 92: 569-573. 10.1093/jnci/92.7.569.View ArticlePubMedGoogle Scholar
- Machado JC, Oliveira C, Carvalho R, Soares P, Berx G, Caldas C, Seruca R, Carneiro F, Sobrinho-Simöes M: E-cadherin gene (CDH1) promoter methylation as the second hit in sporadic diffuse gastric carcinoma. Oncogene. 2001, 20: 1525-1528. 10.1038/sj.onc.1204234.View ArticlePubMedGoogle Scholar
- Seruca R, Suijkerbuijk RF, Gärtner F, Criado B, Veiga I, Olde-Weghuis D, David L, Castedo S, Sobrinho-Simões M: Increasing levels of MYC and MET co-amplification during tumor progression of a case of gastric cancer. Cancer Genet Cytogenet. 1995, 82: 140-145. 10.1016/0165-4608(95)00033-L.View ArticlePubMedGoogle Scholar
- Wiegand KC, Shah SP, Al-Agha OM, Zhao Y, Tse K, Zeng T, Senz J, McConechy MK, Anglesio MS, Kalloger SE, Yang W, Heravi-Moussavi A, Giuliany R, Chow C, Fee J, Zayed A, Prentice L, Melnyk N, Turashvili G, Delaney AD, Madore J, Yip S, McPherson AW, Ha G, Bell L, Fereday S, Tam A, Galletta L, Tonin PN, Provencher D, et al: ARID1A mutations in endometriosis-associated ovarian carcinomas. N Engl J Med. 2010, 363: 1532-1543. 10.1056/NEJMoa1008433.View ArticlePubMedPubMed CentralGoogle Scholar
- Jones S, Wang TL, Shih IM, Mao TL, Nakayama K, Roden R, Glas R, Slamon D, Diaz LA, Vogelstein B, Kinzler KW, Velculescu VE, Papadopoulos N: Frequent mutations of chromatin remodeling gene ARID1A in ovarian clear cell carcinoma. Science. 2010, 330: 228-231. 10.1126/science.1196333.View ArticlePubMedPubMed CentralGoogle Scholar
- Günther T, Schneider-Stock R, Häckel C, Kasper HU, Pross M, Hackelsberger A, Lippert H, Roessner A: Mdm2 gene amplification in gastric cancer correlation with expression of Mdm2 protein and p53 alterations. Mod Pathol. 2000, 13: 621-626. 10.1038/modpathol.3880107.View ArticlePubMedGoogle Scholar
- Wade M, Li YC, Wahl GM: MDM2, MDMX and p53 in oncogenesis and cancer therapy. Nat Rev Cancer. 2013, 13: 83-96. 10.1038/nrc3430.View ArticlePubMedPubMed CentralGoogle Scholar
- Nagar B, Overduin M, Ikura M, Rini JM: Structural basis of calcium-induced E-cadherin rigidification and dimerization. Nature. 1996, 380: 360-364. 10.1038/380360a0.View ArticlePubMedGoogle Scholar
- Handschuh G, Luber B, Hutzler P, Hofler H, Becker KF: Single amino acid substitutions in conserved extracellular domains of E-cadherin differ in their functional consequences. J Mol Biol. 2001, 314: 445-454. 10.1006/jmbi.2001.5143.View ArticlePubMedGoogle Scholar
- Brooks-Wilson AR, Kaurah P, Suriano G, Leach S, Senz J, Grehan N, Butterfield YS, Jeyes J, Schinas J, Bacani J, Kelsey M, Ferreira P, MacGillivray B, MacLeod P, Micek M, Ford J, Foulkes W, Australie K, Greenberg C, LaPointe M, Gilpin C, Nikkel S, Gilchrist D, Hughes R, Jackson CE, Monaghan KG, Oliveira MJ, Seruca R, Gallinger S, Caldas C, et al: Germline E-cadherin mutations in hereditary diffuse gastric cancer: assessment of 42 new families and review of genetic screening criteria. J Med Genet. 2004, 41: 508-517. 10.1136/jmg.2004.018275.View ArticlePubMedPubMed CentralGoogle Scholar
- Ozawa M, Engel J, Kemler R: Single amino acid substitutions in one Ca2+ binding site of uvomorulin abolish the adhesive function. Cell. 1990, 63: 1033-1038. 10.1016/0092-8674(90)90506-A.View ArticlePubMedGoogle Scholar
- Leckband D, Prakasam A: Mechanism and dynamics of cadherin adhesion. Annu Rev Biomed Eng. 2006, 8: 259-287. 10.1146/annurev.bioeng.8.061505.095753.View ArticlePubMedGoogle Scholar
- Garziera M, De Re V, Geremia S, Seruca R, Figueiredo J, Melo S, Simões-Correia J, Caggiari L, De Zorzi M, Canzonieri V, Cannizzaro R, Toffoli G: A novel CDH1 germline missense mutation in a sporadic gastric cancer patient in north-east of Italy. Clin Exp Med. 2013, 13: 149-157. 10.1007/s10238-012-0184-7.View ArticlePubMedGoogle Scholar
- Simões-Correia J, Figueiredo J, Lopes R, Stricher F, Oliveira C, Serrano L, Seruca R: E-cadherin destabilization accounts for the pathogenicity of missense mutations in hereditary diffuse gastric cancer. PLoS One. 2012, 7: e33783-10.1371/journal.pone.0033783.View ArticlePubMedPubMed CentralGoogle Scholar
- Suriano G, Mulholland D, de Wever O, Ferreira P, Mateus AR, Bruyneel E, Nelson CC, Mareel MM, Yokota J, Huntsman D, Seruca R: The intracellular E-cadherin germline mutation V832 M lacks the ability to mediate cell-cell adhesion and to suppress invasion. Oncogene. 2003, 22: 5716-5719. 10.1038/sj.onc.1206672.View ArticlePubMedGoogle Scholar
- Oliveira C, Suriano G, Ferreira P, Canedo P, Kaurah P, Mateus R, Ferreira A, Ferreira AC, Oliveira MJ, Figueiredo C, Carneiro F, Keller G, Huntsman D, Machado JC, Seruca R: Genetic screening for familial gastric cancer. Hered Cancer Clin Pract. 2004, 2: 51-64. 10.1186/1897-4287-2-2-51.View ArticlePubMedPubMed CentralGoogle Scholar
- Becker KF, Höfler H: Frequent somatic allelic inactivation of the E-cadherin gene in gastric carcinomas. J Natl Cancer Inst. 1995, 87: 1082-1084. 10.1093/jnci/87.14.1082.View ArticlePubMedGoogle Scholar
- Shibata D: Cancer. Heterogeneity and tumor history. Science. 2012, 336: 304-305. 10.1126/science.1222361.View ArticlePubMedGoogle Scholar
- Li H, Durbin R: Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009, 25: 1754-1760. 10.1093/bioinformatics/btp324.View ArticlePubMedPubMed CentralGoogle Scholar
- Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R: 1000 Genome Project Data Processing Subgroup: the sequence alignment/map format and SAMtools. Bioinformatics. 2009, 25: 2078-2079. 10.1093/bioinformatics/btp352.View ArticlePubMedPubMed CentralGoogle Scholar
- McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA: The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010, 20: 1297-1303. 10.1101/gr.107524.110.View ArticlePubMedPubMed CentralGoogle Scholar
- Smith TF, Waterman MS: Identification of common molecular subsequences. J Mol Biol. 1981, 147: 195-197. 10.1016/0022-2836(81)90087-5.View ArticlePubMedGoogle Scholar
- Kanehisa M: The KEGG database. Novartis Found Symp. 2002, 247: 91-101. discussion 101–103, 119–128, 244–252View ArticlePubMedGoogle Scholar
- Bamford S, Dawson E, Forbes S, Clements J, Pettett R, Dogan A, Flanagan A, Teague J, Futreal PA, Stratton MR, Wooster R: The COSMIC (Catalogue of Somatic Mutations in Cancer) database and website. Br J Cancer. 2004, 91: 355-358.PubMedPubMed CentralGoogle Scholar
- Chen K, Wallis JW, McLellan MD, Larson DE, Kalicki JM, Pohl CS, McGrath SD, Wendl MC, Zhang Q, Locke DP, Shi X, Fulton RS, Ley TJ, Wilson RK, Ding L, Mardis ER: BreakDancer: an algorithm for high-resolution mapping of genomic structural variation. Nat Methods. 2009, 6: 677-681. 10.1038/nmeth.1363.View ArticlePubMedPubMed CentralGoogle Scholar
- Xi R, Hadjipanayis AG, Luquette LJ, Kim TM, Lee E, Zhang J, Johnson MD, Muzny DM, Wheeler DA, Gibbs RA, Kucherlapati R, Park PJ: Copy number variation detection in whole-genome sequencing data using the Bayesian information criterion. Proc Natl Acad Sci U S A. 2011, 108: E1128-E1136. 10.1073/pnas.1110574108.View ArticlePubMedPubMed CentralGoogle Scholar
- Harrison OJ, Jin X, Hong S, Bahna F, Ahlsen G, Brasch J, Wu Y, Vendome J, Felsovalyi K, Hampton CM, Troyanovsky RB, Ben-Shaul A, Frank J, Troyanovsky SM, Shapiro L, Honig B: The extracellular architecture of adherens junctions revealed by crystal structures of type I cadherins. Structure. 2011, 19: 244-256. 10.1016/j.str.2010.11.016.View ArticlePubMedPubMed CentralGoogle Scholar
- Sali A, Potterton L, Yuan F, van Vlijmen H, Karplus M: Evaluation of comparative protein modeling by MODELLER. Proteins. 1995, 23: 318-326. 10.1002/prot.340230306.View ArticlePubMedGoogle Scholar
- The sequence read archive. http://www.ncbi.nlm.nih.gov/sra,
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