Metastatic tumor evolution and organoid modeling implicate TGFBR2as a cancer driver in diffuse gastric cancer
© Nadauld et al.; licensee BioMed Central 2014
Received: 29 April 2014
Accepted: 27 August 2014
Published: 27 August 2014
Gastric cancer is the second-leading cause of global cancer deaths, with metastatic disease representing the primary cause of mortality. To identify candidate drivers involved in oncogenesis and tumor evolution, we conduct an extensive genome sequencing analysis of metastatic progression in a diffuse gastric cancer. This involves a comparison between a primary tumor from a hereditary diffuse gastric cancer syndrome proband and its recurrence as an ovarian metastasis.
Both the primary tumor and ovarian metastasis have common biallelic loss-of-function of both the CDH1 and TP53 tumor suppressors, indicating a common genetic origin. While the primary tumor exhibits amplification of the Fibroblast growth factor receptor 2 (FGFR2) gene, the metastasis notably lacks FGFR2 amplification but rather possesses unique biallelic alterations of Transforming growth factor-beta receptor 2 (TGFBR2), indicating the divergent in vivo evolution of a TGFBR2-mutant metastatic clonal population in this patient. As TGFBR2 mutations have not previously been functionally validated in gastric cancer, we modeled the metastatic potential of TGFBR2 loss in a murine three-dimensional primary gastric organoid culture. The Tgfbr2 shRNA knockdown within Cdh1 -/- ; Tp53 -/- organoids generates invasion in vitro and robust metastatic tumorigenicity in vivo, confirming Tgfbr2 metastasis suppressor activity.
We document the metastatic differentiation and genetic heterogeneity of diffuse gastric cancer and reveal the potential metastatic role of TGFBR2 loss-of-function. In support of this study, we apply a murine primary organoid culture method capable of recapitulating in vivo metastatic gastric cancer. Overall, we describe an integrated approach to identify and functionally validate putative cancer drivers involved in metastasis.
Worldwide, gastric adenocarcinoma is the fourth most common malignancy and the second leading cause of cancer deaths among men and women. Based on distinctive histopathologic features, gastric adenocarcinoma is categorized into diffuse and intestinal subtypes . In terms of histopathology, diffuse gastric cancers are generally undifferentiated, frequently have signet cell ring features and invasively infiltrate normal stomach tissue. In contrast, the intestinal subtype has epithelial features and forms discrete tumor masses similar to colon cancer. Diffuse gastric cancer has a higher incidence of metastatic disease and a generally worse prognosis compared to the intestinal subtype ,. Currently, the genomic analyses of diffuse gastric cancer have involved a small number of samples including a recent study by the Cancer Genome Atlas Project (TCGA) and a whole genome sequencing survey of a set of diffuse gastric tumors . However, there are few, if any, studies that detail the metastatic evolution of gastric cancer; metastatic tumors are typically absent from large-scale genomic cancer surveys such as TCGA. Overall, little is known about the oncogenic process and tumor evolution of metastatic gastric cancer despite its paramount clinical importance .
In hereditary diffuse gastric cancer (HDGC), germline mutations in CDH1 (that is, E-cadherin) confer a 70% lifetime risk of developing diffuse gastric cancer ,. The CDH1 tumor suppressor gene encodes E-cadherin, a transmembrane glycoprotein that mediates calcium-dependent cell-cell adhesion. Changes in CDH1 function affect the epithelial-mesenchymal transition (EMT) that has been implicated as playing a role in tumorigenesis. Studies of affected HDGC individuals’ tumors provide a unique opportunity to determine the essential drivers of diffuse gastric cancer in the context of CDH1 loss of function. Supporting evidence of the role of CDH1 in sporadic diffuse gastric cancers includes the observation that 50% contain CDH1 mutations or hypermethylation of the CDH1 promoter ,. A recent whole genome sequencing survey of diffuse gastric cancer also identified frequent CDH1 mutations as the most common driver event . The TCGA gastric cancer data also show a high frequency of somatic CDH1 mutations . Significantly less is known about the identity and role of co-occurring drivers that contribute to diffuse gastric metastasis.
We determined if the candidate drivers from this metastatic progression were sufficient to reproduce diffuse gastric cancer. Our cancer modeling methodology used in vitro gastric organoids and allows one to engineer the genetic driver context of these cancers and study the process of metastatic evolution and oncogenic pathway divergence. Integrating genetic analysis and biological modeling, we determined the independent role of TGFBR2 (transforming growth factor-β receptor 2) in the oncogenesis of diffuse gastric cancer. Our experimental cancer modeling relies on an air-liquid interface for primary mouse intestinal culture that contains both epithelial and mesenchymal elements, accurately recapitulates long-term proliferation, multilineage differentiation, the Wnt/Notch-dependent stem cell niche, and peristalsis . We reported an analogous primary gastric organoid culture system that accurately recapitulates multilineage epithelial differentiation and stromal elements . Recently, we achieved robust in vitro oncogenic transformation of primary gastric, colon, and pancreatic organoids via mutations in Kras and Trp53, which induce high-grade dysplasia and invasion in vitro with adenocarcinoma upon subcutaneous transplantation into mice . We demonstrate the functional validation of candidate gastric cancer metastasis drivers from cancer genomic profiling studies, focusing on modeling the TGFBR2 driver as proof of principle.
Diffuse gastric cancer and metastatic progression
At the age of 37 years, the index patient (525) was diagnosed with stage III (T3N1M0) poorly differentiated diffuse gastric adenocarcinoma (Figure 1). Her 42-year-old sister was diagnosed with diffuse gastric adenocarcinoma 2 months earlier. Based on the family history of gastric cancer and the unusually young age of onset, she underwent germline CDH1 mutation testing. The patient and her sister were found to have a germline splice site mutation in intron 10 (c.1565 + 2insT). This germline mutation was subsequently reported in another family with hereditary diffuse gastric cancer (HDGC) . The patient underwent a total gastrectomy to remove her primary tumor and was found to have a single lymph node metastasis. She received standard adjuvant treatment including combined chemotherapy (cisplatinum and 5-fluorouracil) and radiation. Three years after her initial presentation, the patient reported progressive lower abdominal fullness. A computed tomography (CT) scan demonstrated a large pelvic mass consistent with a left ovarian metastasis (Figure 1). Subsequently, the patient underwent laparotomy with bilateral salpingo-oophorectomy and biopsy of the pelvic mass. Pathological studies demonstrated metastatic adenocarcinoma involving the ovary, otherwise referred to as a Krukenberg tumor, with the same histologic appearance as the primary tumor. One study reported that among diffuse gastric cancer with metastatic dissemination, the ovary was a metastatic site in 28.8% of cases . Thus, the ovary is a common site for metastatic disease.
Cancer genome sequencing analysis
Both exome and whole genome paired-end sequencing were performed on the primary tumor, ovarian metastasis, and normal tissue which included blood and normal gastric tissue (Additional file 1: Table S1). Tissue from the lymph node metastasis was not available for analysis. Multiple sequencing methods were employed to compensate for the extent of normal stromal mixture, a direct result of the infiltrative invasiveness of the diffuse gastric cancer subtype. We determined the extent of normal genome mixture and corrected for inclusion of the normal DNA (Additional file 1: Methods). Given the complexity of the tumor samples, we conducted an additional round of targeted sequencing to confirm the presence of mutations and other genetic aberrations that occurred in exons, near exon boundaries or promoters.
Overall, we obtained greater than 100× average coverage for each exome and generally relied on exome data for the discovery of coding region mutations. For whole genome sequencing, we had greater than 60× average coverage for the primary cancer whole genome sample and 30× for the metastatic genome. The whole genome sequencing was used for identifying larger scale genetic aberrations such as copy number variation (CNVs), allelic imbalances, rearrangements, and other classes of structural rearrangements. After alignment, we conducted variant calling to identify somatic mutations and other classes of genetic aberrations. This included somatic mutations, insertion-deletions (indels), CNVs, loss-of-heterozygosity regions (LOH), and cancer rearrangements (Additional file 1: Table S3 and Table S4). As a control for single nucleotide variant calling, we genotyped the samples with Affymetrix 6.0 single nucleotide polymorphism (SNP) arrays; we compared the genotypes to the identified SNPS from the sequence data. The concordance of exome and whole genome SNP data to the array data was 99%.
Coding region mutations and validation with deep sequencing
We identified mutations that occurred in exons and intronic mutations within 100 bases of the exon boundary and the results are summarized in Additional file 1: Table S2. As noted previously, the tumor samples had complex composition that reduced the sequence coverage of some mutations. We proceeded with an additional round of targeted sequencing to validate these mutations and determine their presence in both tumors. We designed an assay for deep targeted resequencing that covered approximately 300 bases around the specific mutation loci (Additional file 1: Table S5). The average targeted sequencing coverage for each putative mutation or loci was 278× for the normal, 251× for the primary tumor and 152× for the metastasis.
Between the two tumors, we independently validated a total of 77 mutations that occurred within or proximal to exons (Additional file 1: Methods and Table S5). Validated genetic aberrations included: (1) non-synonymous mutations, (2) synonymous mutations, (3) insertions, or (4) deletions. With the targeted sequencing data, we determined the mutation allelic frequency (MAF) between the primary tumor and metastasis for each mutation. This involves determining the fraction of a sequence read with a mutation in comparison to the reference sequence reads. We were able to identify which mutations were common or exclusive to the primary tumor versus the metastasis. Among the 77 validated mutations, the distribution was such that mutations were generally unique either to the primary tumor or metastatic site. For example, the primary tumor had eight mutations that were not present in the metastasis while the metastasis had 37 mutations not present in the primary tumor. Common to both cancers were 32 mutations.
Given the interval of three years prior to the detection of the metastasis, there is a possibility that the metastasis-specific mutations occurred independently from the primary tumor. Mutations specific to the primary tumor that were not present in the ovarian metastasis may have been the result of random genetic drift. The mutations common to both indicate a common origin but the exact timing of the differentiation between the two tumors is less clear as noted by those mutations with lower MAF. A subset of these genes had high MAF values, indicating a higher likelihood of being present in all clonal populations in the primary tumor or metastasis. As we describe later, these genes were prioritized for further experimental testing in gastric organoids.
Mutations affecting gene function
Cancer oncogenes with amplifications or cancer drivers with biallelic events
Known or candidate cancer driver
Allelic alteration 1
Mutation or genomic aberration
Chr position or interval
Allelic alteration 2
Unique to the primary
117820033 - 119748751
Common to the primary tumor and metastasis
Partial deletion of exon 9
68847326 - 68847403
Germline mutation in CDH1
5’ splice site mutation
Hemizygous loss of 17p arm
Unique to the metastasis
Stop codon in exon 4
Hemizygous deletion of wild-type TGFBR2 locus
Hemizygous deletion of wild-type 4 arm
Loss of heterozygosity
FERMT1 located in 20p12.3
Loss of heterozygosity
BMP7 located in 20q13.3
Copy number variations and allelic imbalances distinguishing the primary tumor from the metastasis
In contrast to the primary tumor, the metastatic tumor had numerous chromosomal scale LOH events and genomic deletions affecting 12 different chromosomes, the majority of which were unique to the metastatic tumor (Figure 2). This included multiple deletions and copy neutral LOH events that are detailed in Additional file 1: Table S3. There was a five-fold genomic amplification in chromosome 2 but no specific known genes existed in the affected interval. There were no detectable inter-chromosomal translocations in either the primary tumor or metastasis genomes. Other cancer rearrangements were identified but did not point towards aberrations in any candidate driver genes (Additional file 1: Table S4). There were indications of large scale genomic instability based upon allelic imbalance analysis; chromosomes 14, 17, 20, and 22 all involved the entire chromosome.
For copy number aberrations and allelic imbalances, we identified exclusive versus common events between the primary tumor and metastasis. The only common genetic aberration involved the p arm of chromosome 17. Overall, the lack of overlap was indicative of significant genetic divergence from the primary tumor and metastasis despite a common origin as denoted by shared mutations in critical tumor suppressors.
The genomic intervals of the LOH, copy number aberration and rearrangement events were compared with the position of validated gene mutations. This integrated analysis pointed to a number of genes that had biallelic alterations involving both a loss of the wild-type allele from a large interval genomic aberration and a mutant allele. The results for genes with biallelic hits were considered to be strong candidates for a loss-of-function involvement in cancer (Table 1).
Identification of cancer drivers common to the primary tumor and metastasis
Both the primary and the metastasis contained cancer driver events that were likely to be critical for tumorigenesis in the context of the initial CDH1 mutation (Table 1, Figure 2). In addition to the germline CDH1 intronic mutation, the second CDH1 allele had a somatic 77 bp genomic deletion of a portion of exon 9 that affects the downstream coding regions as well. The CDH1 somatic mutation was identical in both the primary and metastatic gastric cancer genomes, demonstrating a common genetic origin and providing strong genetic evidence that this driver had a critical role in diffuse gastric tumorigenesis. Mutations affecting CDH1 exon 9 that lead to loss of protein expression have frequently been detected in diffuse gastric cancer -. This exon’s amino acid sequence is a putative calcium-binding site that is likely important for receptor function.
The primary and metastatic tumor also shared biallelic splice donor site mutation (c.559 + 1G > A) of the fifth intron of TP53 and a chromosome 17p LOH event encompassing the TP53 locus (Additional file 1: Figure S1). The TP53 splicing mutation interrupts RNA splicing  and is a previously reported cancer mutation ,. The analyses of sporadic and inherited gastric cancers have identified TP53 mutations that occur concurrently with CDH1 mutation ,. CDH1 inactivation in gastric parietal cells does not induce gastric carcinoma, suggesting that loss of CDH1 is insufficient for tumor initiation . However, double conditional knockout of CDH1 and TP53 induces development of diffuse gastric carcinoma . Interestingly, the genomic interval of the LOH event affecting the TP53 locus was larger in the metastasis compared to the primary tumor. This could have occurred because of independent genomic instability events given the strong selection for biallelic loss of TP53 function.
FGFRis an actionable cancer driver exclusive to the primary gastric tumor
In the primary tumor, there was a six-fold genomic amplification of a region of chromosome 10 q arm and covered an interval of 1.66 Mb. Within this genomic regions was an oncogenic candidate driver FGFR2 also referred to as the fibroblast growth factor receptor 2 (Figure 3c). This was confirmed with multiple methods including sequencing, array analysis, and validation by quantitative PCR. FGFR2 is a transmembrane receptor that acts as part of a key signal transduction pathway regulating tissue repair and embryonic development among a host of other functions .
In support of its role as a candidate driver, FGFR2 amplification is present in a number of gastric cancer cell lines , and subsequently reported in various gastrointestinal malignancies such as esophageal adenocarcinoma . In addition, treatment of cancer cell lines with FGFR2-specific small molecule inhibitors or shRNAs leads to potent growth inhibition  suggesting a functional role for FGFR2 amplification in the diffuse subtype.
Functional analysis of the FGFR2 driver in combination with CDH1 and TP53
We identified two examples of a primary diffuse gastric cancer with co-occurrence of known and putative cancer drivers involving CDH1, TP53, and FGFR2 as seen in the index patient. The first example included a diffuse gastric cancer sample that was among the gastric adenocarcinomas analyzed by TCGA. Using the cBio TCGA portal , we identified a patient (TCGA-BR-6803) who had a similar complement of genetic aberrations in CDH1, TP53, and FGFR2, all of which have been previously described in cancer as seen in the COSMIC cancer mutation repository. This included the following: a missense mutation in CDH1 (D254Y) that has been described in three other cancers; a missense mutation (L130F) in TP53 where mutations in this codon have been reported in 37 other cancers; the FGFR2 amplification which we and others have identified in diffuse gastric cancer.
As the second example, we identified a human diffuse gastric cancer cell line, KatoIII, which has a similar composition of genetic aberrations affecting the same cancer genes as the primary tumor of our index patient. KatoIII has a CDH1 mutation leading to an intronic sequence insertion in the mRNA ,, a TP53 mutation leading to a complete gene deletion  and the FGFR2 amplification  (Figure 4b). This cell line allowed us to assess the potential oncogenic role of the FGFR2 amplification in the specific genetic context of CDH1 and TP53 mutations, similar to the index patient’s primary tumor.
To determine the contribution of FGFR signaling to neoplastic growth, we treated KatoIII cells with several FGFR2 small molecule tyrosine-kinase inhibitors (TKIs), including Brivanib, TKI258, Ponatinib, and AZD4547 . As a control, we used the gastric cancer cell line AGS which is wild type for FGFR2, CDH1, and TP53, but has mutations in KRAS and PIK3CA (Figure 4b). All FGFR2 inhibitors induced cell death in KatoIII but not AGS cells (Figure 4c and d). The most potent of these TKIs, AZD4547, has an IC50 of approximately 2 nM in KatoIII cells and 39,580 nM in AGS cells (Figure 4c and d). Each of the inhibitors demonstrated a statistically significant lower IC50 in FGFR2-amplified KatoIII cells compared to non-FGFR2-amplified AGS cells at all concentrations tested (Figure 4c and d).
In contrast, treatment of KatoIII and AGS cells with cytotoxic chemotherapeutic agents such as paclitaxel, 5-fluorouracil and carboplatin did not have a significant effect on either KatoIII or AGS lines, with similar IC50 identified in each (Additional file 1: Table S7). For AZD4547, the 20,000-fold difference in sensitivity to FGFR inhibitors suggests that FGF signaling is a critical driver to CDH1-initiated gastric cellular proliferation and this TKI represents a potential targeted therapy in diffuse subtype cancers harboring FGFR2 amplifications.
Biallelic inactivation of TGFBRis exclusive to the ovarian metastasis
Genetic divergence was evident; the metastasis harbored its own unique subset of mutations and genomic aberrations. As we described, the metastasis had the same CDH1 and TP53 mutations as the primary tumor but lacked the FGFR2 amplification found in the primary cancer site (Figure 3c). To eliminate the possibility that the absence of FGFR2 amplification was related to a subpopulation not present in our original metastatic section, we performed a highly sensitive quantitative digital PCR on a separate geographic region from the metastasis (data not shown). This method has been previously been demonstrated to identify FGFR2 copy number amplifications with high sensitivity and specificity, even in the context of diluted mixtures . This independent analysis again confirmed that the FGFR2 locus was not amplified in a separate region of the metastatic tumor.
The most striking event uniquely defining the metastasis was a TGFBR2 deletion in exon 3 (Table 1). We looked for the presence of this somatic mutation among the normal and primary tumor sequence from the independent datasets (for example, whole genome, exome, and deep targeted resequencing). The MAF of the mutation among all of these sequencing datasets indicated exclusivity specific to the ovarian metastasis (Additional file 1: Table S5). The mutation was not found in any significant fraction among the primary and normal genomes.
TGFBR2 encodes a receptor for the transforming growth factor β (TGF-β) pathway. While TGFBR2 is mutated in numerous human cancers with particular prevalence in mismatch repair-deficient colon cancer , its functional relevance in gastric cancer is unknown. This particular deletion markedly reduces mRNA levels, presumably due to nonsense-mediated decay . The metastasis also harbored a unique large genomic deletion of chromosome arm 3p encompassing the TGFBR2 locus as shown by both CNV and LOH events, resulting in biallelic events affecting the wildtype TGFBR2 alleles (Figure 3b).
TGFBR2 exon 3 deletions are typically associated with colorectal tumors displaying microsatellite instability (MSI), a molecular marker for the loss of DNA mismatch repair (MMR). We assessed the primary and metastatic tumor for DNA mismatch repair defects. The primary tumor had normal immunohistochemical staining for the major DNA mismatch repair proteins MLH1, MSH2, PMS2, and MSH6. Neither the primary tumor nor metastasis exhibited elevated MSI at any of the diagnostic genetic markers (Additional file 1: Table S7). In addition, the patient had no germline, primary tumor or metastatic somatic mutations in the MMR genes.
We examined the cBIO TCGA dataset for gastric cancers classified by the Lauren histopathologic criteria as diffuse. Among the TCGA set, three of 79 diffuse gastric tumor samples had mutations in TGFBR2. This included two cancers in which there was biallelic loss of the wild-type allele . These samples were MSI stable. The diffuse subtype samples with TGFBR2 mutations include: a homozygous deletion (TCGA-BR-A4QM); biallelic mutations involving F442S and A426V in (TCGA-D7-6522); Q418 splice site mutation (TCGA-CD-8531). The examples of TGFBR2 mutations existing in diffuse gastric cancers are supportive evidence for the potential role of TGFBR2 as a driver.
Other candidate cancer genes delineating the metastasis from the primary tumor
Additional candidate cancer genes were identified that distinguished the metastasis from the primary gastric tumor (Table 1). A novel predicted pathogenic mutation in BMP7 was identified in the primary and metastatic tumor but the metastatic tumor had a unique copy neutral loss of heterozygosity event encompassing the entire chromosome arm 20 q including the BMP7 locus. BMP7 (that is, bone morphogenic protein) interacts with the TGF- β pathway and has a well-studied role in osteoclast differentiation and bone development . In addition, BMP7 expression has been correlated with tumor recurrence in gastric cancer .
Similarly, a novel DOCK1 mutation was uniquely identified in the metastatic genome. DOCK1 regulates cell motility and migration and has been implicated in ovarian cancer tumorigenesis  (Additional file 1: Table S5). Another genomic amplification unique to the primary tumor occurred in the 5q22.3 locus (Additional file 1: Table S3). Among the 15 genes within the amplification locus, the major oncogenic-related cancer gene was TRIM36 that is overexpressed in prostate cancer. It has been hypothesized its overexpression leads to chromosomal instability ,.
TGFBR2 knockdown in the context of CDH1 and TP53 is sufficient to induce metastatic diffuse gastric cancer in a primary gastric organoid murine model
Given the metastasis-specific, biallelic alteration of TGFBR2, we exploited our validated primary air-liquid interface murine gastric organoid system , to investigate if TGFBR2 knockdown was sufficient to induce gastric cancer metastasis. Its consideration as a candidate was also suggested by the TCGA data. Previously, we observed that Trp53 deletion and Kras G12D induced pronounced in vitro dysplasia and invasion of gastric organoids with in vivo tumorigenicity upon subcutaneous implantation, but spontaneous metastasis was not seen by 50 days .
To model the effect of the TGFBR2 in metastatic oncogenesis, we infected the same Cdh1-/-;Trp53-/- gastric organoids with retrovirus expressing shRNA against Tgfbr2, confirming Tgfbr2 knockdown by immunofluorescence and Western blot analysis (Figure 5a and b). Likewise, gene expression of Tgfbr2 was also reduced as determined by real time PCR (Additional file 1: Figure S3). The Tgfbr2 shRNA did not grossly increase the growth rate of Cdh1-/-;Trp53-/- gastric organoids over a 20-day period, possibly because of dominant effects of the Cdh1 and Trp53 deletions (Figure 5c). However, histologic analysis revealed that the resultant Cdh1-/-;Trp53-/-; Tgfbr2 shRNA gastric organoids but not Cdh1-/-; Trp53-/- controls demonstrated features of diffuse subtype gastric cancer. Severe dysplasia along with focal areas of invasion, signet ring formation, and nuclear pleomorphism were found throughout the analyzed organoids (Figure 5d).
Evaluation for distant disease confirmed the presence of pulmonary metastases in NOG mice harboring Cdh1 -/- ;Trp53 -/- ;Tgfbr2 shRNA tumors, comprised of poorly differentiated adenocarcinoma with signet ring features (Figure 6f, g). Metastatic tumors were located in the lungs bilaterally, were grossly observable upon dissection and had similar histologic appearance to diffuse gastric cancer. Overall, these studies support the role of Tgfbr2 as a putative tumor suppressor gene in diffuse gastric cancer, demonstrate successful in vitro conversion of primary gastric tissue to metastatic adenocarcinoma, and reveal the utility of a primary gastric organoid system for functional validation of candidate metastasis drivers.
To address the question of identifying the genetic drivers of diffuse gastric cancer metastasis, we performed an extensive genome sequencing analysis of the metastatic evolutionary process. This involved sequencing of a matched gastric primary and subsequent metastasis from the same patient. We leveraged the unique Mendelian genetics of a HDCG proband as an `experiment of nature’ to delineate essential cancer drivers in diffuse gastric cancer.
Our genomic analysis revealed FGFR2 amplification exclusive to the primary gastric tumor and not present in the metastasis. Our results fully confirm several descriptions of FGFR2 amplification, as well as increased sensitivity of FGFR2-amplification positive cell lines such as KatoIII to small molecule FGFR inhibitors -, with accompanying implications for FGFR2-targeted treatment . The striking absence of FGFR2 amplification in the metastasis in the context of the common somatic CDH1 and TP53 mutations argues strongly for a tumor evolutionary divergence.
The functional validation of the metastatic potential of Tgfbr2 knockdown in our well-validated air-liquid interface gastric organoid method , provides the first demonstration that TGFBR2 functions as a bona fide metastasis suppressor gene in diffuse gastric cancer. Homozygous TGFBR2 deletion is also present in a subset of TCGA gastric cancers  which are largely comprised of non-metastatic tumors. It will be interesting to evaluate whether TGFBR2 alterations are more prevalent in gastric metastases, such as to ovary or other sites. Furthermore, the functional relevance of other potential loci undergoing alteration in these samples merits additional exploration.
Our study also describes the first successful in vitro conversion of primary gastric tissue to metastatic gastric adenocarcinoma, suggesting the general applicability of the organoid method to the functional validation of gastric cancer loci involved in progression and/or metastasis. As shown here, such three-dimensional organoid-based functional validation strategies can potentially combine both the experimental tractability of two-dimensional culture of transformed cell lines with the accurate tissue ultrastructure and stromal components of transgenic mouse systems.
Exclusive FGFR2 and TGFBR2 genetic aberrations delineated the evolution of metastatic recurrence. Our finding may have implications for targeted cancer therapy. For example, the index patient in this study may have conceivably responded to a FGFR2 inhibitor based on FGFR2 amplification in the primary tumor. However, the patient’s metastatic recurrence did not harbor this same FGFR2 amplification, suggesting that treatment with a therapeutic small molecule inhibitor may not have had a discernible biological effect on the patient’s metastatic disease. In the precision management of individuals with metastatic cancer, one may need to account for the genetic heterogeneity and subsequent variation in cancer biology that differentiates metastatic sites from the primary tumor before the initiation of targeted therapy.
Although a comprehensive knowledge of the metastatic process is crucial for improved cancer treatment, the driver events underlying metastatic spread are unfortunately poorly understood . Our paucity of knowledge regarding metastasis is further compounded by the relative omission of metastatic samples in large-scale genomic cancer surveys such as TCGA. This study is an initial effort to further address these questions about the genetics and biology of metastatic evolution by integrating genomic sequencing analysis with in vitro validation of clonal-specific candidate drivers.
Materials and methods
This study was conducted in compliance with the Helsinki Declaration. The institutional review board (IRB) at Stanford University School of Medicine approved the study protocols (11886 and 19071). For all patients cited in this study, we obtained informed consent to conduct research and publish the results. Samples were obtained from the Stanford Cancer Institute Tissue Bank. Frozen tissue sections were prepared from each tumor and hematoxylin-eosin (H&E) staining was performed on a single section. We estimated overall tumor composition that generally was approximately 50% or greater for most samples. Tumors were macro-dissected to increase tumor cellularity and processed for genomic DNA. Full details are in the Additional file 1: Methods.
Sample preparation for whole genome, exome, and targeted resequencing analysis
Genomic DNA was extracted from blood, normal gastric tissue and tumor samples using the E.Z.N.A. SQ DNA/RNA Protein Kit (Omega Bio-Tek, Norcross, GA, USA). Concentrations of genomic DNA were determined with a Nanodrop instrument (Thermo Scientific, Wilmington, DE, USA). Genomic DNA from matched normal and cancer tissue were then used for creating sequencing libraries. DNA from peripheral leukoctyes was used for the Affymetrix SNP array.
From each sample, we fragmented 4 μg of genomic DNA with a Covaris instrument (Covaris, Woburn, MA, USA). Illumina TruSeq Paired End libraries were constructed from double stranded, fragmented DNA per Illumina’s standard protocol (Illumina, San Diego, CA, USA). The amplified material was recovered with a Qiaquick (Qiagen) column according to the manufacturer’s instructions, except the DNA were eluted in 50 μL water. The sequencing library DNA was quantified using the NanoDrop-1000 and the library was evaluated with an Agilent Bioanalyzer 2100 (Agilent, Santa Clara, CA, USA) using a DNA1000 chip. The mean library fragment size was found to be 300 bp and these libraries were used for whole genome sequencing. For exome capture hybridization, we used Nimblegen SeqCap version 2 enrichment assay (Roche-Nimblegen, Madison, WI, USA). The methods were according to the NimbleGen’s SeqCap EZ Exome Library SR User’s Guide v2.2. Following the final amplification reaction, we purified the exome libraries using a Qiaquick column (Qiagen, Valencia, CA, USA) per the manufacturer’s recommended protocol.
Cancer genome sequencing
See Additional file 1 for complete details regarding the whole genome, exome, and targeted resequencing data analysis. This includes information about the targeted resequencing process, variant calling, allelic frequency determination, and mutation interpretation The oligonucleotide sequences for deep targeted resequencing are listed in Additional file 2.
FGFRamplification analysis from diffuse and intestinal gastric cancers
Quantitative PCR was performed using the Bio-Rad QX100 droplet digital PCR (ddPCR) system (Bio-Rad, Pleasanton, CA, USA). We used a standard set of FGFR2-specific TaqMan primers and probes (Life Technology, Foster City, CA, USA) compared with standard references using an ultra-conserved region on chromosome 1. Briefly, TaqMan PCR reaction mixtures were assembled using 2× ddPCR Supermix for probes, 20× assays (18 μM primers and 5 μM probe) and restriction digested DNA samples (Biorad). To assess FGFR2 copy number, 125 ng of each tumor DNA sample was digested with 1.25 units of BsaJI (NEB) in 15 μL for 1 h at 60×C. The digests were diluted 1.67-fold to 25 μL with nuclease free water then 25 ng (5 μL) was assayed per 20 μL ddPCR reaction. FGFR2 assay sequences were (forward primer) 5’-GGCTGGCTGCTGAAGTCT-3’, (reverse primer) 5’-CTTAATCGCCTGTATGGTGGTAACA-3’, and (probe) 5’-FAM-TCTTGGTCGTGTTCTTCATTCGGCACAG-BHQ1-3’. The FGFR2 assay was duplexed with a standard reference sequence on Chromosome 1. This standard reference assay used the following primers: (forward primer) 5’-TGAGGGATTCGGCAGATGTTG-3’, (reverse primer) 5’-CTGAAAGGCTGGACTTGACAGA-3’, and (probe) 5’-VIC-ACTGTGTGCTGGACCT-MGB-3’. All assay primers were ordered from Integrated DNA Technologies. Thermal cycling conditions were 95°C 10 min (1 cycle), 94°C 30×s and 60×C 60×s (40 cycles), 98°C 10 min (1 cycle), and a 12°C hold. FGFR2 copy number per cell was estimated as the ratio of the FGFR2 and RPP30 concentrations multiplied by two to account for the two copies of RPP30 that are expected per diploid genome. Analysis of the ddPCR data was performed using the CNV mode of the QX100 analysis software (version 184.108.40.206). Quadruplicate ddPCR wells were analyzed for each sample.
FGFRinhibitor sensitivity assay
KatoIII cells (HTB-103, ATCC) and AGS cells (CRL-1739, ATCC) were grown in Dulbecco’s Modified Eagle Medium (DMEM), supplemented with 10% fetal bovine serum and 100 U/mL of Pen Strep Glutamine (Gibco). All cells were cultured at 37°C in a humidified atmosphere and 5% CO2. Survival of KatoIII and AGS cells was determined using the WST-1 Proliferation Assay (Roche). We tested multiple FGFR inhibitors including TKI-258, Brivanib (BMS-540215), Ponatnib (AP24534), and AZD4547 (Selleck Chemical). Cells were seeded at a density of 2 × 104 cells/well in 96-well microtiter plates, 100 μL medium/well and maintained 18 h for attachment. Afterwards, we treated the cultures with varying concentrations of each drug diluted in DMSO. After 30×h incubation, 10 μL of WST-1 reagent was added followed by 1 h at 37°C. The cleavage of tetrazolium salt (WST-1) into a visible formazan by viable cells was spectrophotometrically measured using a reference wavelength of 450 nm. Each test was performed in triplicate. Percentages of cell survival were calculated as follows:% cell survival = (absorbance of treated cells/ absorbance of cells with vehicle solvent) × 100. The half inhibitory concentration (IC50) was calculated with a non-linear regression from the dose–response curve.
Mismatch repair protein immunohistochemistry
Mismatch repair protein immunohistochemistry was performed on the primary diffuse gastric tumor using the standard streptavidin-biotin-peroxidase procedure. Primary monoclonal antibodies against MLH1 (clone G168-728, 1:200, BD PharMingen, San Diego, CA, USA 1:200), MSH2 (clone FE11, 1:100, Oncogene Research Products, Cambridge, MA, USA), MSH6 (clone 44, 1:200, BD Transduction, San Jose, CA, USA) and PMS2 (clone MRQ-28, 1:10, Cell Marque, Rocklin, CA, USA) were applied to formalin-fixed, paraffin embedded sections four microns thick. The sections were deparaffinized in xylene, and rehydrated through graded alcohols to distilled water before undergoing antigen retrieval by heat treatment in either citrate solution pH 6.0 (MLH1, PMS2, and MSH2) or EDTA solution pH 9.0 (MSH6). An automated detection using a Leica Bond Autostainer (Leica, Buffalo Groove, IL, USA) was employed. Normal expression was defined as nuclear staining within tumor cells, using infiltrating lymphocytes as positive internal control. Negative protein expression was defined as complete absence of nuclear staining within tumor cells in the face of concurrent positive labeling in internal non-neoplastic tissues.
Gastric organoid cancer modeling in mice and functional analysis
All procedure involving animal were approved the Stanford University Administrative Panel on Laboratory Animal Care and was fully compliant with the USDA Animal Welfare Act, and our Assurance of Compliance with the PHS Policy on Human Care and Use of Laboratory Animals. Air-liquid interface organoid culture was performed as described ,.
Cdh1flox/flox;Trp53flox/flox mice were generated by crossing Cdh1flox/flox mice, obtained from Jackson Laboratory, and Trp53flox/flox mice, kindly provided by Dr. Anton Berns  NOD.Cg-Prkdcscid Il2rgtm1Sug/JicTac mice were obtained from Taconic Farms, Inc. We dissected stomachs from neonatal mice (age P1-10) and washed them in cold PBS to remove all luminal contents. We extensively minced either large 25% sections or any entire neonatal stomach per dish and embedded the minced tissues in a 3D collagen gel using a double-dish air-liquid interface culture system as previously described . To maintain the organoids, we applied fresh medium (F12, 20%FCS, gentamicin 50 ug/mL) every week.
Tgfbr2 shRNAs were obtained from Origene (catalog TG516186). Retroviral plasmids were cotransfected with pCL-Eco into 293 T cells by Lipofectamine2000 (Invitrogen). Retroviral supernatants were collected 48 and 72 h post-transfection and concentrated by PEG-it virus precipitation solution (5×, System Biosciences). Virus titer was determined by infection of NIH3T3 cells and FACS analysis of GFP positive cells 48 h post infection. Cdh1flox/flox;Trp53flox/flox gastric organoids were infected at day 0 with adenovirus Ad Cre-GFP (University of Iowa Vector Core) or control adenovirus Ad Fc  encoding a mouse immunoglobulin IgG2α Fc fragment by layering viral particles (109 pfu) suspended in 500 μL culture media over the top of the collagen matrix containing primary tissue. For retrovirus infection of secondary organoids, primary organoids at 14 to 20 days of growth were recovered from collagen gel by collagenase IV (Worthington) incubation followed by 0.05% trypsin/EDTA incubation to dissociate organoids into a single cell suspension. Following extensive washing with 10% FBS to inactivate collagenase/trypsin, cells were pelleted by centrifugation and incubated with retroviral particles (2 μL of 108 pfu/mL) encoding Tgfbr2-shRNA in the presence of growth medium and TransDux (System Biosciences) at room temperature for 60×min before serial replating into 3D collagen gel air-liquid interface culture.
Samples were fixed with 4% paraformaldehyde overnight, paraffin-embedded, sectioned, and sections stained by H&E for initial histology analysis. Further immunohistochemistry analysis, used the following antibodies: PCNA (1:300; Invitrogen), CDH1 (1:300; BD Biosciences Pharmagen), TGFBR2 (1:250; Abbiotec), p53 (1:100; Santa Cruz). Cell lysates of mouse gastric culture cell or GSM-06 cells transfected with Tgfbr2-shRNA-GFP were immunoblotted with TGFBR2 (1:2,000, Abbiotec) and β-actin (1:2,000, Abcam).
Cells from gastric organoids were collected from the air-liquid interface collagen gel by disaggregation with collagenase IV (Worthington). For transplantation, 400,000 cells per mouse flank were mixed with matrigel (50% Matrigel, 10%FCS, 40% F12, 100× L of Matrigel mixture per = mouse) and injected into NOD.Cg-Prkdcscid Ilr2rgtm1Sug/JicTac mice. Mice were sacrificed after day 50, after which tumors were dissected and examined by H&E staining. P values were determined using a two-tailed Student’s t-test assuming unequal variances. A P value of 0.05 was considered significant.
The data from this study have been submitted to the NCBI Sequence Read Archive under the accession number SRP044347.
HPJ, LDN, and JMF designed the analysis of the HDGC patient. HX and ESH conducted the sequencing assays. LDN, LM, and JR conducted molecular assays for validation. SG, GN, JMB, PF, CP, HC, NRZ, and HPJ conducted the sequencing data and genetic analysis. RKP provided pathologic review of the gastric cancers and organoids. LDN and LM conducted the cell line and organoid experiments. LDN, AO, and CJK developed the organoid system. CJK supervised the development of cancer organoid modeling. HPJ supervised and coordinated all aspects of the analysis and experiments. All authors revised, read, and approved the final manuscript.
Bone morphogenic protein
Copy number variation
Droplet digital PCR
Epithelial mesenchymal transition
- FGFR2 :
Fibroblast growth factor receptor 2
Green fluorescent protein
Hereditary diffuse gastric cancer
Institutional review board
Loss of heterozygosity
Minor allele frequency
Single nucleotide polymorphism
The Cancer Genome Atlas
- TGFBR2 :
Transforming growth factor-β receptor 2
tyrosine kinase inhibitor
HPJ was supported by a Research Scholar Grant, RSG-13-297-01-TBG, from the American Cancer Society. HPJ received additional support from the Doris Duke Clinical Foundation Clinical Scientist Development Award and a Howard Hughes Medical Institute Early Career Grant. Other support came from the Gastric Cancer Foundation (HPJ, JMF). We also acknowledge support from National Institutes of Health Grants Digestive Disease Center DK56339 (HPJ, LN), P01 HG000205 (EH, JMB, SG, PF, and HPJ), NHGRI RC2 HG005570 (GN, JMB, HX, and HPJ), NCI Innovative Molecular Analysis Technology 1R33CA1745701 (JMB and HPJ), NHGRI R01 HG006137 (HPJ and NZ), NCI Tumor Microenvironment Network Consortium 1U01CA151920 (CJK and HPJ), NCI Cancer Target and Discovery Consortium U01CA17629901 (CJK and HPJ), NIDDK and NIAID Intestinal Stem Cell Consortium 1U01DK085527 (CJK) and K08 CA166512 (LN). LN received additional support from an American Society for Clinical Oncology Young Investigator Award.
- Lauren P: Histogenesis of intestinal and diffuse types of gastric carcinoma. Scand J Gastroenterol Suppl. 1991, 180: 160-164.PubMed
- Cunningham D, Allum WH, Stenning SP, Thompson JN, Van de Velde CJ, Nicolson M, Scarffe JH, Lofts FJ, Falk SJ, Iveson TJ, Smith DB, Langley RE, Verma M, Weeden S, Chua YJ: Perioperative chemotherapy versus surgery alone for resectable gastroesophageal cancer. N Engl J Med. 2006, 355: 11-20. 10.1056/NEJMoa055531.PubMedView Article
- Lim L, Michael M, Mann GB, Leong T: Adjuvant therapy in gastric cancer. J Clin Oncol. 2005, 23: 6220-6232. 10.1200/JCO.2005.11.593.PubMedView Article
- Lee YS, Cho YS, Lee GK, Lee S, Kim YW, Jho S, Kim HM, Hong SH, Hwang JA, Kim SY, Hong D, Choi IJ, Kim BC, Kim BC, Kim CH, Choi H, Kim Y, Kim KW, Kong G, Kim HL, Bhak J, Lee SH, Lee JS: Genomic profile analysis of diffuse-type gastric cancers. Genome Biol. 2014, 15: R55-10.1186/gb-2014-15-4-r55.PubMedPubMed CentralView Article
- Yamashita K, Sakuramoto S, Watanabe M: Genomic and epigenetic profiles of gastric cancer: potential diagnostic and therapeutic applications. Surg Today. 2011, 41: 24-38. 10.1007/s00595-010-4370-5.PubMedView Article
- Cisco RM, Ford JM, Norton JA: Hereditary diffuse gastric cancer: implications of genetic testing for screening and prophylactic surgery. Cancer. 2008, 113: 1850-1856. 10.1002/cncr.23650.PubMedView Article
- 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.PubMedView Article
- Becker KF, Atkinson MJ, Reich U, Becker I, Nekarda H, Siewert JR, Hofler H: E-cadherin gene mutations provide clues to diffuse type gastric carcinomas. Cancer Res. 1994, 54: 3845-3852.PubMed
- Machado JC, Oliveira C, Carvalho R, Soares P, Berx G, Caldas C, Seruca R, Carneiro F, Sobrinho-Simoes 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.PubMedView Article
- Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, Antipin Y, Reva B, Goldberg AP, Sander C, Schultz N: The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012, 2: 401-404. 10.1158/2159-8290.CD-12-0095.PubMedView Article
- Ootani A, Li X, Sangiorgi E, Ho QT, Ueno H, Toda S, Sugihara H, Fujimoto K, Weissman IL, Capecchi MR, Kuo CJ: Sustained in vitro intestinal epithelial culture within a Wnt-dependent stem cell niche. Nat Med. 2009, 15: 701-706. 10.1038/nm.1951.PubMedPubMed CentralView Article
- Katano T, Ootani A, Mizoshita T, Tanida S, Tsukamoto H, Ozeki K, Ebi M, Mori Y, Kataoka H, Kamiya T, Toda S, Joh T: Establishment of a long-term three-dimensional primary culture of mouse glandular stomach epithelial cells within the stem cell niche. Biochem Biophys Res Commun. 2013, 432: 558-563. 10.1016/j.bbrc.2013.02.051.PubMedView Article
- Li X, Nadauld L, Ootani A, Corney DC, Pai RK, Gevaert O, Cantrell MA, Rack PG, Neal JT, Chan CW, Yeung T, Gong X, Yuan J, Wilhelmy J, Robine S, Attardi LD, Plevritis SK, Hung KE, Chen CZ, Ji HP, Kuo JC: Oncogenic transformation of diverse gastrointestinal tissues in primary organoid culture. Nat Med. 2014, 20: 769-777. 10.1038/nm.3585.PubMedPubMed CentralView Article
- Kluijt I, Siemerink EJ, Ausems MG, van Os TA, de Jong D, Simoes-Correia J, van Krieken JH, Ligtenberg MJ, Figueiredo J, van Riel E, Sijmons RH, Plukker JT, van Hillegersberg R, Dekker E, Oliveira C, Cats A, Hoogerbrugge N: CDH1-related hereditary diffuse gastric cancer syndrome: Clinical variations and implications for counseling. Int J Cancer. 2012, 131: 367-376. 10.1002/ijc.26398.PubMedView Article
- Hass HG, Smith U, Jager C, Schaffer M, Wellhausser U, Hehr T, Markmann HU, Nehls O, Denzlinger C: Signet ring cell carcinoma of the stomach is significantly associated with poor prognosis and diffuse gastric cancer (Lauren's): single-center experience of 160 cases. Onkologie. 2011, 34: 682-686. 10.1159/000334545.PubMedView Article
- Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR: A method and server for predicting damaging missense mutations. Nat Methods. 2010, 7: 248-249. 10.1038/nmeth0410-248.PubMedPubMed CentralView Article
- Kumar P, Henikoff S, Ng PC: Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc. 2009, 4: 1073-1081. 10.1038/nprot.2009.86.PubMedView Article
- Berx G, Becker KF, Hofler 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.PubMedView Article
- Gamboa-Dominguez A, Dominguez-Fonseca C, Chavarri-Guerra Y, Vargas R, Reyes-Gutierrez E, Green D, Quintanilla-Martinez L, Luber B, Busch R, Becker KF, Becker I, Hofler H, Fend F: E-cadherin expression in sporadic gastric cancer from Mexico: exon 8 and 9 deletions are infrequent events associated with poor survival. Hum Pathol. 2005, 36: 29-35. 10.1016/j.humpath.2004.09.020.PubMedView Article
- Hiraguri S, Godfrey T, Nakamura H, Graff J, Collins C, Shayesteh L, Doggett N, Johnson K, Wheelock M, Herman J, Baylin S, Pinkel D, Gray J: Mechanisms of inactivation of E-cadherin in breast cancer cell lines. Cancer Res. 1998, 58: 1972-1977.PubMed
- Foti A, Bar-Eli M, Ahuja HG, Cline MJ: A splicing mutation accounts for the lack of p53 gene expression in a CML blast crisis cell line: a novel mechanism of p53 gene inactivation. Br J Haematol. 1990, 76: 143-145. 10.1111/j.1365-2141.1990.tb07849.x.PubMedView Article
- Petitjean A, Achatz MI, Borresen-Dale AL, Hainaut P, Olivier M: TP53 mutations in human cancers: functional selection and impact on cancer prognosis and outcomes. Oncogene. 2007, 26: 2157-2165. 10.1038/sj.onc.1210302.PubMedView Article
- Forbes SA, Bindal N, Bamford S, Cole C, Kok CY, Beare D, Jia M, Shepherd R, Leung K, Menzies A, Teague JW, Campbell PJ, Stratton MR, Futreal PA: COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 2011, 39: D945-D950. 10.1093/nar/gkq929.PubMedPubMed CentralView Article
- Strickler JG, Zheng J, Shu Q, Burgart LJ, Alberts SR, Shibata D: p53 mutations and microsatellite instability in sporadic gastric cancer: when guardians fail. Cancer Res. 1994, 54: 4750-4755.PubMed
- Tamura G, Sakata K, Nishizuka S, Maesawa C, Suzuki Y, Iwaya T, Terashima M, Saito K, Satodate R: Inactivation of the E-cadherin gene in primary gastric carcinomas and gastric carcinoma cell lines. Jpn J Cancer Res. 1996, 87: 1153-1159. 10.1111/j.1349-7006.1996.tb03125.x.PubMedView Article
- Shimada S, Mimata A, Sekine M, Mogushi K, Akiyama Y, Fukamachi H, Jonkers J, Tanaka H, Eishi Y, Yuasa Y: Synergistic tumour suppressor activity of E-cadherin and p53 in a conditional mouse model for metastatic diffuse-type gastric cancer. Gut. 2012, 61: 344-353. 10.1136/gutjnl-2011-300050.PubMedView Article
- Nadauld LD, Regan JF, Miotke L, Pai RK, Longacre TA, Kwok SS, Saxonov S, Ford JM, Ji HP: Quantitative and sensitive detection of cancer genome amplifications from formalin fixed paraffin embedded tumors with droplet digital PCR. Transl Med (Sunnyvale). 2012, 2: 1-5. 10.4172/2161-1025.1000107.View Article
- Kunii K, Davis L, Gorenstein J, Hatch H, Yashiro M, Di Bacco A, Elbi C, Lutterbach B: FGFR2-amplified gastric cancer cell lines require FGFR2 and Erbb3 signaling for growth and survival. Cancer Res. 2008, 68: 2340-2348. 10.1158/0008-5472.CAN-07-5229.PubMedView Article
- Tsujimoto H, Sugihara H, Hagiwara A, Hattori T: Amplification of growth factor receptor genes and DNA ploidy pattern in the progression of gastric cancer. Virchows Arch. 1997, 431: 383-389. 10.1007/s004280050115.PubMedView Article
- Dulak AM, Schumacher SE, van Lieshout J, Imamura Y, Fox C, Shim B, Ramos AH, Saksena G, Baca SC, Baselga J, Tabernero J, Barretina J, Enzinger PC, Corso G, Roviello F, Lin L, Bandla S, Luketich JD, Pennathur A, Meyerson M, Ogino S, Shivdasani RA, Beer DG, Godfrey TE, Beroukhim R, Bass AJ: Gastrointestinal adenocarcinomas of the esophagus, stomach, and colon exhibit distinct patterns of genome instability and oncogenesis. Cancer Res. 2012, 72: 4383-4393. 10.1158/0008-5472.CAN-11-3893.PubMedPubMed CentralView Article
- Bass AJ, Lawrence MS, Brace LE, Ramos AH, Drier Y, Cibulskis K, Sougnez C, Voet D, Saksena G, Sivachenko A, Jing R, Parkin M, Pugh T, Verhakk RG, Stransky N, Boutin AT, Barretina J, Solit DB, Vakiani E, Shao W, Mishina Y, Warmuth M, Jimenez J, Chiang DY, Signoretti S, Kaelin WG, Spardy N, Hahn WC, Hoshida Y, Ogino S, et al: Genomic sequencing of colorectal adenocarcinomas identifies a recurrent VTI1A-TCF7L2 fusion. Nat Genet. 2011, 43: 964-968. 10.1038/ng.936.PubMedPubMed CentralView Article
- Oda T, Kanai Y, Oyama T, Yoshiura K, Shimoyama Y, Birchmeier W, Sugimura T, Hirohashi S: E-cadherin gene mutations in human gastric carcinoma cell lines. Proc Natl Acad Sci U S A. 1994, 91: 1858-1862. 10.1073/pnas.91.5.1858.PubMedPubMed CentralView Article
- Yamada Y, Yoshida T, Hayashi K, Sekiya T, Yokota J, Hirohashi S, Nakatani K, Nakano H, Sugimura T, Terada M: p53 gene mutations in gastric cancer metastases and in gastric cancer cell lines derived from metastases. Cancer Res. 1991, 51: 5800-5805.PubMed
- Gavine PR, Mooney L, Kilgour E, Thomas AP, Al-Kadhimi K, Beck S, Rooney C, Coleman T, Baker D, Mellor MJ, Brooks AN, Klinowska T: AZD4547: an orally bioavailable, potent, and selective inhibitor of the fibroblast growth factor receptor tyrosine kinase family. Cancer Res. 2012, 72: 2045-2056. 10.1158/0008-5472.CAN-11-3034.PubMedView Article
- Yoon YK, Kim HP, Han SW, Hur HS, Oh do Y, Im SA, Bang YJ, Kim TY: Combination of EGFR and MEK1/2 inhibitor shows synergistic effects by suppressing EGFR/HER3-dependent AKT activation in human gastric cancer cells. Mol Cancer Ther. 2009, 8: 2526-2536. 10.1158/1535-7163.MCT-09-0300.PubMedView Article
- Bellam N, Pasche B: Tgf-beta signaling alterations and colon cancer. Cancer Treat Res. 2010, 155: 85-103. 10.1007/978-1-4419-6033-7_5.PubMedView Article
- Markowitz S, Wang J, Myeroff L, Parsons R, Sun L, Lutterbaugh J, Fan RS, Zborowska E, Kinzler KW, Vogelstein B: Inactivation of the type II TGF-beta receptor in colon cancer cells with microsatellite instability. Science. 1995, 268: 1336-1338. 10.1126/science.7761852.PubMedView Article
- Itoh F, Asao H, Sugamura K, Heldin CH, ten Dijke P, Itoh S: Promoting bone morphogenetic protein signaling through negative regulation of inhibitory Smads. EMBO J. 2001, 20: 4132-4142. 10.1093/emboj/20.15.4132.PubMedPubMed CentralView Article
- Aoki M, Ishigami S, Uenosono Y, Arigami T, Uchikado Y, Kita Y, Kurahara H, Matsumoto M, Ueno S, Natsugoe S: Expression of BMP-7 in human gastric cancer and its clinical significance. Br J Cancer. 2011, 104: 714-718. 10.1038/sj.bjc.6606075.PubMedPubMed CentralView Article
- Wang H, Linghu H, Wang J, Che YL, Xiang TX, Tang WX, Yao ZW: The role of Crk/Dock180/Rac1 pathway in the malignant behavior of human ovarian cancer cell SKOV3. Tumour Biol. 2010, 31: 59-67. 10.1007/s13277-009-0009-9.PubMedView Article
- Balint I, Muller A, Nagy A, Kovacs G: Cloning and characterisation of the RBCC728/TRIM36 zinc-binding protein from the tumor suppressor gene region at chromosome 5q22.3. Gene. 2004, 332: 45-50. 10.1016/j.gene.2004.02.045.PubMedView Article
- Miyajima N, Maruyama S, Nonomura K, Hatakeyama S: TRIM36 interacts with the kinetochore protein CENP-H and delays cell cycle progression. Biochem Biophys Res Commun. 2009, 381: 383-387. 10.1016/j.bbrc.2009.02.059.PubMedView Article
- Wei K, Piecewicz SM, McGinnis LM, Taniguchi CM, Wiegand SJ, Anderson K, Chan CW, Mulligan KX, Kuo D, Yuan J, Vallon M, Morton L, Lefai E, Simon MC, Maher JJ, Mithieux G, Rajas F, Annes J, McGuinness OP, Thurston G, Giaccia AJ, Kuo CJ: A liver Hif-2alpha-Irs2 pathway sensitizes hepatic insulin signaling and is modulated by Vegf inhibition. Nat Med. 2013, 19: 1331-1337. 10.1038/nm.3295.PubMedPubMed CentralView Article
- Nakatani H, Sakamoto H, Yoshida T, Yokota J, Tahara E, Sugimura T, Terada M: Isolation of an amplified DNA sequence in stomach cancer. Jpn J Cancer Res. 1990, 81: 707-710. 10.1111/j.1349-7006.1990.tb02631.x.PubMedView Article
- Su X, Zhan P, Gavine PR, Morgan S, Womack C, Ni X, Shen D, Bang YJ, Im SA, Ho Kim W, Jung E-J, Grabsch HI, Kilgour E: FGFR2 amplification has prognostic significance in gastric cancer: results from a large international multicentre study. Br J Cancer. 2014, 110: 967-975. 10.1038/bjc.2013.802.PubMedPubMed CentralView Article
- Xie L, Su X, Zhang L, Yin X, Tang L, Zhang X, Xu Y, Gao Z, Liu K, Zhou M, Gao B, Shen D, Zhang L, Ji J, Gavine PR, Zhang J, Kilgour E, Zhang X, Ji Q: FGFR2 gene amplification in gastric cancer predicts sensitivity to the selective FGFR inhibitor AZD4547. Clin Cancer Res. 2013, 19: 2572-2583. 10.1158/1078-0432.CCR-12-3898.PubMedView Article
- Lee J, Ou SH: Towards the goal of personalized medicine in gastric cancer–time to move beyond HER2 inhibition. Part I: Targeting receptor tyrosine kinase gene amplification. Discov Med. 2013, 15: 333-341.PubMed
- Roychowdhury S, Iyer MK, Robinson DR, Lonigro RJ, Wu YM, Cao X, Kalyana-Sundaram S, Sam L, Balbin OA, Quist MJ, Barrette T, Everett J, Siddiqui J, Kunju LP, Navone N, Araujo JC, Troncoso P, Logothetis CJ, Innis JW, Smith DC, Lao CD, Kim SY, Roberts JS, Gruber SB, Pienta KJ, Talpaz M, Chinnaiyan AM: Personalized oncology through integrative high-throughput sequencing: a pilot study. Sci Transl Med. 2011, 3: 111ra121-PubMedPubMed CentralView Article
- Nguyen DX, Massague J: Genetic determinants of cancer metastasis. Nat Rev Genet. 2007, 8: 341-352. 10.1038/nrg2101.PubMedView Article
- Meuwissen R, Linn SC, Linnoila RI, Zevenhoven J, Mooi WJ, Berns A: Induction of small cell lung cancer by somatic inactivation of both Trp53 and Rb1 in a conditional mouse model. Cancer Cell. 2003, 4: 181-189. 10.1016/S1535-6108(03)00220-4.PubMedView Article
- Fitzgerald RC, Hardwick R, Huntsman D, Carneiro F, Guilford P, Blair V, Chung DC, Norton J, Ragunath K, Van Krieken JH, Dwerryhouse S, Caldas C: Hereditary diffuse gastric cancer: updated consensus guidelines for clinical management and directions for future research. J Med Genet. 2010, 47: 436-444. 10.1136/jmg.2009.074237.PubMedPubMed CentralView Article
- Igartua C, Turner EH, Ng SB, Hodges E, Hannon GJ, Bhattacharjee A, Rieder MJ, Nickerson DA, Shendure J: Targeted enrichment of specific regions in the human genome by array hybridization. Curr Protoc Hum Genet. 2010, Chapter 18: Unit 18 13-
- Li H, Durbin R: Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics. 2010, 26: 589-595. 10.1093/bioinformatics/btp698.PubMedPubMed CentralView Article
- 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.PubMedPubMed CentralView Article
- Koboldt DC, Chen K, Wylie T, Larson DE, McLellan MD, Mardis ER, Weinstock GM, Wilson RK, Ding L: VarScan: variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics. 2009, 25: 2283-2285. 10.1093/bioinformatics/btp373.PubMedPubMed CentralView Article
- DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Rivas MA, Hanna M, McKenna A, Fennell TJ, Kernytsky AM, Sivachenko AY, Cibulskis K, Gabriel SB, Altshuler D, Daly MJ: A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011, 43: 491-498. 10.1038/ng.806.PubMedPubMed CentralView Article
- Bhagwat M: Searching NCBI’s dbSNP database. Curr Protoc Bioinformatics. 2010, Chapter 1: Unit 1 19-PubMed
- Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K: dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001, 29: 308-311. 10.1093/nar/29.1.308.PubMedPubMed CentralView Article
- A map of human genome variation from population-scale sequencing.Nature 2010, 467:1061-1073.,
- Solomon E, Voss R, Hall V, Bodmer WF, Jass JR, Jeffreys AJ, Lucibello FC, Patel I, Rider SH: Chromosome 5 allele loss in human colorectal carcinomas. Nature. 1987, 328: 616-619. 10.1038/328616a0.PubMedView Article
- Bettegowda C, Agrawal N, Jiao Y, Sausen M, Wood LD, Hruban RH, Rodriguez FJ, Cahill DP, McLendon R, Riggins G, Velculescu VE, Oba-Shinjo SM, Marie SK, Vogelstein B, Bigner D, Yan H, Papadopoulos N, Kinzler KW: Mutations in CIC and FUBP1 contribute to human oligodendroglioma. Science. 2011, 333: 1453-1455. 10.1126/science.1210557.PubMedPubMed CentralView Article
- Chen H, Xing H, Zhang NR: Estimation of parent specific DNA copy number in tumors using high-density genotyping arrays. PLoS Comput Biol. 2011, 7: e1001060-10.1371/journal.pcbi.1001060.PubMedPubMed CentralView Article
- Shen JJ, Zhang NR: Change-point model on non-homogeneous Poisson processes with application in copy number profiling by next-generation DNA sequencing. Ann Appl Stat. 2012, 6: 476-496. 10.1214/11-AOAS517.View Article
- 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.PubMedPubMed CentralView Article
- Lam HY, Pan C, Clark MJ, Lacroute P, Chen R, Haraksingh R, O’Huallachain M, Gerstein MB, Kidd JM, Bustamante CD, Snyder M: Detecting and annotating genetic variations using the HugeSeq pipeline. Nat Biotechnol. 2012, 30: 226-229. 10.1038/nbt.2134.PubMedView Article
- Ye K, Schulz MH, Long Q, Apweiler R, Ning Z: Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from pairedend short reads. Bioinformatics. 2009, 25: 2865-2871. 10.1093/bioinformatics/btp394.PubMedPubMed CentralView Article
- Abyzov A, Urban AE, Snyder M, Gerstein M: CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. Genome Res. 2011, 21: 974-984. 10.1101/gr.114876.110.PubMedPubMed CentralView Article
- Lam HY, Mu XJ, Stutz AM, Tanzer A, Cayting PD, Snyder M, Kim PM, Korbel JO, Gerstein MB: Nucleotide-resolution analysis of structural variants using BreakSeq and a breakpoint library. Nat Biotechnol. 2010, 28: 47-55. 10.1038/nbt.1600.PubMedPubMed CentralView Article
- Myllykangas S, Buenrostro JD, Natsoulis G, Ji HP, Bell JM: Efficient targeted resequencing of human germline and cancer genomes by oligonucleotideselective sequencing. Nat Biotechnol. 2011, 29: 1024-1027. 10.1038/nbt.1996.PubMedPubMed CentralView Article
- Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP: Integrative genomics viewer. Nat Biotechnol. 2011, 29: 24-26. 10.1038/nbt.1754.PubMedPubMed CentralView Article
- Zerbino DR, Birney E: Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008, 18: 821-829. 10.1101/gr.074492.107.PubMedPubMed CentralView Article
- Reimand J, Kull M, Peterson H, Hansen J: Vilo J: g:Profiler–a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 2007, 35: W193-W200. 10.1093/nar/gkm226.PubMedPubMed CentralView Article
- Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M: KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 2012, 40: D109-D114. 10.1093/nar/gkr988.PubMedPubMed CentralView Article
- Ding L, Ley TJ, Larson DE, Miller CA, Koboldt DC, Welch JS, Ritchey JK, Young MA, Lamprecht T, McLellan MD, McMichael JF, Wallis JW, Lu C, Shen D, Harris CC, Dooling DJ, Fulton RS, Fulton LL, Chen K, Schmidt H, Kalicki-Veizer J, Magrini VJ, Cook L, McGrath SD, Vickery TL, Wendl MC, Heath S, Watson MA, Link DC, Tomasson MH, et al: Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature. 2012, 481: 506-510. 10.1038/nature10738.PubMedPubMed CentralView Article
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