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Molecular Biology and Genetics |
Cellular and Molecular Research [S. T. T., K. Y., A. A., C. H. L., K. W., P. T.], Division of Medical Sciences [S. H. L., J. V., D. L., K. C. S., O. L. K.], and Defence Medical Research Institute [P. T.], National Cancer Centre, Singapore 169610, Republic of Singapore; Department of Pathology and Laboratory Medicine, Tan Tock Seng Hospital, Singapore 308433, Republic of Singapore [S. Y. T.]; and Department of General Surgery, Singapore General Hospital, Singapore, Republic of Singapore [W. K. W.]
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ABSTRACT |
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 Top ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSMolecular Subtypes of Gastric...Identification of Minimal...DISCUSSIONREFERENCES |
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INTRODUCTION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSMolecular Subtypes of Gastric...Identification of Minimal...DISCUSSIONREFERENCES |
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Recently, it has been shown that the resolving power of classification schemes based on molecular data can be sufficiently sensitive to detect new disease subtypes that have hitherto eluded traditional light microscopy approaches (4) . In this study, we used various molecular assays such as CGH,5 MSI studies, and expression microarrays to characterize a common set of gastric tumors. We identified several novel genomic aberrations associated with gastric cancer and discovered that gastric cancers could be divided into three broad molecular subgroups ("tumorigenic," "reactive," and "gastric-like") on the basis of their expression profiles. Patients belonging to one of these subgroups (gastric-like) exhibited a significantly better overall survival than patients belonging to the other groups. Using a recently described novel methodology for multiclass prediction, we defined various optimal predictor gene sets capable of accurately predicting the class of an unknown tumor sample. Our results show that molecular data can provide a useful framework for furthering our understanding of the taxonomy and pathology of gastric cancer.
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MATERIALS AND METHODS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSMolecular Subtypes of Gastric...Identification of Minimal...DISCUSSIONREFERENCES |
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CGH.
CGH was performed as described elsewhere (6)
. Of the 13 tumors with low CNA, all 13 (100%) had >50% tumor content. Of the 16 tumors with no CNA, 8 (50%) had >60% tumor content, and the remaining 50% had <50% tumor content.
Microsatellite Analysis of Tumors.
Multiplex PCR was performed at five markers (Bat25, Bat26, D5S346, D2S123, D17S250) on tumor DNA and case-matched normal genomic DNA from peripheral blood or histologically verified normal gastric tissues of 59 patients. Microsatellite stability (MSS), MSI-H, and MSI-L were scored by consensus criteria (7)
.
Generation of Expression Profiles.
cDNA microarrays of approximately 13K and 18K array targets were produced using established procedures (8)
with cDNA clones from commercial vendors (Incyte and Research Genetics). Identities of array targets were confirmed by resequencing the parent clones. Expression profiles were generated from tumor specimens containing a minimum of >50% cancer cells as assessed by cryosections. Total RNA was extracted from homogenized gastric tissue and 5 µg were amplified using a single-round T7-polymerase-based linear amplification protocol (9)
. Each microarray hybridization used 1–2 µg aRNA (amplified RNA) and was compared with a common reference RNA pool (Universal Reference RNA; Stratagene).
Microarray Data Analysis.
Microarray data sets are downloadable.6
An initial data set was created from array targets that were well measured across 90% of all of the arrays and normalized by median centering each sample (array) and array target (gene). A truncated data set (764 array targets) was then formed by selecting array targets exhibiting a minimal SD of >0.7 across all of the samples. Minor variations on the gene selection filter (e.g., using a SD of 0.6–0.8) did not significantly affect results of the clustering analysis (data not shown).7
"Semisupervised" clustering was performed using seven distinct expression clusters whose boundaries were visually determined using Treeview software. Supervised clustering was performed using the following algorithms: OVA SVM (10)
, nearest neighbor correlation analysis (NNCA), and GA/MLHD; Ref. 11
; see Supplementary Information2
for details). Accuracy of the supervised classification methodologies was assessed using LVO CV.
Survival Curves.
Kaplan-Meier survival curves were generated using SPSS software. To maximize sample size, clinical data was used from patients whose biological samples are in Fig. 3
, as well as from four additional patients whose samples could be reliably assigned to a specific tumor class (one tumorigenic, one reactive, and two gastric-like) using two independent classification methodologies [OVA SVM and nearest neighbor correlation analysis (NNCA); see Supplementary Information2
].
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RESULTS |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSMolecular Subtypes of Gastric...Identification of Minimal...DISCUSSIONREFERENCES |
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In addition to previously reported gains in 20q, 8q, 7p, 13q, 20p, and 17q (Ref. 12
; Fig. 1
; Supplementary Information2
), several tumors also exhibited novel chromosomal amplifications in 11p, 12p, 14q, 22q, 10q, 17p, 4p, 10p, 16q, 19p, and 4q. Strikingly, 
13% of tumors exhibited a gain of 16q. High-level amplifications were also identified in 20q11.2-q13 (six cases), 6p21.1-p21.3, 16p12 and 19q12-q13 (four cases each), 8q24.1-q24.2, 11p13-p14, 12p11.2-q12, 12q14, and 17q12-q21 (three cases each). Although deletions on 18q, 4q, 5q, 17p, and 9p have been reported by others, we also observed deletions in 8p, 10q, 11q, and 1q. Chromosomal imbalances in 2p, 4p, and 5p occurred only in the intestinal-type tumors but were absent in all of the diffuse-type tumors in this series.
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Identification of Biological Expression Signatures Using Unsupervised Clustering
We then used cDNA microarrays to generate expression profiles for the gastric tumors, focusing initially on samples containing >50% tumor cells as determined by cryosections (47 tumors), and also profiling 3 surgical samples of normal gastric mucosae obtained from patients with benign gastric disease. Using various data filters, we defined a set of 746 array targets representing well-measured genes that exhibited considerable transcriptional variation across all of the gastric samples (see "Materials and Methods"). A two-way unsupervised HC algorithm was then used to order the gastric samples and genes on the basis of their similarity to one another (Fig. 2)
. The gastric tumors segregated into three broad subclasses (discussed in the next section), and the three normal gastric samples exhibited tight cosegregation, indicating that their expression profiles are highly correlated to one another.
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). We identified at least seven specific signatures, each associated with a distinct biological process. In this report, only a few members of each signature are mentioned.6
Cell Growth and Proliferation.
This expression signature contained several genes involved in different aspects of cell growth, e.g., energy metabolism (adenylate kinase), DNA and protein synthesis (various ribosomal proteins and nucleoside phosphorylase), and cell cycle regulation (cyclin D1). Notably, cyclin D1 overexpression has been reported in a gastric cancer subset (14)
. Other relevant genes in this signature (not depicted in Fig. 2
) included thymidine kinase and replication factor C (data not shown).7
Intestinal Metaplasia.
The genes in this expression signature were highly expressed in many of the tumor samples but were down-regulated in the samples of normal gastric mucosae. They included markers of intestinal differentiation such as villin-1, trefoil factor 3(intestinal), the intestinal brush border protein galectin 4, and the intestinal enzyme glutathione peroxidase 2. The cytoskeletal proteins keratin 8 and 18 were also part of this signature suggesting a specific "crypt"-like intestinal character (15)
. The presence of these intestinal markers in tumors but not in normal tissue supports the hypothesis that intestinal metaplasia is a predisposing factor in gastric carcinogenesis.
Immunity.
We detected two distinct clusters (Immunity A and B) related to immunological function. Immunity A contained multiple MHC Class I genes (B, C, and G), whereas Immunity B was composed primarily of MHC Class II genes (DO, DP, DQ, and DR) and 
2-macroglobulin. Previous reports have suggested that gastric cancer cells with a tendency for peritoneal dissemination are associated with the up-regulation of MHC Class I molecules, whereas gastric cancer cells with a tendency for lymph node metastasis tend to up-regulate MHC Class II genes instead (16
, 17)
. Alternatively, it is also possible that the presence of distinct populations of immune cells may contribute to the differential expression of the MHC genes observed in this tumor series.
Tumor-like.
This prominent expression signature contained several genes associated with an active tumorigenic phenotype, such as markers of tumor hypoxia (HIF-1) and reactive angiogenesis (VEGF). Also in this cluster were ß1-integrin and matrix metalloproteinase 9 (MMP-9), both having been implicated in gastric tumor invasion and dissemination (18
, 19)
. Tumor markers in this cluster included tumor rejection antigen gp96 and tumor-associated calcium signal transducer 1. Genes involved in protein degradation, e.g., several 26S proteosome subunits and the E1 ubiquitin-activating enzyme were also prominent in this cluster, as was the transcription factor GATA6, previously shown to be strongly expressed in certain gastric cancer cell lines (20)
.
Remodeling.
This cluster contained genes such as Mucin 5B and FGFR1, which have been reported to be expressed in a subset of gastric cancers (21
, 22)
but not in normal gastric tissue (22
, 23)
. However, the most striking feature of this cluster was the presence of numerous genes involved in stromal remodeling and endothelial growth, suggesting the presence of an active desmoplastic reaction, which is frequently observed in gastric cancer. A number of smooth muscle genes (leiomodin 1, calponin 1) were highly up-regulated, as were the pan-endothelial markers hevin, IGFBP4, and matrix Gla protein (24)
. Also present were genes such as MMP-2 and COL1A1 that behave as specific markers of tumor endothelium (24)
.
Gastric-like.
This final cluster was strongly expressed in the three benign gastric specimens, as well as in several tumor samples, and contained genes associated with gastric epithelia including the digestive proteins pepsinogen C, gastric lipase, and tryptase II/Granzyme K. The tight junction epithelial proteins p55 and desmoplakin were also present, as was the gastric-specific growth hormone ghrelin. The secreted frizzled-related protein hsFRP, shown to be expressed in normal gastric tissues and some gastric cancers (24)
, was also in this cluster. It is important to note that the tumor samples in this group were confirmed by histological examination of cryosections to contain a very high percentage (80–100%) of tumor cells. Thus, it is unlikely that the presence of this gastric-like expression signature in these tumor samples arises from the presence of contaminating normal gastric tissue, but instead is reflective of the endogenous tumor expression profile.
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Molecular Subtypes of Gastric Cancer have Distinct Clinical Behaviors |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSMolecular Subtypes of Gastric...Identification of Minimal...DISCUSSIONREFERENCES |
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. Because the groupings defined in the purely unsupervised and semisupervised clusterings were highly comparable (Supplementary Information),2
such semisupervised clustering was performed primarily to refine the distribution of specific tumors within each group and to minimize "noise" caused by extraneous genes.
We then attempted to determine whether the subtypes defined by the expression analysis might be associated with any clinical or histopathological criteria. To maximize our sample size, we included clinical data from four additional patients whose samples were not used in the initial expression analysis because they contained 40% tumor cells (as assessed by cryosections).The samples, subsequently, could be reliably assigned to a specific class using two independent classification methods (Supplementary Information).2
No significant associations were discovered between the three molecular subgroups and age of diagnosis, patient sex, tumor site, Lauren classification (intestinal or diffuse), tumor differentiation status, or clinical stage at diagnosis (Supplementary Information).2
However, when a survival analysis was performed, we discovered that patients with gastric-like tumors exhibited a significantly better overall survival (P < 0.05) than patients belonging to the other two groups (Fig. 4A)
, suggesting that subtyping gastric cancers by expression profiling might identify clinically relevant features of gastric adenocarcinoma.
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2, and P = 0.51 by subsequent evaluation using ANOVA). This result suggests that the improved prognosis of patients with gastric-like tumors might be attributable to factors independent of tumor stage, and that knowing the molecular subtype of a gastric tumor might serve as a useful adjunct to traditionally used staging systems for disease prognostication. To explore this possibility, we stratified our patients by tumor stage, and we confirmed that patients presenting at both extremes of the clinical spectrum (stages I and IV) were associated with statistically significant "good" and "bad" prognoses, respectively (good, Stage I versus II/III/IV, P < 0.001; bad, Stage IV versus II/III, P < 0.05; data not shown).7
However, although there was an observed tendency for stage II patients to have a better prognosis than stage III patients, this difference was not statistically significant, possibly because of the small sample sizes involved (Fig. 4B
; P = 0.17). Nevertheless, when these same patients (stage II and III) were then restratified according to their molecular subtype, we once again found that, despite the reduced sample size, patients with gastric-like tumors still exhibited a significantly better overall survival than patients belonging to the other two groups (Fig. 4C
; P < 0.05). These results suggest that, for gastric cancer prognostication, classical tumor staging may play a dominant role for early stage (I) and late stage (IV) patients. However, for patients presenting at intermediate clinical stages (Stage II and III, the majority of gastric cancer cases), prognostication by molecular subtype may prove more clinically useful than classical tumor stage. |
Identification of Minimal Predictor Gene Sets for Gastric Cancer Classification |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSMolecular Subtypes of Gastric...Identification of Minimal...DISCUSSIONREFERENCES |
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). Higher predictive accuracies (94%) were obtained, however, when the OVA SVM was trained on only the top 10 genes both positively and negatively correlated to each of the three classes (total number, 60 genes).
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DISCUSSION |
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSMolecular Subtypes of Gastric...Identification of Minimal...DISCUSSIONREFERENCES |
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As an alternative to CGH, which detects large-scale genomic aberrations, we also used MSI studies to address the possibility that the high proportion of low- and no-NA tumors in our series might be associated with microlevel genomic instability. Although only a small fraction of the no- and low-CNA tumors [i.e., 6 (19%) of 32] were MSI-H, this observation carries several caveats. For example, protocol discrepancies may explain much of the reported variation of MSI in gastric cancer (from 9 to 44%; Refs. 28
and 29
), and the standard marker loci used in the MSI assay may not be equally mutable in all DNA mismatch repair-deficient tumors. Indeed, a study of MSI in gastric cancer showed that the rate of instability of different markers in the same tumors ranged from 0 to 77% (30)
. Furthermore, MSI-H gastric cancers are known to harbor mutations in genes that are distinct from those found in other tumor types (31)
. Nevertheless, if a majority of CNA-absent gastric cancers are truly mismatch repair-proficient (as our data indicate), then this may suggest the existence of an alternative pathway capable of driving the oncogenic potential of no/low-CNA gastric tumors in the absence of processes causing either classical micro- or macro-genomic instability (the former being measured by MSI and the latter by CGH).
We also discovered that the gastric cancers could be divided on the basis of their expression profiles into three major groups: tumorigenic, reactive, and gastric-like, and that patients with gastric-like tumors exhibited a significantly better overall survival than patients of the other two groups. The clinical usefulness of the molecular subtypes became more apparent when used to prognosticate patients presenting at intermediate clinical stages. Our current hypothesis is that each of the molecular subtypes is associated with a distinct biological behavior, which ultimately contributes to the differing survival rates. For example, tumorigenic tumors may be more clinically and metabolically aggressive, whereas gastric-like tumors may progress along a more indolent course. In addition, the expression signatures found in each subtype logically suggest that certain therapies may be more effective against certain tumor subtypes than against other therapies. For example, reactive tumors, by virtue of their association with numerous endothelial growth markers, may be more susceptible to antiangiogenic therapies and strategies that target the surrounding normal stroma.
Finally, we used various supervised learning approaches to define a minimal predictor gene set that could accurately classify the class of an unknown gastric sample. To date, much less work has been done specifically on algorithms for multiclass prediction than for binary prediction. The popular OVA SVM approach (10) , for example, is associated with certain issues that render it less than ideal for use in a multiclass setting. Because it relies on converting the multiclass scenario into a series of quasibinary class prediction problems (the OVA approach), distinct sets of predictor genes need to be selected for each quasibinary class distinction, leading to a final combined predictor gene set that can be fairly large and unwieldy, especially for the development of diagnostic assays. As an alternative, we used a methodology that we developed (GA/MLHD) which was created specifically for use in a multiclass prediction setting (11) . In addition to being able to automatically determine the optimal number of genes that should belong to a predictor gene set (a number that normally has to be prespecified), the GA/MLHD approach does not rely on a rank-based gene selection strategy, and deliberately selects genes that are uncorrelated in expression to each other to belong to a predictor gene set. Although the strength of this approach is primarily seen in scenarios involving many classes (i.e., more than five; see Ref. 10 ), the application of the GA/MLHD methodology to the gastric cancer data set allowed us to define a series of small (<20) gene sets that delivered very high classification accuracies (100% CV accuracy, as compared with 87% for a OVA SVM based on 21 genes). We are hopeful that the GA/MLHD methodology will prove useful also in other complex multiclass prediction settings for other cancers. In conclusion, our results offer several insights and suggest multiple logical avenues for future research into gastric cancer, which may ultimately lead to improved methods of diagnosis, treatment, and prevention of this important and complex disease.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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1 Supported in part by National Medical Research Council, National Cancer Centre, and the Lee Foundation. 
2 Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org).
3 S. T. T. and S. H. L. contributed equally to this work.
4 To whom requests for reprints should be addressed, at Defence Medical Research Institute, National Cancer Centre, 11 Hospital Drive, Singapore 169610, Republic of Singapore. E-mail: cmrtan{at}nccs.com.sg
5 The abbreviations used are: CGH, comparative genomic hybridization; CNA, copy number abnormality; MSI, microsatellite instability, HC, hierarchical clustering, SVM, support vector machine, LVO, leave-one-out; CV, cross-validation; OVA, one-versus-all; MSI-H, high MSI; MSI-L, low MSI; GA/MLHD, genetic algorithm/maximum likelihood discriminant analysis.
6 The entire expression data set is available at www.omniarray.com/gastric_cancer.html.
7 P. Tan, unpublished observations.
8 P. Tan and O. L. Kon, unpublished observations.
Received 9/25/02. Accepted 4/11/03.
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TopABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSMolecular Subtypes of Gastric...Identification of Minimal...DISCUSSIONREFERENCES |
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2ß1 and 3ß1-integrin in the peritoneal implantation of scirrhous gastric carcinoma. Br. J. Cancer, 74: 1406-1412, 1996.[Medline]
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