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H R Ali,
M Irwin,
L Morris,
S-J Dawson,
F M Blows,
E Provenzano,
B Mahler-Araujo,
P D Pharoah,
N A Walton,
J D Brenton, C Caldas
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ABSTRACT: Background:High-throughput evaluation of tissue biomarkers in oncology has been greatly accelerated by the widespread use of tissue microarrays (TMAs) and immunohistochemistry. Although TMAs have the potential to facilitate protein expression profiling on a scale to rival experiments of tumour transcriptomes, the bottleneck and imprecision of manually scoring TMAs has impeded progress.Methods:We report image analysis algorithms adapted from astronomy for the precise automated analysis of IHC in all subcellular compartments. The power of this technique is demonstrated using over 2000 breast tumours and comparing quantitative automated scores against manual assessment by pathologists.Results:All continuous automated scores showed good correlation with their corresponding ordinal manual scores. For oestrogen receptor (ER), the correlation was 0.82, P<0.0001, for BCL2 0.72, P<0.0001 and for HER2 0.62, P<0.0001. Automated scores showed excellent concordance with manual scores for the unsupervised assignment of cases to 'positive' or 'negative' categories with agreement rates of up to 96%.Conclusion:The adaptation of astronomical algorithms coupled with their application to large annotated study cohorts, constitutes a powerful tool for the realisation of the enormous potential of digital pathology.British Journal of Cancer advance online publication, 17 January 2013; doi:10.1038/bjc.2012.558 www.bjcancer.com.
British Journal of Cancer 01/2013; · 5.04 Impact Factor
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G C Wishart,
C D Bajdik,
E Dicks,
E Provenzano,
M K Schmidt,
M Sherman,
D C Greenberg,
A R Green,
K A Gelmon,
V-M Kosma, [......],
L J van't Veer,
M Southey,
H Nevanlinna,
A Mannermaa,
A Cox,
M Cheang,
L Baglietto, C Caldas,
M Garcia-Closas,
P D P Pharoah
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ABSTRACT: Predict (www.predict.nhs.uk) is an online, breast cancer prognostication and treatment benefit tool. The aim of this study was to incorporate the prognostic effect of HER2 status in a new version (Predict+), and to compare its performance with the original Predict and Adjuvant!.
The prognostic effect of HER2 status was based on an analysis of data from 10 179 breast cancer patients from 14 studies in the Breast Cancer Association Consortium. The hazard ratio estimates were incorporated into Predict. The validation study was based on 1653 patients with early-stage invasive breast cancer identified from the British Columbia Breast Cancer Outcomes Unit. Predicted overall survival (OS) and breast cancer-specific survival (BCSS) for Predict+, Predict and Adjuvant! were compared with observed outcomes.
All three models performed well for both OS and BCSS. Both Predict models provided better BCSS estimates than Adjuvant!. In the subset of patients with HER2-positive tumours, Predict+ performed substantially better than the other two models for both OS and BCSS.
Predict+ is the first clinical breast cancer prognostication tool that includes tumour HER2 status. Use of the model might lead to more accurate absolute treatment benefit predictions for individual patients.
British Journal of Cancer 07/2012; 107(5):800-7. · 5.04 Impact Factor
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ABSTRACT: Proliferation has emerged as a major prognostic factor in luminal breast cancer. The immunohistochemical (IHC) proliferation marker Ki67 has been most extensively investigated but has not gained widespread clinical acceptance.
We have conducted a head-to-head comparison of a panel of proliferation markers, including Ki67. Our aim was to establish the marker of the greatest prognostic utility. Tumour samples from 3093 women with breast cancer were constructed as tissue microarrays. We used IHC to detect expression of mini-chromosome maintenance protein 2, Ki67, aurora kinase A (AURKA), polo-like kinase 1, geminin and phospho-histone H3. We used a Cox proportional-hazards model to investigate the association with 10-year breast cancer-specific survival (BCSS). Missing values were resolved using multiple imputation.
The prognostic significance of proliferation was limited to oestrogen receptor (ER)-positive breast cancer. Aurora kinase A emerged as the marker of the greatest prognostic significance in a multivariate model adjusted for the standard clinical and molecular covariates (hazard ratio 1.3; 95% confidence interval 1.1-1.5; P=0.005), outperforming all other markers including Ki67.
Aurora kinase A outperforms other proliferation markers as an independent predictor of BCSS in ER-positive breast cancer. It has the potential for use in routine clinical practice.
British Journal of Cancer 04/2012; 106(11):1798-806. · 5.04 Impact Factor
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Breast Cancer Research 04/2012; 10:1-2. · 5.33 Impact Factor
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X Y Goh,
J R E Rees,
A L Paterson,
S F Chin,
J C Marioni,
V Save,
M O'Donovan,
P P Eijk,
D Alderson,
B Ylstra, C Caldas,
R C Fitzgerald
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ABSTRACT: The incidence of oesophageal adenocarcinoma (OAC) has been increasing rapidly with a dismal survival rate of less than 20%. Understanding the genomic aberrations and biology of this cancer may enhance disease interventions. This study aimed to use genome-wide genomic and expression data to enhance the understanding of OAC pathogenesis and identify groups with differential outcomes.
Array-comparative genomic hybridisation (aCGH) analysis was carried out on 56 fresh frozen OAC resection samples with long-term clinical follow-up data. Samples with aberrations were further analysed with whole-genome single-nucleotide polymorphism arrays to confirm aCGH findings. Matched gene expression microarray data were used to identify genes with high copy number-expression correlations. Nested-multiplex PCR on DNA from microdissected specimens and fluorescence in situ hybridisation assays were used for target validation. Immunohistochemistry on the same cohort and independent samples (n=371) was used for subsequent validation. Kaplan-Meier survival analyses were performed based on aCGH data after unsupervised K-means clustering (K=5, 50 iterations) and immunohistochemistry data.
aCGH identified 17 common regions (>5% samples) of gains and 11 common regions of losses, including novel regions in OAC (loci 11p13 and 21q21.2). Integration of aCGH data with matched gene expression microarray data highlighted genes with high copy number-expression correlations: two deletions (p16/CDKN2A, MBNL1) and four gains (EGFR, WT1, NEIL2, MTMR9). Immunohistochemistry demonstrated protein over-expression of targets with gains: EGFR (10%), WT1 (20%), NEIL2 (14%) and MTMR9 (25%). These targets individually (p<0.060) and in combination had prognostic significance (p=0.008). On the genomic level, K-means clustering identified a cluster (32% of cohort) with differential log(2) ratios of 16 CGH probes (p<4×10(-7)) and a worse prognosis (median survival=1.37 years; p=0.015).
Integration of aCGH and gene expression data identified copy number aberrations and novel genes with prognostic potential in OAC.
Gut 04/2011; 60(10):1317-26. · 10.11 Impact Factor
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ABSTRACT: Predict (www.predict.nhs.uk) is a prognostication and treatment benefit tool developed using UK cancer registry data. The aim of this study was to compare the 10-year survival estimates from Predict with observed 10-year outcome from a British Columbia dataset and to compare the estimates with those generated by Adjuvant! (www.adjuvantonline.com).
The analysis was based on data from 3140 patients with early invasive breast cancer diagnosed in British Columbia, Canada, from 1989-1993. Demographic, pathologic, staging and treatment data were used to predict 10-year overall survival (OS) and breast cancer specific survival (BCSS) using Adjuvant! and Predict models. Predicted outcomes from both models were then compared with observed outcomes.
Calibration of both models was excellent. The difference in total number of deaths estimated by Predict was 4.1 percent of observed compared to 0.7 percent for Adjuvant!. The total number of breast cancer specific deaths estimated by Predict was 3.4 percent of observed compared to 6.7 percent for Adjuvant! Both models also discriminate well with similar AUC for Predict and Adjuvant! respectively for both OS (0.709 vs 0.712) and BCSS (0.723 vs 0.727). Neither model performed well in women aged 20-35.
In summary Predict provided accurate overall and breast cancer specific survival estimates in the British Columbia dataset that are comparable with outcome estimates from Adjuvant! Both models appear well calibrated with similar model discrimination. This study provides further validation of Predict as an effective predictive tool following surgery for invasive breast cancer.
European journal of surgical oncology: the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology 03/2011; 37(5):411-7. · 2.56 Impact Factor
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ABSTRACT: Tissue micro-arrays (TMAs) are increasingly used to generate data of the molecular phenotype of tumours in clinical epidemiology studies, such as studies of disease prognosis. However, TMA data are particularly prone to missingness. A variety of methods to deal with missing data are available. However, the validity of the various approaches is dependent on the structure of the missing data and there are few empirical studies dealing with missing data from molecular pathology. The purpose of this study was to investigate the results of four commonly used approaches to handling missing data from a large, multi-centre study of the molecular pathological determinants of prognosis in breast cancer.
We pooled data from over 11,000 cases of invasive breast cancer from five studies that collected information on seven prognostic indicators together with survival time data. We compared the results of a multi-variate Cox regression using four approaches to handling missing data - complete case analysis (CCA), mean substitution (MS) and multiple imputation without inclusion of the outcome (MI-) and multiple imputation with inclusion of the outcome (MI+). We also performed an analysis in which missing data were simulated under different assumptions and the results of the four methods were compared.
Over half the cases had missing data on at least one of the seven variables and 11 percent had missing data on 4 or more. The multi-variate hazard ratio estimates based on multiple imputation models were very similar to those derived after using MS, with similar standard errors. Hazard ratio estimates based on the CCA were only slightly different, but the estimates were less precise as the standard errors were large. However, in data simulated to be missing completely at random (MCAR) or missing at random (MAR), estimates for MI+ were least biased and most accurate, whereas estimates for CCA were most biased and least accurate.
In this study, empirical results from analyses using CCA, MS, MI- and MI+ were similar, although results from CCA were less precise. The results from simulations suggest that in general MI+ is likely to be the best. Given the ease of implementing MI in standard statistical software, the results of MI+ and CCA should be compared in any multi-variate analysis where missing data are a problem.
British Journal of Cancer 02/2011; 104(4):693-9. · 5.04 Impact Factor
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S J Dawson,
N Makretsov,
F M Blows,
K E Driver,
E Provenzano,
J Le Quesne,
L Baglietto,
G Severi,
G G Giles,
C A McLean,
G Callagy,
A R Green,
I Ellis,
K Gelmon,
G Turashvili,
S Leung,
S Aparicio,
D Huntsman, C Caldas,
P Pharoah
British Journal of Cancer 09/2010; 103(7):1137. · 5.04 Impact Factor
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ABSTRACT: This paper describes 'PathGrid'--an analysis and data integration system, developed initially to meet the demands in the analysis of medical microscopy imaging data. An overview of the current system is given, describing the techniques used in developing the data handling infrastructure and the analysis algorithm development. The use of software created in the context of systems designed for the astronomy domain is noted, specifically infrastructure from the astronomy virtual observatory movement for data discovery, access and workflow management, and astronomical image analysis software adapted for the analysis of high-throughput astronomy imaging surveys. This paper notes the applicability of the techniques from the astronomy domain. The testbed infrastructure deployment is described, emphasizing its speed and ease of use and support. The validity of the analysis techniques is confirmed through the pilot study described here--with the application to a large sample of immunohistochemistry microscopy data obtained in part for assessing the oestrogen receptor status of breast cancers. The analysis showed that the specificity and sensitivity values for the automatic scoring using PathGrid were within the errors of those obtained via a 'gold standard' manual pathologist scoring.
Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences 08/2010; 368(1925):3937-52. · 2.77 Impact Factor
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S-J Dawson,
N Makretsov,
F M Blows,
K E Driver,
E Provenzano,
J Le Quesne,
L Baglietto,
G Severi,
G G Giles,
C A McLean,
G Callagy,
A R Green,
I Ellis,
K Gelmon,
G Turashvili,
S Leung,
S Aparicio,
D Huntsman, C Caldas,
P Pharoah
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ABSTRACT: Background: Breast cancer is heterogeneous and the existing prognostic classifiers are limited in accuracy, leading to unnecessary treatment of numerous women. B-cell lymphoma 2 (BCL2), an antiapoptotic protein, has been proposed as a prognostic marker, but this effect is considered to relate to oestrogen receptor (ER) status. This study aimed to test the clinical validity of BCL2 as an independent prognostic marker.
British Journal of Cancer 07/2010; 103(5):668-675. · 5.04 Impact Factor
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ABSTRACT: A synonymous single nucleotide polymorphism (SNP) rs172378 (A>G, Gly->Gly) in the complement component C1QA has been proposed to be associated with distant breast cancer metastasis. We previously reported overexpression of this gene to be significantly associated with better prognosis in oestrogen-receptor-negative tumours. The purpose of this study was to investigate the association of rs172378 with expression of C1QA and breast cancer survival.
We analysed the gene expression pattern of rs172378 in normal and tumour tissue samples, and further explored its involvement in relation to mortality in 2270 women with breast cancer participating in Studies of Epidemiology and Risk factors in Cancer Heredity, a population-based case-control study.
We found that although rs172378 showed differential allelic expression significantly different between normal (preferentially expressing the G allele) and tumour tissue samples (preferentially expressing the A allele), there was no significant difference in survival by rs172378 genotype (per allele hazard ratio (HR) 1.02, 95% CI: 0.88-1.19, P=0.78 for all-cause mortality; HR 1.03, 95% CI: 0.87-1.22, P=0.72 for breast-cancer-specific mortality).
Our study results show that rs172378 is linked to a cis-regulatory element affecting gene expression and that allelic preferential expression is altered in tumour samples, but do not support an association between genetic variation in C1QA and breast cancer survival.
British Journal of Cancer 03/2010; 102(8):1294-9. · 5.04 Impact Factor
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Y L Chua,
Y Ito,
J C M Pole,
S Newman,
S-F Chin,
R C Stein,
I O Ellis, C Caldas,
M J O'Hare,
A Murrell,
P A W Edwards
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ABSTRACT: Neuregulin-1 (NRG1) is both a candidate oncogene and a candidate tumour suppressor gene. It not only encodes the heregulins and other mitogenic ligands for the ERBB family, but also causes apoptosis in NRG1-expressing cells. We found that most breast cancer cell lines had reduced or undetectable expression of NRG1. This included cell lines that had translocation breaks in the gene. Similarly, expression in cancers was generally comparable to or less than that in various normal breast samples. Many non-expressing cell lines had extensive methylation of the CpG island at the principal transcription start site at exon 2 of NRG1. Expression was reactivated by demethylation. Many tumours also showed methylation, whereas normal mammary epithelial fragments had none. Lower NRG1 expression correlated with higher methylation. Small interfering RNA (siRNA)-mediated depletion of NRG1 increased net proliferation in a normal breast cell line and a breast cancer cell line that expressed NRG1. The short arm of chromosome 8 is frequently lost in epithelial cancers, and NRG1 is the most centromeric gene that is always affected. NRG1 may therefore be the major tumour suppressor gene postulated to be on 8p: it is in the correct location, is antiproliferative and is silenced in many breast cancers.
Oncogene 10/2009; 28(46):4041-52. · 6.37 Impact Factor
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ABSTRACT: Despite the perception of many oncologists that tamoxifen is an inferior drug, and should be substituted by an aromatase inhibitor in post-menopausal women, the current evidence strongly supports the view that AIs should be used 2-3 years after tamoxifen to achieve the maximal overall survival (OS) advantage.
British Journal of Cancer 09/2009; 101(6):875-8. · 5.04 Impact Factor
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ABSTRACT: Triple negative breast cancers are defined by the absence of oestrogen, progesterone and HER2 expression. Most triple negative cancers display distinct clinical and pathological characteristics with a high proportion of these tumours occurring at a younger age of onset and in African-American women. Triple negative tumours typically demonstrate high histological grade and are the most common breast cancer subtype in BRCA1 carriers. In addition, many of the features of triple negative cancers are similar to those identified in the basal-like molecular subtype which has recently been characterised by gene expression profiling. Although the two groups overlap, they are not synonymous. Triple negative breast cancers are of pivotal clinical importance given the lack of therapeutic options. The prognostic significance of triple negative tumours remains unclear since the group is heterogeneous and worst prognosis seems to be mostly confined to those that express basal cytokeratins or epidermal growth factor receptor (EGFR). This review focuses on outlining the pathological, molecular, and clinical features of triple negative breast cancers, discusses its prognostic value and summarises current therapeutic approaches and future directions of research.
European journal of cancer (Oxford, England: 1990) 09/2009; 45 Suppl 1:27-40. · 4.12 Impact Factor
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ABSTRACT: Several recent studies have shown that screen detection remains an independent prognostic factor after adjusting for disease stage at presentation. This study compares the molecular characteristics of screen-detected with symptomatic breast cancers to identify if differences in tumour biology may explain some of the survival benefit conferred by screen detection.
A total of 1379 women (aged 50-70 years) with invasive breast cancer from a large population-based case-control study were included in the analysis. Individual patient data included tumour size, grade, lymph node status, adjuvant therapy, mammographic screening status and mortality. Immunohistochemistry was performed on tumour samples using 11 primary antibodies to define five molecular subtypes. The effect of screen detection compared with symptomatic diagnosis on survival was estimated after adjustment for grade, nodal status, Nottingham Prognostic Index (NPI) and the molecular markers.
Fifty-six per cent of the survival benefit associated with screen-detected breast cancer was accounted for by a shift in the NPI, a further 3-10% was explained by the biological variables and more than 30% of the effect remained unexplained.
Currently known biomarkers remain limited in their ability to explain the heterogeneity of breast cancer fully. A more complete understanding of the biological profile of breast tumours will be necessary to assess the true impact of tumour biology on the improvement in survival seen with screen detection.
British Journal of Cancer 09/2009; 101(8):1338-44. · 5.04 Impact Factor
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P Britton,
S W Duffy,
R Sinnatamby,
M G Wallis,
S Barter,
M Gaskarth,
A O'Neill, C Caldas,
J D Brenton,
P Forouhi,
G C Wishart
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ABSTRACT: The aim of this study was to estimate the number of patients discharged from a symptomatic breast clinic who subsequently develop breast cancer and to determine how many of these cancers had been 'missed' at initial assessment. Over a 3-year period, 7004 patients were discharged with a nonmalignant diagnosis. Twenty-nine patients were subsequently diagnosed with breast cancer over the next 36 months. This equates to a symptomatic 'interval' cancer rate of 4.1 per 1000 women in the 36 months after initial assessment (0.9 per 1000 women within 12 months, 2.6 per 1000 women within 24 months). The lowest sensitivity of initial assessment was seen in patients of 40-49 years of age, and these patients present the greatest imaging and diagnostic challenge. Following multidisciplinary review, a consensus was reached on whether a cancer had been missed or not. No delay occurred in 10 patients (35%) and probably no delay in 7 patients (24%). Possible delay occurred in three patients (10%) and definite delay in diagnosis (i.e., a 'missed' cancer) occurred in only nine patients (31%). The overall diagnostic accuracy of 'triple' assessment is 99.6% and the 'missed' cancer rate is 1.7 per 1000 women discharged.
British Journal of Cancer 06/2009; 100(12):1873-8. · 5.04 Impact Factor
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ABSTRACT: The last decade has witnessed unprecedented developments in the genetic and epigenetic analyses of solid tumours. Transcriptional and DNA copy-number studies have improved our understanding and classification of solid tumours and highlighted the patterns of genomic aberrations associated with outcome. The identification of altered transcriptional and translational silencing by microRNAs and epigenetic modification by methylation in tumours has showed a layer of additional intricacy to the regulation of gene expression in different tumour types. The advent of massive parallel sequencing has allowed whole cancer genomes to be sequenced with extraordinary speed and accuracy providing insight into the bewildering complexity of gene mutations present in solid tumours. Functional genomic studies using RNA interference-screening tools promises to improve the classification of solid tumours by probing the relevance of each gene to tumour phenotype. In this review, we discuss how these studies have contributed to solid tumour classification and why such studies are central to the future of oncology. We suggest that these developments are gradually leading to a change in emphasis of early clinical trials to a therapeutic model guided by the molecular classification of tumours. The investigation of drug efficacy later in development is beginning to rely on patient selection defined by predictive molecular criteria that complement solid tumour classification based on anatomic site.
British Journal of Cancer 05/2009; 100(10):1517-22. · 5.04 Impact Factor
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J B A Crusius,
F Canzian,
G Capellá,
A S Peña,
G Pera,
N Sala,
A Agudo,
F Rico,
G Del Giudice,
D Palli, [......],
P H M Peeters,
M E Numans,
F Clavel-Chapelon,
A Trichopoulou,
E Lund,
M Jenab,
S Rinaldi,
P Ferrari,
E Riboli,
C A González
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ABSTRACT: The relative contribution to gastric cancer (GC) risk of variants in genes that determine the inflammatory response remains mostly unknown and results from genotyping studies are inconsistent.
A nested case-control study within the prospective European Prospective Investigation into Cancer and Nutrition cohort was carried out, including 248 gastric adenocarcinomas and 770 matched controls. Twenty common polymorphisms at cytokine genes [interleukin (IL)1A, IL1B, IL1RN, IL4, IL4R, IL6, IL8, IL10, IL12A, IL12B, lymphotoxin alpha and tumor necrosis factor (TNF)] were analyzed. Antibodies against Helicobacter pylori (Hp) and CagA were measured.
IL1RN 2R/2R genotype [odds ratio (OR) 2.43; 95% confidence interval (CI) 1.19-4.96] and allele IL1RN Ex5-35C were associated with an increased risk of Hp(+) non-cardia GC. IL8 -251AA genotype was associated with a decreased risk of Hp(+) non-cardia GC (OR 0.51; 95% CI 0.32-0.81), mainly of the intestinal type. These associations were not modified by CagA status. Carriers of IL1B -580C and TNF -487A alleles did not associate with an increased risk. A moderately increased risk of Hp(+) non-cardia GC for IL4R -29429T variant was observed (OR 1.74; 95% CI 1.15-2.63).
This prospective study confirms the association of IL1RN polymorphisms with the risk of non-cardia GC and indicates that IL8 -251T>A may modify the risk for GC.
Annals of Oncology 11/2008; 19(11):1894-902. · 6.43 Impact Factor
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M Barber,
A Murrell,
Y Ito,
A-T Maia,
S Hyland,
C Oliveira,
V Save,
F Carneiro,
A L Paterson,
N Grehan,
S Dwerryhouse,
P Lao-Sirieix, C Caldas,
R C Fitzgerald
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ABSTRACT: Around 25-40% of cases of hereditary diffuse gastric cancer (HDGC) are caused by heterozygous E-cadherin (CDH1) germline mutations. The mechanisms for loss of the second allele still remain unclear. The aims of this study were to elucidate mechanisms for somatic inactivation of the wild-type CDH1 allele and to seek evidence for cadherin switching. Archival tumour material was analysed from 16 patients with CDH1 germline mutations and seven patients fulfilling HDGC criteria without CDH1 germline mutations. The 16 CDH1 exons were sequenced. E-cadherin promoter methylation was analysed by bisulphite sequencing and pyrosequencing and allele specificity was determined using polymorphic loci. Loss of heterozygosity was analysed using microsatellite markers. Cadherin expression levels were determined by real-time RT-PCR and immunohistochemistry. Six of 16 individuals with germline mutations had at least one second hit mechanism. Two exonic mutations (exon 9 truncating, exon 3 missense) and four intronic mutations which may affect splicing were identified. Tumours from 4/16 individuals had promoter hypermethylation that was restricted to the A allele haplotype in three cases. E-cadherin loss (mRNA and protein) generally correlated with identification of a second hit. In cases without germline E-cadherin mutations there was no evidence for somatic mutation or significant promoter methylation. P-cadherin (>25% cells) was expressed in 7/13 (54%) and 4/5 (80%) with and without germline CDH1 mutations, respectively, independent of complete E-cadherin loss. Overall, inactivation of the second CDH1 allele occurs by mutation and methylation events. Methylation is commonly allele-specific and is uncommon without germline mutations. P-cadherin over-expression commonly occurs in individuals with diffuse type gastric cancer.
The Journal of Pathology 09/2008; 216(3):295-306. · 6.32 Impact Factor
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ABSTRACT: Hereditary diffuse gastric cancer (HDGC) is caused by germline E-cadherin (CDH1) mutations in 25-40% of tested families. Management options for asymptomatic mutation carriers are fraught, since endoscopic surveillance can miss cancer foci and prophylactic gastrectomy has profound clinical sequelae. The aims of this study were to evaluate the impact of current surveillance practices on pre-operative diagnosis and to characterize the microscopic lesions in gastrectomy specimens to better inform clinical practice. Histological assessment and mapping of endoscopic surveillance and gastrectomy specimens were performed for eight asymptomatic CDH1 mutation carriers. E-cadherin expression and proliferation were analysed and evidence of epithelial-mesenchymal transition (EMT) was sought by immunohistochemistry for vimentin and cytokeratin 8/18. Four of eight patients had lesions detected at endoscopic surveillance. A median of 20.5 (range 0-66) signet ring foci were identified per gastrectomy (including in situ lesions and pagetoid spread). Foci were predominantly identified in the fundus and body (90% endoscopic biopsies and 85% in gastrectomy). The likelihood of detecting foci pre-operatively was positively correlated with the number of biopsies taken and the number of lesions in the gastrectomy specimen. E-cadherin expression in gastrectomy specimens was reduced or absent in all of the foci compared with the intervening gastric tissue, suggesting that these lesions are polyclonal. The foci had a low proliferative index (<2%) and there was no evidence for EMT. Multiple endoscopic biopsy sampling of the gastric mucosa increases the yield of microscopic cancer foci. The low proliferative index and lack of EMT suggests that these foci may represent an indolent stage of HDGC.
The Journal of Pathology 08/2008; 216(3):286-94. · 6.32 Impact Factor
