Publications
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Baggaley AE, Lafaurie GBRC, Tate SJ, et al., 2023, Pressurized intraperitoneal aerosol chemotherapy (PIPAC): updated systematic review using the IDEAL framework, British Journal of Surgery, Vol: 110, Pages: 10-18, ISSN: 0007-1323
Savva K-V, Das B, Antonowicz S, et al., 2022, Progress with metabolomic blood tests for gastrointestinal cancer diagnosis-an assessment of biomarker translation, Cancer Epidemiology, Biomarkers and Prevention, Vol: 31, Pages: 2095-2105, ISSN: 1055-9965
There is an urgent need for cost-effective, non-invasive tools to detect early stages of gastrointestinal cancer (colorectal, gastric, and esophageal cancers). Despite many publications suggesting circulating metabolites acting as accurate cancer biomarkers, few have reached the clinic. In upper gastrointestinal cancer this is critically important, as there is no test to complement gold-standard endoscopic evaluation in patients with mild symptoms that do not meet referral criteria. Therefore, this study aimed to describe and solve this translational gap. Studies reporting diagnostic accuracy of metabolomic blood-based gastrointestinal cancer biomarkers from 2007 to 2020 were systematically reviewed and progress of each biomarker along the discovery–validation–adoption pathway was mapped. Successful biomarker translation was defined as a composite endpoint, including patent protection/FDA approval/recommendation in national guidelines. The review found 77 biomarker panels of gastrointestinal cancer, including 25 with an AUROC >0.9. All but one was stalled at the discovery phase, 9.09% were patented and none were clinically approved, confirming the extent of biomarker translational gap. In addition, there were numerous “re-discoveries,” including histidine, discovered in 7 colorectal studies. Finally, this study quantitatively supports the presence of a translational gap between discovery and clinical adoption, despite clear evidence of highly performing biomarkers with significant potential clinical value.
Case A, Prosser S, Peters CJ, et al., 2022, Pressurised intraperitoneal aerosolised chemotherapy (PIPAC) for gastric cancer with peritoneal metastases: a systematic review by the PIPAC UK collaborative, Critical Reviews in Oncology Hematology, Vol: 180, Pages: 1-16, ISSN: 1040-8428
INTRODUCTION: Gastric cancer with peritoneal metastases (GCPM) carries a poor prognosis. Pressurised Intraperitoneal Aerosolised Chemotherapy (PIPAC) offers pharmacokinetic advantages over intravenous therapy, resulting in higher chemotherapy concentrations in peritoneal deposits, and potentially reduced systemic absorption/toxicity. This review evaluates efficacy, tolerability and impact on quality of life (QOL) of PIPAC for GCPM. METHODS: Following registration with PROSPERO (CRD42021281500), MEDLINE, EMBASE and The Cochrane Library were searched for PIPAC in patients with peritoneal metastases, in accordance with PRISMA standards RESULTS: Across 18 included reports representing 751 patients with GCPM (4 prospective, 11 retrospective, 3 abstracts, no phase III studies), median overall survival (mOS) was 8 - 19.1 months, 1-year OS 49.8-77.9%, complete response (PRGS1) 0-35% and partial response (PRGS2/3) 0-83.3%. Grade 3 and 4 toxicity was 0.7-25% and 0-4.1% respectively. Three studies assessing QOL reported no significant difference. CONCLUSION: PIPAC may offer promising survival benefits, toxicity, and QOL for GCPM.
Nazarian S, Gkouzionis I, Kawka M, et al., 2022, Real-time tracking and classification of tumour and non-tumour tissue in upper gastrointestinal cancers using diffuse reflectance spectroscopy for resection margin assessment, JAMA Surgery, ISSN: 2168-6254
Importance:Cancers of the upper gastrointestinal tract remain a major contributor to the global cancer burden. The accurate mapping of tumour margins is of particular importance for curative cancer resection and improvement in overall survival. Current mapping techniques preclude a full resection margin assessment in real-time.Objective:We aimed to use diffuse reflectance spectroscopy on gastric and oesophageal cancer specimens to differentiate tissue types and provide real-time feedback to the operator.Design:This was a prospective ex vivo validation study. Patients undergoing oesophageal or gastric cancer resection were prospectively recruited into the study between July 2020 and July 2021 at Hammersmith Hospital in London, United Kingdom.Setting:This was a single-centre study based at a tertiary hospital.Participants:Tissue specimens were included for patients undergoing elective surgery for either oesophageal carcinoma (adenocarcinoma or squamous cell carcinoma) or gastric adenocarcinoma.Exposure:A hand-held diffuse reflectance spectroscopy probe and tracking system was used on freshly resected ex vivo tissue to obtain spectral data. Binary classification, following histopathological validation, was performed using four supervised machine learning classifiers. Main Outcomes and Measures:Data were divided into training and testing sets using a stratified 5-fold cross-validation method. Machine learning classifiers were evaluated in terms of sensitivity, specificity, overall accuracy, and the area under the curve.Results:A total of 14,097 mean spectra for normal and cancerous tissue were collected from 37 patients. The machine learning classifier achieved an overall normal versus cancer diagnostic accuracy of 93.86±0.66 for stomach tissue and 96.22±0.50 for oesophageal tissue, and sensitivity and specificity of 91.31% and 95.13% for stomach and 94.60% and 97.28% for oesophagus, respectively. Real-time tissue tracking and classification was achieved a
Elson D, Nazarian S, Gkouzionis I, et al., 2022, Real-time Classification of Colorectal Tissue Using Diffuse Reflectance Spectroscopy to Aid Margin Assessment, European Society of Coloproctology Scientific Conference
Salem V, Hirani D, Lloyd C, et al., 2022, Why are women still leaving academic medicine? A qualitative study within a London Medical School, BMJ Open, Vol: 12, ISSN: 2044-6055
Objectives: To identify factors that influenced women who chose to leave academic medicine.Design and main outcome measures: Independent consultants led a focus group of women in medicine who had left academia after completion of their postgraduate research degree at Imperial College London Faculty of Medicine. Thematic analysis was performed on the transcribed conversations.Participants and setting: Nine women physicians who completed a postgraduate degree (MD or PhD) at a large London Medical School and Academic Health Sciences Centre, Imperial College London, but did not go on to pursue a career in academic medicine.Results: Influences to leave clinical academia were summarised under eight themes—career intentions, supervisor support, institutional human resources support, inclusivity, work–life balance, expectations, mentors and role models, and pregnancy and maternity leave.Conclusion: The women in our focus group reported several factors contributing to their decision to leave clinical academia, which included lack of mentoring tailored to specific needs, low levels of acceptance for flexible working to help meet parental responsibilities and perceived explicit gender biases. We summarise the multiple targeted strategies that Imperial College London has implemented to promote retention of women in academic medicine, although more research needs to be done to ascertain the most effective interventions.
Boshier PR, Swaray A, Vadhwana B, et al., 2022, Systematic review and validation of clinical models predicting survival after oesophagectomy for adenocarcinoma, British Journal of Surgery, Vol: 109, Pages: 418-425, ISSN: 0007-1323
BACKGROUND: Oesophageal adenocarcinoma poses a significant global health burden, yet the staging used to predict survival has limited ability to stratify patients by outcome. This study aimed to identify published clinical models that predict survival in oesophageal adenocarcinoma and to evaluate them using an independent international multicentre dataset. METHODS: A systematic literature search (title and abstract) using the Ovid Embase and MEDLINE databases (from 1947 to 11 July 2020) was performed. Inclusion criteria were studies that developed or validated a clinical prognostication model to predict either overall or disease-specific survival in patients with oesophageal adenocarcinoma undergoing surgical treatment with curative intent. Published models were validated using an independent dataset of 2450 patients who underwent oesophagectomy for oesophageal adenocarcinoma with curative intent. RESULTS: Seventeen articles were eligible for inclusion in the study. Eleven models were suitable for testing in the independent validation dataset and nine of these were able to stratify patients successfully into groups with significantly different survival outcomes. Area under the receiver operating characteristic curves for individual survival prediction models ranged from 0.658 to 0.705, suggesting poor-to-fair accuracy. CONCLUSION: This study highlights the need to concentrate on robust methodologies and improved, independent, validation, to increase the likelihood of clinical adoption of survival predictions models.
Ng AWT, Contino G, Killcoyne S, et al., 2022, Rearrangement processes and structural variations show evidence of selection in oesophageal adenocarcinomas, Communications Biology, Vol: 5, ISSN: 2399-3642
Oesophageal adenocarcinoma (OAC) provides an ideal case study to characterize large-scale rearrangements. Using whole genome short-read sequencing of 383 cases, for which 214 had matched whole transcriptomes, we observed structural variations (SV) with a predominance of deletions, tandem duplications and inter-chromosome junctions that could be identified as LINE-1 mobile element (ME) insertions. Complex clusters of rearrangements resembling breakage-fusion-bridge cycles or extrachromosomal circular DNA accounted for 22% of complex SVs affecting known oncogenes. Counting SV events affecting known driver genes substantially increased the recurrence rates of these drivers. After excluding fragile sites, we identified 51 candidate new drivers in genomic regions disrupted by SVs, including ETV5, KAT6B and CLTC. RUNX1 was the most recurrently altered gene (24%), with many deletions inactivating the RUNT domain but preserved the reading frame, suggesting an altered protein product. These findings underscore the importance of identification of SV events in OAC with implications for targeted therapies.
Sivakumar J, Forshaw MJ, Lam S, et al., 2022, Identifying the limitations of cardiopulmonary exercise testing prior to esophagectomy using a pooled analysis of patient-level data, DISEASES OF THE ESOPHAGUS, Vol: 35, ISSN: 1120-8694
Gkouzionis I, Nazarian S, Kawka M, et al., 2022, Real-time tracking of a diffuse reflectance spectroscopy probe used to aid histological validation of margin assessment in upper gastrointestinal cancer resection surgery, Journal of Biomedical Optics, Vol: 27, ISSN: 1083-3668
Significance: Diffuse reflectance spectroscopy (DRS) allows discrimination of tissue type. Its application is limited by the inability to mark the scanned tissue and the lack of real-time measurements.Aim: This study aimed to develop a real-time tracking system to enable localization of a DRS probe to aid the classification of tumor and non-tumor tissue.Approach: A green-colored marker attached to the DRS probe was detected using hue-saturation-value (HSV) segmentation. A live, augmented view of tracked optical biopsy sites was recorded in real time. Supervised classifiers were evaluated in terms of sensitivity, specificity, and overall accuracy. A developed software was used for data collection, processing, and statistical analysis.Results: The measured root mean square error (RMSE) of DRS probe tip tracking was 1.18 ± 0.58 mm and 1.05 ± 0.28 mm for the x and y dimensions, respectively. The diagnostic accuracy of the system to classify tumor and non-tumor tissue in real time was 94% for stomach and 96% for the esophagus.Conclusions: We have successfully developed a real-time tracking and classification system for a DRS probe. When used on stomach and esophageal tissue for tumor detection, the accuracy derived demonstrates the strength and clinical value of the technique to aid margin assessment in cancer resection surgery.
Gkouzionis I, Nazarian S, Patel N, et al., 2022, Towards real-time upper gastrointestinal resection margin assessment using a diffuse reflectance spectroscopy probe
The use of a diffuse reflectance spectroscopy probe for real-time classification of stomach and oesophageal tissue specimen can aid resection margin assessment in upper gastrointestinal cancer surgery.
Gkouzionis I, Nazarian S, Darzi A, et al., 2022, Three-dimensional tissue reconstruction and tracking of a diffuse reflectance spectroscopy probe for real-time tissue classification in upper gastrointestinal cancer surgery, Photonics Europe: Clinical Biophotonics II
Gkouzionis I, Nazarian S, Kawka M, et al., 2022, Real-time tissue classification in stomach and oesophageal cancer based on optical tracking of a diffuse reflectance spectroscopy probe, Photonics West: Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XX
Gkouzionis I, Nazarian S, Darzi A, et al., 2022, Discriminating between cancer and healthy tissue in upper gastrointestinal cancer surgery using deep learning and diffuse reflectance spectroscopy, London Surgery Symposium
Elson D, Gkouzionis I, Nazarian S, et al., 2022, Real-time tracking of a diffuse reflectance spectroscopy probe for tissue classification in colorectal cancer surgery, Hamlyn Symposium on Medical Robotics Workshop on Sensing and biophotonics for surgical robotics and in vivo diagnostics
Elson D, Gkouzionis I, Nazarian S, et al., 2022, Using diffuse reflectance spectroscopy for real-time tissue assessment during upper gastrointestinal cancer surgery, IEEE International Conference on Biomedical and Health Informatics
Nazarian S, Gkouzionis I, Kawka M, et al., 2021, Real-time tracking and classification of tumour and non-tumour tissue in upper gastrointestinal cancer specimens using diffuse reflectance spectroscopy, UGI Congress 2021, ISSN: 0007-1323
Gkouzionis I, Nazarian S, Anandakumar A, et al., 2021, Using diffuse reflectance spectroscopy probe tracking to identify non-tumour and tumour tissue in upper gastrointestinal specimens, Translational Biophotonics: Diagnostics and Therapeutics, Publisher: SPIE
The use of a diffuse reflectance spectroscopy probe and tracking system was successfully used in real-time for automated tissue classification in upper gastrointestinal surgery to aid resection margin assessment.
Ococks E, Sharma S, Ng AWT, et al., 2021, Serial Circulating tumor DNA detection using a personalized, tumor-informed assay in esophageal adenocarcinoma patients following resection, Gastroenterology, Vol: 161, Pages: 1705-1708.e2, ISSN: 0016-5085
Savva K-V, Hage L, Belluomo I, et al., 2021, Assessment of the Burden of Small Intestinal Bacterial Overgrowth (SIBO) in Patients After Oesophagogastric (OG) Cancer Resection, JOURNAL OF GASTROINTESTINAL SURGERY, Vol: 26, Pages: 924-926, ISSN: 1091-255X
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Nazarian S, Gkouzionis I, Anandakumar A, et al., 2021, Using diffuse reflectance spectroscopy (DRS) to identify tumour and non-tumour tissue in upper gastrointestinal specimens, Association of Surgeons of Great Britain and Ireland Virtual Congress, Publisher: British Journal of Surgery Society, Pages: 41-41, ISSN: 0007-1323
AimCancers of the upper gastrointestinal (GI) tract remain a major contributor to the global cancer risk. Surgery aims to completely resect tumour with clear margins, whilst preserving as much surrounding tissue. Diffuse reflectance spectroscopy (DRS) is a novel technique that presents a promising advancement in cancer diagnosis. We have developed a novel DRS system with tracking capability. Our aim is to classify tumour and non-tumour GI tissue in real-time using this device to aid intra-operative analysis of resection margins.MethodAn ex-vivo study was undertaken in which data was collected from consecutive patients undergoing upper GI cancer resection surgery between August 2020- January 2021. A hand-held DRS probe and tracking system was used on normal and cancerous tissue to obtain spectral information. After acquisition of all spectra, a classification system using histopathology results was created. A user interface was developed using Python 3.6 and Qt5. A support vector machine was used to classify the results.ResultsThe data included 4974 normal spectra and 2108 tumour spectra. The overall accuracy of the DRS probe in differentiating normal versus tumour tissue was 88.08% for the stomach (sensitivity 84.8%, specificity 89.3%), and 91.42% for the oesophagus (sensitivity 76.3%, specificity 98.9%).ConclusionWe have developed a successful DRS system with tracking capability, able to process thousands of spectra in a small timeframe, which can be used in real-time to distinguish tumour and non-tumour tissue. This can be used for intra-operative decision-making during upper GI cancer surgery to help select the best resection plane.
Antonowicz S, Bodai Z, Wiggins T, et al., 2021, Endogenous aldehyde accumulation generates genotoxicity and exhaled biomarkers in esophageal adenocarcinoma, Nature Communications, Vol: 12, ISSN: 2041-1723
Volatile aldehydes are enriched in esophageal adenocarcinoma (EAC) patients’ breath and could improve early diagnosis, however the mechanisms of their production are unknown. Here, we show that weak aldehyde detoxification characterizes EAC, which is sufficient to cause endogenous aldehyde accumulation in vitro. Two aldehyde groups are significantly enriched in EAC biopsies and adjacent tissue: (i) short-chain alkanals, and (ii) medium-chain alkanals, including decanal. The short-chain alkanals form DNA-adducts, which demonstrates genotoxicity and confirms inadequate detoxification. Metformin, a putative aldehyde scavenger, reduces this toxicity. Tissue and breath concentrations of the medium-chain alkanal decanal are correlated, and increased decanal is linked to reduced ALDH3A2 expression, TP53 deletion, and adverse clinical features. Thus, we present a model for increased exhaled aldehydes based on endogenous accumulation from reduced detoxification, which also causes therapeutically actionable genotoxicity. These results support EAC early diagnosis trials using exhaled aldehyde analysis.
Gkouzionis I, Nazarian S, Kawka M, et al., 2021, The use of machine learning for real-time detection of oesophageal and gastric cancer based on diffuse reflectance spectroscopy: a validation study, Joint Congress of the European Society for Diseases of the Esophagus and the International Gastric Cancer Association European Chapter
Nazarian S, Gkouzionis I, Kawka M, et al., 2021, The use of a diffuse reflectance spectroscopy probe and tracking system to classify tumour and non-tumour tissue in upper gastrointestinal cancer specimens to aid margin assessment, London Surgical Symposium
Sharma A, Khan S, Chicco M, et al., 2020, New onset diabetes presenting with DKA , spontaneous pneumomediastinum and subcutaneous emphysema: a case series, Practical Diabetes, Vol: 37, Pages: 183-187, ISSN: 2047-2897
Knight WRC, McEwen R, Byrne BE, et al., 2020, Endoscopic tumour morphology impacts survival in adenocarcinoma of the oesophagus, European Journal of Surgical Oncology, ISSN: 0748-7983
BackgroundPrognostication in oesophageal cancer on the basis of preoperative variables is challenging. Many of the accepted predictors of survival are only derived after surgical treatment and may be influenced by neoadjuvant therapy. This study aims to explore the relationship between pre-treatment endoscopic tumour morphology and postoperative survival.MethodsPatients with endoscopic descriptions of tumours were identified from the prospectively managed databases including the OCCAMS database. Tumours were classified as exophytic, ulcerating or stenosing. Kaplan Meier survival analysis and multivariable Cox regression analyses were performed to determine hazard ratios (HR) with 95% confidence intervals.Results262 patients with oesophageal adenocarcinoma undergoing potentially curative resection were pooled from St Thomas’ Hospital (161) and the OCCAMS database (101). There were 70 ulcerating, 114 exophytic and 78 stenosing oesophageal adenocarcinomas. Initial tumour staging was similar across all groups (T3/4 tumours 71.4%, 70.2%, 74.4%). Median survival was 55 months, 51 months and 36 months respectively (p < 0.001). Rates of lymphovascular invasion (P = 0.0176), pathological nodal status (P = 0.0195) and pathological T stage (P = 0.0007) increased from ulcerating to exophytic to stenosing lesions. Resection margin positivity was 21.4% in ulcerating tumours compared to 54% in stenosing tumours (p < 0.001). When compared to stenosing lesions, exophytic and ulcerating lesions demonstrated a significant survival advantage on multivariable analysis (HR 0.56 95% CI 0.31–0.93, HR 0.42 95% CI 0.21–0.82).ConclusionThis study demonstrates that endoscopic morphology may be an important pre-treatment prognostic factor in oesophageal cancer. Ulcerating, exophytic and stenosing tumours may represent different pathological processes and tumour biology.
Rahman SA, Walker RC, Lloyd MA, et al., 2020, Machine learning to predict early recurrence after oesophageal cancer surgery., Br J Surg, Vol: 107, Pages: 1042-1052
BACKGROUND: Early cancer recurrence after oesophagectomy is a common problem, with an incidence of 20-30 per cent despite the widespread use of neoadjuvant treatment. Quantification of this risk is difficult and existing models perform poorly. This study aimed to develop a predictive model for early recurrence after surgery for oesophageal adenocarcinoma using a large multinational cohort and machine learning approaches. METHODS: Consecutive patients who underwent oesophagectomy for adenocarcinoma and had neoadjuvant treatment in one Dutch and six UK oesophagogastric units were analysed. Using clinical characteristics and postoperative histopathology, models were generated using elastic net regression (ELR) and the machine learning methods random forest (RF) and extreme gradient boosting (XGB). Finally, a combined (ensemble) model of these was generated. The relative importance of factors to outcome was calculated as a percentage contribution to the model. RESULTS: A total of 812 patients were included. The recurrence rate at less than 1 year was 29·1 per cent. All of the models demonstrated good discrimination. Internally validated areas under the receiver operating characteristic (ROC) curve (AUCs) were similar, with the ensemble model performing best (AUC 0·791 for ELR, 0·801 for RF, 0·804 for XGB, 0·805 for ensemble). Performance was similar when internal-external validation was used (validation across sites, AUC 0·804 for ensemble). In the final model, the most important variables were number of positive lymph nodes (25·7 per cent) and lymphovascular invasion (16·9 per cent). CONCLUSION: The model derived using machine learning approaches and an international data set provided excellent performance in quantifying the risk of early recurrence after surgery, and will be useful in prognostication for clinicians and patients.
Jammula S, Katz-Summercorn AC, Li X, et al., 2020, Identification of Subtypes of Barrett's Esophagus and Esophageal Adenocarcinoma Based on DNA Methylation Profiles and Integration of Transcriptome and Genome Data., Gastroenterology, Vol: 158, Pages: 1682-1697.e1
BACKGROUND & AIMS: Esophageal adenocarcinomas (EACs) are heterogeneous and often preceded by Barrett's esophagus (BE). Many genomic changes have been associated with development of BE and EAC, but little is known about epigenetic alterations. We performed epigenetic analyses of BE and EAC tissues and combined these data with transcriptome and genomic data to identify mechanisms that control gene expression and genome integrity. METHODS: In a retrospective cohort study, we collected tissue samples and clinical data from 150 BE and 285 EAC cases from the Oesophageal Cancer Classification and Molecular Stratification consortium in the United Kingdom. We analyzed methylation profiles of all BE and EAC tissues and assigned them to subgroups using non-negative matrix factorization with k-means clustering. Data from whole-genome sequencing and transcriptome studies were then incorporated; we performed integrative methylation and RNA-sequencing analyses to identify genes that were suppressed with increased methylation in promoter regions. Levels of different immune cell types were computed using single-sample gene set enrichment methods. We derived 8 organoids from 8 EAC tissues and tested their sensitivity to different drugs. RESULTS: BE and EAC samples shared genome-wide methylation features, compared with normal tissues (esophageal, gastric, and duodenum; controls) from the same patients and grouped into 4 subtypes. Subtype 1 was characterized by DNA hypermethylation with a high mutation burden and multiple mutations in genes in cell cycle and receptor tyrosine signaling pathways. Subtype 2 was characterized by a gene expression pattern associated with metabolic processes (ATP synthesis and fatty acid oxidation) and lack methylation at specific binding sites for transcription factors; 83% of samples of this subtype were BE and 17% were EAC. The third subtype did not have changes in methylation pattern, compared with control tissue, but had a gene expression pattern t
Gkouzionis IA, Avila-Rencoret F, Peters C, et al., 2020, Hyperspectral circumferential resection margin assessment for gastrointestinal cancer surgery, Biophotonics and Imaging Graduate Summer School 2020
Bornschein J, Wernisch L, Secrier M, et al., 2019, Transcriptomic profiling reveals three molecular phenotypes of adenocarcinoma at the gastroesophageal junction, International Journal of Cancer, Vol: 145, Pages: 3389-3401, ISSN: 0020-7136
Cancers occurring at the gastroesophageal junction (GEJ) are classified as predominantly esophageal or gastric, which is often difficult to decipher. We hypothesized that the transcriptomic profile might reveal molecular subgroups which could help to define the tumor origin and behavior beyond anatomical location. The gene expression profiles of 107 treatment‐naïve, intestinal type, gastroesophageal adenocarcinomas were assessed by the Illumina‐HTv4.0 beadchip. Differential gene expression (limma), unsupervised subgroup assignment (mclust) and pathway analysis (gage) were undertaken in R statistical computing and results were related to demographic and clinical parameters. Unsupervised assignment of the gene expression profiles revealed three distinct molecular subgroups, which were not associated with anatomical location, tumor stage or grade (p > 0.05). Group 1 was enriched for pathways involved in cell turnover, Group 2 was enriched for metabolic processes and Group 3 for immune‐response pathways. Patients in group 1 showed the worst overall survival (p = 0.019). Key genes for the three subtypes were confirmed by immunohistochemistry. The newly defined intrinsic subtypes were analyzed in four independent datasets of gastric and esophageal adenocarcinomas with transcriptomic data available (RNAseq data: OCCAMS cohort, n = 158; gene expression arrays: Belfast, n = 63; Singapore, n = 191; Asian Cancer Research Group, n = 300). The subgroups were represented in the independent cohorts and pooled analysis confirmed the prognostic effect of the new subtypes. In conclusion, adenocarcinomas at the GEJ comprise three distinct molecular phenotypes which do not reflect anatomical location but rather inform our understanding of the key pathways expressed.
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