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Pseudomyxoma peritonei (PMP) is a rare, progressive, slowly growing neoplastic condition which is poorly understood, with a 5-year progression-free survival rate as low as 48%. PMP is most commonly caused by appendiceal mucinous neoplasms (AMN), and understanding their genetic biology and pathogenicity may allow for the development of better novel systemic treatments to target key deleterious mutations and the implicated pathways. The primary aim of this systematic review was to identify the genetic profile of histologically confirmed human PMP or AMN samples. The secondary aim was to identify whether genetic marks could be used to predict patient survival. Ovid EMBASE, Ovid MEDLINE, PubMed, and Web of Science were searched to identify studies investigating the genetic profile of histologically-confirmed human PMP or AMN samples. We review findings of 46 studies totalling 2181 tumour samples. The most frequently identified somatic gene mutations in patients with PMP included KRAS (38–100%), GNAS (17–100%), and TP53 (5–23%); however, there were conflicting results of their effect on survival. Three studies identified molecular subtypes based on gene expression profiles classifying patients into oncogene-enriched, immune-enriched, and mixed molecular subtypes with prognostic value. This review summarises the current literature surrounding genetic aberrations in PMP and AMNs and their potential utility for targeted therapy. Given the recent advances in clinical trials to directly target KRAS and GNAS mutations in other cancers, we propose a rationale to explore these mutations in future pre-clinical studies in PMP with a view for a future clinical trial. Graphical Abstract

Background: Esophageal adenocarcinoma (EAC) is usually resistant to cytotoxic therapies and immunotherapies have gained little traction. Cancer-associated fibroblasts (CAF) are a major stromal cell population in the EAC tumor microenvironment associated with prognosis and treatment outcomes. Recent evidence suggests that cancer cell phenotypes related to EMT may determine CAF heterogeneity, but the molecular and cellular biology that underlies myofibroblast fate in EAC is not well understood. Methods: To obtain the most comprehensive profile of CAF heterogeneity in EAC, we performed histopathologic, single-cell RNA sequencing and transcriptomic analyses on 28 samples from 26 patients and prognostic validation using two EAC cohorts from genomic consortia. Results: Combining histologic and molecular profiles revealed five CAF phenotypes, including three myofibroblast phenotypes, associated with EMT-related signatures in EAC cells and cellular interactions that promote tumor progression and metastasis. We identified a specific myofibroblast subtype (CAF5) in close proximity to cancer cells and tumor vasculature with exclusive expression of TRPA1, offering a potential therapeutic vulnerability. We reconstructed CAF differentiation trajectories from esophagus-resident universal fibroblasts to identify candidate genes central to the CAF phenotype, and used this knowledge to construct a combined EMT-myofibroblast four gene signature (GSN, ATF1, ZEB2 and POSTN) that was highly prognostic in EAC and several other solid tumors. Conclusions: This study stratifies EAC patients into two prognostic groups and reports important data supporting a cancer – CAF signalling axis, promoting myofibroblastic differentiation, that may inform more effective treatment strategies.

Background The complexity of the upper gastrointestinal (UGI) multidisciplinary team (MDT) is continually growing, leading to rising clinician workload, time pressures, and demands. This increases heterogeneity or ‘noise’ within decision-making for patients with oesophageal cancer (OC) and may lead to inconsistent treatment decisions. In recent decades, the application of artificial intelligence (AI) and more specifically the branch of machine learning (ML) has led to a paradigm shift in the perceived utility of statistical modelling within healthcare. Within oesophageal cancer (OC) care, ML techniques have already been applied with early success to the analyses of histological samples and radiology imaging; however, it has not yet been applied to the MDT itself where such models are likely to benefit from incorporating information-rich, diverse datasets to increase predictive model accuracy. Methods This review discusses the current role the MDT plays in modern UGI cancer care as well as the utilisation of ML techniques to date using histological and radiological data to predict treatment response, prognostication, nodal disease evaluation, and even resectability within OC. Results The review finds that an emerging body of evidence is growing in support of ML tools within multiple domains relevant to decision-making within OC including automated histological analysis and radiomics. However, to date, no specific application has been directed to the MDT itself which routinely assimilates this information. Conclusions The authors feel the UGI MDT offers an information-rich, diverse array of data from which ML offers the potential to standardise, automate, and produce more consistent, data-driven MDT decisions.

Background: Sarcopenia (low skeletal muscle mass), myosteatosis (low skeletal muscle radiation-attenuation) and fitness are independently associated with postoperative outcomes in oesophago-gastric cancer. This study aimed to investigate (1) the effect of neoadjuvant therapy (NAT) on sarcopenia, myosteatosis and cardiopulmonary exercise testing (CPET), (2) the relationship between these parameters, and (3) their association with postoperative morbidity and survival.Methods: Body composition analysis used single slice computed tomography (CT) images from chest (superior to aortic arch) and abdominal CT scans (third lumbar vertebrae). Oxygen uptake at anaerobic threshold (VO2 at AT) and at peak exercise (VO2 Peak) were measured using CPET. Measurements were performed before and after NAT and an adjusted regression model assessed their association.Results: Of the 184 patients recruited, 100 underwent surgical resection. Following NAT skeletal muscle mass, radiation-attenuation and fitness reduced significantly (p < 0.001). When adjusted for age, sex, and body mass index, only pectoralis muscle mass was associated with VO2 Peak (p = 0.001). VO2 at AT and Peak were associated with 1-year survival, while neither sarcopenia nor myosteatosis were associated with morbidity or survival.Conclusion: Skeletal muscle and CPET variables reduced following NAT and were positively associated with each other. Cardiorespiratory function significantly contributes to short-term survival after oesophago-gastric cancer surgery.Keywords: body composition; myosteatosis; neoadjuvant therapy; oxygen uptake; physical fitness; sarcopenia.

Background No well validated and contemporaneous tools for personalized prognostication of gastric adenocarcinoma exist. This study aimed to derive and validate a prognostic model for overall survival after surgery for gastric adenocarcinoma using a large national dataset. Methods National audit data from England and Wales were used to identify patients who underwent a potentially curative gastrectomy for adenocarcinoma of the stomach. A total of 2931 patients were included and 29 clinical and pathological variables were considered for their impact on survival. A non-linear random survival forest methodology was then trained and validated internally using bootstrapping with calibration and discrimination (time-dependent area under the receiver operator curve (tAUC)) assessed. Results The median survival of the cohort was 69 months, with a 5-year survival of 53.2 per cent. Ten variables were found to influence survival significantly and were included in the final model, with the most important being lymph node positivity, pT stage and achieving an R0 resection. Patient characteristics including ASA grade and age were also influential. On validation the model achieved excellent performance with a 5-year tAUC of 0.80 (95 per cent c.i. 0.78 to 0.82) and good agreement between observed and predicted survival probabilities. A wide spread of predictions for 3-year (14.8–98.3 (i.q.r. 43.2–84.4) per cent) and 5-year (9.4–96.1 (i.q.r. 31.7–73.8) per cent) survival were seen. Conclusions A prognostic model for survival after a potentially curative resection for gastric adenocarcinoma was derived and exhibited excellent discrimination and calibration of predictions.

Prognostication 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.Patients 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.262 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).This 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.

Background: esophageal cancer is increasingly common and carries a poor prognosis. The optimal treatment modality for locally advanced cancer is unknown, with current guidance recommending either neoadjuvant chemotherapy (CT) or chemoradiotherapy (CRT) followed by surgery. There is a lack of adequately powered trials comparing CT against CRT. We retrospectively compared CT versus CRT using a propensity score weighting approach.Methods: demographic, disease, treatment and outcome data were retrieved from a local database for patients who received neoadjuvant CT or CRT followed by surgery. Inverse probability of treatment weighting (IPTW) was used to balance groups using a propensity score-weighting approach. Groups were assessed for differences in postoperative outcomes and survival. Kaplan-Meier and non-parametric tests were used to compare survival and outcome data as appropriate.Results: data for 284 patients were retrieved. Following IPTW groups were well matched. No significant differences were seen for postoperative complications (CT 64.9% vs. CRT 63.3%, p=0.807), including major complications (24.0% vs. 23.6%, p=0.943) and anastomotic leak (7.8% vs. 5.6%, p=0.526). Significantly higher rates of clinical regression and complete pathological response were seen following CRT (p=0.002 for both). Rates of R0 resection were higher with CRT, CT 79.1% vs. CRT 93.1%, p=0.006. There was no difference between groups for overall or disease-free survival.Conclusion: this study suggests that the significant improvements in local tumour response seen after neoadjuvant CRT compared to CT may not translate to different survival outcomes. However, it must be stressed that adequately powered prospective trials are sti

The chemotherapy resistance of esophageal adenocarcinomas (EACs) is underpinned by cancer cell extrinsic mechanisms of the tumor microenvironment (TME). We demonstrate that, by targeting the tumor-promoting functions of the predominant TME cell type, cancer-associated fibroblasts (CAFs) with phosphodiesterase type 5 inhibitors (PDE5i), we can enhance the efficacy of standard-of-care chemotherapy. In ex vivo conditions, PDE5i prevent the transdifferentiation of normal fibroblasts to CAF and abolish the tumor-promoting function of established EAC CAFs. Using shotgun proteomics and single-cell RNA-seq, we reveal PDE5i-specific regulation of pathways related to fibroblast activation and tumor promotion. Finally, we confirm the efficacy of PDE5i in combination with chemotherapy in close-to-patient and in vivo PDX-based model systems. These findings demonstrate that CAFs drive chemotherapy resistance in EACs and can be targeted by repurposing PDE5i, a safe and well-tolerated class of drug administered to millions of patients world-wide to treat erectile dysfunction.

For patients with esophageal cancer, producing accurate prediction models for long-term survival after esophagectomy has proved challenging. We investigated whether Random Survival Forests (RSF), a machine learning method, could produce an accurate prognostic model for overall survival after esophagectomy. Methods The study used data from the 'National Oesophago-Gastric Cancer Audit' (NOGCA) and included patients diagnosed with esophageal adenocarcinoma or squamous cell carcinoma between 2012 and 2018 in England and Wales and who underwent a curative esophagectomy with adequate lymphadenectomy (15 lymph nodes) and survived to discharge (n = 6838). Missing data was handled using multiple imputation. 15 variables were selected for inclusion using Random Forest variable importance and used to train the final model. The same variables with non-linearity transformations were used to develop a traditional Cox regression model for comparison. Results Median survival was 50 months. The final RSF model had good discrimination on internal validation with a C-index of 0.7627 (0.7625–0.7629), exceeding the cox model 0.7539 (0.7541–0.7537). At 3 years post-surgery, overall survival was 56.2%. The RSF yielded a mean predicted survival of 59.3% (IQR 33.3–87.1%) with good calibration (Figure 1) compared to 57.4% (38.4%–79.8%) for the cox model. The most influential variables were lymph node involvement and pT/ypT stage, however other variables including neoadjuvant treatment completion and surgical complications were also important. Decision curve analysis was undertaken which also showed an increased net benefit with the RSF model. Conclusion A Random Forest survival model provided better performance in predicting survival after curative esophagectomy. This will allow more personalised predictions to be delivered clinicians and patients. An online web app is provided at https://uoscancer.shinyapps.io/NOGCA/

Simple Summary Despite advances in both the surgical and medical management of oesophageal adenocarcinoma (OAC), overall prognosis remains poor, with less than 1 in 5 patients surviving more than 5 years from diagnosis. Patients suitable for treatment with curative intent receive pre-operative chemotherapy, but few (

Background: Over 1500 patients with oesophageal cancer undergo a resection in the UK each year. At surgery, patients commonly have a nasogastric tube (NGT) placed and may undergo a pyloric intervention. There is conflicting evidence on the use of both NGTs and pyloric interventions during oesophageal resections. We performed a national survey of oesophageal centres and assessed practice variation.Material and methods: An electronic survey was distributed to all resection centres in England, Wales and Scotland. Variations in practice regarding NGTs and pyloric intervention were assessed, and compared to nationally reported centre volumes and length-of-stay data.Results: Most centres (31/39, 79%) responded to the survey. All centres reported routine NGT use. The majority of centres (19/31, 61%) did not perform pyloric interventions. When used, surgical pyloroplasty was the most frequent strategy (8/31, 26%). Routine post-operative radiological assessment was utilised in 9/31 (29%) of centres. Criteria for NGT removal and dietary progression was highly variable, with every centre reporting different protocols. There were no significant differences in practice between high and low volume centres. There were also no trends seen when comparing centres above vs at-or-below the median length-of-stay. The majority (68%) of centres were willing to take part in a trial assessing NGT use and pyloric interventions.Conclusions: Pyloric intervention use varies widely, with no clear link to outcomes. NGT use remains standard practice despite evidence for safe omission. Surgeons require and recognise the need for a trial to assess requirement for NGTs and pyloric intervention after oesophageal resection.

Neoadjuvant therapy followed by surgery is the standard of care for locally advanced esophageal adenocarcinoma (EAC). Unfortunately, response to neoadjuvant chemotherapy (NAC) is poor (20–37%), as is the overall survival benefit at five years (9%). The EAC genome is complex and heterogeneous between patients, and it is not yet understood whether specific mutational patterns may result in chemotherapy sensitivity or resistance. To identify associations between genomic events and response to NAC in EAC, a comparative genomic analysis was performed in 65 patients with extensive clinical and pathological annotation using whole-genome sequencing (WGS). We defined response using Mandard Tumor Regression Grade (TRG), with responders classified as TRG1–2 (n = 27) and non-responders classified as TRG4–5 (n =38). We report a higher non-synonymous mutation burden in responders (median 2.08/Mb vs. 1.70/Mb, p = 0.036) and elevated copy number variation in non-responders (282 vs. 136/patient, p &lt, 0.001). We identified copy number variants unique to each group in our cohort, with cell cycle (CDKN2A, CCND1), c-Myc (MYC), RTK/PIK3 (KRAS, EGFR) and gastrointestinal differentiation (GATA6) pathway genes being specifically altered in non-responders. Of note, NAV3 mutations were exclusively present in the non-responder group with a frequency of 22%. Thus, lower mutation burden, higher chromosomal instability and specific copy number alterations are associated with resistance to NAC.

PURPOSE OF REVIEW: This review examines current developments and controversies in the multimodal management of oesophageal cancer, with an emphasis on surgical dilemmas and outcomes from the surgeon's perspective.RECENT FINDINGS: Despite the advancement of oncological neoadjuvant treatments, there is still no consensus on what regimen is superior. The majority of patients may still fail to respond to neoadjuvant therapy and suffer potential harm without any survival advantage as a result. In patients who do not respond, adjuvant therapy is still often recommended after surgery despite any evidence for its benefit. We examine the implications of different regimens and treatment approaches for both squamous cell cancer and adenocarcinoma of the oesophagus.SUMMARY: The efficacy of neoadjuvant treatment is highly variable and likely relates to variability of tumour biology. Ongoing work to identify responders, or optimize treatment on an individual patient, should increase the efficacy of multimodal therapy and improve patient outcomes.

Esophago-gastric malignancies are associated with a high recurrence rate; yet there is a lack of evidence to inform guidelines for the standardization and structure of postoperative surveillance after curatively intended treatment. This study aimed to capture the variation in postoperative surveillance strategies across the UK and Ireland, and enquire the opinions and beliefs around surveillance from practicing clinicians. A web-based survey consisting of 40 questions was sent to surgeons or allied health professionals performing or involved in surgical care for esophago-gastric cancers at high-volume centers in the UK. Respondents from each center completed the survey on what best represented their center. The first section of the survey evaluated the timing and components of follow-ups, and their variation between centers. The second section evaluated respondents perspective on how surveillance can be structured. Thirty-five respondents from 27 centers consisting 28 consultants, 6 senior trainees and 1 specialist nurse had completed the questionnaire; 45.7% of responders arranged clinical follow-up at 2–4 weeks. Twenty responders had a specific postoperative surveillance protocol for their patients. Of these, 31.4% had a standardized protocol for all patients, while 25.7% tailored it to patient needs. Patient preference, comorbidities and chance of recurrence were considered as major factors for necessitating more intense surveillance than currently practiced. There is a significant variation in how patients are monitored after surgery between centers in the UK. Randomized controlled trials are necessary to link surveillance strategies to both survival outcomes and quality of life of patients and to evaluate the prognostic value of different postoperative surveillance strategies.

Objective: To determine the relationship between body composition (BC), specifically low skeletal muscle mass (sarcopenia) and poor muscle quality (myosteatosis) and outcomes in emergency laparotomy patients. Background: Emergency laparotomy has one of the highest morbidity and mortality rates of all surgical interventions. BC objectively identifies patients at risk of adverse outcomes in elective cancer cohorts, however evidence is lacking in emergency surgery. Methods: An observational cohort study of patients undergoing emergency laparotomy at ten English hospitals was performed. BC analyses were performed at the third lumbar vertebrae level using pre-operative CT images to quantify skeletal muscle index (SMI) and skeletal muscle radiation attenuation (SM-RA). Sex-specific SMI and SM-RA were determined, with the lower tertile splits defining sarcopenia (low SMI) and myosteatosis (low SM-RA). Accuracy of mortality risk prediction, incorporating SMI and SM-RA variables into risk models was assessed with regression modelling. Results: Six hundred and ten patients were included. Sarcopenia and myosteatosis were both associated with increased risk of morbidity (52.1% vs. 45.1%, p = 0.028; 57.5% vs. 42.6%, p = 0.014), 30-day (9.5% vs. 3.6%, p = 0.010; 14.9% vs. 3.4%, p < 0.001), and 1-year mortality (27.4% vs. 11.5%, p < 0.001; 29.7% vs.12.5%, p < 0.001). Risk-adjusted 30-day mortality was significantly increased by sarcopenia (OR 2.56 (95%CI 1.12-5.84), p = 0.026) and myosteatosis (OR 4.26 (2.01-9.06), p < 0.001), similarly at 1-year (OR 2.66 (95%CI 1.57-4.52), p < 0.001; OR 2.08 (95%CI 1.26-3.41), p = 0.004). BC data increased discrimination of an existing mortality risk-prediction model (AUC 0.838, 95%CI 0.835-0.84). Conclusion: Sarcopenia and myosteatosis are associated with increased adverse outcomes in emergency laparotomy patients.

Background and aims: malnutrition is prevalent in oesophageal cancer. Evidence for the use of nutrition support and prehabilitation in this cohort is variable. The aim of this study was to examine the effect of early nutrition support and functional measures of nutritional status on post-operative outcomes in adult patients with oesophageal cancer. Methods: retrospective review of adults with oesophageal cancer undergoing oesophagectomy (n = 151). Early nutrition support was defined as: oral or enteral nutrition supplementation during neoadjuvant treatment. Late nutrition support defined as: oral or enteral nutrition supplementation prescribed post-operatively. Nutrition outcome measures were; percentage weight loss from 3 to 6 months prior to diagnosis, peri- and post-operatively, and pre-operative assessment of handgrip-strength (HGS). Results: pre-operative weight loss ≥10% was a significant predictor of mortality at 1 year (OR 2.84, 95%CI 1.03–7.83, p = 0.04) independent of tumour stage, adjuvant treatment, age and gender. Adults prescribed early nutrition support during neoadjuvant treatment experienced less weight loss at 12-months post-oesophagectomy compared to adults prescribed late oral nutrition support (p=Conclusions: pre-operative weight loss ≥10% was associated with mortality. Early nutrition support was associated with less weight loss at 12-months post-operatively. Pre-operative HGS measures did not have prognostic value as a stand-alone measure. Future work should investigate the efficacy of early nutrition support in reducing both pre- and post-operative weight loss to improve nutritional status and surgical outcomes as part of a multimodal prehabilitation programme in adults with oesophageal cancer.

The future of single cell diversity screens involves ever-larger sample sizes, dictating the need for higher throughput methods with low analytical noise to accurately describe the nature of the cellular system. Current approaches are limited by the Poisson statistic, requiring dilute cell suspensions and associated losses in throughput. In this contribution, we apply Dean entrainment to both cell and bead inputs, defining different volume packets to effect efficient co-encapsulation. Volume ratio scaling was explored to identify optimal conditions. This enabled the co-encapsulation of single cells with reporter beads at rates of ~1 million cells/hour, while increasing assay signal-to-noise with cell multiplet rates of ~2.5% and capturing ~70% of cells. The method, called Pirouette-seq, extends our capacity to investigate biological systems.TOC AbstractPirouette-seq involves cell and reporter bead inertial ordering for efficient co-encapsulation, achieving a throughput of 1 million cells/hour, a 2.5% multiplet rate and a 70% cell capture efficiency.

Neoadjuvant therapy followed by surgery is the standard of care for locally advanced esophageal adenocarcinoma (EAC). Unfortunately, response to neoadjuvant chemotherapy (NAC) is poor (P=0.036) and elevated copy number variation in non-responders (282 vs 136/patient,PCDKN2A, CCND1), c-Myc (MYC), RTK/PIK3 (KRAS, EGFR) and gastrointestinal differentiation (GATA6) pathway genes being specifically altered in non-responders. Of note,NAV3mutations were exclusively present in the non-responder group with a frequency of 22%. Thus, lower mutation burden, higher chromosomal instability and specific copy number alterations are associated with resistance to NAC.

Introduction In oesophageal adenocarcinoma with an apparent pathological complete response (pCR) to neoadjuvant therapy (NAT) there remains debate as to whether oesophagectomy is required. Single Cell RNA sequencing (scRNAseq) enables identification and characterisation of cell populations at higher resolution than diagnostic techniques. Method ScRNAseq was used to determine transcriptomic profiles of cell populations in 24 OAC tumours and 13 matched normal samples. Five were also analysed using bulk RNA sequencing and high-precision mass spectrometry proteomics. Immunohistochemistry (IHC) was used to validate pCR. Paired scRNAseq analysis of pre-and post-treatment specimens from three further patients was used to compare transcriptomic profiles before and after NAT. Cancer cells (CCs) were assigned a cancer stem cell (CSC) score curated from published gene sets. Result We analysed a total of 22,738 single cells forming 29 different cell phenotypes. In two samples with apparent pCR, IHC staining, bulk RNA sequencing and proteomics of post-treatment samples failed to identify CCs. ScRNAseq, conversely, revealed persistent CCs (12/978 and 45/774). Transcriptomic analysis identified upregulation of stem cell markers and high CSC scores in these cells. Conclusion We have shown that CCs persist beneath the lower detection limit of standard approaches in apparent pCR. These cells express marker genes and expression programs consistent with CSCs. CSCs are a critical subpopulation that drive tumour initiation, growth, invasion, metastasis and resistance to therapy. These gene expression programs are not enriched in non-responders and straight to surgery samples. Oesophagus sparing treatment algorithms in pCR may subject patients to unnecessary risk of progression. Take-home message Cancer cells remain within tumours after apparent complete pathological response. These cells express stem cell markers associated with resistance to therapy and cancer progression.

Introduction Gastric mesenchymal tumours are a rare group of neoplasms, which include gastrointestinal stromal tumours (GISTs) and leiomyomas. To date, there is limited information on the tumour microenvironment (TME) in these neoplasms, despite the TME widely known to influence the hallmarks of cancer. In this study we used single cell RNA sequencing (scRNAseq) to profile individual cells of the TME in GIST and leiomyoma. Method The two gastric mesenchymal tumours and two normal gastric samples were analysed using DropSeq, where single cell transcriptomes are captured onto barcoded beads using a microfluidic device before next generation sequencing. For comparison, we performed bulk RNA-sequencing and CIBERSORT to estimate the abundance of 22 immune cell populations. Furthermore, we used immunohistochemistry to elucidate the presence and location of several immune cells. Result Both neoplasms had diverse immune and stromal cell populations with a greater proportion of macrophages but less B cells than normal gastric tissue. ScRNAseq was able to identify subpopulations of B cells and T cells not detected with CIBERSORT. Interstitial cells of cajal, believed to be the pre-cursor to GISTs, were observed through scRNAseq and confirmed through immunohistochemistry. Conclusion To our knowledge, this is the first study to utilise scRNAseq on GISTs and leiomyomas, which enabled characterisation of the TME at a cellular level. Using this platform in future studies will enable better characterisation of the TME and may inform the discovery of therapeutic targets. Take-home message Single cell RNA sequencing enables the ability to explore the tumour microenvironment of mesenchymal tumours at an enhanced resolution, paving the way for potential future therapeutic targets.

Aims We currently cannot predict which patients with locally advanced oesophageal adenocarcinoma will be amongst the 15-20% to gain a clinically important response to neoadjuvant therapy (NAT). This pilot study aimed to identify differentially expressed genes from oesophageal adenocarcinoma pre-treatment biopsies between responders and non-responders to NAT and develop methodology for predicting response. Method Response to NAT was assessed pathologically using Tumour Regression Grading (TRG). Pre-treatment formalin-fixed paraffin embedded samples were analysed with two nuclease protection assays (EdgeSeq, HTG = Oncology Biomarker Panel (OBP) and Precision Immuno-Oncology Panel (PIP)). Sequencing was performed on the NextSeq500 (Illumina). Result Whilst there was no difference in pre-treatment characteristics, responders (TRG1-2, n=26) had significantly better post-treatment pathology and overall survival than non-responders (TRG4-5, n=30). Genes up-regulated in responders were involved in regulating cell cycling, whereas genes up-regulated in non-responders were involved in cytokine signalling and the immune response. Neuronal artificial network models could predict response to NAT with overall accuracy of 73% and 68% for the OBP and PIP, respectively, which is promising considering the small sample size. As no model will be 100% accurate, we developed a model that could take patient's views into consideration with an adjustable probability threshold for classification. Conclusion This pilot study informs a biologically sound hypothesis for the basis of response to NAT and suggests prediction from pre-treatment biopsies may be possible using EdgeSeq. We now aim to validate these results in a larger study to inform a bespoke classifier of response to enable delivery of precision therapy. Take-home message In oesophageal adenocarcinoma, responders and non-responders to neoadjuvant therapy have different expression profiles. Through using EdgeSeq in larger studies, we may be able to predict which patients will respond to treatment, allowing for delivery of precision therapy.

Introduction For patients with oesophageal cancer, producing accurate prediction models for survival after oesophagectomy has proved challenging. We investigated whether Random Survival Forests (RSF), a novel machine learning method, could produce an accurate prognostic model for overall survival after oesophagectomy. Method The study used data from the National Oesophago-Gastric Cancer Audit and included patients diagnosed with oesophageal adenocarcinoma or squamous cell carcinoma between 2012 and 2018 in England and Wales and who underwent a curative oesophagectomy with adequate lymphadenectomy (15 LN) and survived to discharge (n=6198). Missing data was handled using multiple imputation and the data was split into training and validation cohorts. 13 variables were selected for inclusion using Random Forest variable importance and used to train the final model. The same variables were used to develop a traditional Cox regression model. Result Median survival was 53 months in both cohorts. The final RSF model had good discrimination in the validation cohort with a C-index of 0.757(0.755-0.759), exceeding the Cox model; 0.748(0.746-0.750). At 3 years post-surgery, overall survival was 56.2%. The RSF yielded a mean predicted survival of 55.8%(IQR 29.5%-81.7%) compared to 55.4%(40.0%-77.7%) for the Cox model. The most important variables were lymph node involvement and pT/ypT stage, however other variables including neoadjuvant treatment completion and surgical complications were also found to be important. Conculsion A Random Forest survival model provided better performance in predicting survival after curative oesophagectomy. This will allow more personalised predictions to be delivered clinicians and patients. Take-home message Random Forest survival models can accurately predict post-operative prognosis after oesophagectomy.

OBJECTIVE: To develop a predictive model for overall survival after esophagectomy using pre/postoperative clinical data and machine learning. SUMMARY BACKGROUND DATA: For patients with esophageal cancer, accurately predicting long-term survival after esophagectomy is challenging. This study investigated survival prediction after esophagectomy using a Random Survival Forest (RSF) model derived from routine data from a large, well curated, national dataset. METHODS: Patients diagnosed with esophageal adenocarcinoma or squamous cell carcinoma between 2012 and 2018 in England and Wales who underwent an esophagectomy were included. Prediction models for overall survival were developed using the RSF method and Cox regression from 41 patient and disease characteristics. Calibration and discrimination (time dependent AUC) were validated internally using bootstrap resampling. RESULTS: The study analysed 6399 patients, with 2625 deaths during follow-up. Median follow-up was 41 months. Overall survival was 47.1% at 5 years. The final RSF model included 14 variables and had excellent discrimination with a 5-year tAUC of 83.9% (95%CI 82.6-84.9%), compared to 82.3% (95%CI 81.1-83.3%) for the Cox model. The most important variables were lymph node involvement, pT stage, CRM involvement (tumour at

Background Histopathological outcomes, such as lymph node yield and margin positivity, are used to benchmark and assess surgical centre quality, and are reported annually by the National Oesophago-Gastric Cancer Audit (NOGCA) in England and Wales. The variation in pathological specimen assessment and how this affects these outcomes is not known. Methods A survey of practice was circulated to all tertiary oesophagogastric cancer centres across England and Wales. Questions captured demographic data, and information on how specimens were prepared and analysed. National performance data were retrieved from the NOGCA. Survey results were compared for tertiles of lymph node yield, and circumferential and longitudinal margins. Results Survey responses were received from 32 of 37 units (86 per cent response rate), accounting for 93.1 per cent of the total oesophagectomy volume in England and Wales. Only 5 of 32 units met or exceeded current guidelines on specimen preparation according to the Royal College of Pathologists guidelines. There was wide variation in how centres defined positive (R1) margins, and how margins and lymph nodes were assessed. Centres with the highest nodal yield were more likely to use systematic fat blocking, and to re-examine specimens when the initial load was low. Systematic blocking of lesser curve fat resulted in significantly higher rates of patients with at least 15 lymph nodes examined (91.4 versus 86.5 per cent; P = 0.027). Conclusion Preparation and histopathological assessment of specimens varies significantly across institutions. This challenges the validity of currently used surgical quality metrics for oesophageal and other tumours.

Introduction: Boerhaave's syndrome is a life-threatening spontaneous perforation of the esophagus associated with significant morbidity and mortality. Historically, thoracotomy has been the mainstay of treatment, but is associated with high morbidity and pain. Minimally invasive approaches provide alternative treatment possibilities. This systematic review together with inclusion of a case series from a tertiary esophagogastric unit assesses current evidence focused on minimally invasive surgical management of this condition. Methods: A systematic review was conducted in line with MOOSE (Meta-analyses Of Observational Studies in Epidemiology) guidelines. Electronic databases PubMed, MEDLINE, and Cochrane Library were searched, and articles focusing on the minimally invasive management of Boerhaave's syndrome were identified and scrutinized. We also report demographics and outcomes for a consecutive case series of patients with acute Boerhaave's syndrome managed via thoracoscopy. Results: Fifteen studies were included comprising 5 retrospective cohort studies and 10 case reports. Management strategies were divided into three categories: thoracoscopic, endoscopic, and laparoscopic. Overall mortality rates for each treatment modality were 2%, 13%, and 33%, and treatment success rates 98%, 38%, and 67%, respectively. Postoperative infective complications were seen in 79% of patients, with only 2 patients requiring salvage thoracotomy. In our local case series, we report 4 consecutive patients managed with thoracoscopy, with a 100% treatment success rate. Conclusion: Minimally invasive approaches in the management of Boerhaave's syndrome are safe and effective. We propose a selective management algorithm involving a minimally invasive approach to management of this life-threatening condition. Registered with local clinical outcomes team as service evaluation. (Approval number sev/0171).

The chemotherapy resistance of esophageal adenocarcinomas (EAC) is underpinned by cancer cell extrinsic mechanisms of the tumor microenvironment (TME). We demonstrate that by targeting the tumor-promoting functions of the predominant TME cell type, cancer associated fibroblasts (CAF) with phosphodiesterase type 5 inhibitors (PDE5i) we can enhance the efficacy of standard-of-care chemotherapy. In ex-vivo conditions PDE5i prevented the transdifferation of normal fibroblasts to CAF and abolished the tumor-promoting function of established EAC CAF. Using shotgun proteomics and single cell RNA-Seq we revealed PDE5i specific regulation of pathways related to fibroblast activation and tumor promotion. Finally, we confirmed the efficacy of PDE5i in combination with chemotherapy in close-to-patient and in vivo PDX-based model systems. These findings demonstrate that CAF drive chemotherapy resistance in EAC and can be targeted by repurposing PDE5i, a safe and well tolerated class of drug administered to millions of patients world-wide to treat erectile dysfunction.

In esophageal adenocarcinoma (EAC) with apparent pathological complete response (pCR) to neoadjuvant therapy (NAT) debate remains as to whether esophagectomy is required. Recurrence after pCR is not limited to distant metastases outside radiation or resection fields. It is unknown if cancer persists below the lower detection limit of current diagnostic technology. Trials randomising patients with apparent pCR to esophagectomy or active surveillance are currently recruiting in an attempt to spare patients the morbidity of esophagectomy. Methods Single cell (scRNAseq) was performed on fresh tissue taken at surgical resection to determine the transcriptomic profiles of cell populations in 24 EAC tumours and matched normal samples. Five tumour-normal pairs were also analysed using bulk RNA sequencing and high-precision mass spectrometry proteomics. Immunohistochemistry (IHC) was used to confirm pCR. Paired scRNAseq analysis of pre-and post-treatment specimens from three patients was used to compare transcriptomic profiles before and after NAT. Cancer cells were assigned a cancer stem cell module score curated from published gene sets. Results We analysed a total of 22,738 single cells forming 29 different cell states. In two samples with pCR, we repeated the pathological analysis and performed IHC using antibodies to known cancer proteins and identified no residual cancer cells (figure 1A). Bulk tissue RNA (figure 1B) and proteomic analysis did not detect cancer genes or proteins in the pCR samples. ScRNAseq, conversely, revealed small populations of persistent cancer cells in both complete responders (12/978 and 45/774) (figure 1C). Transcriptomic analysis of these residual cells identified upregulation of stem cell markers and high cancer stem cell scores in these remaining cells (figure 1D). Conclusion We have determined the cell states present across multiple different EAC and normal esophageal samples. We have shown cancer cells persisting after NAT, in patients with apparent pCR, beneath the lower detection limit of standard diagnostic approaches. These cells express gene programs consistent with cancer stem cells. Cancer stem cells are a critical subpopulation that drive tumour initiation, growth, and resistance to therapy. Esophagus sparing approaches in pCR may subject patients to risk of progression.

In the UK, neoadjuvant chemotherapy (NAC) for locally advanced esophageal adenocarcinoma (EAC) is the standard of care. Unfortunately, response to NAC, following surgery is often low ( Methods We defined response as Mandard Tumor Regression Grade (TRG) 1-2 (n = 15) and non-response as TRG4-5 (n = 33). WGS of 50x coverage and calling of genomic events (Strelka2, SCAT/GISTIC) was performed according to Frankell Nat Genet 2019 with modifications, to differentiate driver from passenger mutations (dNdScv, oncodriveCLUST), determine variant allele frequencies (VAF; copy number-adjusted) and mutation signatures (NMF R-package). Following stringent variant QC (Phred score ≥ 30), filtering (VAF >0.02) and annotation (ANNOVAR, cancer census/helper, the 77 known EAC drivers and false positive genes) and visualisation in maftools, we evaluated the data for associations between genomic events and response to NAC. Results COSMIC mutation signatures 2 (TRG1-2; Cosθ = 0.89) and 6 (TRG4-5; Cosθ = 0.82) were enriched, suggesting defective mismatch repair in non-responders. Using 5,193 high-confidence non-silent mutations (TRG1-2,n = 1969; TRG4-5, n = 3224), we identified 39 mutated driver genes (Figure-1) with a median of 2.9(0-5) (TRG1-2) and 2.7(0-9) (TRG4-5) driver events/patient. Shared dysregulated pathways, included DNA-damage (TP53), cell cycle (CDKN2A), TGF-β (SMAD4) and chromatin regulation (ARID1A). There was no difference in the prognostic of SMAD4 and GATA4 genes. Interestingly, NAV3 mutations were only present in non-responders (21%,7/33); and in responders TP53 incidence was higher (93% vs. 58%) with a concomitant reduction in clonal cell fraction (0.25 vs. 0.39;Wilcoxon, p Conclusion The data suggest that specific genomic events, such as NAV3 mutation and the intra-tumoral heterogeneity of mutated DNA-damage response genes may offer additional predictive value. Ongoing work includes analysis of the impact of non-coding variation on response. While our analysis is limited by sample size and requires validation in additional cohorts, it demonstrates the potential of genomic sequencing to identify NAC response biomarkers and may guide alternative or novel treatment modalities for chemo-resistant tumours.

IntroductionSurgery is the primary curative treatment for oesophageal cancer, with considerable recent improvements in long-term survival. However, surgery has a long-lasting impact on patient’s health-related quality of life (HRQOL). Through a multicentre European study, our research group was able to identify key symptoms that affect patient’s HRQOL. These symptoms were combined to produce a tool to identify poor HRQOL following oesophagectomy (LAsting Symptoms after Oesophageal Resection (LASOR) tool). The objective of this multicentre study is to validate a six-symptom clinical tool to identify patients with poor HRQOL for use in everyday clinical practice.Methods and analysisIncluded patients will: (1) be aged 18 years or older, (2) have undergone an oesophagectomy for cancer between 2015 and 2019, and (3) be at least 12 months after the completion of adjuvant oncological treatments. Patients will be given the previously created LASOR questionnaire. Each symptom from the LASOR questionnaire will be graded according to impact on quality of life and frequency of the symptom, with a composite score from 0 to 5. The previously developed LASOR symptom tool will be validated against HRQOL as measured by the European Organisation for Research and Treatment of Cancer QLQC30 and OG25.Sample sizeWith a predicted prevalence of poor HRQOL of 45%, based on the previously generated LASOR clinical symptom tool, to validate this tool with a sensitivity and specificity of 80%, respectively, a minimum of 640 patients will need to be recruited to the study.Ethics and disseminationNHS Health Research Authority (North East—York Research Ethics Committee) approval was gained 8 November 2019 (REC reference 19/NE/0352). Multiple platforms will be used for the dissemination of the research data, including international clinical and patient group presentations and publication of research outputs in a high impact clinical journal.

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 that indicated immune cell infiltration; this tumor type was associated with the shortest time of patient survival. The fourth subtype was characterized by DNA hypomethylation associated with structure rearrangements, copy number alterations, with preferential amplification of CCNE1 (cells with this gene amplification have been reported to be sensitive to CDK2 inhibitors). Organoids with reduced levels of MGMT and CHFR expression were sensitive to temozolomide and taxane drugs.CONCLUSIONS: In a comprehensive integrated analysis of methylation, transcriptome, and genome profiles of more than 400 BE and EAC tissues, along with clinical data, we identified 4 subtypes that were associated with patient outcomes and potential responses to therapy.

Background and aimsEsophageal adenocarcinoma (EAC) is chemoresistant in the majority of cases. The tumor-promoting biology of cancer associated fibroblasts (CAF) make them a target for novel therapies. Phosphodiesterase type 5 inhibitors (PDE5i) have been shown to regulate the activated fibroblast phenotype in benign disease. We investigated the potential for CAF modulation in EAC using PDE5i to enhance the efficacy of chemotherapy.MethodsEAC fibroblasts were treated with PDE5i and phenotypic effects examined using immunoblotting, immunohistochemistry, gel contraction, transwell invasion, organotypics, single cell RNAseq and shotgun proteomics. The combination of PDE5i with standard-of-care chemotherapy (Epirubicin, 5-Fluorouracil and Cisplatin) was tested for safety and efficacy in validated near-patient model systems (3D tumor growth assays (3D-TGAs) and patient derived xenograft (PDX) mouse models).ResultsPDE5i treatment reduced α–SMA expression in CAFs by 50% (pConclusionPDE5 is a candidate for clinical trials to alter the fibroblast phenotype in esophageal cancer. We demonstrate the specificity of PDE5i for fibroblasts to prevent transdifferentiation and revert the CAF phenotype. Finally, we confirm the efficacy of PDE5i in combination with chemotherapy in close-to-patientin vitroandin vivoPDX-based model systems.

MC3 Working Group: Rehan Akbani21, Pavana Anur22, Matthew H. Bailey1,2,3, Alex Buchanan9, Kami Chiotti9, Kyle Covington12,23, Allison Creason9, Li Ding1,2,3,20, Kyle Ellrott9, Yu Fan21, Steven Foltz1,2, Gad Getz8,14,15,16, Walker Hale12, David Haussler24,25, Julian M. Hess8,26, Carolyn M. Hutter27, Cyriac Kandoth28, Katayoon Kasaian29,30, Melpomeni Kasapi27, Dave Larson1 , Ignaty Leshchiner8, John Letaw31, Singer Ma32, Michael D. McLellan1,3,20, Yifei Men32, Gordon B. Mills33,34, Beifang Niu35, Myron Peto22, Amie Radenbaugh24, Sheila M. Reynolds36, Gordon Saksena8, Heidi Sofia27, Chip Stewart8, Adam J. Struck31, Joshua M. Stuart24,37, Wenyi Wang21, John N. Weinstein38, David A. Wheeler12,13, Christopher K. Wong24,39, Liu Xi12 & Kai Ye40,41 21Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 22Molecular and Medical Genetics, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 23Castle Biosciences Inc, Friendswood, TX 77546, USA. 24UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 25Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 26Massachusetts General Hospital Center for Cancer Research, Charlestown, MA 02114, USA. 27National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20894, USA. 28Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. 29Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada. 30Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada. 31Computational Biology Program, School of Medicine, Oregon Health and Science University, Portland, OR 97239, USA. 32DNAnexus Inc, Mountain View, CA 94040, USA. 33Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA. 34Precision Oncology, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 35Computer Network Information Center, Chinese Academy of Sciences, Beijing, China. 36Institute for Systems Biology, Seattle, WA 98109, USA. 37Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 38Department of Bioinformatics and Computational Biology and Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 39Biomolecular Engineering Department, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 40School of Elect, PCAWG novel somatic mutation calling methods working group: Matthew H. Bailey1,2,3, Beifang Niu35, Matthias Bieg42,43, Paul C. Boutros6,44,45,46, Ivo Buchhalter43,47,48, Adam P. Butler49, Ken Chen50, Zechen Chong51, Li Ding1,2,3,20, Oliver Drechsel52,53, Lewis Jonathan Dursi6,7, Roland Eils47,48,54,55, Kyle Ellrott9, Shadrielle M. G. Espiritu6, Yu Fan21, Robert S. Fulton1,3,20, Shengjie Gao56, Josep L. l. Gelpi57,58, Mark B. Gerstein5,18,19, Gad Getz8,14,15,16, Santiago Gonzalez59,60, Ivo G. Gut52,61, Faraz Hach62,63, Michael C. Heinold47,48, Julian M. Hess8,26, Jonathan Hinton49, Taobo Hu64, Vincent Huang6, Yi Huang65,66, Barbara Hutter43,67,68, David R. Jones49, Jongsun Jung69, Natalie Jäger47, Hyung-Lae Kim70, Kortine Kleinheinz47,48, Sushant Kumar5,19, Yogesh Kumar64, Christopher M. Lalansingh6, Ignaty Leshchiner8, Ivica Letunic71, Dimitri Livitz8, Eric Z. Ma64, Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Michael D. McLellan1,3,20, Andrew Menzies49, Ana Milovanovic57, Morten Muhlig Nielsen73, Stephan Ossowski52,53,74, Nagarajan Paramasivam43,47, Jakob Skou Pedersen73,75, Marc D. Perry76,77, Montserrat Puiggròs57, Keiran M. Raine49, Esther Rheinbay8,14,72, Romina Royo57, S. Cenk Sahinalp62,78,79, Gordon Saksena8, Iman Sarrafi62,78, Matthias Schlesner47,80, Jared T. Simpson6,17, Lucy Stebbings49, Chip Stewart8, Miranda D. Stobbe52,61, Jon W. Teague49, Grace Tiao8, David Torrents57,81, Jeremiah A. Wala8,14,82, Jiayin Wang1,40,66, Wenyi Wang21, Sebastian M. Waszak60, Joachim Weischenfeldt60,83,84, Michael C. Wendl1,10,11, Johannes Werner47,85, Zhenggang Wu64, Hong Xue64, Sergei Yakneen60, Takafumi N. Yamaguchi6, Kai Ye40,41, Venkata D. Yellapantula20,86, Christina K. Yung76 & Junjun Zhang76, PCAWG Consortium: Lauri A. Aaltonen87, Federico Abascal49, Adam Abeshouse88, Hiroyuki Aburatani89, David J. Adams49, Nishant Agrawal90, Keun Soo Ahn91, Sung-Min Ahn92, Hiroshi Aikata93, Rehan Akbani21, Kadir C. Akdemir50, Hikmat Al-Ahmadie88, Sultan T. Al-Sedairy94, Fatima Al-Shahrour95, Malik Alawi96,97, Monique Albert98, Kenneth Aldape99,100, Ludmil B. Alexandrov49,101,102, Adrian Ally30, Kathryn Alsop103, Eva G. Alvarez104,105,106, Fernanda Amary107, Samirkumar B. Amin108,109,110, Brice Aminou76, Ole Ammerpohl111,112, Matthew J. Anderson113, Yeng Ang114, Davide Antonello115, Pavana Anur22, Samuel Aparicio116, Elizabeth L. Appelbaum1,117, Yasuhito Arai118, Axel Aretz119, Koji Arihiro93, Shun-ichi Ariizumi120, Joshua Armenia121, Laurent Arnould122, Sylvia Asa123,124, Yassen Assenov125, Gurnit Atwal6,126,127, Sietse Aukema112,128, J. Todd Auman129, Miriam R. Aure130, Philip Awadalla6,126, Marta Aymerich131, Gary D. Bader126, Adrian Baez-Ortega132, Matthew H. Bailey1,2,3, Peter J. Bailey133, Miruna Balasundaram30, Saianand Balu134, Pratiti Bandopadhayay8,135,136, Rosamonde E. Banks137, Stefano Barbi138, Andrew P. Barbour139,140, Jonathan Barenboim6, Jill Barnholtz-Sloan141,142, Hugh Barr143, Elisabet Barrera59, John Bartlett98,144, Javier Bartolome57, Claudio Bassi115, Oliver F. Bathe145,146, Daniel Baumhoer147, Prashant Bavi148, Stephen B. Baylin149,150, Wojciech Bazant59, Duncan Beardsmore151, Timothy A. Beck152,153, Sam Behjati49, Andreas Behren154, Beifang Niu35, Cindy Bell155, Sergi Beltran52,61, Christopher Benz156, Andrew Berchuck157, Anke K. Bergmann158, Erik N. Bergstrom101,102, Benjamin P. Berman159,160,161, Daniel M. Berney162, Stephan H. Bernhart163,164,165, Rameen Beroukhim8,14,82, Mario Berrios166, Samantha Bersani167, Johanna Bertl73,168, Miguel Betancourt169, Vinayak Bhandari6,44, Shriram G. Bhosle49, Andrew V. Biankin133,170,171,172, Matthias Bieg42,43, Darell Bigner173, Hans Binder163,164, Ewan Birney59, Michael Birrer72, Nidhan K. Biswas174, Bodil Bjerkehagen147,175, Tom Bodenheimer134, Lori Boice176, Giada Bonizzato177, Johann S. De Bono178, Arnoud Boot179,180, Moiz S. Bootwalla166, Ake Borg181, Arndt Borkhardt182, Keith A. Boroevich183,184, Ivan Borozan6, Christoph Borst185, Marcus Bosenberg186, Mattia Bosio52,53,57, Jacqueline Boultwood187, Guillaume Bourque188,189, Paul C. Boutros6,44,45,46, G. Steven Bova190, David T. Bowen49,191, Reanne Bowlby30, David D. L. Bowtell103, Sandrine Boyault192, Rich Boyce59, Jeffrey Boyd193, Alvis Brazma59, Paul Brennan194, Daniel S. Brewer195,196, Arie B. Brinkman197, Robert G. Bristow44,198,199,200,201, Russell R. Broaddus99, Jane E. Brock202, Malcolm Brock203, Annegien Broeks204, Angela N. Brooks8,24,37,82, Denise Brooks30, Benedikt Brors67,205,206, Søren Brunak207,208, Timothy J. C. Bruxner113,209, Alicia L. Bruzos104,105,106, Alex Buchanan9, Ivo Buchhalter43,47,48, Christiane Buchholz210, Susan Bullman8,82, Hazel Burke211, Birgit Burkhardt212, Kathleen H. Burns213,214, John Busanovich8,215, Carlos D. Bustamante216,217, Adam P. Butler49, Atul J. Butte218, Niall J. Byrne76, Anne-Lise Børresen-Dale130,219, Samantha J. Caesar-Johnson220, Andy Cafferkey59, Declan Cahill221, Claudia Calabrese59,60, Carlos Caldas222,223, Fabien Calvo224, Niedzica Camacho178, Peter J. Campbell49,225, Elias Campo226,227, Cinzia Cantù177, Shaolong Cao21, Thomas E. Carey228, Joana Carlevaro-Fita229,230,231, Rebecca Carlsen30, Ivana Cataldo167,177, Mario Cazzola232, Jonathan Cebon154, Robert Cerfolio233, Dianne E. Chadwick234, Dimple Chakravarty235, Don Chalmers236, Calvin Wing Yiu Chan47,237, Kin Chan238, Michelle Chan-Seng-Yue148, Vishal S. Chandan239, David K. Chang133,170, Stephen J. Chanock240, Lorraine A. Chantrill170,241, Aurélien Chateigner76,242, Nilanjan Chatterjee149,243, Kazuaki Chayama93, Hsiao-Wei Chen114,121, Jieming Chen218, Ken Chen50, Yiwen Chen21, Zhaohong Chen244, Andrew D. Cherniack8,82, Jeremy Chien245, Yoke-Eng Chiew246,247, Suet-Feung Chin222,223, Juok Cho8, Sunghoon Cho248, Jung Kyoon Choi249, Wan Choi250, Christine Chomienne251, Zechen Chong51, Su Pin Choo252, Angela Chou170,246, Angelika N. Christ113, Elizabeth L. Christie103, Eric Chuah30, Carrie Cibulskis8, Kristian Cibulskis8, Sara Cingarlini253, Peter Clapham49, Alexander Claviez254, Sean Cleary148,255, Nicole Cloonan256, Marek Cmero257,258,259, Colin C. Collins62, Ashton A. Connor255,260, Susanna L. Cooke133, Colin S. Cooper178,196,261, Leslie Cope149, Vincenzo Corbo138,177, Matthew G. Cordes1,262, Stephen M. Cordner263, Isidro Cortés-Ciriano264,265,266, Kyle Covington12,23, Prue A. Cowin267, Brian Craft24, David Craft8,268, Chad J. Creighton269, Yupeng Cun270, Erin Curley271, Ioana Cutcutache179,180, Karolina Czajka272, Bogdan Czerniak99,273, Rebecca A. Dagg274, Ludmila Danilova149, Maria Vittoria Davi275, Natalie R. Davidson276,277,278,279,280, Helen Davies49,281,282, Ian J. Davis283, Brandi N. Davis-Dusenbery284, Kevin J. Dawson49, Francisco M. De La Vega216,217,285, Ricardo De Paoli-Iseppi211, Timothy Defreitas8, Angelo P. Dei Tos286, Olivier Delaneau287,288,289, John A. Demchok220, Jonas Demeulemeester290,291, German M. Demidov52,53,74, Deniz Demircioğlu292,293, Nening M. Dennis221, Robert E. Denroche148, Stefan C. Dentro49,290,294, Nikita Desai76, Vikram Deshpande72, Amit G. Deshwar295, Christine Desmedt296,297, Jordi Deu-Pons298,299, Noreen Dhalla30, Neesha C. Dhani300, Priyanka Dhingra301,302, Rajiv Dhir303, Anthony DiBiase304, Klev Diamanti305, Li Ding1,2,3,20, Shuai Ding306, Huy Q. Dinh159, Luc Dirix307, HarshaVardhan Doddapaneni12, Nilgun Donmez62,78, Michelle T. Dow244, Ronny Drapkin308, Oliver Drechsel52,53, Ruben M. Drews223, Serge Serge49, Tim Dudderidge150,221, Ana Dueso-Barroso57, Andrew J. Dunford8, Michael Dunn309, Lewis Jonathan Dursi6,7, Fraser R. Duthie133,310, Ken Dutton-Regester311, Jenna Eagles272, Douglas F. Easton312,313, Stuart Edmonds314, Paul A. Edwards223,315, Sandra E. Edwards178, Rosalind A. Eeles178,221, Anna Ehinger316, Juergen Eils54,55, Roland Eils47,48,54,55, Adel El-Naggar99,273, Matthew Eldridge223, Kyle Ellrott9, Serap Erkek60, Georgia Escaramis53,317,318, Shadrielle M. G. Espiritu6, Xavier Estivill53,319, Dariush Etemadmoghadam103, Jorunn E. Eyfjord320, Bishoy M. Faltas280, Daiming Fan321, Yu Fan21, William C. Faquin72, Claudiu Farcas244, Matteo Fassan322, Aquila Fatima323, Francesco Favero324, Nodirjon Fayzullaev76, Ina Felau220, Sian Fereday103, Martin L. Ferguson325, Vincent Ferretti76,326, Lars Feuerbach205, Matthew A. Field327, J. Lynn Fink57,113, Gaetano Finocchiaro328, Cyril Fisher221, Matthew W. Fittall290, Anna Fitzgerald329, Rebecca C. Fitzgerald282, Adrienne M. Flanagan330, Neil E. Fleshner331, Paul Flicek59, John A. Foekens332, Kwun M. Fong333, Nuno A. Fonseca59,334, Christopher S. Foster335,336, Natalie S. Fox6, Michael Fraser6, Scott Frazer8, Milana Frenkel-Morgenstern337, William Friedman338, Joan Frigola298, Catrina C. Fronick1,262, Akihiro Fujimoto184, Masashi Fujita184, Masashi Fukayama339, Lucinda A. Fulton1 , Robert S. Fulton1,3,20, Mayuko Furuta184, P. Andrew Futreal340, Anja Füllgrabe59, Stacey B. Gabriel8, Steven Gallinger148,255,260, Carlo Gambacorti-Passerini341, Jianjiong Gao121, Shengjie Gao56, Levi Garraway82, Øystein Garred342, Erik Garrison49, Dale W. Garsed103, Nils Gehlenborg8,343, Josep L. l. Gelpi57,58, Joshy George110, Daniela S. Gerhard344, Clarissa Gerhauser345, Jeffrey E. Gershenwald346,347, Mark B. Gerstein5,18,19, Moritz Gerstung59,60, Gad Getz8,14,15,16, Mohammed Ghori49, Ronald Ghossein348, Nasra H. Giama349, Richard A. Gibbs12, Anthony J. Gill170,350, Pelvender Gill351, Dilip D. Giri348, Dominik Glodzik49, Vincent J. Gnanapragasam352,353, Maria Elisabeth Goebler354, Mary J. Goldman24, Carmen Gomez355, Santiago Gonzalez59,60, Abel Gonzalez-Perez298,299,356, Dmitry A. Gordenin357, James Gossage358, Kunihito Gotoh359, Ramaswamy Govindan3, Dorthe Grabau360, Janet S. Graham133,361, Robert C. Grant148,260, Anthony R. Green315, Eric Green27, Liliana Greger59, Nicola Grehan282, Sonia Grimaldi177, Sean M. Grimmond362, Robert L. Grossman363, Adam Grundhoff97,364, Gunes Gundem88, Qianyun Guo75, Manaswi Gupta8, Shailja Gupta365, Ivo G. Gut52,61, Marta Gut52,61, Jonathan Göke292,366, Gavin Ha8, Andrea Haake111, David Haan37, Siegfried Haas185, Kerstin Haase290, James E. Haber367, Nina Habermann60, Faraz Hach62,63, Syed Haider6, Natsuko Hama118, Freddie C. Hamdy351, Anne Hamilton267, Mark P. Hamilton368, Leng Han369, George B. Hanna370, Martin Hansmann371, Nicholas J. Haradhvala8,72, Olivier Harismendy102,372, Ivon Harliwong113, Arif O. Harmanci5,373, Eoghan Harrington374, Takanori Hasegawa375, David Haussler24,25, Steve Hawkins223, Shinya Hayami376, Shuto Hayashi375, D. Neil Hayes134,377,378, Stephen J. Hayes379,380, Nicholas K. Hayward211,311, Steven Hazell221, Yao He381, Allison P. Heath382, Simon C. Heath52,61, David Hedley300, Apurva M. Hegde38, David I. Heiman8, Michael C. Heinold47,48, Zachary Heins88, Lawrence E. Heisler152, Eva Hellstrom-Lindberg383, Mohamed Helmy384, Seong Gu Heo385, Austin J. Hepperla134, José María Heredia-Genestar386, Carl Herrmann47,48,387, Peter Hersey211, Julian M. Hess8,26, Holmfridur Hilmarsdottir320, Jonathan Hinton49, Satoshi Hirano388, Nobuyoshi Hiraoka389, Katherine A. Hoadley134,390, Asger Hobolth75,168, Ermin Hodzic78, Jessica I. Hoell182, Steve Hoffmann163,164,165,391, Oliver Hofmann392, Andrea Holbrook166, Aliaksei Z. Holik53, Michael A. Hollingsworth393, Oliver Holmes209,311, Robert A. Holt30, Chen Hong205,237, Eun Pyo Hong385, Jongwhi H. Hong394, Gerrit K. Hooijer395, Henrik Hornshøj73, Fumie Hosoda118, Yong Hou56,396, Volker Hovestadt397, William Howat352, Alan P. Hoyle134, Ralph H. Hruban149, Jianhong Hu12, Taobo Hu64, Xing Hua240, Kuan-lin Huang1,398, Mei Huang176, Mi Ni Huang179,180, Vincent Huang6, Yi Huang65,66, Wolfgang Huber60, Thomas J. Hudson272,399, Michael Hummel400, Jillian A. Hung246,247, David Huntsman401, Ted R. Hupp402, Jason Huse88, Matthew R. Huska403, Barbara Hutter43,67,68, Carolyn M. Hutter27, Daniel Hübschmann48,54,404,405,406, Christine A. Iacobuzio-Donahue348, Charles David Imbusch205, Marcin Imielinski407,408, Seiya Imoto375, William B. Isaacs409, Keren Isaev6,44, Shumpei Ishikawa410, Murat Iskar397, S. M. Ashiqul Islam244, Michael Ittmann411,412,413, Sinisa Ivkovic284, Jose M. G. Izarzugaza414, Jocelyne Jacquemier415, Valerie Jakrot211, Nigel B. Jamieson133,172,416, Gun Ho Jang148, Se Jin Jang417, Joy C. Jayaseelan12, Reyka Jayasinghe1 , Stuart R. Jefferys134, Karine Jegalian418, Jennifer L. Jennings419, Seung-Hyup Jeon250, Lara Jerman60,420, Yuan Ji421,422, Wei Jiao6, Peter A. Johansson311, Amber L. Johns170, Jeremy Johns272, Rory Johnson230,423, Todd A. Johnson183, Clemency Jolly290, Yann Joly424, Jon G. Jonasson320, Corbin D. Jones425, David R. Jones49, David T. W. Jones426,427, Nic Jones428, Steven J. M. Jones30, Jos Jonkers204, Young Seok Ju49,249, Hartmut Juhl429, Jongsun Jung69, Malene Juul73, Randi Istrup Juul73, Sissel Juul374, Natalie Jäger47, Rolf Kabbe47, Andre Kahles276,277,278,279,430, Abdullah Kahraman431,432,433, Vera B. Kaiser434, Hojabr Kakavand211, Sangeetha Kalimuthu148, Christof von Kalle405, Koo Jeong Kang91, Katalin Karaszi351, Beth Karlan435, Rosa Karlić436, Dennis Karsch437, Katayoon Kasaian29,30, Karin S. Kassahn113,438, Hitoshi Katai439, Mamoru Kato440, Hiroto Katoh410, Yoshiiku Kawakami93, Jonathan D. Kay117, Stephen H. Kazakoff209,311, Marat D. Kazanov441,442,443, Maria Keays59, Electron Kebebew444,445, Richard F. Kefford446, Manolis Kellis8,447, James G. Kench170,350,448, Catherine J. Kennedy246,247, Jules N. A. Kerssemakers47, David Khoo273, Vincent Khoo221, Narong Khuntikeo115,449, Ekta Khurana301,302,450,451, Helena Kilpinen117, Hark Kyun Kim452, Hyung-Lae Kim70, Hyung-Yong Kim415, Hyunghwan Kim250, Jaegil Kim8, Jihoon Kim453, Jong K. Kim454, Youngwook Kim455,456, Tari A. King457,458,459, Wolfram Klapper128, Kortine Kleinheinz47,48, Leszek J. Klimczak460, Stian Knappskog49,461, Michael Kneba437, Bartha M. Knoppers424, Youngil Koh462,463, Jan Komorowski305,464, Daisuke Komura410, Mitsuhiro Komura375, Gu Kong415, Marcel Kool426,465, Jan O. Korbel59,60, Viktoriya Korchina12, Andrey Korshunov465, Michael Koscher465, Roelof Koster466, Zsofia Kote-Jarai178, Antonios Koures244, Milena Kovacevic284, Barbara Kremeyer49, Helene Kretzmer164,165, Markus Kreuz467, Savitri Krishnamurthy99,468, Dieter Kube469, Kiran Kumar8, Pardeep Kumar221, Sushant Kumar5,19, Yogesh Kumar64, Ritika Kundra114,121, Kirsten Kübler8,14,72, Ralf Küppers470, Jesper Lagergren383,471, Phillip H. Lai166, Peter W. Laird472, Sunil R. Lakhani473, Christopher M. Lalansingh6, Emilie Lalonde6, Fabien C. Lamaze6, Adam Lambert351, Eric Lander8, Pablo Landgraf474,475, Luca Landoni115, Anita Langerød130, Andrés Lanzós230,231,423, Denis Larsimont476, Erik Larsson477, Mark Lathrop189, Loretta M. S. Lau478, Chris Lawerenz55, Rita T. Lawlor177, Michael S. Lawrence8,72,183, Alexander J. Lazar99,108, Xuan Le479, Darlene Lee30, Donghoon Lee5, Eunjung Alice Lee480, Hee Jin Lee417, Jake June-Koo Lee264,266, Jeong-Yeon Lee481, Juhee Lee482, Ming Ta Michael Lee340, Henry Lee-Six49, Kjong-Van Lehmann276,277,278,279,430, Hans Lehrach483, Dido Lenze400, Conrad R. Leonard209,311, Daniel A. Leongamornlert49,178, Ignaty Leshchiner8, Louis Letourneau484, Ivica Letunic71, Douglas A. Levine88,485, Lora Lewis12, Tim Ley486, Chang Li56,396, Constance H. Li6,44, Haiyan Irene Li30, Jun Li21, Lin Li56, Shantao Li5, Siliang Li56,396, Xiaobo Li56,396, Xiaotong Li5, Xinyue Li56, Yilong Li49, Han Liang21, Sheng-Ben Liang234, Peter Lichter68,397, Pei Lin8, Ziao Lin8,487, W. M. Linehan488, Ole Christian Lingjærde489, Dongbing Liu56,396, Eric Minwei Liu88,301,302, Fei-Fei Liu201,490, Fenglin Liu381,491, Jia Liu492, Xingmin Liu56,396, Julie Livingstone6, Dimitri Livitz8, Naomi Livni221, Lucas Lochovsky5,19,110, Markus Loeffler467, Georgina V. Long211, Armando Lopez-Guillermo493, Shaoke Lou5,19, David N. Louis72, Laurence B. Lovat117, Yiling Lu38, Yong-Jie Lu162,494, Youyong Lu495,496,497, Claudio Luchini167, Ilinca Lungu144,148, Xuemei Luo152, Hayley J. Luxton117, Andy G. Lynch223,315,498, Lisa Lype36, Cristina López111,112, Carlos López-Otín499, Eric Z. Ma64, Yussanne Ma30, Gaetan MacGrogan500, Shona MacRae501, Geoff Macintyre223, Tobias Madsen73, Kazuhiro Maejima184, Andrea Mafficini177, Dennis T. Maglinte166,502, Arindam Maitra174, Partha P. Majumder174, Luca Malcovati232, Salem Malikic62,78, Giuseppe Malleo115, Graham J. Mann211,246,503, Luisa Mantovani-Löffler504, Kathleen Marchal505,506, Giovanni Marchegiani115, Elaine R. Mardis1,193,507, Adam A. Margolin31, Maximillian G. Marin37, Florian Markowetz223,315, Julia Markowski403, Jeffrey Marks508, Tomas Marques-Bonet61,81,386,509, Marco A. Marra30, Luke Marsden351, John W. M. Martens332, Sancha Martin49,510, Jose I. Martin-Subero81,511, Iñigo Martincorena49, Alexander Martinez-Fundichely301,302,451 Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Charlie E. Massie223, Thomas J. Matthew37, Lucy Matthews178, Erik Mayer221,512, Simon Mayes513, Michael Mayo30, Faridah Mbabaali272, Karen McCune514, Ultan McDermott49, Patrick D. McGillivray19, Michael D. McLellan1,3,20, John D. McPherson148,272,515, John R. McPherson179,180, Treasa A. McPherson260, Samuel R. Meier8, Alice Meng516, Shaowu Meng134, Andrew Menzies49, Neil D. Merrett115,517, Sue Merson178, Matthew Meyerson8,14,82, William U. Meyerson4,5, Piotr A. Mieczkowski518, George L. Mihaiescu76, Sanja Mijalkovic284, Ana Mijalkovic Mijalkovic-Lazic284, Tom Mikkelsen519, Michele Milella253, Linda Mileshkin103, Christopher A. Miller1 , David K. Miller113,170, Jessica K. Miller272, Gordon B. Mills33,34, Ana Milovanovic57, Sarah Minner520, Marco Miotto115, Gisela Mir Arnau267, Lisa Mirabello240, Chris Mitchell103, Thomas J. Mitchell49,315,352, Satoru Miyano375, Naoki Miyoshi375, Shinichi Mizuno521, Fruzsina Molnár-Gábor522, Malcolm J. Moore300, Richard A. Moore30, Sandro Morganella49, Quaid D. Morris127,490, Carl Morrison523,524, Lisle E. Mose134, Catherine D. Moser349, Ferran Muiños298,299, Loris Mularoni298,299, Andrew J. Mungall30, Karen Mungall30, Elizabeth A. Musgrove133, Ville Mustonen525,526,527, David Mutch528, Francesc Muyas52,53,74, Donna M. Muzny12, Alfonso Muñoz59, Jerome Myers529, Ola Myklebost461, Peter Möller530, Genta Nagae89, Adnan M. Nagrial170, Hardeep K. Nahal-Bose76, Hitoshi Nakagama531, Hidewaki Nakagawa184, Hiromi Nakamura118, Toru Nakamura388, Kaoru Nakano184, Tannistha Nandi532, Jyoti Nangalia49, Mia Nastic284, Arcadi Navarro61,81,386, Fabio C. P. Navarro19, David E. Neal223,352, Gerd Nettekoven533, Felicity Newell209,311, Steven J. Newhouse59, Yulia Newton37, Alvin Wei Tian Ng534, Anthony Ng535, Jonathan Nicholson49, David Nicol221, Yongzhan Nie321,536, G. Petur Nielsen72, Morten Muhlig Nielsen73, Serena Nik-Zainal49,281,282,537, Michael S. Noble8, Katia Nones209,311, Paul A. Northcott538, Faiyaz Notta148,539, Brian D. O’Connor76,540, Peter O’Donnell541, Maria O’Donovan282, Sarah O’Meara49, Brian Patrick O’Neill542, J. Robert O’Neill543, David Ocana59, Angelica Ochoa88, Layla Oesper544, Christopher Ogden221, Hideki Ohdan93, Kazuhiro Ohi375, Lucila Ohno-Machado244, Karin A. Oien523,545, Akinyemi I. Ojesina546,547,548, Hidenori Ojima549, Takuji Okusaka550, Larsson Omberg551, Choon Kiat Ong552, Stephan Ossowski52,53,74, German Ott553, B. F. Francis Ouellette76,554, Christine P’ng6, Marta Paczkowska6, Salvatore Paiella115, Chawalit Pairojkul523, Marina Pajic170, Qiang Pan-Hammarström56,555, Elli Papaemmanuil49, Irene Papatheodorou59, Nagarajan Paramasivam43,47, Ji Wan Park385, Joong-Won Park556, Keunchil Park557,558, Kiejung Park559, Peter J. Park264,266, Joel S. Parker518, Simon L. Parsons124, Harvey Pass560, Danielle Pasternack272, Alessandro Pastore276, Ann-Marie Patch209,311, Iris Pauporté251, Antonio Pea115, John V. Pearson209,311, Chandra Sekhar Pedamallu8,14,82, Jakob Skou Pedersen73,75, Paolo Pederzoli115, Martin Peifer270, Nathan A. Pennell561, Charles M. Perou129,518, Marc D. Perry76,77, Gloria M. Petersen562, Myron Peto22, Nicholas Petrelli563, Robert Petryszak59, Stefan M. Pfister426,465,564, Mark Phillips424, Oriol Pich298,299, Hilda A. Pickett478, Todd D. Pihl565, Nischalan Pillay566, Sarah Pinder567, Mark Pinese170, Andreia V. Pinho568, Esa Pitkänen60, Xavier Pivot569, Elena Piñeiro-Yáñez95, Laura Planko533, Christoph Plass345, Paz Polak8,14,15, Tirso Pons570, Irinel Popescu571, Olga Potapova572, Aparna Prasad52, Shaun R. Preston573, Manuel Prinz47, Antonia L. Pritchard311, Stephenie D. Prokopec6, Elena Provenzano574, Xose S. Puente499, Sonia Puig176, Montserrat Puiggròs57, Sergio Pulido-Tamayo505,506, Gulietta M. Pupo246, Colin A. Purdie575, Michael C. Quinn209,311, Raquel Rabionet52,53,576, Janet S. Rader577, Bernhard Radlwimmer397, Petar Radovic284, Benjamin Raeder60, Keiran M. Raine49, Manasa Ramakrishna49, Kamna Ramakrishnan49, Suresh Ramalingam578, Benjamin J. Raphael579, W. Kimryn Rathmell580, Tobias Rausch60, Guido Reifenberger475, Jüri Reimand6,44, Jorge Reis-Filho348, Victor Reuter348, Iker Reyes-Salazar298, Matthew A. Reyna579, Sheila M. Reynolds36, Esther Rheinbay8,14,72, Yasser Riazalhosseini189, Andrea L. Richardson323, Julia Richter111,128, Matthew Ringel581, Markus Ringnér181, Yasushi Rino582, Karsten Rippe405, Jeffrey Roach583, Lewis R. Roberts349, Nicola D. Roberts49, Steven A. Roberts584, A. Gordon Robertson30, Alan J. Robertson113, Javier Bartolomé Rodriguez57, Bernardo Rodriguez-Martin104,105,106, F. Germán Rodríguez-González83,332, Michael H. A. Roehrl44,123,148,234,585,586, Marius Rohde587, Hirofumi Rokutan440, Gilles Romieu588, Ilse Rooman170, Tom Roques262, Daniel Rosebrock8, Mara Rosenberg8,72, Philip C. Rosenstiel589, Andreas Rosenwald590, Edward W. Rowe221,591, Romina Royo57, Steven G. Rozen179,180,592, Yulia Rubanova17,127, Mark A. Rubin423,593,594,595,596, Carlota Rubio-Perez298,299,597, Vasilisa A. Rudneva60, Borislav C. Rusev177, Andrea Ruzzenente598, Gunnar Rätsch276,277,278,279,280,430, Radhakrishnan Sabarinathan298,299,599, Veronica Y. Sabelnykova6, Sara Sadeghi30, S. Cenk Sahinalp62,78,79, Natalie Saini357, Mihoko Saito-Adachi440, Gordon Saksena8, Adriana Salcedo6, Roberto Salgado600, Leonidas Salichos5,19, Richard Sallari8, Charles Saller601, Roberto Salvia115, Michelle Sam272, Jaswinder S. Samra115,602, Francisco Sanchez-Vega114,121, Chris Sander276,603,604, Grant Sanders134, Rajiv Sarin605, Iman Sarrafi62,78, Aya Sasaki-Oku184, Torill Sauer489, Guido Sauter520, Robyn P. M. Saw211, Maria Scardoni167, Christopher J. Scarlett170,606, Aldo Scarpa177, Ghislaine Scelo194, Dirk Schadendorf68,607, Jacqueline E. Schein30, Markus B. Schilhabel589, Matthias Schlesner47,80, Thorsten Schlomm84,608, Heather K. Schmidt1 , Sarah-Jane Schramm246, Stefan Schreiber609, Nikolaus Schultz121, Steven E. Schumacher8,323, Roland F. Schwarz59,403,405,610, Richard A. Scolyer211,448,602, David Scott428, Ralph Scully611, Raja Seethala612, Ayellet V. Segre8,613, Iris Selander260, Colin A. Semple434, Yasin Senbabaoglu276, Subhajit Sengupta614, Elisabetta Sereni115, Stefano Serra585, Dennis C. Sgroi72, Mark Shackleton103, Nimish C. Shah352, Sagedeh Shahabi234, Catherine A. Shang329, Ping Shang211, Ofer Shapira8,323, Troy Shelton271, Ciyue Shen603,604, Hui Shen615, Rebecca Shepherd49, Ruian Shi490, Yan Shi134, Yu-Jia Shiah6, Tatsuhiro Shibata118,616, Juliann Shih8,82, Eigo Shimizu375, Kiyo Shimizu617, Seung Jun Shin618, Yuichi Shiraishi375, Tal Shmaya285, Ilya Shmulevich36, Solomon I. Shorser6, Charles Short59, Raunak Shrestha62, Suyash S. Shringarpure217, Craig Shriver619, Shimin Shuai6,126, Nikos Sidiropoulos83, Reiner Siebert112,620, Anieta M. Sieuwerts332, Lina Sieverling205,237, Sabina Signoretti202,621, Katarzyna O. Sikora177, Michele Simbolo138, Ronald Simon520, Janae V. Simons134, Jared T. Simpson6,17, Peter T. Simpson473, Samuel Singer115,458, Nasa Sinnott-Armstrong8,217, Payal Sipahimalani30, Tara J. Skelly390, Marcel Smid332, Jaclyn Smith622, Karen Smith-McCune514, Nicholas D. Socci276, Heidi J. Sofia27, Matthew G. Soloway134, Lei Song240, Anil K. Sood623,624,625, Sharmila Sothi626, Christos Sotiriou244, Cameron M. Soulette37, Paul N. Span627, Paul T. Spellman22, Nicola Sperandio177, Andrew J. Spillane211, Oliver Spiro8, Jonathan Spring628, Johan Staaf181, Peter F. Stadler163,164,165, Peter Staib629, Stefan G. Stark277,279,618,630, Lucy Stebbings49, Ólafur Andri Stefánsson631, Oliver Stegle59,60,632, Lincoln D. Stein6,126, Alasdair Stenhouse633, Chip Stewart8, Stephan Stilgenbauer634, Miranda D. Stobbe52,61, Michael R. Stratton49, Jonathan R. Stretch211, Adam J. Struck31, Joshua M. Stuart24,37, Henk G. Stunnenberg396,635, Hong Su56,396, Xiaoping Su99, Ren X. Sun6, Stephanie Sungalee60, Hana Susak52,53, Akihiro Suzuki89,636, Fred Sweep637, Monika Szczepanowski128, Holger Sültmann67,638, Takashi Yugawa617, Angela Tam30, David Tamborero298,299, Benita Kiat Tee Tan639, Donghui Tan518, Patrick Tan180,532,592,640, Hiroko Tanaka375, Hirokazu Taniguchi616, Tomas J. Tanskanen641, Maxime Tarabichi49,290, Roy Tarnuzzer220, Patrick Tarpey642, Morgan L. Taschuk152, Kenji Tatsuno89, Simon Tavaré223,643, Darrin F. Taylor113, Amaro Taylor-Weiner8, Jon W. Teague49, Bin Tean Teh180,592,640,644,645, Varsha Tembe246, Javier Temes104,105, Kevin Thai76, Sarah P. Thayer393, Nina Thiessen30, Gilles Thomas646, Sarah Thomas221, Alan Thompson221, Alastair M. Thompson633, John F. Thompson211, R. Houston Thompson647, Heather Thorne103, Leigh B. Thorne176, Adrian Thorogood424, Grace Tiao8, Nebojsa Tijanic284, Lee E. Timms272, Roberto Tirabosco648, Marta Tojo106, Stefania Tommasi649, Christopher W. Toon170, Umut H. Toprak48,650, David Torrents57,81, Giampaolo Tortora651,652, Jörg Tost653, Yasushi Totoki118, David Townend654, Nadia Traficante103, Isabelle Treilleux655,656, Jean-Rémi Trotta61, Lorenz H. P. Trümper469, Ming Tsao124,539, Tatsuhiko Tsunoda183,657,658,659, Jose M. C. Tubio104,105,106, Olga Tucker660, Richard Turkington661, Daniel J. Turner513, Andrew Tutt323, Masaki Ueno376, Naoto T. Ueno662, Christopher Umbricht151,213,663, Husen M. Umer305,664, Timothy J. Underwood665, Lara Urban59,60, Tomoko Urushidate616, Tetsuo Ushiku339, Liis Uusküla-Reimand666,667, Alfonso Valencia57,81, David J. Van Den Berg166, Steven Van Laere307, Peter Van Loo290,291, Erwin G. Van Meir668, Gert G. Van den Eynden307, Theodorus Van der Kwast123, Naveen Vasudev137, Miguel Vazquez57,669, Ravikiran Vedururu267, Umadevi Veluvolu518, Shankar Vembu490,670, Lieven P. C. Verbeke506,671, Peter Vermeulen307, Clare Verrill351,672, Alain Viari177, David Vicente57, Caterina Vicentini177, K. Vijay Raghavan365, Juris Viksna673, Ricardo E. Vilain674, Izar Villasante57, Anne Vincent-Salomon635, Tapio Visakorpi190, Douglas Voet8, Paresh Vyas311,351, Ignacio Vázquez-García49,86,675,676, Nick M. Waddell209, Nicola Waddell209,311, Claes Wadelius677, Lina Wadi6, Rabea Wagener111,112, Jeremiah A. Wala8,14,82, Jian Wang56, Jiayin Wang1,40,66, Linghua Wang12, Qi Wang465, Wenyi Wang21, Yumeng Wang21, Zhining Wang220, Paul M. Waring523, Hans-Jörg Warnatz483, Jonathan Warrell5,19, Anne Y. Warren352,678, Sebastian M. Waszak60, David C. Wedge49,294,679, Dieter Weichenhan345, Paul Weinberger680, John N. Weinstein38, Joachim Weischenfeldt60,83,84, Daniel J. Weisenberger166, Ian Welch681, Michael C. Wendl1,10,11, Johannes Werner47,85, Justin P. Whalley61,682, David A. Wheeler12,13, Hayley C. Whitaker117, Dennis Wigle683, Matthew D. Wilkerson518, Ashley Williams244, James S. Wilmott211, Gavin W. Wilson6,148, Julie M. Wilson148, Richard K. Wilson1,684, Boris Winterhoff685, Jeffrey A. Wintersinger17,127,384, Maciej Wiznerowicz686,687, Stephan Wolf688, Bernice H. Wong689, Tina Wong1,30, Winghing Wong690, Youngchoon Woo250, Scott Wood209,311, Bradly G. Wouters44, Adam J. Wright6, Derek W. Wright133,691, Mark H. Wright217, Chin-Lee Wu72, Dai-Ying Wu285, Guanming Wu692, Jianmin Wu170, Kui Wu56,396, Yang Wu179,180, Zhenggang Wu64, Liu Xi12, Tian Xia693, Qian Xiang76, Xiao Xiao66, Rui Xing497, Heng Xiong56,396, Qinying Xu209,311, Yanxun Xu694, Hong Xue64, Shinichi Yachida118,695, Sergei Yakneen60, Rui Yamaguchi375, Takafumi N. Yamaguchi6, Masakazu Yamamoto120, Shogo Yamamoto89, Hiroki Yamaue376, Fan Yang490, Huanming Yang56, Jean Y. Yang696, Liming Yang220, Lixing Yang697, Shanlin Yang306, Tsun-Po Yang270, Yang Yang369, Xiaotong Yao408,698, Marie-Laure Yaspo483, Lucy Yates49, Christina Yau156, Chen Ye56,396, Kai Ye40,41, Venkata D. Yellapantula20,86, Christopher J. Yoon249, Sung-Soo Yoon463, Fouad Yousif6, Jun Yu699, Kaixian Yu700, Willie Yu701, Yingyan Yu702, Ke Yuan223,510,703, Yuan Yuan21, Denis Yuen6, Takashi Yugawa617, Christina K. Yung76, Olga Zaikova704, Jorge Zamora49,104,105,106, Marc Zapatka397, Jean C. Zenklusen220, Thorsten Zenz67, Nikolajs Zeps705,706, Cheng-Zhong Zhang8,707, Fan Zhang381, Hailei Zhang8, Hongwei Zhang494, Hongxin Zhang121, Jiashan Zhang220, Jing Zhang5, Junjun Zhang76, Xiuqing Zhang56, Xuanping Zhang66,369, Yan Zhang5,708,709, Zemin Zhang381,710, Zhongming Zhao711, Liangtao Zheng381, Xiuqing Zheng381, Wanding Zhou615, Yong Zhou56, Bin Zhu240, Hongtu Zhu700,712, Jingchun Zhu24, Shida Zhu56,396, Lihua Zou713, Xueqing Zou49, Anna deFazio246,247,714, Nicholas van As221, Carolien H. M. van Deurzen715, Marc J. van de Vijver523, L. van’t Veer716 & Christian von Mering433,717, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

In vivo studies in animal models are critical tools necessary to study the fundamental complexity of carcinogenesis. A constant strive to improve animal models in cancer exists, especially those investigating the use of chemotherapeutic effectiveness. In the present systematic review, colorectal cancer (CRC) is used as an example to highlight and critically evaluate the range of reporting strategies used when investigating chemotherapeutic agents in the preclinical setting. A systematic review examining the methodology and reporting of preclinical chemotherapeutic drug studies using CRC murine models was conducted. A total of 45 studies were included in this systematic review. The literature was found to be highly heterogeneous with various cell lines, animal strains, animal ages and chemotherapeutic compounds/regimens tested, proving difficult to compare outcomes between similar studies or indeed gain any significant insight into which chemotherapeutic regimen caused adverse events. From this analysis we propose a minimum core outcome dataset that could be regarded as a standardised way of reporting results from in vivo experimentation.

IntroductionSurgery (oesophagectomy), with neoadjuvant chemo(radio)therapy, is the main curative treatment for patients with oesophageal cancer. Several surgical approaches can be used to remove an oesophageal tumour. The Ivor Lewis (two-phase procedure) is usually used in the UK. This can be performed as an open oesophagectomy (OO), a laparoscopically assisted oesophagectomy (LAO) or a totally minimally invasive oesophagectomy (TMIO). All three are performed in the National Health Service, with LAO and OO the most common. However, there is limited evidence about which surgical approach is best for patients in terms of survival and postoperative health-related quality of life.Methods and analysisWe will undertake a UK multicentre randomised controlled trial to compare LAO with OO in adult patients with oesophageal cancer. The primary outcome is patient-reported physical function at 3 and 6 weeks postoperatively and 3 months after randomisation. Secondary outcomes include: postoperative complications, survival, disease recurrence, other measures of quality of life, spirometry, success of patient blinding and quality assurance measures. A cost-effectiveness analysis will be performed comparing LAO with OO. We will embed a randomised substudy to evaluate the safety and evolution of the TMIO procedure and a qualitative recruitment intervention to optimise patient recruitment. We will analyse the primary outcome using a multi-level regression model. Patients will be monitored for up to 3 years after their surgery.Ethics and disseminationThis study received ethical approval from the South-West Franchay Research Ethics Committee. We will submit the results for publication in a peer-reviewed journal.Trial registration numberNCT10386621.

Particle sedimentation is deleterious to a tremendous variety of microfluidic applications. Using an open instrumentation approach we show that syringe rotation retains particles in a suspended state, providing a universal solution for the continuous delivery of particulate samples to microfluidic processors.

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., Early recurrence after surgery for adenocarcinoma of the oesophagus is common. A risk prediction model was derived using modern machine learning methods that accurately predicts risk of early recurrence using postoperative pathology. Machine learning may help

The molecular pathways involved in the development and treatment of oesophageal cancer are complex. Recent large-scale genome sequencing studies have delivered novel insights into aetiology and possible targeted treatments. Oesophageal squamous cell carcinoma (OSCC) and adenocarcinoma (OAC) are distinct entities. At the molecular level OSCC is more similar to squamous cell cancers in other organs than OAC. Whilst considerable heterogeneity exists in both tumour types new data suggests that driver gene events and mutational signatures may be able to categorise tumours into potentially actionable subtypes. Taken together these findings not only suggest new avenues for treatments in a cancer type with appalling outcomes, but also a new era of molecular rather than purely anatomical classification and staging of oesophageal cancer.

Background & Aims: Most patients with esophageal adenocarcinoma (EAC) present with de novo tumors. Although this could be due to inadequate screening strategies, the precise reason for this observation is not clear. We compared survival of patients with prevalent EAC with and without synchronous Barrett esophagus (BE) with intestinal metaplasia (IM) at the time of EAC diagnosis. Methods: Clinical data were studied using Cox proportional hazards regression to evaluate the effect of synchronous BE-IM on EAC survival independent of age, sex, TNM stage, and tumor location. We analyzed data from a cohort of patients with EAC from the Mayo Clinic (n=411; 203 with BE and IM) and a multicenter cohort from the United Kingdom (n=1417; 638 with BE and IM). Results: In the Mayo cohort, BE with IM had a reduced risk of death compared to patients without BE and IM (hazard ratio [HR] 0.44; 95% CI, 0.34–0.57; PConclusion: Two types of EAC can be characterized based on the presence or absence of BE. These findings could increase our understanding the etiology of EAC, and be used in management and prognosis of patients.

Background: Esophageal adenocarcinoma is a male-dominant disease, but the role of androgens is unclear. Aims: To examine the expression and clinical correlates of the androgen receptor (AR) and the androgen-responsive gene FK506-binding protein 5 (FKBP5) in esophageal adenocarcinoma. Methods: Expression of AR and FKBP5 was determined by immunohistochemistry. The effect of the AR ligand 5α-dihydrotestosterone (DHT) on the expression of a panel of androgen-responsive genes was measured in AR-positive and AR-negative esophageal adenocarcinoma cell lines. Correlations in expression between androgen-responsive genes were analyzed in an independent cohort of esophageal adenocarcinoma tissues. Results: There was AR staining in 75 of 77 cases (97 %), and FKBP5 staining in 49 (64 %), all of which had nuclear AR. Nuclear AR with FKBP5 expression was associated with decreased median survival (451 vs. 2800 days) and was an independent prognostic indicator (HR 2.894, 95 % CI 1.396–6.002, p = 0.0043) in multivariable Cox proportional hazards models. DHT induced a significant increase in expression of the androgen-responsive genes FKBP5, HMOX1, FBXO32, VEGFA, WNT5A, and KLK3 only in AR-positive cells in vitro. Significant correlations in expression were observed between these androgen-responsive genes in an independent cohort of esophageal adenocarcinoma tissues. Conclusion: Nuclear AR and expression of FKBP5 is associated with decreased survival in esophageal adenocarcinoma. Refereed/Peer-reviewed

Interactions between cancer cells and cancer-associated fibroblasts (CAF) play an important role in tumour development and progression. In this study we investigated the functional role of CAF in oesophageal adenocarcinoma (EAC). We used immunochemistry to analyse a cohort of EAC patients (183 patients) for CAF markers related to disease mortality. We characterized CAF and normal oesophageal fibroblasts (NOF) using western blotting, immunofluorescence and gel contraction. Transwell assays, 3-D organotypic culture and xenograft models were used to examine effects on EAC cell function, and dissect molecular mechanisms regulating invasion. Most EAC (93%) contained CAF with a myofibroblastic (?-SMA-positive) phenotype, which correlated significantly with poor survival (p?=?0.016; HR 7. 1 (1.7-29.4). Primary CAF, isolated from EAC, have a contractile, myofibroblastic phenotype, and promote EAC cell invasion in vitro (Transwell assays, p?=?