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Cancer cells are shaped through an evolutionary process of DNA mutation, cell selection and population expansion. Early steps in this process are driven by a set of mutated driver genes and structural alterations to the genome through copy number gains or losses. Oesophageal adenocarcinoma (EAC) and the pre-invasive tissue, Barrett’s oesophagus (BE), provide an ideal example in which to observe and study this evolution. BE displays early genomic instability, specifically in copy number changes that may later be observed in EAC. Furthermore, these early changes result in patterns of progression (that is, ‘born bad’, gradual or catastrophic) that may help to describe the evolution of EAC. As only a small proportion of patients with BE will go on to develop cancer, a better understanding of these patterns and the resulting genomic changes should improve early detection in EAC and may provide clues for the evolution of cancer more broadly.This Review discusses the genomic evolution of Barrett’s oesophagus, which can sometimes progress to oesophageal adenocarcinoma (EAC). Understanding this evolution should improve early detection of EAC and may provide clues for the evolution of cancer more broadly.

Recent studies show that aneuploidy and driver gene mutations precede cancer diagnosis by many years 1 – 4 . We assess whether these genomic signals can be used for early detection and pre-emptive cancer treatment using the neoplastic precursor lesion Barrett’s esophagus as an exemplar 5 . Shallow whole-genome sequencing of 777 biopsies, sampled from 88 patients in Barrett’s esophagus surveillance over a period of up to 15 years, shows that genomic signals can distinguish progressive from stable disease even 10 years before histopathological transformation. These findings are validated on two independent cohorts of 76 and 248 patients. These methods are low-cost and applicable to standard clinical biopsy samples. Compared with current management guidelines based on histopathology and clinical presentation, genomic classification enables earlier treatment for high-risk patients as well as reduction of unnecessary treatment and monitoring for patients who are unlikely to develop cancer. Longitudinal molecular profiling of copy number alterations in patients with Barrett’s esophagus can identify patients at higher risk of developing esophageal cancer.

Summary Barrett’s oesophagus has been known for many years to display early changes to the genome consistent with the risk for oesophageal adenocarcinoma. Recently we have shown that this information can be used without knowledge of individual gene mutations to accurately predict a patient’s future risk of malignant progression.

Endoscopic surveillance is recommended for patients with Barrett's oesophagus because, although the progression risk is low, endoscopic intervention is highly effective for high-grade dysplasia and cancer. However, repeated endoscopy has associated harms and access has been limited during the COVID-19 pandemic. We aimed to evaluate the role of a non-endoscopic device (Cytosponge) coupled with laboratory biomarkers and clinical factors to prioritise endoscopy for Barrett's oesophagus. We first conducted a retrospective, multicentre, cross-sectional study in patients older than 18 years who were having endoscopic surveillance for Barrett's oesophagus (with intestinal metaplasia confirmed by TFF3 and a minimum Barrett's segment length of 1 cm [circumferential or tongues by the Prague C and M criteria]). All patients had received the Cytosponge and confirmatory endoscopy during the BEST2 (ISRCTN12730505) and BEST3 (ISRCTN68382401) clinical trials, from July 7, 2011, to April 1, 2019 (UK Clinical Research Network Study Portfolio 9461). Participants were divided into training (n=557) and validation (n=334) cohorts to identify optimal risk groups. The biomarkers evaluated were overexpression of p53, cellular atypia, and 17 clinical demographic variables. Endoscopic biopsy diagnosis of high-grade dysplasia or cancer was the primary endpoint. Clinical feasibility of a decision tree for Cytosponge triage was evaluated in a real-world prospective cohort from Aug 27, 2020 (DELTA; ISRCTN91655550; n=223), in response to COVID-19 and the need to provide an alternative to endoscopic surveillance. The prevalence of high-grade dysplasia or cancer determined by the current gold standard of endoscopic biopsy was 17% (92 of 557 patients) in the training cohort and 10% (35 of 344) in the validation cohort. From the new biomarker analysis, three risk groups were identified: high risk, defined as atypia or p53 overexpression or both on Cytosponge; moderate risk, defined by the presence of a clinical risk factor (age, sex, and segment length); and low risk, defined as Cytosponge-negative and no clinical risk factors. The risk of high-grade dysplasia or intramucosal cancer in the high-risk group was 52% (68 of 132 patients) in the training cohort and 41% (31 of 75) in the validation cohort, compared with 2% (five of 210) and 1% (two of 185) in the low-risk group, respectively. In the real-world setting, Cytosponge results prospectively identified 39 (17%) of 223 patients as high risk (atypia or p53 overexpression, or both) requiring endoscopy, among whom the positive predictive value was 31% (12 of 39 patients) for high-grade dysplasia or intramucosal cancer and 44% (17 of 39) for any grade of dysplasia. Cytosponge atypia, p53 overexpression, and clinical risk factors (age, sex, and segment length) could be used to prioritise patients for endoscopy. Further investigation could validate their use in clinical practice and lead to a substantial reduction in endoscopy procedures compared with current surveillance pathways. Medical Research Council, Cancer Research UK, Innovate UK.

Timely detection of Barrett’s esophagus, the pre-malignant condition of esophageal adenocarcinoma, can improve patient survival rates. The Cytosponge-TFF3 test, a non-endoscopic minimally invasive procedure, has been used for diagnosing intestinal metaplasia in Barrett’s. However, it depends on pathologist’s assessment of two slides stained with H&E and the immunohistochemical biomarker TFF3. This resource-intensive clinical workflow limits large-scale screening in the at-risk population. To improve screening capacity, we propose a deep learning approach for detecting Barrett’s from routinely stained H&E slides. The approach solely relies on diagnostic labels, eliminating the need for expensive localized expert annotations. We train and independently validate our approach on two clinical trial datasets, totaling 1866 patients. We achieve 91.4% and 87.3% AUROCs on discovery and external test datasets for the H&E model, comparable to the TFF3 model. Our proposed semi-automated clinical workflow can reduce pathologists’ workload to 48% without sacrificing diagnostic performance, enabling pathologists to prioritize high risk cases. Diagnosis of Barrett’s esophagus depends on pathologist assessment of stained slides. Here, the authors utilise a deep learning approach to prioritize potential cases using diagnostic labels in two datasets, with the aim to improve Barrett’s screening capacity.

Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.

Background For shotgun mass spectrometry based proteomics the most computationally expensive step is in matching the spectra against an increasingly large database of sequences and their post-translational modifications with known masses. Each mass spectrometer can generate data at an astonishingly high rate, and the scope of what is searched for is continually increasing. Therefore solutions for improving our ability to perform these searches are needed. Results We present a sequence database search engine that is specifically designed to run efficiently on the Hadoop MapReduce distributed computing framework. The search engine implements the K-score algorithm, generating comparable output for the same input files as the original implementation. The scalability of the system is shown, and the architecture required for the development of such distributed processing is discussed. Conclusion The software is scalable in its ability to handle a large peptide database, numerous modifications and large numbers of spectra. Performance scales with the number of processors in the cluster, allowing throughput to expand with the available resources.

Background The advances in high-throughput sequencing technologies and growth in data sizes has highlighted the need for scalable tools to perform quality assurance testing. These tests are necessary to ensure that data is of a minimum necessary standard for use in downstream analysis. In this paper we present the SAMQA tool to rapidly and robustly identify errors in population-scale sequence data. Results SAMQA has been used on samples from three separate sets of cancer genome data from The Cancer Genome Atlas (TCGA) project. Using technical standards provided by the SAM specification and biological standards defined by researchers, we have classified errors in these sequence data sets relative to individual reads within a sample. Due to an observed linearithmic speedup through the use of a high-performance computing (HPC) framework for the majority of tasks, poor quality data was identified prior to secondary analysis in significantly less time on the HPC framework than the same data run using alternative parallelization strategies on a single server. Conclusions The SAMQA toolset validates a minimum set of data quality standards across whole-genome and exome sequences. It is tuned to run on a high-performance computational framework, enabling QA across hundreds gigabytes of samples regardless of coverage or sample type.

Background As the volume, complexity and diversity of the information that scientists work with on a daily basis continues to rise, so too does the requirement for new analytic software. The analytic software must solve the dichotomy that exists between the need to allow for a high level of scientific reasoning, and the requirement to have an intuitive and easy to use tool which does not require specialist, and often arduous, training to use. Information visualization provides a solution to this problem, as it allows for direct manipulation and interaction with diverse and complex data. The challenge addressing bioinformatics researches is how to apply this knowledge to data sets that are continually growing in a field that is rapidly changing. Results This paper discusses an approach to the development of visual mining tools capable of supporting the mining of massive data collections used in systems biology research, and also discusses lessons that have been learned providing tools for both local researchers and the wider community. Example tools were developed which are designed to enable the exploration and analyses of both proteomics and genomics based atlases. These atlases represent large repositories of raw and processed experiment data generated to support the identification of biomarkers through mass spectrometry (the PeptideAtlas) and the genomic characterization of cancer (The Cancer Genome Atlas). Specifically the tools are designed to allow for: the visual mining of thousands of mass spectrometry experiments, to assist in designing informed targeted protein assays; and the interactive analysis of hundreds of genomes, to explore the variations across different cancer genomes and cancer types. Conclusions The mining of massive repositories of biological data requires the development of new tools and techniques. Visual exploration of the large-scale atlas data sets allows researchers to mine data to find new meaning and make sense at scales from single samples to entire populations. Providing linked task specific views that allow a user to start from points of interest (from diseases to single genes) enables targeted exploration of thousands of spectra and genomes. As the composition of the atlases changes, and our understanding of the biology increase, new tasks will continually arise. It is therefore important to provide the means to make the data available in a suitable manner in as short a time as possible. We have done this through the use of common visualization workflows, into which we rapidly deploy visual tools. These visualizations follow common metaphors where possible to assist users in understanding the displayed data. Rapid development of tools and task specific views allows researchers to mine large-scale data almost as quickly as it is produced. Ultimately these visual tools enable new inferences, new analyses and further refinement of the large scale data being provided in atlases such as PeptideAtlas and The Cancer Genome Atlas.

IntroductionOesophageal adenocarcinoma (OAC) represents a significant clinical challenge, characterised by a rising prevalence and often detected in advanced stages, leading to a dismal 5-year overall survival rate of less than 20%. The development of OAC from Barrett’s Oesophagus (BO) through various dysplastic stages underscores a critical window for early diagnosis and intervention. Capsule sponge testing has been established in various clinical settings and is currently undergoing widespread adoption. This non-endoscopic approach facilitates cell collection combined with biomarker testing for the detection and surveillance of BO. Adoption within the National Health Services of England and Scotland, has seen the utilisation of two distinct cell collection platforms: the EndoSign® cell collection device (Cyted, UK) and the Cytosponge™ cell collection device (Medtronic, US). The differences in device design and operation raises questions regarding consistency in sample collection and, consequently, diagnosis rates across patient pathways.MethodsThe primary objective of this study is to assess the equivalency of the EndoSign® and Cytosponge™ cell collection devices, measured by a comparison of diagnosis rates and test result recommendations for endoscopic investigation.A total of 755 Cytosponge™ samples and 743 EndoSign® samples from 2023 were retrospectively analysed. All adequate samples per device were subdivided into two distinct patient groups, Symptomatic Referral (N = 582) and Barrett’s Surveillance (N = 786). The total number of patients who were recommended an onward endoscopy for further investigation was identified in each pathway based on their biomarker results.To examine if there was a difference between the endoscopy referral recommendations for the two cell collection devices, two-proportion Z-tests were performed for the individual pathways.ResultsIn the Symptomatic Referral pathway, 27 (10.6%) patients with EndoSign® samples (n=255) and 21 (6.4%) patients with Cytosponge™ samples (n=327) were recommended a confirmatory endoscopy for abnormal biomarker results. In the Barrett’s surveillance pathway, 29 (6.9%) patients with EndoSign® samples (n=422) and 19 (5.2%) patients with Cytosponge™ samples (n=364) were recommended for further endoscopic investigation.A two-proportion Z-test confirmed no statistically significant difference between the Cytosponge™ and EndoSign® devices in onward recommendation to endoscopy in both patient pathways (Symptomatic Referral: Z=1.81, p=0.07; Barrett’s Surveillance: Z=0.96, p=0.33).ConclusionsThis analysis demonstrates that biomarker performance is equivalent across the two cell collection devices. Further studies on healthcare provider experience are ongoing.