Multimorbidity in patients living with and beyond cancer: protocol for a scoping review.

INTRODUCTION: The number of people living with and beyond cancer is increasing rapidly. Many of them will experience ongoing physical or psychological sequelae as a result of their original cancer diagnosis or comorbidities arising from risk factors common to cancers and other long-term conditions. This poses the complex problem of managing cancer as a 'chronic' illness along with other existing comorbidities. This scoping review aims to map the literature available on multimorbidity in patients living with and beyond cancer, to explore, quantify and understand the impact of comorbid illnesses to inform work around cancer care in UK primary care settings. METHODS AND ANALYSIS: This review will be guided by Joanna Briggs Institute Reviewer's manual for scoping reviews. A systematic literature search using Medical Subject Heading and text words related to cancer survivors and multimorbidity will be performed in MEDLINE, CINAHL, Embase and Web of Science, from 1990. Results will be described in a narrative style, reported in extraction tables and diagrams, and where appropriate in themes and text. ETHICS AND DISSEMINATION: The scoping review will undertake secondary analysis of published literature; therefore, ethics committee approval is not required. Results will be disseminated through a peer-reviewed scientific journal and presented in relevant conferences. The scoping review will inform understanding of the burden of multimorbidity for cancer survivors, thus allow families, practitioners, clinicians and researchers to take the steps necessary to improve patient-centred care.

Differentiating Ductal Adenocarcinoma of the Pancreas from Benign Conditions Using Routine Health Records: A Prospective Case-Control Study.

The study aimed to develop a prediction model for differentiating suspected PDAC from benign conditions. We used a prospective cohort of patients with pancreatic disease (n = 762) enrolled at the Barts Pancreas Tissue Bank (2008-2021) and performed a case-control study examining the association of PDAC (n = 340) with predictor variables including demographics, comorbidities, lifestyle factors, presenting symptoms and commonly performed blood tests. Age (over 55), weight loss in hypertensive patients, recent symptoms of jaundice, high serum bilirubin, low serum creatinine, high serum alkaline phosphatase, low red blood cell count and low serum sodium were identified as the most important features. These predictors were then used for training several machine-learning-based risk-prediction models on 75% of the cohort. Models were assessed on the remaining 25%. A logistic regression-based model had the best overall performance in the validation cohort (area-under-the-curve = 0.90; Spiegelhalter's z = -1·82, p = 0.07). Setting a probability threshold of 0.15 guided by the maximum F2-score of 0.855, 96.8% sensitivity was reached in the full cohort, which could lead to earlier detection of 84.7% of the PDAC patients. The prediction model has the potential to be applied in primary, secondary and emergency care settings for the early distinction of suspected PDAC patients and expedited referral to specialist hepato-pancreatico-biliary services.

Temporality of clinical factors associated with pancreatic cancer: a case-control study using linked electronic health records.

BACKGROUND: Pancreatic cancer risk is poorly quantified in relation to the temporal presentation of medical comorbidities and lifestyle. This study aimed to examine this aspect, with possible influence of demographics. METHODS: We conducted a retrospective case-control study on the ethnically-diverse population of East London, UK, using linked electronic health records. We evaluated the independent and two-way interaction effects of 19 clinico-demographic factors in patients with pancreatic cancer (N = 965), compared with non-malignant pancreatic conditions (N = 3963) or hernia (control; N = 4355), reported between April 1, 2008 and March 6, 2020. Risks were quantified by odds ratios (ORs) and 95% confidence intervals (CIs) from multivariable logistic regression models. RESULTS: We observed increased odds of pancreatic cancer incidence associated with recent-onset diabetes occurring within 6 months to 3 years before cancer diagnosis (OR 1.95, 95% CI 1.25-3.03), long-standing diabetes for over 3 years (OR 1.74, 95% CI 1.32-2.29), recent smoking (OR 1.81, 95% CI 1.36-2.4) and drinking (OR 1.76, 95% CI 1.31-2.35), as compared to controls but not non-malignant pancreatic conditions. Pancreatic cancer odds was highest for chronic pancreatic disease patients (recent-onset: OR 4.76, 95% CI 2.19-10.3, long-standing: OR 5.1, 95% CI 2.18-11.9), amplified by comorbidities or harmful lifestyle. Concomitant diagnosis of diabetes, upper gastrointestinal or chronic pancreatic conditions followed by a pancreatic cancer diagnosis within 6 months were common, particularly in South Asians. Long-standing cardiovascular, respiratory and hepatobiliary conditions were associated with lower odds of pancreatic cancer. CONCLUSIONS: Several factors are, independently or via effect modifications, associated with higher incidence of pancreatic cancer, but some established risk factors demonstrate similar magnitude of risk measures of developing non-malignant pancreatic conditions. The findings may inform refined risk-stratification strategies and better surveillance for high-risk individuals, and also provide a means for systematic identification of target population for prospective cohort-based early detection research initiatives.

COVID-19 in patients with hepatobiliary and pancreatic diseases: a single-centre cross-sectional study in East London.

OBJECTIVE: To explore risk factors associated with COVID-19 susceptibility and survival in patients with pre-existing hepato-pancreato-biliary (HPB) conditions. DESIGN: Cross-sectional study. SETTING: East London Pancreatic Cancer Epidemiology (EL-PaC-Epidem) Study at Barts Health National Health Service Trust, UK. Linked electronic health records were interrogated on a cohort of participants (age ≥18 years), reported with HPB conditions between 1 April 2008 and 6 March 2020. PARTICIPANTS: EL-PaC-Epidem Study participants, alive on 12 February 2020, and living in East London within the previous 6 months (n=15 440). The cohort represents a multi-ethnic population with 51.7% belonging to the non-White background. MAIN OUTCOME MEASURE: COVID-19 incidence and mortality. RESULTS: Some 226 (1.5%) participants had confirmed COVID-19 diagnosis between 12 February and 12 June 2020, with increased odds for men (OR 1.56; 95% CI 1.2 to 2.04) and Black ethnicity (2.04; 1.39 to 2.95) as well as patients with moderate to severe liver disease (2.2; 1.35 to 3.59). Each additional comorbidity increased the odds of infection by 62%. Substance misusers were at more risk of infection, so were patients on vitamin D treatment. The higher ORs in patients with chronic pancreatic or mild liver conditions, age >70, and a history of smoking or obesity were due to coexisting comorbidities. Increased odds of death were observed for men (3.54; 1.68 to 7.85) and Black ethnicity (3.77; 1.38 to 10.7). Patients having respiratory complications from COVID-19 without a history of chronic respiratory disease also had higher odds of death (5.77; 1.75 to 19). CONCLUSIONS: In this large population-based study of patients with HPB conditions, men, Black ethnicity, pre-existing moderate to severe liver conditions, six common medical multimorbidities, substance misuse and a history of vitamin D treatment independently posed higher odds of acquiring COVID-19 compared with their respective counterparts. The odds of death were significantly high for men and Black people.

SNPnexus: a web server for functional annotation of human genome sequence variation (2020 update).

SNPnexus is a web-based annotation tool for the analysis and interpretation of both known and novel sequencing variations. Since its last release, SNPnexus has received continual updates to expand the range and depth of annotations provided. SNPnexus has undergone a complete overhaul of the underlying infrastructure to accommodate faster computational times. The scope for data annotation has been substantially expanded to enhance biological interpretations of queried variants. This includes the addition of pathway analysis for the identification of enriched biological pathways and molecular processes. We have further expanded the range of user directed annotation fields available for the study of cancer sequencing data. These new additions facilitate investigations into cancer driver variants and targetable molecular alterations within input datasets. New user directed filtering options have been coupled with the addition of interactive graphical and visualization tools. These improvements streamline the analysis of variants derived from large sequencing datasets for the identification of biologically and clinically significant subsets in the data. SNPnexus is the most comprehensible web-based application currently available and these new set of updates ensures that it remains a state-of-the-art tool for researchers. SNPnexus is freely available at https://www.snp-nexus.org.

'Multi-omic' data analysis using O-miner.

Innovations in -omics technologies have driven advances in biomedical research. However, integrating and analysing the large volumes of data generated from different high-throughput -omics technologies remain a significant challenge to basic and clinical scientists without bioinformatics skills or access to bioinformatics support. To address this demand, we have significantly updated our previous O-miner analytical suite, to incorporate several new features and data types to provide an efficient and easy-to-use Web tool for the automated analysis of data from '-omics' technologies. Created from a biologist's perspective, this tool allows for the automated analysis of large and complex transcriptomic, genomic and methylomic data sets, together with biological/clinical information, to identify significantly altered pathways and prioritize novel biomarkers/targets for biological validation. Our resource can be used to analyse both in-house data and the huge amount of publicly available information from array and sequencing platforms. Multiple data sets can be easily combined, allowing for meta-analyses. Here, we describe the analytical pipelines currently available in O-miner and present examples of use to demonstrate its utility and relevance in maximizing research output. O-miner Web server is free to use and is available at http://www.o-miner.org.

SNPnexus: assessing the functional relevance of genetic variation to facilitate the promise of precision medicine.

Broader functional annotation of genetic variation is a valuable means for prioritising phenotypically-important variants in further disease studies and large-scale genotyping projects. We developed SNPnexus to meet this need by assessing the potential significance of known and novel SNPs on the major transcriptome, proteome, regulatory and structural variation models. Since its previous release in 2012, we have made significant improvements to the annotation categories and updated the query and data viewing systems. The most notable changes include broader functional annotation of noncoding variants and expanding annotations to the most recent human genome assembly GRCh38/hg38. SNPnexus has now integrated rich resources from ENCODE and Roadmap Epigenomics Consortium to map and annotate the noncoding variants onto different classes of regulatory regions and noncoding RNAs as well as providing their predicted functional impact from eight popular non-coding variant scoring algorithms and computational methods. A novel functionality offered now is the support for neo-epitope predictions from leading tools to facilitate its use in immunotherapeutic applications. These updates to SNPnexus are in preparation for its future expansion towards a fully comprehensive computational workflow for disease-associated variant prioritization from sequencing data, placing its users at the forefront of translational research. SNPnexus is freely available at http://www.snp-nexus.org.

IW-Scoring: an Integrative Weighted Scoring framework for annotating and prioritizing genetic variations in the noncoding genome.

The vast majority of germline and somatic variations occur in the noncoding part of the genome, only a small fraction of which are believed to be functional. From the tens of thousands of noncoding variations detectable in each genome, identifying and prioritizing driver candidates with putative functional significance is challenging. To address this, we implemented IW-Scoring, a new Integrative Weighted Scoring model to annotate and prioritise functionally relevant noncoding variations. We evaluate 11 scoring methods, and apply an unsupervised spectral approach for subsequent selective integration into two linear weighted functional scoring schemas for known and novel variations. IW-Scoring produces stable high-quality performance as the best predictors for three independent data sets. We demonstrate the robustness of IW-Scoring in identifying recurrent functional mutations in the TERT promoter, as well as disease SNPs in proximity to consensus motifs and with gene regulatory effects. Using follicular lymphoma as a paradigmatic cancer model, we apply IW-Scoring to locate 11 recurrently mutated noncoding regions in 14 follicular lymphoma genomes, and validate 9 of these regions in an extension cohort, including the promoter and enhancer regions of PAX5. Overall, IW-Scoring demonstrates greater versatility in identifying trait- and disease-associated noncoding variants. Scores from IW-Scoring as well as other methods are freely available from http://www.snp-nexus.org/IW-Scoring/.

The Pancreatic Expression Database: 2018 update.

The Pancreatic Expression Database (PED, http://www.pancreasexpression.org) continues to be a major resource for mining pancreatic -omics data a decade after its initial release. Here, we present recent updates to PED and describe its evolution into a comprehensive resource for extracting, analysing and integrating publicly available multi-omics datasets. A new analytical module has been implemented to run in parallel with the existing literature mining functions. This analytical module has been created using rich data content derived from pancreas-related specimens available through the major data repositories (GEO, ArrayExpress) and international initiatives (TCGA, GENIE, CCLE). Researchers have access to a host of functions to tailor analyses to meet their needs. Results are presented using interactive graphics that allow the molecular data to be visualized in a user-friendly manner. Furthermore, researchers are provided with the means to superimpose layers of molecular information to gain greater insight into alterations and the relationships between them. The literature-mining module has been improved with a redesigned web appearance, restructured query platforms and updated annotations. These updates to PED are in preparation for its integration with the Pancreatic Cancer Research Fund Tissue Bank (PCRFTB), a vital resource of pancreas cancer tissue for researchers to support and promote cutting-edge research.

BCCTBbp: the Breast Cancer Campaign Tissue Bank bioinformatics portal.

BCCTBbp (http://bioinformatics.breastcancertissue bank.org) was initially developed as the data-mining portal of the Breast Cancer Campaign Tissue Bank (BCCTB), a vital resource of breast cancer tissue for researchers to support and promote cutting-edge research. BCCTBbp is dedicated to maximising research on patient tissues by initially storing genomics, methylomics, transcriptomics, proteomics and microRNA data that has been mined from the literature and linking to pathways and mechanisms involved in breast cancer. Currently, the portal holds 146 datasets comprising over 227,795 expression/genomic measurements from various breast tissues (e.g. normal, malignant or benign lesions), cell lines and body fluids. BCCTBbp can be used to build on breast cancer knowledge and maximise the value of existing research. By recording a large number of annotations on samples and studies, and linking to other databases, such as NCBI, Ensembl and Reactome, a wide variety of different investigations can be carried out. Additionally, BCCTBbp has a dedicated analytical layer allowing researchers to further analyse stored datasets. A future important role for BCCTBbp is to make available all data generated on BCCTB tissues thus building a valuable resource of information on the tissues in BCCTB that will save repetition of experiments and expand scientific knowledge.

The pancreatic expression database: recent extensions and updates.

The Pancreatic Expression Database (PED, http://www.pancreasexpression.org) is the only device currently available for mining of pancreatic cancer literature data. It brings together the largest collection of multidimensional pancreatic data from the literature including genomic, proteomic, microRNA, methylomic and transcriptomic profiles. PED allows the user to ask specific questions on the observed levels of deregulation among a broad range of specimen/experimental types including healthy/patient tissue and body fluid specimens, cell lines and murine models as well as related treatments/drugs data. Here we provide an update to PED, which has been previously featured in the Database issue of this journal. Briefly, PED data content has been substantially increased and expanded to cover methylomics studies. We introduced an extensive controlled vocabulary that records specific details on the samples and added data from large-scale meta-analysis studies. The web interface has been improved/redesigned with a quick search option to rapidly extract information about a gene/protein of interest and an upload option allowing users to add their own data to PED. We added a user guide and implemented integrated graphical tools to overlay and visualize retrieved information. Interoperability with biomart-compatible data sets was significantly improved to allow integrative queries with pancreatic cancer data.

A practical guide for the functional annotation of genetic variations using SNPnexus.

Broader functional annotation of known as well as putative genetic variations is a valuable mean for prioritizing targets in disease studies and large-scale genotyping projects. In this article, we present a practical guide to SNPnexus, a web-based tool that provides an aggregate set of functional annotations for genomic variation data by characterizing related consequences at the transcriptome/proteome levels with in-depth analysis of potential deleterious effects, inferring physical and cytogenetic mapping, reporting related HapMap data, finding overlaps with potential regulatory, structural as well as conserved elements and retrieving links with previously reported genetic disease studies. We focus on the SNPnexus query system, its annotation categories and the biological interpretation of results.

SNPnexus: a web server for functional annotation of novel and publicly known genetic variants (2012 update).

Broader functional annotation of single nucleotide variations is a valuable mean for prioritizing targets in further disease studies and large-scale genotyping projects. We originally developed SNPnexus to assess the potential significance of known and novel SNPs on the major transcriptome, proteome, regulatory and structural variation models in order to identify the phenotypically important variants. Being committed to providing continuous support to the scientific community, we have substantially improved SNPnexus over time by incorporating a broader range of variations such as insertions/deletions, block substitutions, IUPAC codes submission and region-based analysis, expanding the query size limit, and most importantly including additional categories for the assessment of functional impact. SNPnexus provides a comprehensive set of annotations for genomic variation data by characterizing related functional consequences at the transcriptome/proteome levels of seven major annotation systems with in-depth analysis of potential deleterious effects, inferring physical and cytogenetic mapping, reporting information on HapMap genotype/allele data, finding overlaps with potential regulatory elements, structural variations and conserved elements, and retrieving links with previously reported genetic disease studies. SNPnexus has a user-friendly web interface with an improved query structure, enhanced functional annotation categories and flexible output presentation making it practically useful for biologists. SNPnexus is freely available at http://www.snp-nexus.org.

O-miner: an integrative platform for automated analysis and mining of -omics data.

High-throughput profiling has generated massive amounts of data across basic, clinical and translational research fields. However, open source comprehensive web tools for analysing data obtained from different platforms and technologies are still lacking. To fill this gap and the unmet computational needs of ongoing research projects, we developed O-miner, a rapid, comprehensive, efficient web tool that covers all the steps required for the analysis of both transcriptomic and genomic data starting from raw image files through in-depth bioinformatics analysis and annotation to biological knowledge extraction. O-miner was developed from a biologist end-user perspective. Hence, it is as simple to use as possible within the confines of the complexity of the data being analysed. It provides a strong analytical suite able to overlay and harness large, complicated, raw and heterogeneous sets of profiles with biological/clinical data. Biologists can use O-miner to analyse and integrate different types of data and annotations to build knowledge of relevant altered mechanisms and pathways in order to identify and prioritize novel targets for further biological validation. Here we describe the analytical workflows currently available using O-miner and present examples of use. O-miner is freely available at www.o-miner.org.