Field cancerization in breast cancer.

Breast cancer affects one in seven women worldwide during their lifetime. Widespread mammographic screening programs and education campaigns allow for early detection of the disease, often during its asymptomatic phase. Current practice in treatment and recurrence monitoring is based primarily on pathological evaluations but can also encompass genomic evaluations, both of which focus on the primary tumor. Although breast cancer is one of the most studied cancers, patients still recur at a rate of up to 15% within the first 10 years post-surgery. Local recurrence was originally attributed to tumor cells contaminating histologically normal (HN) tissues beyond the surgical margin, but advances in technology have allowed for the identification of distinct aberrations that exist in the peri-tumoral tissues themselves. One leading theory to explain this phenomenon is the field cancerization theory. Under this hypothesis, tumors arise from a field of molecularly altered cells that create a permissive environment for malignant evolution, which can occur with or without morphological changes. The traditional histopathology paradigm dictates that molecular alterations are reflected in the tissue phenotype. However, the spectrum of inter-patient variability of normal breast tissue may obfuscate recognition of a cancerized field during routine diagnostics. In this review, we explore the concept of field cancerization focusing on HN peri-tumoral tissues: we present the pathological and molecular features of field cancerization within these tissues and discuss how the use of peri-tumoral tissues can affect research. Our observations suggest that pathological and molecular evaluations could be used synergistically to assess risk and guide the therapeutic management of patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Longitudinal profiling of circulating tumour DNA for tracking tumour dynamics in pancreatic cancer.

BACKGROUND: The utility of circulating tumour DNA (ctDNA) for longitudinal tumour monitoring in pancreatic ductal adenocarcinoma (PDAC) has not been explored beyond mutations in the KRAS proto-oncogene. Here, we aimed to characterise and track patient-specific somatic ctDNA variants, to assess longitudinal changes in disease burden and explore the landscape of actionable alterations. METHODS: We followed 3 patients with resectable disease and 4 patients with unresectable disease, including 4 patients with ≥ 3 serial follow-up samples, of whom 2 were rare long survivors (> 5 years). We performed whole exome sequencing of tumour gDNA and plasma ctDNA (n = 20) collected over a ~ 2-year period from diagnosis through treatment to death or final follow-up. Plasma from 3 chronic pancreatitis cases was used as a comparison for analysis of ctDNA mutations. RESULTS: We detected > 55% concordance between somatic mutations in tumour tissues and matched serial plasma. Mutations in ctDNA were detected within known PDAC driver genes (KRAS, TP53, SMAD4, CDKN2A), in addition to patient-specific variants within alternative cancer drivers (NRAS, HRAS, MTOR, ERBB2, EGFR, PBRM1), with a trend towards higher overall mutation loads in advanced disease. ctDNA alterations with potential for therapeutic actionability were identified in all 7 patients, including DNA damage response (DDR) variants co-occurring with hypermutation signatures predictive of response to platinum chemotherapy. Longitudinal tracking in 4 patients with follow-up > 2 years demonstrated that ctDNA mutant allele fractions and clonal trends were consistent with CA19-9 measurements and/or clinically reported disease burden. The estimated prevalence of 'stem clones' was highest in an unresectable patient where changes in ctDNA dynamics preceded CA19-9 levels. Longitudinal evolutionary trajectories revealed ongoing subclonal evolution following chemotherapy. CONCLUSION: These results provide proof-of-concept for the use of exome sequencing of serial plasma to characterise patient-specific ctDNA profiles, and demonstrate the sensitivity of ctDNA in monitoring disease burden in PDAC even in unresectable cases without matched tumour genotyping. They reveal the value of tracking clonal evolution in serial ctDNA to monitor treatment response, establishing the potential of applied precision medicine to guide stratified care by identifying and evaluating actionable opportunities for intervention aimed at optimising patient outcomes for an otherwise intractable disease.

The breast cancer oncogene IKKε coordinates mitochondrial function and serine metabolism.

IκB kinase ε (IKKε) is a key molecule at the crossroads of inflammation and cancer. Known to regulate cytokine secretion via NFκB and IRF3, the kinase is also a breast cancer oncogene, overexpressed in a variety of tumours. However, to what extent IKKε remodels cellular metabolism is currently unknown. Here, we used metabolic tracer analysis to show that IKKε orchestrates a complex metabolic reprogramming that affects mitochondrial metabolism and consequently serine biosynthesis independently of its canonical signalling role. We found that IKKε upregulates the serine biosynthesis pathway (SBP) indirectly, by limiting glucose-derived pyruvate utilisation in the TCA cycle, inhibiting oxidative phosphorylation. Inhibition of mitochondrial function induces activating transcription factor 4 (ATF4), which in turn drives upregulation of the expression of SBP genes. Importantly, pharmacological reversal of the IKKε-induced metabolic phenotype reduces proliferation of breast cancer cells. Finally, we show that in a highly proliferative set of ER negative, basal breast tumours, IKKε and PSAT1 are both overexpressed, corroborating the link between IKKε and the SBP in the clinical context.

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.

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.

The Sharing Experimental Animal Resources, Coordinating Holdings (SEARCH) Framework: Encouraging Reduction, Replacement, and Refinement in Animal Research.

While significant medical breakthroughs have been achieved through using animal models, our experience shows that often there is surplus material remaining that is frequently never revisited but could be put to good use by other scientists. Recognising that most scientists are willing to share this material on a collaborative basis, it makes economic, ethical, and academic sense to explore the option to utilise this precious resource before generating new/additional animal models and associated samples. To bring together those requiring animal tissue and those holding this type of archival material, we have devised a framework called Sharing Experimental Animal Resources, Coordinating Holdings (SEARCH) with the aim of making remaining material derived from animal studies in biomedical research more visible and accessible to the scientific community. We encourage journals, funding bodies, and scientists to unite in promoting a new way of approaching animal research by adopting the SEARCH framework.

The integrin αvß6 drives pancreatic cancer through diverse mechanisms and represents an effective target for therapy.

Pancreatic ductal adenocarcinoma (PDAC) has a 5-year survival rate of less than 4% and desperately needs novel effective therapeutics. Integrin αvß6 has been linked with poor prognosis in cancer but its potential as a target in PDAC remains unclear. We report that transcriptional expression analysis revealed that high levels of ß6 mRNA correlated strongly with significantly poorer survival (n = 491 cases, p = 3.17 × 10-8 ). In two separate cohorts, we showed that over 80% of PDACs expressed αvß6 protein and that paired metastases retained αvß6 expression. In vitro, integrin αvß6 promoted PDAC cell growth, survival, migration, and invasion. Treatment of both αvß6-positive human PDAC xenografts and transgenic mice bearing αvß6-positive PDAC with the αvß6 blocking antibody 264RAD, combined with gemcitabine, significantly reduced tumour growth (p < 0.0001) and increased survival (log-rank test, p < 0.05). Antibody therapy was associated with suppression of tumour cell activity (suppression of pErk growth signals, increased apoptosis seen as activated caspase-3) and suppression of the pro-tumourigenic microenvironment (suppression of TGFß signalling, fewer αSMA-positive myofibroblasts, decreased blood vessel density). These data show that αvß6 promotes PDAC growth through both tumour cell and tumour microenvironment mechanisms and represents a valuable target for PDAC therapy. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

BCNTB bioinformatics: the next evolutionary step in the bioinformatics of breast cancer tissue banking.

Here, we present an update of Breast Cancer Now Tissue Bank bioinformatics, a rich platform for the sharing, mining, integration and analysis of breast cancer data. Its modalities provide researchers with access to a centralised information gateway from which they can access a network of bioinformatic resources to query findings from publicly available, in-house and experimental data generated using samples supplied from the Breast Cancer Now Tissue Bank. This in silico environment aims to help researchers use breast cancer data to their full potential, irrespective of any bioinformatics barriers. For this new release, a complete overhaul of the IT and bioinformatic infrastructure underlying the portal has been conducted and a host of novel analytical modules established. We developed and adopted an automated data selection and prioritisation system, expanded the data content and included tissue and cell line data generated from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia, designed a host of novel analytical modalities and enhanced the query building process. Furthermore, the results are presented in an interactive format, providing researchers with greater control over the information on which they want to focus. Breast Cancer Now Tissue Bank bioinformatics can be accessed at http://bioinformatics.breastcancertissuebank.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/.

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.

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.

Novel Role for p110ß PI 3-Kinase in Male Fertility through Regulation of Androgen Receptor Activity in Sertoli Cells.

The organismal roles of the ubiquitously expressed class I PI3K isoform p110ß remain largely unknown. Using a new kinase-dead knockin mouse model that mimics constitutive pharmacological inactivation of p110ß, we document that full inactivation of p110ß leads to embryonic lethality in a substantial fraction of mice. Interestingly, the homozygous p110ß kinase-dead mice that survive into adulthood (maximum ~26% on a mixed genetic background) have no apparent phenotypes, other than subfertility in females and complete infertility in males. Systemic inhibition of p110ß results in a highly specific blockade in the maturation of spermatogonia to spermatocytes. p110ß was previously suggested to signal downstream of the c-kit tyrosine kinase receptor in germ cells to regulate their proliferation and survival. We now report that p110ß also plays a germ cell-extrinsic role in the Sertoli cells (SCs) that support the developing sperm, with p110ß inactivation dampening expression of the SC-specific Androgen Receptor (AR) target gene Rhox5, a homeobox gene critical for spermatogenesis. All extragonadal androgen-dependent functions remain unaffected by global p110ß inactivation. In line with a crucial role for p110ß in SCs, selective inactivation of p110ß in these cells results in male infertility. Our study is the first documentation of the involvement of a signalling enzyme, PI3K, in the regulation of AR activity during spermatogenesis. This developmental pathway may become active in prostate cancer where p110ß and AR have previously been reported to functionally interact.

Disease evolution and outcomes in familial AML with germline CEBPA mutations.

In-depth molecular investigation of familial leukemia has been limited by the rarity of recognized cases. This study examines the genetic events initiating leukemia and details the clinical progression of disease across multiple families harboring germ-line CEBPA mutations. Clinical data were collected from 10 CEBPA-mutated families, representing 24 members with acute myeloid leukemia (AML). Whole-exome (WES) and deep sequencing were performed to genetically profile tumors and define patterns of clonal evolution. Germline CEBPA mutations clustered within the N-terminal and were highly penetrant, with AML presenting at a median age of 24.5 years (range, 1.75-46 years). In all diagnostic tumors tested (n = 18), double CEBPA mutations (CEBPAdm) were detected, with acquired (somatic) mutations preferentially targeting the C-terminal. Somatic CEBPA mutations were unstable throughout the disease course, with different mutations identified at recurrence. Deep sequencing of diagnostic and relapse paired samples confirmed that relapse-associated CEBPA mutations were absent at diagnosis, suggesting recurrence was triggered by novel, independent clones. Integrated WES and deep sequencing subsequently revealed an entirely new complement of mutations at relapse, verifying the presentation of a de novo leukemic episode. The cumulative incidence of relapse in familial AML was 56% at 10 years (n = 11), and 3 patients experienced ≥3 disease episodes over a period of 17 to 20 years. Durable responses to secondary therapies were observed, with prolonged median survival after relapse (8 years) and long-term overall survival (10-year overall survival, 67%). Our data reveal that familial CEBPA-mutated AML exhibits a unique model of disease progression, associated with favorable long-term outcomes.

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 BioMart community portal: an innovative alternative to large, centralized data repositories.

The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.

SEARCHBreast: a new resource to locate and share surplus archival material from breast cancer animal models to help address the 3Rs.

Animal models have contributed to our understanding of breast cancer, with publication of results in high-impact journals almost invariably requiring extensive in vivo experimentation. As such, many laboratories hold large collections of surplus animal material, with only a fraction being used in publications relating to the original projects. Despite being developed at considerable cost, this material is an invisible and hence an underutilised resource, which often ends up being discarded. Within the breast cancer research community there is both a need and desire to make this valuable material available for researchers. Lack of a coordinated system for visualisation and localisation of this has prevented progress. To fulfil this unmet need, we have developed a novel initiative called Sharing Experimental Animal Resources: Coordinating Holdings-Breast (SEARCHBreast) which facilitates sharing of archival tissue between researchers on a collaborative basis and, de facto will reduce overall usage of animal models in breast cancer research. A secure searchable database has been developed where researchers can find, share, or upload materials related to animal models of breast cancer, including genetic and transplant models. SEARCHBreast is a virtual compendium where the physical material remains with the original laboratory. A bioanalysis pipeline is being developed for the analysis of transcriptomics data associated with mouse models, allowing comparative study with human and cell line data. Additionally, SEARCHBreast is committed to promoting the use of humanised breast tissue models as replacement alternatives to animals. Access to this unique resource is freely available to all academic researchers following registration at https://searchbreast.org.

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.