Acquire: an open-source comprehensive cancer biobanking system.

MOTIVATION: The probability of effective treatment of cancer with a targeted therapeutic can be improved for patients with defined genotypes containing actionable mutations. To this end, many human cancer biobanks are integrating more tightly with genomic sequencing facilities and with those creating and maintaining patient-derived xenografts (PDX) and cell lines to provide renewable resources for translational research. RESULTS: To support the complex data management needs and workflows of several such biobanks, we developed Acquire. It is a robust, secure, web-based, database-backed open-source system that supports all major needs of a modern cancer biobank. Its modules allow for i) up-to-the-minute 'scoreboard' and graphical reporting of collections; ii) end user roles and permissions; iii) specimen inventory through caTissue Suite; iv) shipping forms for distribution of specimens to pathology, genomic analysis and PDX/cell line creation facilities; v) robust ad hoc querying; vi) molecular and cellular quality control metrics to track specimens' progress and quality; vii) public researcher request; viii) resource allocation committee distribution request review and oversight and ix) linkage to available derivatives of specimen.

Identification and classification of principal features for analyzing unwarranted clinical variation.

RATIONALE, AIMS, AND OBJECTIVE: Unwarranted clinical variation (UCV) is an undesirable aspect of a healthcare system, but analyzing for UCV can be difficult and time-consuming. No analytic feature guidelines currently exist to aid researchers. We performed a systematic review of UCV literature to identify and classify the features researchers have identified as necessary for the analysis of UCV. METHODS: The literature search followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. We looked for articles with the terms 'medical practice variation' and 'unwarranted clinical variation' from four databases: Medline, Web of Science, EMBASE and CINAHL. The search was performed on 24 March 2023. The articles selected were original research articles in the English language reporting on UCV analysis in adult populations. Most of the studies were retrospective cohort analyses. We excluded studies reporting geographic variation based on the Atlas of Variation or small-area analysis methods. We used ASReview Lab software to assist in identifying articles for abstract review. We also conducted subsequent reference searches of the primary articles to retrieve additional articles. RESULTS: The search yielded 499 articles, and we reviewed 46. We identified 28 principal analytic features utilized to analyze for unwarranted variation, categorised under patient-related or local healthcare context factors. Within the patient-related factors, we identified three subcategories: patient sociodemographics, clinical characteristics, and preferences, and classified 17 features into seven subcategories. In the local context category, 11 features are classified under two subcategories. Examples are provided on the usage of each feature for analysis. CONCLUSION: Twenty-eight analytic features have been identified, and a categorisation has been established showing the relationships between features. Identifying and classifying features provides guidelines for known confounders during analysis and reduces the steps required when performing UCV analysis; there is no longer a need for a UCV researcher to engage in time-consuming feature engineering activities.

Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials.

Co-clinical trials are the concurrent or sequential evaluation of therapeutics in both patients clinically and patient-derived xenografts (PDX) pre-clinically, in a manner designed to match the pharmacokinetics and pharmacodynamics of the agent(s) used. The primary goal is to determine the degree to which PDX cohort responses recapitulate patient cohort responses at the phenotypic and molecular levels, such that pre-clinical and clinical trials can inform one another. A major issue is how to manage, integrate, and analyze the abundance of data generated across both spatial and temporal scales, as well as across species. To address this issue, we are developing MIRACCL (molecular and imaging response analysis of co-clinical trials), a web-based analytical tool. For prototyping, we simulated data for a co-clinical trial in "triple-negative" breast cancer (TNBC) by pairing pre- (T0) and on-treatment (T1) magnetic resonance imaging (MRI) from the I-SPY2 trial, as well as PDX-based T0 and T1 MRI. Baseline (T0) and on-treatment (T1) RNA expression data were also simulated for TNBC and PDX. Image features derived from both datasets were cross-referenced to omic data to evaluate MIRACCL functionality for correlating and displaying MRI-based changes in tumor size, vascularity, and cellularity with changes in mRNA expression as a function of treatment.

Transcriptomine, a web resource for nuclear receptor signaling transcriptomes.

The nuclear receptor (NR) superfamily of ligand-regulated transcription factors directs ligand- and tissue-specific transcriptomes in myriad developmental, metabolic, immunological, and reproductive processes. The NR signaling field has generated a wealth of genome-wide expression data points, but due to deficits in their accessibility, annotation, and integration, the full potential of these studies has not yet been realized. We searched public gene expression databases and MEDLINE for global transcriptomic datasets relevant to NRs, their ligands, and coregulators. We carried out extensive, deep reannotation of the datasets using controlled vocabularies for RNA Source and regulating molecule and resolved disparate gene identifiers to official gene symbols to facilitate comparison of fold changes and their significance across multiple datasets. We assembled these data points into a database, Transcriptomine (http://www.nursa.org/transcriptomine), that allows for multiple, menu-driven querying strategies of this transcriptomic "superdataset," including single and multiple genes, Gene Ontology terms, disease terms, and uploaded custom gene lists. Experimental variables such as regulating molecule, RNA Source, as well as fold-change and P value cutoff values can be modified, and full data records can be either browsed or downloaded for downstream analysis. We demonstrate the utility of Transcriptomine as a hypothesis generation and validation tool using in silico and experimental use cases. Our resource empowers users to instantly and routinely mine the collective biology of millions of previously disparate transcriptomic data points. By incorporating future transcriptome-wide datasets in the NR signaling field, we anticipate Transcriptomine developing into a powerful resource for the NR- and other signal transduction research communities.

The Signaling Pathways Project, an integrated 'omics knowledgebase for mammalian cellular signaling pathways.

Mining of integrated public transcriptomic and ChIP-Seq (cistromic) datasets can illuminate functions of mammalian cellular signaling pathways not yet explored in the research literature. Here, we designed a web knowledgebase, the Signaling Pathways Project (SPP), which incorporates community classifications of signaling pathway nodes (receptors, enzymes, transcription factors and co-nodes) and their cognate bioactive small molecules. We then mapped over 10,000 public transcriptomic or cistromic experiments to their pathway node or biosample of study. To enable prediction of pathway node-gene target transcriptional regulatory relationships through SPP, we generated consensus 'omics signatures, or consensomes, which ranked genes based on measures of their significant differential expression or promoter occupancy across transcriptomic or cistromic experiments mapped to a specific node family. Consensomes were validated using alignment with canonical literature knowledge, gene target-level integration of transcriptomic and cistromic data points, and in bench experiments confirming previously uncharacterized node-gene target regulatory relationships. To expose the SPP knowledgebase to researchers, a web browser interface was designed that accommodates numerous routine data mining strategies. SPP is freely accessible at https://www.signalingpathways.org .

Improving the discoverability, accessibility, and citability of omics datasets: a case report.

Although omics datasets represent valuable assets for hypothesis generation, model testing, and data validation, the infrastructure supporting their reuse lacks organization and consistency. Using nuclear receptor signaling transcriptomic datasets as proof of principle, we developed a model to improve the discoverability, accessibility, and citability of published omics datasets. Primary datasets were retrieved from archives, processed to extract data points, then subjected to metadata enrichment and gap filling. The resulting secondary datasets were exposed on responsive web pages to support mining of gene lists, discovery of related datasets, and single-click citation integration with popular reference managers. Automated processes were established to embed digital object identifier-driven links to the secondary datasets in associated journal articles, small molecule and gene-centric databases, and a dataset search engine. Our model creates multiple points of access to reprocessed and reannotated derivative datasets across the digital biomedical research ecosystem, promoting their visibility and usability across disparate research communities.

Discovering relationships between nuclear receptor signaling pathways, genes, and tissues in Transcriptomine.

We previously developed a web tool, Transcriptomine, to explore expression profiling data sets involving small-molecule or genetic manipulations of nuclear receptor signaling pathways. We describe advances in biocuration, query interface design, and data visualization that enhance the discovery of uncharacterized biology in these pathways using this tool. Transcriptomine currently contains about 45 million data points encompassing more than 2000 experiments in a reference library of nearly 550 data sets retrieved from public archives and systematically curated. To make the underlying data points more accessible to bench biologists, we classified experimental small molecules and gene manipulations into signaling pathways and experimental tissues and cell lines into physiological systems and organs. Incorporation of these mappings into Transcriptomine enables the user to readily evaluate tissue-specific regulation of gene expression by nuclear receptor signaling pathways. Data points from animal and cell model experiments and from clinical data sets elucidate the roles of nuclear receptor pathways in gene expression events accompanying various normal and pathological cellular processes. In addition, data sets targeting non-nuclear receptor signaling pathways highlight transcriptional cross-talk between nuclear receptors and other signaling pathways. We demonstrate with specific examples how data points that exist in isolation in individual data sets validate each other when connected and made accessible to the user in a single interface. In summary, Transcriptomine allows bench biologists to routinely develop research hypotheses, validate experimental data, or model relationships between signaling pathways, genes, and tissues.

Nuclear Receptor Signaling Atlas: Opening Access to the Biology of Nuclear Receptor Signaling Pathways.

Signaling pathways involving nuclear receptors (NRs), their ligands and coregulators, regulate tissue-specific transcriptomes in diverse processes, including development, metabolism, reproduction, the immune response and neuronal function, as well as in their associated pathologies. The Nuclear Receptor Signaling Atlas (NURSA) is a Consortium focused around a Hub website (www.nursa.org) that annotates and integrates diverse 'omics datasets originating from the published literature and NURSA-funded Data Source Projects (NDSPs). These datasets are then exposed to the scientific community on an Open Access basis through user-friendly data browsing and search interfaces. Here, we describe the redesign of the Hub, version 3.0, to deploy "Web 2.0" technologies and add richer, more diverse content. The Molecule Pages, which aggregate information relevant to NR signaling pathways from myriad external databases, have been enhanced to include resources for basic scientists, such as post-translational modification sites and targeting miRNAs, and for clinicians, such as clinical trials. A portal to NURSA's Open Access, PubMed-indexed journal Nuclear Receptor Signaling has been added to facilitate manuscript submissions. Datasets and information on reagents generated by NDSPs are available, as is information concerning periodic new NDSP funding solicitations. Finally, the new website integrates the Transcriptomine analysis tool, which allows for mining of millions of richly annotated public transcriptomic data points in the field, providing an environment for dataset re-use and citation, bench data validation and hypothesis generation. We anticipate that this new release of the NURSA database will have tangible, long term benefits for both basic and clinical research in this field.

Research resource: the Endometrium Database Resource (EDR).

In order to understand the biology of the endometrium and potentially develop new diagnostic tools and treatments for endometrial diseases, the highly orchestrated gene expression/regulation that occurs within the uterus must first be understood. Even though a wealth of information on endometrial gene expression/regulation is available, this information is scattered across several different resources in formats that can be difficult for the average bench scientist to query, integrate, and utilize. The Endometrium Database Resource (EDR) was created as a single evolving resource for protein- and micro-RNA-encoding genes that have been shown by gene expression microarray, Northern blot, or other experiments in the literature to have their expression regulated in the uterus of humans, mice, rats, cows, domestic pigs, guinea pigs, and sheep. Genes are annotated in EDR with basic gene information (eg, gene symbol and chromosome), gene orthologs, and gene ontologies. Links are also provided to external resources for publication/s, nucleic and amino acid sequence, gene product function, and Gene Expression Omnibus (GEO) phase expression graph information. The resource also allows for direct comparison of relative gene expression in different microarray experiments for genes shown in the literature to be differentially expressed in the uterus. It is available via a user-friendly, web-based interface and is available without charge or restriction to the entire scientific community. The EDR can be accessed at http://edr.research.bcm.edu.