Castration‐resistant prostate cancer (CRPC) is the most aggressive form of prostate cancer (PCa) and remains a significant therapeutic challenge. The key to the development of novel therapeutic targets for CRPC is to decipher the molecular alterations underlying this lethal disease. The aim of our study was to identify therapeutic targets for CRPC by assessing somatic copy number alterations (SCNAs) by whole‐exome sequencing on five CRPC/normal paired formalin‐fixed paraffin‐embedded (FFPE) samples, using the SOLiD4 next‐generation sequencing (NGS) platform. Data were validated using fluorescence in situ hybridization (FISH) on a PCa progression cohort. PTK2 and YWHAZ amplification, mRNA and protein expression were determined in selected PCa cell lines. Effects of PTK2 inhibition using TAE226 inhibitor and YWHAZ knock‐down on cell proliferation and migration were tested in PC3 cells in vitro. In a larger validation cohort, the amplification frequency of YWHAZ was 3% in localized PCa and 48% in CRPC, whereas PTK2 was amplified in 1% of localized PCa and 35% in CRPC. YWHAZ knock‐down and PTK2 inhibition significantly affected cell proliferation and migration in the PC3 cells. Our findings suggest that inhibition of YWHAZ and PTK2 could delay the progression of the disease in CRPC patients harbouring amplification of the latter genes. Furthermore, our validated whole‐exome sequencing data show that FFPE tissue could be a promising alternative for SCNA screening using next‐generation sequencing technologies. Copyright © 2013 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Somatic copy number alterations by whole-exome sequencing implicates YWHAZ and PTK2 in castration-resistant prostate cancer : YWHAZ and PTK2 in castration-resistant prostate cancer

Imaging has become an invaluable tool in preclinical research for its capability to non-invasively detect and monitor disease and assess treatment response. With the increased use of preclinical imaging, large volumes of image data are being generated requiring critical data management tools. Due to proprietary issues and continuous technology development, preclinical images, unlike DICOM-based images, are often stored in an unstructured data file in company-specific proprietary formats. This limits the available DICOM-based image management database to be effectively used for preclinical applications. A centralized image registry and management tool is essential for advances in preclinical imaging research. Specifically, such tools may have a high impact in generating large image datasets for the evolving artificial intelligence applications and performing retrospective analyses of previously acquired images. In this study, a web-based server application is developed to address some of these issues. The application is designed to reflect the actual experimentation workflow maintaining detailed records of both individual images and experimental data relevant to specific studies and/or projects. The application also includes a web-based 3D/4D image viewer to easily and quickly view and evaluate images. This paper briefly describes the initial implementation of the web-based application.

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Web-Based Application for Biomedical Image Registry, Analysis, and Translation (BiRAT)

Introduction: In biobanks, participants’ biological samples are stored for future research. The application of artificial intelligence (AI) involves the analysis of data and the prediction of any pathological outcomes. In AI, models are used to diagnose diseases as well as classify and predict disease risks. Our research analyzed AI’s role in the development of biobanks in the healthcare industry, systematically. Methods: The literature search was conducted using three digital reference databases, namely PubMed, CINAHL, and WoS. Guidelines for preferred reporting elements for systematic reviews and meta-analyses (PRISMA)-2020 in conducting the systematic review were followed. The search terms included “biobanks”, “AI”, “machine learning”, and “deep learning”, as well as combinations such as “biobanks with AI”, “deep learning in the biobanking field”, and “recent advances in biobanking”. Only English-language papers were included in the study, and to assess the quality of selected works, the Newcastle–Ottawa scale (NOS) was used. The good quality range (NOS ≥ 7) is only considered for further review. Results: A literature analysis of the above entries resulted in 239 studies. Based on their relevance to the study’s goal, research characteristics, and NOS criteria, we included 18 articles for reviewing. In the last decade, biobanks and artificial intelligence have had a relatively large impact on the medical system. Interestingly, UK biobanks account for the highest percentage of high-quality works, followed by Qatar, South Korea, Singapore, Japan, and Denmark. Conclusions: Translational bioinformatics probably represent a future leader in precision medicine. AI and machine learning applications to biobanking research may contribute to the development of biobanks for the utility of health services and citizens.

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A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review

https://doi.org/10.1016/j.mcpro.2023.100561

Multi-Omics Profiling for Health

Abstract Motivation Environmental DNA (eDNA), as a rapidly expanding research field, stands to benefit from shared resources including sampling protocols, study designs, discovered sequences, and taxonomic assignments to sequences. High-quality community shareable eDNA resources rely heavily on comprehensive metadata documentation that captures the complex workflows covering field sampling, molecular biology lab work, and bioinformatic analyses. There are limited sources that provide documentation of database development on comprehensive metadata for eDNA and these workflows and no open-source software. Results We present medna-metadata, an open-source, modular system that aligns with Findable, Accessible, Interoperable, and Reusable guiding principles that support scholarly data reuse and the database and application development of a standardized metadata collection structure that encapsulates critical aspects of field data collection, wet lab processing, and bioinformatic analysis. Medna-metadata is showcased with metabarcoding data from the Gulf of Maine (Polinski et al., 2019). Availability and implementation The source code of the medna-metadata web application is hosted on GitHub (https://github.com/Maine-eDNA/medna-metadata). Medna-metadata is a docker-compose installable package. Documentation can be found at https://medna-metadata.readthedocs.io/en/latest/?badge=latest. The application is implemented in Python, PostgreSQL and PostGIS, RabbitMQ, and NGINX, with all major browsers supported. A demo can be found at https://demo.metadata.maine-edna.org/. Supplementary information Supplementary data are available at Bioinformatics online.

/pdf/medna-metadata-an-open-source-data-management-system-for-1r124l6l.pdf

medna-metadata: an open-source data management system for tracking environmental DNA samples and metadata

As technical developments in omics and biomedical imaging increase the throughput of data generation in life sciences, the need for information systems capable of managing heterogeneous digital assets is increasing. In particular, systems supporting the findability, accessibility, interoperability, and reusability (FAIR) principles of scientific data management.We propose a Service Oriented Architecture approach for integrated management and analysis of multi-omics and biomedical imaging data. Our architecture introduces an image management system into a FAIR-supporting, web-based platform for omics data management. Interoperable metadata models and middleware components implement the required data management operations. The resulting architecture allows for FAIR management of omics and imaging data, facilitating metadata queries from software applications. The applicability of the proposed architecture is demonstrated using two technical proofs of concept and a use case, aimed at molecular plant biology and clinical liver cancer research, which integrate various imaging and omics modalities.We describe a data management architecture for integrated, FAIR-supporting management of omics and biomedical imaging data, and exemplify its applicability for basic biology research and clinical studies. We anticipate that FAIR data management systems for multi-modal data repositories will play a pivotal role in data-driven research, including studies which leverage advanced machine learning methods, as the joint analysis of omics and imaging data, in conjunction with phenotypic metadata, becomes not only desirable but necessary to derive novel insights into biological processes. 

/pdf/a-data-management-infrastructure-for-the-integration-of-21ro06b0.pdf

A data management infrastructure for the integration of imaging and omics data in life sciences

/pdf/a-data-management-infrastructure-for-the-integration-of-3vomxmiy.pdf

In personalized medicine, SNPs are used to identify specific diseases of a patient. However, for many SNPs, no information about the pathogenicity is available. Current programs try to predict the effect of a SNP on the function of a protein, but give no possibility for visual interpretation. We have developed SNPViz, a program that first finds 3D structures of affected proteins and then highlights the affected amino acid in the 3D structure. This can give researchers and doctors more information about the probable pathogenicity of the SNP. In the future, we plan to add also further information, such as whether the position of the SNP is in a binding domain, or is involved in a protein-protein interaction.

https://genomicscomputbiol.org/ojs3/GCB/article/download/48/182

Tobias Koch

Papers

A data management infrastructure for the integration of imaging and omics data in life sciences

A data management infrastructure for the integration of imaging and omics data in life sciences

SNPViz - Visualization of SNPs in proteins