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Next Generation Biorepository Informatics: Supporting Genomics, Imaging, and Innovations in Spatial Biology

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Clinical Research Informatics

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Abstract

The past three decades catapulted biorepositories and their informatics innovation to the forefront of Precision Medicine and particularly Precision Oncology. The National Institute of Health (NIH)‘s programs, particularly the All of Us Research Program and the Biden Cancer Moonshot initiatives, have been key enablers. The importance of biospecimens and their derivatives, particularly genomic sequencing and expression data coupled with deep clinical annotation from electronic health records, are fueling a new era of deep biologic interrogation of both the cell biology of human tissues and their diseased counterparts. Clear evidence of this is the Chan-Zuckerberg BioHub (CZ BioHub), the Human Biomolecular Atlas (HuBMAP), the Human Tumor Atlas Network (HTAN), the Cellular Senescence Network (SenNet), and international initiatives such as LifeTime. Biorepositories that support spatial biology and cellular microenvironment imaging efforts are fueling deeper understanding of the host microenvironment and how Precision Medicine/Oncology can lead to more effective therapies. The tools of the next-generation biorepository include single cell genomics, whole-slide imaging, and multiplexed analyses to understand cell-cell messaging. Thus, “next-gen” tools are positioned to advance a deeper understanding of therapies that can be used to exploit cellular and molecular interactions within disease tissues. Biorepositories like the National Mesothelioma Virtual Bank (NMVB) are striving to provide an example of what a traditional biorepository can do to become a “Next-Generation” biorepository that not only provides clinical samples with extensive clinical annotations but also enables access to genomic, imaging, and informatics data and supports experimental innovation. This chapter hopes to establish a vision for biorepository informatics fueled by innovative approaches that enable efficient, cost-effective, and sustainable models for advancing biomedical discovery and clinical translation.

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Acknowledgments

This work was supported by the following funding sources for MJB: (1) Center for Disease Control and National Institute for Occupational Safety and Health-5U24 OH009077-15; (2) National Center for Advancing Translational Science-2UL1 TR001857-06; and (3) Patient Centered Outcomes Research Institute Clinical Research Network-RI-CRN-2020-006. For RR: (1) National Institute of General Medical Sciences of the National Institutes of Health-5T32 GM 8208-33; (2) National Library of Medicine-2T15LM007059-36. NOTE: The content is solely the responsibility of the authors and does not necessarily represent the official views of the above listed funding sources. The co-authors would like to acknowledge the excellent editing assistance by Lorrie Ogden.

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  1. Chenyu Li and Rumana Rashid contributed equally with all other contributors.

Authors and Affiliations

  1. Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Chenyu Li, Rumana Rashid, Eugene M. Sadhu & Michael J. Becich

  2. Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA

    Rumana Rashid & Sandro Santagata

  3. Laboratory for Systems Pharmacology, Harvard Medical School, Boston, MA, USA

    Rumana Rashid & Sandro Santagata

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  3. Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA

    Kate Fultz Hollis

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Li, C., Rashid, R., Sadhu, E.M., Santagata, S., Becich, M.J. (2023). Next Generation Biorepository Informatics: Supporting Genomics, Imaging, and Innovations in Spatial Biology. In: Richesson, R.L., Andrews, J.E., Fultz Hollis, K. (eds) Clinical Research Informatics. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-031-27173-1_5

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