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. 2023 Feb 18;13(2):360.
doi: 10.3390/jpm13020360.

Dynamic Biobanking for Advancing Breast Cancer Research

Maryam Abdollahyan  1 , Emanuela Gadaleta  1 , Millahat Asif  1 , Jorge Oscanoa  1 , Rachel Barrow-McGee  2 , Samantha Jones  2 , Louise J Jones  2 , Claude Chelala  1
Affiliations

Dynamic Biobanking for Advancing Breast Cancer Research

Maryam Abdollahyan et al. J Pers Med. .

Abstract

Longitudinal patient biospecimens and data advance breast cancer research through enabling precision medicine approaches for identifying risk, early diagnosis, improved disease management and targeted therapy. Cancer biobanks must evolve to provide not only access to high-quality annotated biospecimens and rich associated data, but also the tools required to harness these data. We present the Breast Cancer Now Tissue Bank centre at the Barts Cancer Institute as an exemplar of a dynamic biobanking ecosystem that hosts and links longitudinal biospecimens and multimodal data including electronic health records, genomic and imaging data, offered alongside integrated data sharing and analytics tools. We demonstrate how such an ecosystem can inform precision medicine efforts in breast cancer research.

Keywords: bioinformatics; breast cancer; electronic health records; genomics; health informatics; precision medicine; translational research.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Overview of BCNTB-BCI infrastructure showing the centralised REDCap database where BCNTB data are stored, linkage to EHRs from BH and genomic data from 100K GP, the integrated Biospecimen and Data Request System and Bioinformatics Portal.
Figure 2
Figure 2
Evolution of disease status in BCNTB-BCI donors over 5 years of follow-up (the ‘Other’ category consists of cosmetic and high-risk cases; new and existing cases at the end of each follow-up year are shown separately).
Figure 3
Figure 3
Prevalence and co-occurrence of conditions comorbid with breast cancer in BCNTB-BCI patients (the numbers are calculated based on the ICD-10 codes used to compute the Elixhauser comorbidity index [28]).
Figure 4
Figure 4
Age at diagnosis of primary breast cancer and ethnicity of BCNTB-BCI patients.
Figure 5
Figure 5
Example patient journey based on BCNTB-BCI data.
Figure 6
Figure 6
Example patient journey based on the structured parts of EHRs from BH (non-cancer-related information are omitted; ICD-10 and OPCS-4 descriptions are shown for diagnoses and procedures, respectively; drugs are shown next to the condition they were prescribed for).
Figure 7
Figure 7
Example patient journey based on imaging reports in EHRs from BH (non-cancer-related information are omitted; keywords highlighted by the NLP algorithm are shown in purple; reports are paraphrased and summarised).
Figure 8
Figure 8
Example of a patient’s sequencing data from 100K GP analysed on Analytics Hub (only gene-drug pairs where alteration match is complete are shown).
See this image and copyright information in PMC

References

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