Big data in healthcare can enable unprecedented understanding of diseases and their treatment, particularly in oncology. These data may include electronic health records, medical imaging, genomic sequencing, payor records, and data from pharmaceutical research, wearables, and medical devices. The ability to combine datasets and use data across many analyses is critical to the successful use of big data and is a concern for those who generate and use the data. Interoperability and data quality continue to be major challenges when working with different healthcare datasets. Mapping terminology across datasets, missing and incorrect data, and varying data structures make combining data an onerous and largely manual undertaking. Data privacy is another concern addressed by the Health Insurance Portability and Accountability Act, the Common Rule, and the General Data Protection Regulation. The use of big data is now included in the planning and activities of the US Food and Drug Administration and the European Medicines Agency. The willingness of organizations to share data in a precompetitive fashion, agreements on data quality standards, and institution of universal and practical tenets on data privacy will be crucial to fully realizing the potential for big data in medicine.

The analysis of big healthcare data has enormous potential as a tool for advancing oncology drug development and patient treatment, particularly in the context of precision medicine. However, there are challenges in organizing, sharing, integrating, and making these data readily accessible to the research community. This review presents five case studies illustrating various successful approaches to addressing such challenges. These efforts are CancerLinQ, the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange, Project Data Sphere, the National Cancer Institute Genomic Data Commons, and the Veterans Health Administration Clinical Data Initiative. Critical factors in the development of these systems include attention to the use of robust pipelines for data aggregation, common data models, data de-identification to enable multiple uses, integration of data collection into physician workflows, terminology standardization and attention to interoperability, extensive quality assurance and quality control activity, incorporation of multiple data types, and understanding how data resources can be best applied. By describing some of the emerging resources, we hope to inspire consideration of the secondary use of such data at the earliest possible step to ensure the proper sharing of data in order to generate insights that advance the understanding and treatment of cancer.

Precancer atlases have the potential to revolutionize how we think about the topographic and morphologic structures of precancerous lesions in relation to cellular, molecular, genetic, and pathophysiologic states. This mini review uses the Human Tumor Atlas Network (HTAN), established by the National Cancer Institute (NCI), to illustrate the construction of cellular and molecular three-dimensional atlases of human cancers as they evolve from precancerous lesions to advanced disease. We describe the collaborative nature of the network and the research to determine how and when premalignant lesions progress to invasive cancer, regress or obtain a state of equilibrium. We have attempted to highlight progress made by HTAN in building precancer atlases and discuss possible future directions. It is hoped that the lessons from our experience with HTAN will help other investigators engaged in the construction of precancer atlases to crystallize their thoughts on logistics, rationale, and implementation.

Introduction: Prostate cancer (CaP) is predominantly indolent disease with a small fraction of patients undergoing metastatic progression. Therefore, it is critical to identify prognostic markers for the early detection of an aggressive subtype. We hypothesized that tissue specimens from early-stage, low risk CaP may harbor predictive prognostic signatures for disease progression after radical prostatectomy. Previously, we had systematically evaluated and optimized the NanoString platform for CaP gene expression analysis in formalin fixed paraffin embedded (FFPE) whole mounted prostate specimens. The goal of the study is to validate the CPDR findings on the differential mRNA expression of candidate markers from patients with BCR (biochemical recurrence) and non-BCR in an independent cohort. Methods: This is a retrospective case cohort study based on 78 tumor-normal paired (N=156) archived whole mounted FFPE prostate samples received from biobank at UTHSCSA. We performed an optimized NanoString analysis of 203-CaP probe set to evaluate the association of markers with BCR outcome in a racially diverse patient population comprising of 21.6% AA and 78.38% CA men. The cohort comprised of 28 patients with BCR, and 50 patients with no BCR (based on a minimum of 5 years of follow-up). ERG gene expression was validated by immunohistochemistry (IHC) assay using CPDR ERG-MAb (9FY). Normalized nCounter gene expression data was analyzed using the NanoString nSolver (v 4.0) platform. Fisher exact and Wilcoxon-Mann-Whitney tests were used for categorical and continuous variables respectively. Results: We found 96.1% (73/76) concordance between ERG IHC and NanoString datasets, which provides strong QC and validation for NanoString data. ERG positive tumors had strong expression of both TMPRSS2-ERG fusion and several other ERG splice forms. Additionally, the prostate epithelial cell markers PSA, MSMB and PAP had the highest signals in all samples reflecting the prostate epithelial origin of the specimens. Differential gene expression analysis showed that a total of 37 genes were found to be significantly (P adj <0.05) different across tumors and normal. Established CaP genes like AMACR, PCA3, OR51E2, ERG, HOXC6, DLX1, CACNA1D and PSGR were most highly over-expressed in tumors compared to matched normal while GSTP1, CAV1, PAP and MSMB were significantly downregulated in tumors compared to adjacent normal prostate epithelia. Race stratified analysis in this randomly selected patient cohort showed that AA men were found to be significantly younger (median age: 56 years AA vs. 63 years CA; p 0.01) and had higher frequency of BCR events (p 0.004) than CA men. Data analysis for disease outcome across AA and CA men is ongoing. Conclusions: We confirmed that NanoString approach is useful for evaluation of prognostic biomarker candidates in prostate cancer using FFPE specimens. Citation Format: Indu Kohaar, Kaitlyn Bejar, Denise Young, Yingjie Song, Jiji Jiang, Jacob Kagan, Sudhir Srivastava, Javier Hernandez, Gregory Chesnut, Isabell A. Sesterhenn, Robin J. Leach, Gyorgy Petrovics. Discovery and validation of prostate cancer biomarkers of biochemical recurrence in low-risk prostate cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1961.

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