Opportunities and Challenges for Analyzing Cancer Data at the Inter- and Intra-Institutional Levels

Julie Wu; Jordan Bryan; Samuel M Rubinstein; Lucy Wang; Michele Lenoue-Newton; Raed Zuhour; Mia Levy; Christine Micheel; Yaomin Xu; Suresh K Bhavnani; Lester Mackey; Jeremy L Warner

doi:10.1200/PO.19.00394

Opportunities and Challenges for Analyzing Cancer Data at the Inter- and Intra-Institutional Levels

JCO Precis Oncol. 2020 Jun 25:4:PO.19.00394. doi: 10.1200/PO.19.00394. eCollection 2020.

Authors

Julie Wu¹, Jordan Bryan², Samuel M Rubinstein³, Lucy Wang³, Michele Lenoue-Newton³, Raed Zuhour⁴, Mia Levy^{1

3

5}, Christine Micheel³, Yaomin Xu^{5

6}, Suresh K Bhavnani⁷, Lester Mackey⁸, Jeremy L Warner^{1

3

5}

Affiliations

¹ Department of Internal Medicine, Vanderbilt University, Nashville, TN.
² Duke University, Durham, NC.
³ Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN.
⁴ Department of Radiation Oncology, University of Texas Medical Branch, Galveston, TX.
⁵ Department of Biomedical Informatics, Vanderbilt University, Nashville, TN.
⁶ Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN.
⁷ Institute for Translational Sciences, University of Texas Medical Branch, Galveston, TX.
⁸ Microsoft Research, Cambridge, MA.

Abstract

Purpose: Our goal was to identify the opportunities and challenges in analyzing data from the American Association of Cancer Research Project Genomics Evidence Neoplasia Information Exchange (GENIE), a multi-institutional database derived from clinically driven genomic testing, at both the inter- and the intra-institutional level. Inter-institutionally, we identified genotypic differences between primary and metastatic tumors across the 3 most represented cancers in GENIE. Intra-institutionally, we analyzed the clinical characteristics of the Vanderbilt-Ingram Cancer Center (VICC) subset of GENIE to inform the interpretation of GENIE as a whole.

Methods: We performed overall cohort matching on the basis of age, ethnicity, and sex of 13,208 patients stratified by cancer type (breast, colon, or lung) and sample site (primary or metastatic). We then determined whether detected variants, at the gene level, were associated with primary or metastatic tumors. We extracted clinical data for the VICC subset from VICC's clinical data warehouse. Treatment exposures were mapped to a 13-class schema derived from the HemOnc ontology.

Results: Across 756 genes, there were significant differences in all cancer types. In breast cancer, ESR1 variants were over-represented in metastatic samples (odds ratio, 5.91; q < 10^-6). TP53 mutations were over-represented in metastatic samples across all cancers. VICC had a significantly different cancer type distribution than that of GENIE but patients were well matched with respect to age, sex, and sample type. Treatment data from VICC was used for a bipartite network analysis, demonstrating clusters with a mix of histologies and others being more histology specific.

Conclusion: This article demonstrates the feasibility of deriving meaningful insights from GENIE at the inter- and intra-institutional level and illuminates the opportunities and challenges of the data GENIE contains. The results should help guide future development of GENIE, with the goal of fully realizing its potential for accelerating precision medicine.

Abstract

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