Enabling Precision Medicine in Cancer Care Through a Molecular Data Warehouse: The Moffitt Experience

PURPOSE

The use of genomics within cancer research and clinical oncology practice has become commonplace. Efforts such as The Cancer Genome Atlas have characterized the cancer genome and suggested a wealth of targets for implementing precision medicine strategies for patients with cancer. The data produced from research studies and clinical care have many potential secondary uses beyond their originally intended purpose. Effective storage, query, retrieval, and visualization of these data are essential to create an infrastructure to enable new discoveries in cancer research.

METHODS

Moffitt Cancer Center implemented a molecular data warehouse to complement the extensive enterprise clinical data warehouse (Health and Research Informatics). Seven different sequencing experiment types were included in the warehouse, with data from institutional research studies and clinical sequencing.

RESULTS

The implementation of the molecular warehouse involved the close collaboration of many teams with different expertise and a use case-focused approach. Cornerstones of project success included project planning, open communication, institutional buy-in, piloting the implementation, implementing custom solutions to address specific problems, data quality improvement, and data governance, unique aspects of which are featured here. We describe our experience in selecting, configuring, and loading molecular data into the molecular data warehouse. Specifically, we developed solutions for heterogeneous genomic sequencing cohorts (many different platforms) and integration with our existing clinical data warehouse.

CONCLUSION

The implementation was ultimately successful despite challenges encountered, many of which can be generalized to other research cancer centers.

The pan-cancer, pan-biomarker landscape for precision immuno-oncology

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Background: Precision medicine has the opportunity to also change medical practice. However, there are many cancer treatments for which there are few tools for individualized selection based on patient-specific attributes. This is especially the case for the immune check point inhibitors, where a subset of patients receive almost unimaginable benefit, there is a significant incidence of severe, life threatening, and persistent autoimmune oriented toxicities, and the financial burden is real for patients. At least 3 distinct predictive immunooncology biomarkers (IOMark) have been proposed. This includes tumor mutation analysis, expression of PD-L1, and tumor-specific t cell subset (Weber Predictor). However, there is little data on the intrapatient relationship of these putative response markers. Methods: Analysis of individual patient IOMark status was determined in TCGA patients that had cancers for which immunotherapy is FDA approved (n = 2039; bladder, colorectal, kidney, lung, melanoma, stomach). Patients were categorized as putative high/low predicted responders for each biomarker [neoantigen burden (whole exome cutpoint = 225), PD-L1 (median), Weber predictor (upper 50% for each of CD8/Ki67/EOMES)]. Results: A high neoantigen burden was detected in 1%-60% of the tumors (median 31%; range kidney to melanoma). There was less variance for high PD-L1 (26%-78%, median 49%; colorectal to lung). The Weber predictor ranged from 2% to 41% (kidney to stomach). A subset of each cancer type were positive for each of the response markers (0% to 12%; median 6%; renal to stomach). There is not a high association between the IOMarks. Conclusions: Immuno-oncology is in need of robust IOMark, to allow more informed patient treatment. Analysis of the currently most promising IOMarks show high variation across tumor types and within tumor types. Only a subset of patients has high response predicted for all 3 IOMarks. The IOMarks are independent and identify distinct patient features.