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.

Accessible Pipeline for Translational Research Using TCGA: Examples of Relating Gene Mechanism to Disease-Specific Outcomes.

Bioinformatic scientists are often asked to do widespread analyses of publicly available datasets in order to identify genetic alterations in cancer for genes of interest; therefore, we sought to create a set of tools to conduct common statistical analyses of The Cancer Genome Atlas (TCGA) data. These tools have been developed in response to requests from our collaborators to ask questions, validate findings, and better understand the function of their gene of interest. We describe here what data we have used, how to obtain it, and what figures we have found useful.

Effects of Tobacco Smoking on the Tumor Immune Microenvironment in Head and Neck Squamous Cell Carcinoma.

PURPOSE: Patients with head and neck squamous cell carcinoma (HNSCC) who actively smoke during treatment have worse survival compared with never-smokers and former-smokers. We hypothesize the poor prognosis in tobacco smokers with HNSCC is, at least in part, due to ongoing suppression of immune response. We characterized the tumor immune microenvironment (TIM) of HNSCC in a retrospective cohort of 177 current, former, and never smokers. EXPERIMENTAL DESIGN: Tumor specimens were subjected to analysis of CD3, CD8, FOXP3, PD-1, PD-L1, and pancytokeratin by multiplex immunofluorescence, whole-exome sequencing, and RNA sequencing. Immune markers were measured in tumor core, tumor margin, and stroma. RESULTS: Our data indicate that current smokers have significantly lower numbers of CD8+ cytotoxic T cells and PD-L1+ cells in the TIM compared with never- and former-smokers. While tumor mutation burden and mutant allele tumor heterogeneity score do not associate with smoking status, gene-set enrichment analyses reveal significant suppression of IFNα and IFNγ response pathways in current smokers. Gene expression of canonical IFN response chemokines, CXCL9, CXCL10, and CXCL11, are lower in current smokers than in former smokers, suggesting a mechanism for the decreased immune cell migration to tumor sites. CONCLUSIONS: These results suggest active tobacco use in HNSCC has an immunosuppressive effect through inhibition of tumor infiltration of cytotoxic T cells, likely as a result of suppression of IFN response pathways. Our study highlights the importance of understanding the interaction between smoking and TIM in light of emerging immune modulators for cancer management.

epiTAD: a web application for visualizing chromosome conformation capture data in the context of genetic epidemiology.

SUMMARY: Complementary advances in genomic technology and public data resources have created opportunities for researchers to conduct multifaceted examination of the genome on a large scale. To meet the need for integrative genome wide exploration, we present epiTAD. This web-based tool enables researchers to compare genomic 3D organization and annotations across multiple databases in an interactive manner to facilitate in silico discovery. AVAILABILITY AND IMPLEMENTATION: epiTAD can be accessed at https://apps.gerkelab.com/epiTAD/ where we have additionally made publicly available the source code and a Docker containerized version of the application.

Rough set theory based prognostic classification models for hospice referral.

BACKGROUND: This paper explores and evaluates the application of classical and dominance-based rough set theory (RST) for the development of data-driven prognostic classification models for hospice referral. In this work, rough set based models are compared with other data-driven methods with respect to two factors related to clinical credibility: accuracy and accessibility. Accessibility refers to the ability of the model to provide traceable, interpretable results and use data that is relevant and simple to collect. METHODS: We utilize retrospective data from 9,103 terminally ill patients to demonstrate the design and implementation RST- based models to identify potential hospice candidates. The classical rough set approach (CRSA) provides methods for knowledge acquisition, founded on the relational indiscernibility of objects in a decision table, to describe required conditions for membership in a concept class. On the other hand, the dominance-based rough set approach (DRSA) analyzes information based on the monotonic relationships between condition attributes values and their assignment to the decision class. CRSA decision rules for six-month patient survival classification were induced using the MODLEM algorithm. Dominance-based decision rules were extracted using the VC-DomLEM rule induction algorithm. RESULTS: The RST-based classifiers are compared with other predictive and rule based decision modeling techniques, namely logistic regression, support vector machines, random forests and C4.5. The RST-based classifiers demonstrate average AUC of 69.74 % with MODLEM and 71.73 % with VC-DomLEM, while the compared methods achieve average AUC of 74.21 % for logistic regression, 73.52 % for support vector machines, 74.59 % for random forests, and 70.88 % for C4.5. CONCLUSIONS: This paper contributes to the growing body of research in RST-based prognostic models. RST and its extensions posses features that enhance the accessibility of clinical decision support models. While the non-rule-based methods-logistic regression, support vector machines and random forests-were found to achieve higher AUC, the performance differential may be outweighed by the benefits of the rule-based methods, particularly in the case of VC-DomLEM. Developing prognostic models for hospice referrals is a challenging problem resulting in substandard performance for all of the evaluated classification methods.