Literature DB >> 33989014

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

Dana E Rollison1, Steven A Eschrich2, Jamie K Teer2, Phillip Reisman3, Erin Siegel4, Chandan Challa5, Patricia Lewis6, Katherine Fellows6, Everin Malpica5, Rodrigo Carvajal7, Guillermo Gonzalez7, Scott Cukras7, Miguel Betin-Montes7, Garrick Aden-Buie8, Melissa Avedon9, Daniel Manning10, Aik Choon Tan2, Brooke L Fridley2, Travis Gerke3, Mattias Van Looveren5, Amilcar Blake5, Jennifer Greenman10.   

Abstract

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.

Entities:  

Mesh:

Year:  2021        PMID: 33989014      PMCID: PMC8240785          DOI: 10.1200/CCI.20.00175

Source DB:  PubMed          Journal:  JCO Clin Cancer Inform        ISSN: 2473-4276


  28 in total

Review 1.  Implementing personalized cancer care.

Authors:  Richard L Schilsky
Journal:  Nat Rev Clin Oncol       Date:  2014-04-01       Impact factor: 66.675

Review 2.  An Architecture for Translational Cancer Research As Exemplified by the German Cancer Consortium.

Authors:  Martin Lablans; Esther Erika Schmidt; Frank Ückert
Journal:  JCO Clin Cancer Inform       Date:  2018-12

Review 3.  Implementing personalized medicine in a cancer center.

Authors:  David A Fenstermacher; Robert M Wenham; Dana E Rollison; William S Dalton
Journal:  Cancer J       Date:  2011 Nov-Dec       Impact factor: 3.360

4.  Key Lessons Learned from Moffitt's Molecular Tumor Board: The Clinical Genomics Action Committee Experience.

Authors:  Todd C Knepper; Gillian C Bell; J Kevin Hicks; Eric Padron; Jamie K Teer; Teresa T Vo; Nancy K Gillis; Neil T Mason; Howard L McLeod; Christine M Walko
Journal:  Oncologist       Date:  2017-02-08

5.  BigQ: a NoSQL based framework to handle genomic variants in i2b2.

Authors:  Matteo Gabetta; Ivan Limongelli; Ettore Rizzo; Alberto Riva; Daniele Segagni; Riccardo Bellazzi
Journal:  BMC Bioinformatics       Date:  2015-12-29       Impact factor: 3.169

6.  Roadmap to a Comprehensive Clinical Data Warehouse for Precision Medicine Applications in Oncology.

Authors:  David J Foran; Wenjin Chen; Huiqi Chu; Evita Sadimin; Doreen Loh; Gregory Riedlinger; Lauri A Goodell; Shridar Ganesan; Kim Hirshfield; Lorna Rodriguez; Robert S DiPaola
Journal:  Cancer Inform       Date:  2017-03-02

Review 7.  Big Data Analytics for Genomic Medicine.

Authors:  Karen Y He; Dongliang Ge; Max M He
Journal:  Int J Mol Sci       Date:  2017-02-15       Impact factor: 5.923

8.  Combining clinical and genomics queries using i2b2 - Three methods.

Authors:  Shawn N Murphy; Paul Avillach; Riccardo Bellazzi; Lori Phillips; Matteo Gabetta; Alal Eran; Michael T McDuffie; Isaac S Kohane
Journal:  PLoS One       Date:  2017-04-07       Impact factor: 3.240

9.  Architecture and Implementation of a Clinical Research Data Warehouse for Prostate Cancer.

Authors:  Martin G Seneviratne; Tina Seto; Douglas W Blayney; James D Brooks; Tina Hernandez-Boussard
Journal:  EGEMS (Wash DC)       Date:  2018-06-01

Review 10.  Next-generation sequencing to guide cancer therapy.

Authors:  Jeffrey Gagan; Eliezer M Van Allen
Journal:  Genome Med       Date:  2015-07-29       Impact factor: 11.117

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  3 in total

1.  Sensor Data Integration: A New Cross-Industry Collaboration to Articulate Value, Define Needs, and Advance a Framework for Best Practices.

Authors:  Ieuan Clay; Christian Angelopoulos; Anne Lord Bailey; Aaron Blocker; Simona Carini; Rodrigo Carvajal; David Drummond; Kimberly F McManus; Ingrid Oakley-Girvan; Krupal B Patel; Phillip Szepietowski; Jennifer C Goldsack
Journal:  J Med Internet Res       Date:  2021-11-09       Impact factor: 5.428

Review 2.  Optimizing the Retrieval of the Vital Status of Cancer Patients for Health Data Warehouses by Using Open Government Data in France.

Authors:  Olivier Lauzanne; Jean-Sébastien Frenel; Mustapha Baziz; Mario Campone; Judith Raimbourg; François Bocquet
Journal:  Int J Environ Res Public Health       Date:  2022-04-02       Impact factor: 3.390

3.  The Challenges of Implementing Comprehensive Clinical Data Warehouses in Hospitals.

Authors:  François Bocquet; Mario Campone; Marc Cuggia
Journal:  Int J Environ Res Public Health       Date:  2022-06-16       Impact factor: 4.614

  3 in total

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