Molecular medicine is a broad field, where physical, chemical, biological and medical techniques are used to describe molecular structures and mechanisms, identify fundamental molecular and genetic errors of disease, and to develop molecular interventions to correct them. The molecular medicine perspective emphasizes cellular and molecular phenomena and interventions rather than the previous conceptual and observational focus on patients and their organs.1
The phrase “Precision Medicine” encapsulates the idea that molecular information improves the precision with which patients are treated. In cancer, patients will have the tumor’s DNA sequenced, and the readout will be used to select drugs known to work against a particular mutation found in the genome. While few patients today are treated in this way, many academic medical centers are studying precision medicine and hoping to make it standard in cancer care.
Next year, the National Institutes of Health (NIH) expects to begin recruiting at least 1 million people for what may become the world’s largest study of how genes influence disease risks and drug responses. Plans for the study came into focus last week with the release of a blueprint from a panel of human geneticists, medical researchers, and other experts. It urged that NIH recruit participants not only through academic medical centers and health care organizations, but also by issuing an open invitation to anyone living in the United States. The ability to provide precision medicine to patients in routine clinical testing depends on the availability of molecular profiling tests. Many different aspects of precision medicine are tested in research settings (e.g., proteome, microbiome), but routine practice may not use all available inputs. The ability to practice precision medicine is also dependent on the available knowledge bases available to assist clinician in taking action based on test results and the ability to correlate all the data associated with the patient.2
The Future of Big Data
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As I talk to HIT leaders across the Academic Medical Center and Children’s Hospital landscape, I see several themes emerging related to advancing precision medicine:
- Making EMR data available for identifying predictive cohorts. EMR adoption is now providing more data on a wider scale.
- Making omics data available for bio-marker guided therapies. This is historically been a one-off study by study activity.
- Recruiting major players in clinical genomics to bring whole teams for genome sequencing. To date, a relatively small fraction of the patient population has had their DNA fully sequenced, while a larger segment has undergone limited analysis in the area of oncology. 3
- Running pilot projects experimenting with Big Data solutions to explore correlations with genomic and clinical data, environmental data and/or device data.
- Investing in pre-packaged software for storing both clinical and omics data for cohort identification and verification alleviating a heavy IT component for these transformative projects on a wider regional scale for the applicability of precision medicine.
- Building capabilities around using NLP to discover cohorts.
- Broadening the discussion to include consideration for how precision medicine correlates to population health initiatives, including investigating how to optimize the financial impact of disease spending.
- Investigating how to recruit patients for clinical trials using social media including considerations for how to use other social data in the 360 view of both patients and study participants.
The common thread here is “data”, isn’t it? How can we correlate all the data associated with the patient? The key is connecting the records in the hospital with the records out in the doctor’s office, from the laboratory, and all the different components around that particular patient. This information can give out medication, dosing, frequency, adverse reactions. The patient’s record needs to be extended to encompass oncology tumor profiling, residual disease testing and other aspects of the genomic space, in order to get a progression analysis around the disease. Most medical treatments have been designed for the “average patient.” As a result of this “one-size-fits-all-approach,” treatments can be very successful for some patients but not for others. This is changing with the emergence of precision medicine.
To manage the entire lifecycle of biomarker discovery through application with the traceability needed to validate hypotheses, Oracle’s Enterprise Healthcare Analytics platform provides a complete solution that enables the use of open source, proprietary, and acquired algorithms as well as data. Perficient is helping clients such as the University of Colorado to establish a fully integrated informatics “highway” for precision medicine using the Oracle Health Sciences Translational Research Center platform to address formidable challenges such as:
- Data Integration – the most foundational challenge is the need for an integrated data infrastructure spanning fully the genotype and phenotype (i.e., molecular and clinical attributes) of research subjects and patients
- Individual Data Records – relevant data from the basic science, translational, clinical research, and health care domains need to be fully integrated at the individual data record, in order to produce “personalized medicine” models.4
Perficient has the expertise in all these areas and implementing solutions – including strategy and / or the physical implementation or guiding an organization through the entire process. We will be at Oracle OpenWorld this year so stop by booth #1809 in Moscone South to see our demo! #OOW15
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1 https://www.bostonglobe.com/lifestyle/health-wellness/2015/08/29/cancer-treatment-precision-medicine-less-precise-than-promised/tqnqPMC7E8nNoDSPWOeUlK/story.html
2 NIH OPENS PRECISION MEDICINE STUDY TO NATION, Science 25 September 2015: Vol. 349 no. 6255 pp. 1433DOI:10.1126/science.349.6255.1433
3 Tal Behar, Co-founder & Executive Director at PMWC Intl
4 Michael Ames, MBI, Associate Director, Health Data Compass, Center for Biomedical Informatics and Personalized Medicine, University of Colorado
