Toward Precision Medicine Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease (2011) / Chapter Skim
Currently Skimming:
2 Why Now?
2 Why Now?
Pages 21-40
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 21... ...
Biology has acquired the capacity to systematically compile molecular data on a scale that was unimaginable 20 years ago. Diverse technological advances make it possible to gather, integrate, ana lyze, and disseminate health-related biological data in ways that could greatly 21
|
From page 22... ...
(Wolinsky 2007; MITRE Corporation 2010; Mardis 2011) While whole-genome sequencing remains ex pensive by the standards of most clinical laboratory tests, the trend-line leaves little doubt that costs will drop into the range of many routine clinical tests within a few years.
|
From page 23... ...
Admittedly, the cost trajectory of DNA sequencing is an unusual success story, even by high-tech standards. However, it is by no means unique: parallel developments in other areas of molecular analysis, such as the analysis of large numbers of small-molecule metabolites and proteins, and the detection of single molecules, are likely to sweep away purely economic barriers to the diffusion of many data-intensive molecular methods into biomedical research and clinical medicine.
|
From page 24... ...
Some successes along these lines have already occurred; however, the scale of these efforts is currently limited by the lack of the infrastructure that would be required to integrate molecular information with electronic medical records during the ordinary course of health care. The human microbiome project represents an additional opportunity to inform human health care.
|
From page 25... ...
A dynamic, continuously evolving Knowledge Network of Disease will be needed to accommo date future additions to this list of specific genetic predispositions to
|
From page 26... ...
This led to the design of much more effective clinical trials as well as reduced treatment costs and increased treatment effectiveness. Since then, many studies have further divided lung cancers into subsets that can be defined by driver mutations.
|
From page 27... ...
Figure 2-2 The traditional characterization of lung cancers based on histology has been re Bitmapped placed over the past 20 years by classifications based on driver mutations. In 1987, this classification was rudimentary as only one driver mutation had been identified, KRAS.
|
From page 28... ...
It is similarly likely that other molecular data (such as epigenetic or metabolomic data) , information on the patient's history of exposure to environmental agents, and psychosocial or behavioral information will all need to be incorporated into a Knowledge Network and New Taxonomy that would enhance the diagnosis and treatment of disease.
|
From page 29... ...
By integrating patient genotype with health information and outcomes data a New Taxonomy could identify many new genetic variants with significant implications for health care. There is every reason to expect that the genetic influences on most common diseases will be complex.
|
From page 30... ...
. The Risk Score was based on data from slightly more than 5,500 subjects, among whom several hundred coronary heart disease (CHD)
|
From page 31... ...
Larger prospective cohort studies such as the Nurses' Health Study (Missmer et al.
|
From page 32... ...
For instance, a particular challenge has been to achieve both meaning ful data sharing and respect for patient privacy concerns, while adhering to ap plicable regulations and laws (Kho et al. 2011; Masys 2011; McGuire et al.
|
From page 33... ...
GATHERING INFORMATION FROM INFORMAL DATA SOURCES The explosive growth of social networks, particularly in the context of healthcare issues, may also serve as a novel source of data on health and disease. Evidence is already accumulating that these alternative and "informal" sources of health-care data, including information shared by individuals from ubiqui tous technologies such as smart phones and social networks, can contribute significantly to collecting disease and health data (Brownstein et al.
|
From page 34... ...
Once enrolled in a research study, the patient -- or, in some cases, simply a tissue sample and a little clinical information -- passed into a research setting that maintained its own infrastructure, including Institutional Review Boards (IRBs) , patient coordinators, clinical evaluation centers, instrumenta tion, laboratory facilities, and data analysis centers.
|
From page 35... ...
• Involve high costs that are largely unnecessary because of increas ing redundancy between the infrastructure present in research and clinical settings. Most of what is needed to carry out data-intensive molecular studies of huge patient populations already exists in the health-care system or, increasingly, will exist as large coordinated health-care organizations absorb increasing portions of the patient population, EHRs are more widely implemented, medical decisions are increasingly driven by molecular analyses (particularly in the realm of oncology, but increasingly in other subspecialties as well)
|
From page 36... ...
Cost constraints on health-care services -- as well as an increasing appreciation of how often conventional medical wisdom is wrong -- has led to a growing outcomes-research enterprise that barely existed a few decades ago. The requirements of outcomes researchers for access to uniform medical records of large patient populations are remarkably similar to those of molecularly oriented researchers.
|
From page 37... ...
The pharmaceutical and biotechnology industries are now lead ing proponents for developing public–private collaborations and consortia in which longitudinal clinical outcomes data can be combined with new molecular technology to develop the deep biological understanding needed to re-define disease based on biological mechanisms. Given the time scale on which private entities must seek return on investment, there is an increased willingness to regard much of this information as precompetitive.
|
From page 38... ...
Broad engagement of a vast array of public and private stakeholders, includ ing university scientists, regulators, health-care providers, payers, government, and perhaps most importantly the public at large, will be required to support and sustain the changes required for development of innovative new therapies that improve health outcomes based on the proposed Knowledge Network of Disease and associated New Taxonomy.
|
From page 39... ...
THE PROPOSED KNOWLEDGE NETWORK OF DISEASE COULD CATALYZE CHANGES IN BIOLOGY, INFORMATION TECHNOLOGY, MEDICINE, AND SOCIETY The powerful forces affecting basic biological research, information tech nology, clinical medicine, and public attitudes toward the privacy of health records and personal genetic information create an unprecedented opportunity to change how biomedical research is conducted and to improve health out comes. The development of the proposed Knowledge Network of Disease and its associated New Taxonomy could take advantage of these forces to inspire revolutionary change.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.