Toward Precision Medicine Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease (2011) / Chapter Skim
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1 Introduction
1 Introduction
Pages 9-20
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From page 9... ...
Patient 1 is consulting with her medical oncologist following breast cancer surgery. Twenty-five years ago, the patient's mother had breast cancer, when therapeutic options were few: hormonal suppression or broad-spectrum che motherapy with significant side effects.
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From page 10... ...
Consider a world where clinical information, including molecular features, becomes part of a vast "Knowledge Network of Disease" that would support precise diagnosis and individualized treatment. What if the potential of molecular features shared by seemingly disparate diseases to suggest radically new treatment regimens were fully realized?
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From page 11... ...
(see Appendix C) that convened diverse experts in both basic biology and clinical medicine to address the feasibility, need, scope, impact, and consequences of creating a "New Taxonomy of human diseases based on molecular biology".
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The vision for a New Taxonomy informed by the proposed "Knowledge Network" shares some similarities with the widely discussed concept of "Personalized Medicine," recently defined by the President's Council on Advisors on Science and Technology (PCAST) as "the tailoring of medical treatment to the individual characteristics of each patient .
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From page 13... ...
Thus, two extensive stakeholder groups, repre sented on one hand by biomedical researchers, and biotechnology and pharmaceutical industries, and on the other by clinicians, health agencies and payers, are widely perceived to be largely unrelated, and to have distinct interests and goals, and therefore taxonomic needs. This is unfortunate because new insights into human disease emerging from basic research and the explosion of information both in basic biology and medicine have the potential to revolutionize disease taxonomy, diagnosis, therapeutic development, and clinical decisions.
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From page 14... ...
MISSED OPPORTUNITIES OF CURRENT TAXONOMIES Currently used disease classifications have properties that limit their information content and usability. Most importantly, current disease taxonomies, including ICD-10, are primarily based on symptoms, on microscopic examina tion of diseased tissues and cells, and on other forms of laboratory and imaging studies and are not designed optimally to incorporate or exploit rapidly emerg ing molecular data, incidental patient characteristics, or socio-environmental influences on disease.
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From page 15... ...
It also can lead to the artificial separation of diseases based on distinct symptoms that have related underlying molecular mechanisms. For example, mutations in the LMNA gene give rise to a remarkably diverse set of diseases, including Emery-Dreyfus mus cular dystrophy, Charcot-Marie-Tooth axonal neuropathy, lipodystrophy, and premature aging disorders.
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From page 16... ...
Although the release of ICD-11 will mark an important step forward, the Committee thinks that the amount of information available for this effort can be vastly increased by a two-stage process leading to a Knowledge Network of Disease. As discussed in detail in following sections of this report, the first stage in developing this Knowledge Network would involve creating an Information Commons containing a combination of molecular data, medical histories (including information about social and physical environments)
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From page 17... ...
This "New Taxonomy" could, for example, lead to more specific diagnosis and targeted therapies for muscular dystrophy patients based on the specific mutations in their genes. In other cases, it could suggest targeted therapies for patients with the same genetic mechanism of disease despite very different clinical presentations.
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From page 18... ...
The Knowledge Network of Disease would allow researchers to hypothesize new intralayer cluster and interlayer connections. Validated findings that emerge from the Knowledge Network, such as those which define new diseases or subtypes of diseases that are clinically relevant (e.g., which have implications for patient prognosis or therapy)
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From page 19... ...
" It examines basic trends in research, information technology, clinical medicine, and public attitudes that have created an unprecedented opportunity to influ ence biomedical research and health-care delivery in ways that will benefit all stakeholders. Chapter 3 asks "What would a Knowledge Network of Disease and New Taxonomy look like?
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