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Trusheim MR, Berndt ER, Douglas FL. Stratified medicine: strategic and economic implications of combining drugs and clinical biomarkers. Nat Rev Drug Discov 6: 287-293

MIT Center for Biomedical Innovation, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139-4307, USA.
Nature Reviews Drug Discovery (Impact Factor: 41.91). 05/2007; 6(4):287-93. DOI: 10.1038/nrd2251
Source: PubMed

ABSTRACT

The potential to use biomarkers for identifying patients that are more likely to benefit or experience an adverse reaction in response to a given therapy, and thereby better match patients with therapies, is anticipated to have a major effect on both clinical practice and the development of new drugs and diagnostics. In this article, we consider current and emerging examples in which therapies are matched with specific patient population characteristics using clinical biomarkers - which we call stratified medicine - and discuss the implications of this approach to future product development strategies and market structures.

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    • "Contract grant sponsors: Leeds CR-UK Centre Award (to G.R.T.); Yorkshire Cancer Research Program (to P.Q.); Leeds University Scholarship (to K.M.S.). Grant number C37059/A11941 from Cancer Research UK to PAC patient [Trusheim et al., 2007] "
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    ABSTRACT: Most cancers arise and evolve as a consequence of somatic mutations. These mutations influence tumor behavior and clinical outcome. Consequently, there is considerable interest in identifying somatic variants within specific genes (such as BRAF, KRAS and EGFR) so that chemotherapy can be tailored to the patient's tumor genotype rather than using a generic treatment based on histological diagnosis alone. Owing to the heterogeneous nature of tumors, a somatic mutation may be present in only a subset of cells, necessitating the use of quantitative techniques to detect rare variants. The highly quantitative nature of next-generation sequencing (NGS), together with the ability to multiplex numerous samples, makes NGS an attractive choice with which to screen for somatic variants. However, the large volumes of sequence data present significant difficulties when applying NGS for the detection of somatic mutations. To alleviate this, we have developed methodologies including a set of data analysis programs, which allow the rapid screening of multiple formalin-fixed, paraffin-embedded samples for the presence of specified somatic variants using unaligned Illumina NGS data.Laboratory Investigation advance online publication, 28 July 2014; doi:10.1038/labinvest.2014.96.
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    • "Matching patients to treatments using particular patient characteristics (such as genetic polymorphisms or symptom profile) is called stratified or personalised medicine. Its benefits have been demonstrated with a number of anticancer medications (Trusheim et al., 2007). Psychiatrists commonly select antidepressants based on symptom profile (Zimmerman et al., 2004), but due to an absence of established clinical moderators (Simon and Perlis, 2010), there is limited evidence to inform this choice. "
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    • "For example, the promise of personalized genomic medicine depends more on the identification of genetic risk markers—which can allow health researchers to classify patients into different risk groups—than it does on being able to " tailor " interventions to a specific individual's genome[24]. In the United Kingdom, this approach is sometimes called stratified medi- cine[25], which more accurately represents both its logic and its potential for social fallout. If the primary use for large medical data sets is to sort our population into strata of medical risk, that suggests that not all groups are created equal; some groups will perforce fall into higher-risk strata for particular health problems. "
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