new methods, it will be increasingly possible to group individual cancers into subpopulations with similar characteristics to predict patient outcomes for cancer therapies. That will help to ensure that the treatments prescribed for patients will be more effective.

Rising health care costs, the increasing availability of new therapies, and the promise of delivering more effective care make it more important than ever to advance the science underlying personalized medicine (PCAST, 2008). Identifying those subpopulations that are likely to respond to therapies can improve and hasten the success rate for the development of new treatments (PCAST, 2008). Being able to predict the therapeutic response and, therefore, being able to deliver safer and more effective treatments to patients will reduce the number of adverse drug events and thus provide cost savings to the entire health care system.

Advances in personalized medicine are rooted in the discovery, validation, and qualification of biomarkers that can be measured by in vitro diagnostic tests on samples from patients or through in vivo biomedical imaging. For example, cancer biomarkers can be used to develop and deliver improved patient care by predicting the likelihood of the response to treatment or the likelihood that an adverse reaction to the treatment will develop (IOM, 2007). Examples of biomarkers routinely used in the treatment of cancer are shown in Table 2-1.

Most diagnostics that are in use today assess a single target; however, it is widely believed that as technologies in genomics, proteomics, metabolomics, and molecular profiling mature, diagnostic platforms capable of simultaneously examining a large number of potential markers will improve the predictive powers of these tests (IOM, 2007). For example, the cost of DNA sequencing is continually decreasing with advances in sequencing technologies, making it more feasible to identify the gene defects underlying a particular type of cancer. These technological advancements could dramatically change how cancer is diagnosed and treated (Niederhuber, 2009). For instance, by applying new sequencing technologies to genome analysis,

TABLE 2-1 Examples of Validated Biomarkers Routinely Used to Predict Response to Cancer Therapy

Therapeutic Agent

Biomarker

Cancer Type

Endocrine therapies (e.g., tamoxifen)

Estrogen receptor

Breast

Trastuzumab

HER-2

Breast

Imatinib mesylate

BCR-ALB

Leukemia

Cetuximab and panitumumab

KRAS

Colorectal

Irinotecan

UGT1A1

Colorectal



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