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Reinventing Diagnostics for Personalized Therapy in Oncology
Centre for Translational and Applied Genomics (CTAG), Provincial Health Services Authority (PHSA) Laboratories, Vancouver, British Columbia, Canada
Department of Pathology and Laboratory Medicine, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
Department of Pathology, British Columbia Cancer Agency (BCCA), 600 West 10th Avenue, Vancouver, British Columbia, V5Z4E6, Canada
Received: 6 April 2010; in revised form: 15 May 2010 / Accepted: 28 May 2010 / Published: 2 June 2010
Abstract: Human cancers are still diagnosed and classified using the light microscope. The criteria are based upon morphologic observations by pathologists and tend to be subject to interobserver variation. In preoperative biopsies of non-small cell lung cancers, the diagnostic concordance, even amongst experienced pulmonary pathologists, is no better than a coin-toss. Only 25% of cancer patients, on average, benefit from therapy as most therapies do not account for individual factors that influence response or outcome. Unsuccessful first line therapy costs Canada CAN$1.2 billion for the top 14 cancer types, and this extrapolates to $90 billion globally. The availability of accurate drug selection for personalized therapy could better allocate these precious resources to the right therapies. This wasteful situation is beginning to change with the completion of the human genome sequencing project and with the increasing availability of targeted therapies. Both factors are giving rise to attempts to correlate tumor characteristics and response to specific adjuvant and neoadjuvant therapies. Static cancer classification and grading systems need to be replaced by functional classification systems that not only account for intra- and inter- tumor heterogeneity, but which also allow for the selection of the correct chemotherapeutic compounds for the individual patient. In this review, the examples of lung and breast cancer are used to illustrate the issues to be addressed in the coming years, as well as the emerging technologies that have great promise in enabling personalized therapy.
Keywords: lung; breast; genomics; classification; biomarkers; personalized therapy
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Cite This Article
MDPI and ACS Style
Banerjee, D. Reinventing Diagnostics for Personalized Therapy in Oncology. Cancers 2010, 2, 1066-1091.
Banerjee D. Reinventing Diagnostics for Personalized Therapy in Oncology. Cancers. 2010; 2(2):1066-1091.
Banerjee, Diponkar. 2010. "Reinventing Diagnostics for Personalized Therapy in Oncology." Cancers 2, no. 2: 1066-1091.