Tumor classification for tailored cancer therapy

Fiona McLaughlin and Nick LaThangue Prolifix Ltd

91 Milton Park, Abingdon, Oxon 0X14 4RY

Introduction - Cancer is an unmet clinical disease that remains the second largest killer in the Western world, with a steady rise in both the occurrence and death rates due to cancer throughout the last century. Cancer is a genetic disease which, together with its multi-stage nature and the realization that many environmental and dietary factors can influence the disease, has wide ranging implications for its treatment and prevention in the 21st century. Although chemotherapy is currently the most promising treatment in clinical use, its success is greatly limited by a severe lack of understanding of tumor biology. This leads to an inability to predict the response of a given tumor type to a particular treatment regime, and consequently a considerable failure rate in clinical oncology.

The inherent heterogeneity of tumors makes classification frequently difficult and often impossible. Current methods for classifying tumors rely on pathological and clinical diagnosis to determine the stage of progression of the tumor, prediction of the course of disease, interval to metastasis and response to treatment. In this respect, the concept of a tailored therapeutic approach based upon a particular subtype of a disease has received much attention with the advent of the wealth of genetic data resulting from the Human Genome Project. The identification of new genes involved in processes such as cell proliferation, differentiation, apoptosis and tumor suppression has, and continues to increase our understanding of the basic molecular and biological mechanisms of tumor growth. Such advances provide a significant aid to classification and treatment and therefore the overall prognosis of cancer as a disease.

This review focuses on the technologies which offer the most promise in enhancing the ability to classify tumors, based on gene and protein expression profiling. The application of pharmacogenomics to profile both tumors and patients with the aim of defining the most appropriate treatment regime, with minimum toxicology and maximum efficacy, will also be discussed.

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