At present it is estimated that only about 15 out of 100 women with breast cancer treated with chemotherapy achieve any benefit. Current cancer treatments have limited efficacy, are toxic, and are typically quite costly.
The monoclonal antibody-based drug cetuximab, which is used to treat colorectal cancer, for example, costs about $12,000 for a four-week treatment. The goal of personalized medicine is to identify novel therapies that more effectively target a well-defined population of tumor cells and to pinpoint the patients in whom these treatments will be more likely to produce positive outcomes, thereby improving efficacy, limiting toxicity, and reducing treatment costs. Although no one drug is likely to stop cancer, the hope is that combinations of compounds that target multiple sites in a relevant biochemical pathway or signaling cascade will be efficacious.
Arul Chinnaiyan, M.D., Ph.D., professor of pathology and urology at the University of Michigan Medical School, chaired a plenary session on the complexity of the cancer genome at the American Association of Cancer Researchers (AACR) annual meeting held recently in Washington, D.C.
Dr. Chinnaiyan spoke of the need for comprehensive analysis to dissect molecular differences between tumor and normal cells and between different tumor subtypes. Enabling technologies such as amplification techniques, high-throughput parallel sequencing, and, more recently, single molecule sequencing are making this possible. Now the focus is moving to the transcriptome and the identification of chimeric RNAs, he told AACR attendees, as well as to epigenetics and understanding the methylation states of normal versus cancer genomes.
Illustrating the complexity in trying to characterize the cancer genome, Dr. Chinnaiyan explained that some mutations can cross tumor types whereas others will not be shared by tumors of the same type. He pointed to the challenges in trying to connect the dots between the genome and the epigenome and to define the mechanisms by which epigenomic alterations, such as changes affecting histones and chromosomal structure, can affect gene expression.
How can you personalize cancer therapy when many driver mutations may be present in 1% or fewer tumors, and how can you use this information to design clinical trials? he asked. In addition, he wondered, “Should we begin to increase the importance of driver mutations versus tissue of origin for classifying cancer?”