The concept of personalized medicine refers to the notion that information about the DNA sequence of a patient’s tumors can help select a treatment that has the best chance to be successful for that particular patient. The idea behind this concept is rooted in discoveries from molecular studies in the past decade. These have taught us that even though tumors may appear similar under the microscope, they are, in fact, extremely heterogeneous at the molecular level. Breast tumors, for instance, can vary greatly in the mutations they carry in their DNA. Scientists now believe that tumor heterogeneity may largely explain our limited success in the treatment of cancer. The promise of personalized medicine lies in the fact that these mutations can be used to predict the response of a patient to anti-cancer drugs, and hence guide the choice of the most appropriate drug for each patient.
Voest, Bernards, and the members of their team will focus on patients from three clinical studies; one on breast cancer, and two on colorectal tumors. They will collect DNA from these patients’ tumors before, and two months after, the start of defined treatment regimens. They will then correlate the genetic changes in the tumors during that interval with treatment outcome. Armed with this information, they will use state-of-the-art computational biology methods to generate DNA “profiles” that will predict whether patients will respond to a given treatment. By discovering how mutations in tumor DNA are linked to responses to anti-cancer drugs, the team hopes to be able to make far better educated choices for the treatment for individual patients, leading to greater therapeutic benefit, while at the same time reducing the toxicity of non-effective cancer drugs.
The outcome of this project will have an impact at multiple levels of clinical cancer care. First, it will generate novel DNA analysis tools to better select cancer patients for specific treatments. Second, it will expedite cancer drug development by delivering tools that can match the patient with the most appropriate drug. Third, it will show the value of dynamic tumor assessments by repeated biopsies to understand mechanisms of cancer drug resistance. But most importantly, it will contribute to increased cancer survival and quality of life by helping to deliver the most effective drug to the patient early on, while reducing the toxicity of ineffective drugs.
Specific Research Goals:
- Collect DNA from patients’ tumors both before and two months after the start of defined treatment regimens and correlate the genetic changes in the tumors during that interval with treatment outcome;
- Use state-of-the-art computational biology methods to generate DNA “profiles” that will predict whether patients will respond to a given treatment; and
- By discovering how mutations in tumor DNA are linked to responses to anti-cancer drugs, the team hopes to be able to make far more educated choices for the treatment for individual patients, leading to greater therapeutic benefit, while at the same time reducing the toxicity of noneffective cancer drugs.
Amount of Funding:
€1.2 million over a four-year period
Emile E. Voest, M.D., Ph.D., head of the Department of Medical Oncology at the University Medical Center (UMC) Utrecht in the Netherlands
René Bernards, Ph.D., head of the Division of Molecular Carcinogenesis at the Netherlands Cancer Institute
Stefan Sleijfer, M.D., Ph.D., medical oncologist, Erasmus MC, Rotterdam
Laura van ’t Veer, Ph.D., molecular biologist, University of California San Francisco School of Medicine
Trey Ideker, Ph.D., computational scientist, pharmaceutical sciences, University of California San Diego
Page updated: August 2, 2012