Gene Mutation Helps To Predict Drug Responsiveness In Cancer Patients
Main Category: Cancer / OncologyAlso Included In: Genetics
Article Date: 27 Jul 2010 - 2:00 PDT
Drugs such as everolimus that target the protein mTOR are used to treat several forms of cancer, but not all patients respond to the treatment. A team of researchers, led by Alberto Bardelli, at the University of Turin Medical School, Italy, has now identified a way to help predict which patients will respond to such drugs.
Specifically, the team found that human cancer cells with mutations in the PIK3CA gene responded to everolimus in vitro except when a KRAS gene mutation was also present. Importantly, in a cohort of metastatic cancer patients, the presence of KRAS gene mutations was associated with lack of response to treatment with everolimus therapy. These data suggest that by looking for the presence or absence of PIK3CA and KRAS mutations in a person's tumor it will be possible to predict whether or not that person will benefit from treatment with a drug that targets mTOR. However, as noted in an accompanying commentary, by Morassa Mohseni and Ben Ho Park, at The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, while these data have enormous potential to change clinical practice, larger prospective studies are required to verify them.
Title:
Deregulation of the PI3K and KRAS signaling pathways in human cancer cells determines their response to everolimus. View this article here.
Accompanying Commentary:
Title:
PIK3CA and KRAS mutations predict for response to everolimus therapy: now that's RAD001. View this article here.
Source:
Karen Honey
Journal of Clinical Investigation
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MLA
14 Feb. 2012. <http://www.medicalnewstoday.com/releases/195899.php>
APA
http://www.medicalnewstoday.com/releases/195899.php.
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Gene Mutation Helps To Predict Drug Responsiveness In Cancer Patients?
posted by Gregory D. Pawelski on 1 Aug 2010 at 8:04 pmAlthough the theory behind inhibitor targeted therapy is appealing, the reality is more complex. Cancer cells often have many mutations in many different pathways, so even if one route is shut down by a targeted treatment, the cancer cell may be able to use other routes.
In other words, cancer cells have "backup systems" that allow them to survive. The result is that the drug does not affect the tumor as expected. The cancer state is typically characterized by a signaling process that is unregulated and in a continuous state of activation.
In chemotherapy selection, molecular profiling examines a single process within the cell or a relatively small number of processes. All a gene mutation study can tell is whether or not the cells are potentially susceptible to a mechanism of attack. The aim is to tell if there is a theoretical predisposition to drug response.
It doesn't tell you the effectiveness of one drug (or combination) or any other drug which may target this in the individual. There are many mechanisms/pathways to altered cellular function.
Another more practical approach to this problem would be to target multiple pathways in a cancer cell. The challenge is to identify for which patients the targeted treatment will be effective (enzyme inhibitors, proteasome inhibitors, angiogenesis inhibitors, and monoclonal antibodies).
What is needed is to test the concept of targeted cancer drugs with biomarkers as pharmacodynamic endpoints, and with the ability to measure multiple parameters in cellular screens now in hand using flow cytometry. And actually test these targeted drugs against the fresh tumor specimen.
I believe functional profiling would be more beneficial. It measures what happens at the end, rather than the status of the individual mechanisms/pathways. It assesses the activity of a drug (or combinations) upon combined effect of all cellular processes, using combined metabolic and morphologic endpoints, at the cell population level, measuring the interaction of the entire genome.
No matter which mutation is being affected, functional profiling is measuring it through the surrogate of measuring if the cell is alive or dead. The key to understanding the genome is understanding how cells work. How is the cell being killed regardless of the mechanism/pathway.
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