Predicting the response of breast cancer to systemic therapy

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Published: 17 Aug 2010
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Prof Marc van de Vijver - Academic Medical Centre, Amsterdam, Netherlands
Prof van de Vijver discusses his work identifying genes that can be used to predict response to breast cancer treatment. Gene expression profiles of tumour samples are used to identify which genes are associated with response or no response to therapy. Prof van de Vijver explains how genetic profiles can be an important tool for clinicians, allowing them to tailor therapy to individual patients.

EACR 21, 26—29 June 2010, Oslo

Interview with Professor Marc van de Vijver (Academic Medical Centre, Amsterdam, Netherlands)

Predicting the response of breast cancer to systemic therapy

Here at the EACR I’m talking about trying to predict responsiveness of breast cancer to systemic therapy and more specific to chemotherapy and to targeted therapy directed against HER2. So we have been doing gene expression profiling experiments over the last ten years or so to try to identify gene expression signatures that are associated with prognosis and with response to specific therapies. The approach that we are taking is to use tumours from patients that have either been biopsied or operated on, to use the RNA from those tumours to perform gene expression profiling and then to relate the gene expression profiles to outcome and using that approach to identify gene expression signatures associated with specific disease parameters such as outcome and response to therapy.

What we are doing is gene expression profiling using the current techniques can assess gene expression levels for each gene in the human genome in tumour material and so you take an unbiased approach by not first selecting genes but you look at all the genes that are present in the genome and then you ask, for example, you take a group of patients where you know whether the tumours have responded to chemotherapy or not and then using statistical approaches you interrogate the gene expression database which you have and you ask, for example, are there gene expression signatures that are associated with response to therapy or no response to therapy. Then when you identify genes with that approach the first, very important step is to do validation because basically what you do is 25,000 statistical tests and simply by chance you can identify genes that, in the end, are not associated with chemotherapy response. So first of all you have to validate the findings from such experiments and then, of course, also you can try to do functional experiments to further experimentally validate that the genes that you have identified really play a role in chemotherapy responsiveness.

How do you see this impacting the clinic?

The impact in the clinic is greatly needed because what’s happening now is that there is an increasing number of systemic therapies, chemotherapies and targeted therapies, and we know that for each of those therapies, for each single agent in those therapies we know that there is a proportion of tumours that respond to the therapy but there is also a large proportion of tumours that do not respond to the therapy. At the moment we hardly know how to predict responsiveness so we have to treat all patients with the same regimens of chemotherapy and targeted therapies. By knowing up front to which agents tumours will respond, we can tailor the therapy to the individual tumour and to the individual patient. So I expect that in the coming years new diagnostic tests will emerge that will make it possible to optimise treatment for individual patients.

What about cost?

There will be a decreasing cost because we will not treat all patients with an agent but we will only treat those patients that have a tumour that we can predict will respond to that therapy. And the cost of the diagnostic tests is much lower than the cost of the treatments.