{"id":4540,"global_id":"www.ibs.re.kr\/bimag?id=4540","global_id_lineage":["www.ibs.re.kr\/bimag?id=4540"],"author":"3","status":"publish","date":"2021-09-02 01:00:00","date_utc":"2021-05-05 14:22:11","modified":"2021-12-30 12:08:25","modified_utc":"2021-12-30 03:08:25","url":"https:\/\/www.ibs.re.kr\/bimag\/event\/2021-09-02\/","rest_url":"https:\/\/www.ibs.re.kr\/bimag\/wp-json\/tribe\/events\/v1\/events\/4540","title":"Exploiting evolution to design better cancer therapies","description":"
This talk will be presented online. Zoom link: 709 120 4849<\/a> (pw: 1234)<\/div>\n
<\/div>\n
Abstract: Our current approach to cancer treatment has been largely driven by finding molecular targets, those patients fortunate enough to have a targetable mutation will receive a fixed treatment schedule designed to deliver the maximum tolerated dose (MTD). These therapies generally achieve impressive short-term responses, that unfortunately give way to treatment resistance and tumor relapse. The importance of evolution during both tumor progression, metastasis and treatment response is becoming more widely accepted. However, MTD treatment strategies continue to dominate the precision oncology landscape and ignore the fact that treatments drive the evolution of resistance. Here we present an integrated theoretical\/experimental\/clinical approach to develop treatment strategies that specifically embrace cancer evolution. We will consider the importance of using treatment response as a critical driver of subsequent treatment decisions, rather than fixed strategies that ignore it. We will also consider using mathematical models to drive treatment decisions based on limited clinical data. Through the integrated application of mathematical and experimental models as well as clinical data we will illustrate that, evolutionary therapy can drive either tumor control or extinction using a combination of drug treatments and drug holidays. Our results strongly indicate that the future of precision medicine shouldn\u2019t be in the development of new drugs but rather in the smarter evolutionary, and model informed, application of preexisting ones.<\/div>\n
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