BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Leili Shahriyari (University of Massachusetts Amherst) DTSTART:20200624T150000Z DTEND:20200624T160000Z DTSTAMP:20260423T040739Z UID:GSMMA/9 DESCRIPTION:Title: A path toward personalized cancer treatments\nby Leili Shahriyari (Unive rsity of Massachusetts Amherst) as part of Global Seminar on Mathematical Modeling and Applications\n\n\nAbstract\nA major clinical challenge for ca ncer therapies is to obtain an effective treatment strategy for each patie nt or at least identify a subset of patients who could benefit from a par ticular treatment. Since each cancer has its own unique features\, it is v ery important to obtain personalized cancer treatments and find a way to tailor treatment strategies for each patient. Recently\, mathematical mode ls have been commonly used to discover\, validate\, and test drugs. Since these models are a complex system of nonlinear equations with many unknown parameters\, estimating the values of the model's parameters is extremely difficult. Existing parameter estimation methods for these models often use assembled data from various sources rather than a single curated datas et. These datasets are usually obtained through various biological experim ents\, in vitro and in vivo animal studies. To arrive at personalized trea tments\, we need to obtain values of parameters of the model for each pati ent separately. Since the set of variables of the model includes relative amount of each cell type and cytokines in the tumor\, we developed a tumor deconvolution software\, which is a combination of recently developed met hods\, to predict the relative amount of these variables from the gene exp ression profile of the tumor. The output of the tumor deconvolution softwa re can be used to predict the values of the parameters for each patient. I n other words\, we propose to use patients’ gene expression data of prim ary tumor to estimate the values of parameters of the mathematical model f or each patient separately\, instead of the common approach of assuming th ese parameters have the same values across all patients and using animal s tudies to estimate them. This new approach provides us with a unique oppor tunity to suggest the optimal treatment strategy for each patient and pred ict the efficacy of each treatment for each patient.\n LOCATION:https://researchseminars.org/talk/GSMMA/9/ END:VEVENT END:VCALENDAR