Discovery of Biomarkers Predictive of Anticancer Drug Response in Preclinical Settings
1.1 هزار بار بازدید -
6 سال پیش
-
Presented At:Cancer Research & Oncology
Presented At:
Cancer Research & Oncology 2018
Presented By:
Benjamin Haibe-Kains, PhD - Assistant Professor, Princess Margaret Cancer Center, University of Toronto
Speaker Biography:
Benjamin Haibe-Kains is a Scientist at the Princess Margaret Cancer Centre (PM), University Health Network, and Assistant Professor in the Medical Biophysics and Computer Science departments of the University of Toronto. Dr. Haibe-Kains earned his PhD in Bioinformatics at the Université Libre de Bruxelles (Belgium), for which he was awarded a Solvay Award (Belgium). Supported by a Fulbright Award, Dr. Haibe-Kains did his postdoctoral fellowship at the Dana-farber Cancer Institute and Harvard School of Public Health (USA). Dr. Haibe-Kains started his own laboratory at the Institut de Recherches Cliniques de Montréal (Canada) and moved to PM in November 2013. Dr. Haibe-Kains' research focuses on the integration of high-throughput data from various sources to simultaneously analyze multiple facets of carcinogenesis. Dr. Haibe-Kains and his team are analyzing high-throughput (pharmaco)genomic datasets to develop new prognostic and predictive models and to discover new therapeutic regimens in order to significantly improve disease management. Dr. Haibe-Kains' main scientific contributions include several prognostic gene signatures in breast cancer, subtype classification models for ovarian and breast cancers, as well as genomic predictors of drug response in cancer cell lines.
Webinar:
Discovery of Biomarkers Predictive of Anticancer Drug Response in Preclinical Settings
Webinar Abstract:
One of the main challenges in precision medicine consists of developing predictors of drug response to select the most beneficial therapy for each individual patient. In this context, preclinical models are crucial to study the association between molecular features of tumor cells and response to chemical perturbations. However, only few predictors have been successfully translated to clinical settings. Such a low success rate is due not only to the complexity of the mechanisms underlying anticancer drug response, but also to the lack of robustness of the predictors developed in preclinical settings. To address this issue we developed PharmacoGx, a computational platform enabling meta-analysis of large-scale drug screenings of in vitro and in vivo model systems, and PharmacoDB (pharmacodb.pmgenomics.ca), a web-application enabling quick access to a large compendium of pharmacogenomics datasets. In this presentation I will show how we used our new platforms to (i) identify pathways whose genomic and transcriptomic patterns are preserved between model systems and patient tumors, and (ii) develop univariate and multivariate predictors of drug response that can be validated in clinical trial data.
Learning Objectives:
1. Learn about the potential and limitations of biomarker discovery using preclinical pharmacogenomic data
2. Learn about the computational tools available for biomarker discovery in preclinical settings
Earn PACE/CME Credits:
1. Make sure you’re a registered member of LabRoots (https://www.labroots.com/virtual-even...)
2. Watch the webinar on YouTube above or on the LabRoots Website (https://www.labroots.com/virtual-even...)
3. Click Here to get your PACE (Expiration date – October 10, 2020 12:00 PM) – https://www.labroots.com/credit/pace-...
LabRoots on Social:
Facebook: Facebook: LabRootsInc
Twitter: Twitter: LabRoots
LinkedIn: LinkedIn: labroots
Instagram: Instagram: labrootsinc
Pinterest: Pinterest: labroots
SnapChat: labroots_inc
Cancer Research & Oncology 2018
Presented By:
Benjamin Haibe-Kains, PhD - Assistant Professor, Princess Margaret Cancer Center, University of Toronto
Speaker Biography:
Benjamin Haibe-Kains is a Scientist at the Princess Margaret Cancer Centre (PM), University Health Network, and Assistant Professor in the Medical Biophysics and Computer Science departments of the University of Toronto. Dr. Haibe-Kains earned his PhD in Bioinformatics at the Université Libre de Bruxelles (Belgium), for which he was awarded a Solvay Award (Belgium). Supported by a Fulbright Award, Dr. Haibe-Kains did his postdoctoral fellowship at the Dana-farber Cancer Institute and Harvard School of Public Health (USA). Dr. Haibe-Kains started his own laboratory at the Institut de Recherches Cliniques de Montréal (Canada) and moved to PM in November 2013. Dr. Haibe-Kains' research focuses on the integration of high-throughput data from various sources to simultaneously analyze multiple facets of carcinogenesis. Dr. Haibe-Kains and his team are analyzing high-throughput (pharmaco)genomic datasets to develop new prognostic and predictive models and to discover new therapeutic regimens in order to significantly improve disease management. Dr. Haibe-Kains' main scientific contributions include several prognostic gene signatures in breast cancer, subtype classification models for ovarian and breast cancers, as well as genomic predictors of drug response in cancer cell lines.
Webinar:
Discovery of Biomarkers Predictive of Anticancer Drug Response in Preclinical Settings
Webinar Abstract:
One of the main challenges in precision medicine consists of developing predictors of drug response to select the most beneficial therapy for each individual patient. In this context, preclinical models are crucial to study the association between molecular features of tumor cells and response to chemical perturbations. However, only few predictors have been successfully translated to clinical settings. Such a low success rate is due not only to the complexity of the mechanisms underlying anticancer drug response, but also to the lack of robustness of the predictors developed in preclinical settings. To address this issue we developed PharmacoGx, a computational platform enabling meta-analysis of large-scale drug screenings of in vitro and in vivo model systems, and PharmacoDB (pharmacodb.pmgenomics.ca), a web-application enabling quick access to a large compendium of pharmacogenomics datasets. In this presentation I will show how we used our new platforms to (i) identify pathways whose genomic and transcriptomic patterns are preserved between model systems and patient tumors, and (ii) develop univariate and multivariate predictors of drug response that can be validated in clinical trial data.
Learning Objectives:
1. Learn about the potential and limitations of biomarker discovery using preclinical pharmacogenomic data
2. Learn about the computational tools available for biomarker discovery in preclinical settings
Earn PACE/CME Credits:
1. Make sure you’re a registered member of LabRoots (https://www.labroots.com/virtual-even...)
2. Watch the webinar on YouTube above or on the LabRoots Website (https://www.labroots.com/virtual-even...)
3. Click Here to get your PACE (Expiration date – October 10, 2020 12:00 PM) – https://www.labroots.com/credit/pace-...
LabRoots on Social:
Facebook: Facebook: LabRootsInc
Twitter: Twitter: LabRoots
LinkedIn: LinkedIn: labroots
Instagram: Instagram: labrootsinc
Pinterest: Pinterest: labroots
SnapChat: labroots_inc
6 سال پیش
در تاریخ 1397/08/02 منتشر شده
است.
1,186
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