Evolutionary Dynamics in Cancer

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Image of bifurcating tree next to cancer patient
November 4 - November 6, 2019
8:00AM - 5:00PM
Location
MBI Auditorium, Jennings Hall 355

Date Range
Add to Calendar 2019-11-04 08:00:00 2019-11-06 17:00:00 Evolutionary Dynamics in Cancer

Cancer is a heterogeneous disease, with variation across individuals with regard to aspects such as rate of progression, response to treatment, and survival time. Although the polyclonal nature of many cancers and the role of evolutionary processes in tumor progression was first established in the late 1970’s, increased attention has recently been given to these processes as their potential role in clinical outcomes has become more widely recognized.  This has in part been motivated by the availability of data at a finer scale than ever before, though technological developments such as single-cell sequencing. Given the availability of such data, there is now a need for models and methods that can produce realistic pictures of the evolutionary history of a tumor. In this workshop, we explore the use of mathematical and statistical models that include an evolutionary component to study cancer at various scales, including evolution that occurs within individual tumors, across transmissible cancers, and within populations, possibly in conjunction with environmental and genetic factors.

MBI Auditorium, Jennings Hall 355 Mathematical Biosciences Institute mbi-webmaster@osu.edu America/New_York public
Description

Cancer is a heterogeneous disease, with variation across individuals with regard to aspects such as rate of progression, response to treatment, and survival time. Although the polyclonal nature of many cancers and the role of evolutionary processes in tumor progression was first established in the late 1970’s, increased attention has recently been given to these processes as their potential role in clinical outcomes has become more widely recognized.  This has in part been motivated by the availability of data at a finer scale than ever before, though technological developments such as single-cell sequencing. Given the availability of such data, there is now a need for models and methods that can produce realistic pictures of the evolutionary history of a tumor. In this workshop, we explore the use of mathematical and statistical models that include an evolutionary component to study cancer at various scales, including evolution that occurs within individual tumors, across transmissible cancers, and within populations, possibly in conjunction with environmental and genetic factors.

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Organizers

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Julia Chifman
Department of Mathematics and Statistics
American University
chifman@american.edu

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Kevin Coombes
Department of Biomedical Informatics
The Ohio State University
Kevin.Coombes@osumc.edu​

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Laura Kubatko
Departments of Statistics and Evolution, Ecology, and Organismal Biology
The Ohio State University
lkubatko@stat.osu.edu

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Diego Mallo
Biodesign Institute
Arizona State University
Diego.Malloadan@asu.edu

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Marc Suchard
Departments of Biomathematics, Biostatistics, and Human Genetics
University of California, Los Angeles
msuchard@ucla.edu

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Speakers

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Name Affiliation Email
Amir Asiaee T. Mathematical Biosciences Institute, The Ohio State University asiaeetaheri.1@mbi.osu.edu
David Basanta Moffitt Cancer Center david.basanta@moffitt.org
Kimberly Bussey Department of Biomedical Informatics, Arizona State University Kimberly.Bussey@asu.edu
Jasmine Foo School of Mathematics, University of Minnesota Twin Cities jyfoo@math.umn.edu
Katharina Jahn Department of Biosystems Science and Engineering, ETH Zürich katharina.jahn@bsse.ethz.ch
Harsh Jain Department of Mathematics, Florida State University jain@math.fsu.edu
Mary Kuhner Department of Statistics, University of Washington mkkuhner@u.washington.edu
Carlo Maley School of Life Sciences, Arizona State University maley@asu.edu
Michael Metzger Metzger Lab, Pacific Northwest Research Institute metzgerm@pnri.org
Luay Nakhleh Department of Computer Science, Rice University nakhleh@rice.edu
Ben Raphael Computer Science Department, Princeton University braphael@cs.princeton.edu
Russell Schwartz Department ofBiological Sciences, Carnegie Mellon University russells@andrew.cmu.edu
Daniel Stover Department of Internal Medicine, The Ohio State University daniel.stover@osumc.edu
Chi Wang College of Public Health, University of Kentucky chi.wang@uky.edu
Tianjian Zhou University of Chicago tjzhou95@gmail.com

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