Workshop 5: Treatment, Clinical Trials, Resistance

(February 16,2015 - February 20,2015 )

Organizers


Guido Marcucci
Comprehensive Cancer Center, The Ohio State University
Kristin Swanson
Neurological Surgery, Northwestern University
Jack Tuszynski
Oncology, University of Alberta

While the primary forms of tumor treatment remain chemotherapy and radiation, generic cytotoxic therapies, the increasing understanding of the nature of the disease as being both heterogeneous and genetically unstable has induced a trend to design and create therapies tailored to the specific tumor (patient-specific) and to combat the many different subpopulations of cells with combination therapies. Despite these efforts, tumor resistance and recurrence remain an unfortunate challenge of clinical trials. However, the clinical focus has been primarily on the genetic heterogeneity in tumor cell populations, with minimal focus on the impacts of treatment on the subpopulations phenotypic interactions, either competitive or cooperative, the induced microenvironment, or the evolutionary pressures created. One likely reason is the inability of traditional clinical trials to quantify or meaningfully analyze these phenomena. Examining the impact of particular drug therapies and their scheduling on the local microenvironment and individual cellular behavior in both the long and short term is almost impossible in a clinical setting and extremely difficult in laboratory experiments. Limiting factors include inadequate observation tools, e.g. most imaging methodologies are too coarse to properly resolve the dynamics, changing the system by observing it, such as when resecting grown tumors in animals for closer observation, time for disease development and money. Mathematical models offer an approach to investigate many different types of therapies along with their impact on the microenvironment, and to explore optimal dosing combinations and schedules while bypassing the many limitations encountered in the clinic and laboratory. There are many different varieties of models, though they can generally be categorized into discrete, continuum, or statistical, each offering its own advantage for considering various scales or effects. They can be designed utilizing a basic understanding of the primary phenotypes and genotypes present in a tumor to investigate the likely induced microenvironment from various therapies and evolutionary selection pressures leading to resistance. It is even possible to use them to perform virtual clinical trials and compare different treatments on theoretical populations. This workshop will focus on two broad topics: Mathematical modeling of cancer treatment strategies and how to model resistance of cancers to drug treatments. Use of mathematical models to compare clinical trial arms and virtually simulate clinical trials outcomes. The workshop will highlight modeling applications that are as close as possible to direct clinical impact including design of multi-institutional clinical trials for patient-specific radiation dose strategies, quantification of patient-specific response to treatment that can be useful in predicting outcomes and treatment design, as well as include discussions of sequencing of drug treatments, optimal scheduling, and modeling of combination therapies which are useful in rapidly mutating diseases, such as cancer and HIV. The workshop will also discuss ways to implement the use of mathematical models in a clinical setting.

Accepted Speakers

Alexander Anderson
Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute
Donald Berry
Biostatistics, University of Texas M.D. Anderson Cancer Center
Dean Bottino
Clinical Pharmacology, Takeda Pharmaceuticals
Robert Gatenby
H. Lee Moffitt Cancer Center & Research Institute
Susan Geyer
Andrea Hawkins-Daarud
Neurological Surgery, Northwestern University
Thomas Hillen
Mathematical and Statistical Sciences, University of Alberta
Robert Jeraj
Medical Physics, University of Wisconsin
Kevin Leder
Industrial and Systems Engineering, University of Minnesota
Savannah Partridge
Radiology, University of Washington
Russell Rockne
Neurological Surgery, Northwestern University
Ariosto Silva
Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center
Richard Simon
Biometric Research Branch, National Cancer Institute
Andrew Trister
Cancer Biology, Sage Bionetworks
Thomas Yankeelov
Radiology, Vanderbilt University
Monday, February 16, 2015
Time Session
Tuesday, February 17, 2015
Time Session
Wednesday, February 18, 2015
Time Session
Thursday, February 19, 2015
Time Session
Friday, February 20, 2015
Time Session
Name Affiliation
Anderson, Alexander alexander.Anderson@moffitt.org Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center & Research Institute
Berry, Donald dberry@mdanderson.org Biostatistics, University of Texas M.D. Anderson Cancer Center
Bottino, Dean Dean.bottino@gmail.com Clinical Pharmacology, Takeda Pharmaceuticals
Gatenby, Robert robert.gatenby@moffitt.org H. Lee Moffitt Cancer Center & Research Institute
Geyer, Susan susan.geyer@osumc.edu
Hawkins-Daarud, Andrea andrea.hawkins-daarud@northwestern.edu Neurological Surgery, Northwestern University
Hillen, Thomas thillen@ualberta.ca Mathematical and Statistical Sciences, University of Alberta
Jeraj, Robert rjeraj@wisc.edu Medical Physics, University of Wisconsin
Leder, Kevin kevin.leder@isye.umn.edu Industrial and Systems Engineering, University of Minnesota
Partridge, Savannah spartrid@seattlecca.org Radiology, University of Washington
Rockne, Russell russell.rockne@northwestern.edu Neurological Surgery, Northwestern University
Silva, Ariosto ariosto.silva@moffitt.org Cancer Imaging and Metabolism, H. Lee Moffitt Cancer Center
Simon, Richard rsimon@mail.nih.gov Biometric Research Branch, National Cancer Institute
Swanson, Kristin kristin.swanson@northwestern.edu Neurological Surgery, Northwestern University
Trister, Andrew trister@u.washington.edu Cancer Biology, Sage Bionetworks
Tuszynski, Jack jackt@ualberta.ca Oncology, University of Alberta
Yankeelov, Thomas thomas.yankeelov@vanderbilt.edu Radiology, Vanderbilt University