Thesis: Ph. D., Massachusetts Institute of Technology, Computational and Systems Biology Program, 2016. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 119-121). / Cancer is a clonal evolutionary process. This results in complex clonal architecture and intratumoral heterogeneity in each patient. This also presents challenges for effective therapeutic intervention - with constant selective pressure to induce or select pre-existing resistant subclones toward drug resistance. Mathematical/computational modeling from population genetics, evolutionary dynamics, and engineering are being utilized to a greater extent in recent times to study tumor progression, intratumoral heterogeneity, drug resistance, and rational drug scheduling/combinations design. In this thesis we present several joint quantitative and experimental approaches for the rational design of drug combinations to tackle the issue of intratumoral heterogeneity and clonal evolution. Using a tractable experimental system with pre-defined tumor compositions, we derived computational approaches to rationally design drug combinations with the goal of minimizing a given heterogeneous tumor. We found that the best drug combinations can oftentimes be non-intuitive as they do not contain component drugs most effective for the individual subpopulations. This was the result of a need for combinatorial considerations on the effects of each drug on all subpopulations, hence at times leading to non-intuitive drug regimens. We validated our computational model predictions in vitro and in vivo in a preclinical model of Burkitt's lymphoma, with predictable evolutionary trajectories upon treatment. Next, we extended this methodology to study the effects of more complex tumor heterogeneity on combinatorial drug design, with similar conclusions. Sampling and statistical analyses over a range of tumor compositions can further inform effective drug combinations under some uncertainty in initial tumor heterogeneity. Moving beyond a model where we have control of initial tumor composition, we sought to examine collateral resistance and sensitivity during clonal evolution. Using a murine model of Ph+ acute lymphoblastic leukemia, we performed drug selection and pharmacological screen experiments. We observed important evolutionary processes of selection and drift in giving rise to resistance to clinically used BCR-ABL1 inhibitors. Remarkably, the resistant population also became hyper-sensitized to nonclassical BCR-ABL1 inhibitors at intermediate stages of the clonal evolution, in this so-called 'temporally collateral sensitivity'. Mathematical modeling and experimentation brought additional insight into the evolutionary dynamics and mechanism of action, with demonstrated in vivo efficacy. / by Boyang Zhao. / Ph. D.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/103271 |
| Date | January 2016 |
| Creators | Zhao, Boyang |
| Contributors | Michael T. Hemann and Douglas A. Lauffenburger., Massachusetts Institute of Technology. Computational and Systems Biology Program., Massachusetts Institute of Technology. Computational and Systems Biology Program. |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 158 pages, application/pdf |
| Rights | M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission., http://dspace.mit.edu/handle/1721.1/7582 |
Page generated in 0.0329 seconds