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Mathematical modeling of tumor progression and chemotherapy regimens
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Miranda Santana, Leonardo. Mathematical modeling of tumor progression and chemotherapy regimens. Retrieved from https://doi.org/doi:10.7282/t3-ey4r-q552

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TitleMathematical modeling of tumor progression and chemotherapy regimens
NameMiranda Santana, Leonardo (author); Bhanot, Gyan (chair); Coleman, Piers (internal member); Khiabanian, Hossein (internal member); Morozov, Alexandre (internal member); Ganesan, Shridar (outside member); Rutgers University; School of Graduate Studies
Date Created2020
Other Date2020-10 (degree)
SubjectChemotherapy, Physics and Astronomy
Extent1 online resource (ix, 119 pages) : illustrations
DescriptionDespite all important advances in the treatment of cancer over the last decades, preventing disease recurrence remains a challenge. Resistance of tumors to chemotherapy can be caused not only by selection of drug-resistant clones over the course of treatment, but also by the presence of tumor foci that are in a dormant state and are not targeted/killed by DNA-damaging agents. Such dormant tumor foci can eventually transition to a state of active growth, causing disease recurrence. In this thesis, we propose a stochastic model to describe recurrence of generic tumors, in which tumor foci can transition between a chemoresistant dormant state and a chemosensitive state of active growth. We develop a framework to determine the time-dependent probability that an undetectable residual tumor will become large enough to be detectable, and model the effect of chemotherapy on recurrence by switching the death rate of active tumor foci at the treatment time cap. We fit our model to data from a clinical trial for maintenance chemotherapy with the poly(ADP-ribose) polymerase (PARP) inhibitor olaparib in ovarian cancer, and use parameters from the fits to predict recurrence-free survival when chemotherapy dosage or duration are increased. In this context, we also investigate how recurrence and cure are affected by transition rates between dormant and active states within the tumor, and predict how the effectiveness of increasing chemotherapy dosage or duration for improving long-term recurrence-free survival depends on these rates. Our results should be useful in planning optimized chemotherapy dosage and duration for cancer treatment, especially in cancer types for which no targeted therapy is available.
NotePh.D.
NoteIncludes bibliographical references
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/t3-ey4r-q552
LanguageEnglish
CollectionSchool of Graduate Studies Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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