Machine Learning Techniques and Stochastic Modeling in Mathematical Oncology
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Date
2022-07-18
Authors
Eastman, Brydon
Advisor
Kohandel, Mohammad
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
The cancer stem cell hypothesis claims that tumor growth and progression are driven
by a (typically) small niche of the total cancer cell population called cancer stem cells
(CSCs). These CSCs can go through symmetric or asymmetric divisions to differentiate
into specialised, progenitor cells or reproduce new CSCs. While it was once held that this
differentiation pathway was unidirectional, recent research has demonstrated that differenti-
ated cells are more plastic than initially considered. In particular, differentiated cells can
de-differentiate and recover their stem-like capacity. Two recent papers have considered how
this rate of plasticity affects the evolutionary dynamic of an invasive, malignant population
of stem cells and differentiated cells into existing tissue [64, 109]. These papers arrive at
seemingly opposing conclusions, one claiming that increased plasticity results in increased
invasive potential, and the other that increased plasticity decreases invasive potential. Here,
we show that what is most important, when determining the effect on invasive potential,
is how one distributes this increased plasticity between the compartments of resident and
mutant-type cells. We also demonstrate how these results vary, producing non-monotone
fixation probability curves, as inter-compartmental plasticity changes when differentiated
cell compartments are allowed to continue proliferating, highlighting a fundamental dif-
ference between the two models. We conclude by demonstrating the stability of these
qualitative results over various parameter ranges.
Imaging flow cytometry is a tool that uses the high-throughput capabilities of conven-
tional flow cytometry for the purposes of producing single cell images. We demonstrate
the label free prediction of mitotic cell cycle phases in Jurkat cells by utilizing brightfield
and darkfield images from an imaging flow cytometer. The method is a non destructive
method that relies upon images only and does not introduce (potentially confounding) dies
or biomarkers to the cell cycles. By utilizing deep convolutional neural networks regularized
by generated, synthetic images in the presence of severe class imbalance we are able to
produce an estimator that outperforms the previous state of the art on the dataset by
10-15%.
The in-silico development of a chemotherapeutic dosing schedule for treating cancer relies
upon a parameterization of a particular tumour growth model to describe the dynamics
of the cancer in response to the dose of the drug. In practice, it is often prohibitively
difficult to ensure the validity of patient-specific parameterizations of these models for any
particular patient. As a result, sensitivities to these particular parameters can result in
therapeutic dosing schedules that are optimal in principle not performing well on particular
patients. In this study, we demonstrate that chemotherapeutic dosing strategies learned
via reinforcement learning methods are more robust to perturbations in patient-specific
parameter values than those learned via classical optimal control methods. By training a
reinforcement learning agent on mean-value parameters and allowing the agent periodic
access to a more easily measurable metric, relative bone marrow density, for the purpose of
optimizing dose schedule while reducing drug toxicity, we are able to develop drug dosing
schedules that outperform schedules learned via classical optimal control methods, even
when such methods are allowed to leverage the same bone marrow measurements.
Description
Keywords
mathematical oncology, machine learning, computer imaging, reinforcement learning, chemotherapy