Frontiers in MathOnco, Part 1

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Heiko Enderling, Alexander R.A. Anderson


The mathematical oncology subgroup of the Society for Mathematical Biology is growing, reflecting the increasing number of scientific publications on the mathematical modeling of cancer. Cancer models are being developed to answer a diversity of questions, that cover almost every cancer type and stage, including how we might better understand or treat this deadly disease. The specific modelling approach used varies widely, while some models are phenomenological in nature following a bottom-up approach, other models are more top-down data-driven. This two-part minisymposium showcases the breadth of mathematical oncology research from scientists at both Universities and Hospitals at different career stages form around the world to spur discussions and collaborations in the field of mathematical modeling of cancer.

Morgan L. Craig

Université de Montréal, Canada
"Leveraging patient-specific heterogeneity to establish effective immunotherapeutic protocols"
Cancer is an intrinsically heterogeneous disease distinguished by disparate outcomes based on cancer types, patient-specific characteristics, and treatment modalities. Further complicating this picture, therapeutic resistance poses a major challenge to the design and implementation of effective cancer treatments. To overcome these hurdles, it is crucial to characterize heterogeneity within and around the tumour and to quantify the effects that neighbouring tumour and immune cells have on therapeutic success. In contrast to generalized and cytotoxic chemotherapies, immunotherapies aim to harness an individual’s immune system to elicit a targeted immune response and hopefully provide durable therapeutic benefits. Unfortunately, recent disappointing trial results for a variety of immunotherapies stress the need for a more tailored approach to immunotherapeutic scheduling that takes into account patient-specific heterogeneity and the potential for developing resistance. In response, quantitative approaches provide a way to test therapeutic protocols before they are used in patients, ultimately reducing bottlenecks along the drug development pipeline, rationalizing therapeutic scheduling, and improving patient outcomes. Here I will discuss two recent projects focused on establishing effective therapeutic protocols for mono- and poly-oncolytic virus treatments. Using mathematical and computational modelling, we constructed models that recapitulated realistic patient cohorts to study combined vaccinia and vesicular stomatitis oncolytic viruses, and to understand the impact of the tumour microenvironment in glioblastoma multiforme (a deadly central nervous system tumour) on oncolytic virotherapy penetration and efficacy. In both cases, we showed that therapeutic success was principally determined by tailoring treatment to underlying patient characteristics, including tumour aggressivity and spatial structure. Our results highlight the relevance of quantitative approaches to pre-clinical development and therapeutic design, and underline the impact of inter- and intra-individual variability on treatment outcomes.

David Basanta

Moffitt Cancer Center, Tampa, USA
"Innocent bystander? The role of stromal cells in cancer evolution and treatment resistance"
Much work on cancer’s evolutionary dynamics is focused on the genetic mutations that characterize the different stages of cancer progression or the competition between different clones as the tumor grows and copes with different treatment options and schedules. Those evolutionary dynamics are shaped by the tumor ecosystem that, in the context of skeletal cancers, include cells such as osteoclasts, osteoblasts, macrophages and mesenchymal stem cells. Those cells perform a variety of roles in a normal bone and also have an impact on cancer. In this talk I will describe mathematical models that allow us to understand the role of those cells in the normal bone, which is a key step to uncover how they can be co-opted by a tumor and what role do they play as the tumor grows and undergoes treatment.

Kaitlyn Johnson

University of Texas at Austin, USA
"Towards an integrated framework for incorporating multimodal data sets into mechanistic models of treatment response dynamics in cancer"
In the field of mathematical oncology, we commonly look to longitudinal data to calibrate and validate models of tumor progression. Longitudinal data allow for precise model fitting and parameter estimation which can be used to predict tumor behavior. However, molecular level data, although often available at few snapshots in time, is what biologists and clinicians typically use to better understand underlying disease biology in both experimental and clinical settings. While the quantitative nature of these snapshot data sets has vastly improved with technologies such as single cell RNA sequencing (scRNAseq), there exists a need for integrating snapshot and longitudinal data into mathematical frameworks in order to develop the most informed models to describe and predict cancer progression. In this work, we integrate longitudinal drug-response data with snapshot scRNAseq data at just three times points, thus calibrating model outputs to experimental data for two distinct modes of data. We demonstrate that direct incorporation of high-resolution scRNAseq snapshot data into the parameter estimation improves the identifiability of the mathematical model and its predictive power. We present this work as an example of how mathematical oncology can develop novel workflows for incorporating the available biological data to better understand cancer treatment response.

Khaphetsi Joseph Mahasa

"Mesenchymal stem cells used as carrier cells of oncolytic adenovirus results in enhanced oncolytic virotherapy"
Mesenchymal stem cells (MSCs) loaded with oncolytic viruses are presently being investigated as a new modality of advanced/metastatic tumors treatment and enhancement of virotherapy. MSCs can, however, either promote or suppress tumor growth. To address the critical question of how MSCs loaded with oncolytic viruses affect virotherapy outcomes and tumor growth patterns in a tumor microenvironment, we developed and analyzed an integrated mathematical-experimental model. We used the model to describe both the growth dynamics in our experiments of firefly luciferase-expressing Hep3B tumor xenografts and the effects of the immune response during the MSCs-based virotherapy. We further employed it to explore the conceptual clinical feasibility, particularly, in evaluating the relative significance of potential immune promotive/suppressive mechanisms induced by MSCs loaded with oncolytic viruses. We were able to delineate conditions which may significantly contribute to the success or failure of MSC-based virotherapy as well as generate new hypotheses. In fact, one of the most impactful outcomes shown by this investigation, not inferred from the experiments alone, was the initially counter-intuitive fact that using tumor-promoting MSCs as carriers is not only helpful but necessary in achieving tumor control. Considering the fact that it is still currently a controversial debate whether MSCs exert a pro- or anti-tumor action, mathematical models such as this one help to quantitatively predict the consequences of using MSCs for delivering virotherapeutic agents in vivo. Taken together, our results show that MSC-mediated systemic delivery of oncolytic viruses is a promising strategy for achieving synergistic anti-tumor efficacy with improved safety profiles.

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