ONCO Subgroup Contributed Talks

Rafael Bravo

Moffitt Cancer Center

"Investigating the impact of tissue density on tumor growth and evolution in a 3D whole-organ model of lung cancer"

A spatial mathematical model investigates how well-known tumor traits: increased resource consumption and angiogenesis, may alter tumor growth in different densities of normal tissue, using CT scan data to initialize the model.A novel modeling paradigm was developed specifically to model tissue at the resolution of CT imaging: the population-based model (PBM). The PBM uses discrete agents, however agents are compartmentalized into homogenous populations, which simplifies computation and allows modeling much larger populations than with conventional agent-based modeling. This PBM method allows us to seed a model with individual cells that operates at the CT scale of cubic millimeters. A virtual tumor can then be grown in this environment.Extensive parameter sweeping was done on different tumor phenotypes and normal tissue densities to assess how these affect marginal tumor growth rate and cell count.We find an optimal balance between angiogenesis and resource consumption by the tumor is needed to maximize invasiveness and tumor bulk, and that this balance changes depending on surrounding tissue density. These results suggest that such a balance may evolve in patient tumors and change depending on the density of the tissue on the tumor margin.

Stefano Pasetto

H. Lee Moffitt Cancer Center & Research Institute

"Intermittent hormone therapy models analysis and Bayesian-model-comparison for prostate cancer"

The prostate is an exocrine gland of the male reproductive system dependent on androgens (testosterone and dihydrotestosterone) for development and maintenance. Since prostate cells and their malignant counterparts require androgen stimulation to grow, prostate cancer can be treated by androgen deprivation therapy (ADT). A significant problem in a continuous PCa ADT treatment at the maximum tolerable dose is the insurgence of cancer cell resistance; thus, intermittent adaptive therapy (IAT) is invoked to delay time to progression (TTP).Several mathematical models with different biological resistance mechanisms have been considered to simulate intermittent ADT treatment response dynamics. We present a comparison between 12 of these intermittent prostate-specific antigens (PSA) dynamical models over the Canadian Prospective Phase II Trial of IADT for locally advanced prostate cancer.We identified a few models with critical abilities to disentangle between relapsing and not relapsing patients, which can be exploited for clinical purposes. Finally, within the Bayesian framework, we detected the most compelling models in the trial description.

Kevin Murgas

Stony Brook University Dept. of Biomedical Informatics

" Hierarchical Modeling of DNA Methylation Conservation in Colon Cancer"

Conservation is broadly used to identify biologically important genomic regions. Indeed, preferential DNA methylation conservation during tumor growth can indicate areas of particular functional importance to the tumor. In a cohort of 21 colorectal cancer (CRC) patients with multiple tissue samples per patient, we measured methylation at over 850,000 CpG sites using the Infinium Methylation EPIC microarray. Next, we developed a Bayesian hierarchical model that allows for variance decomposition of methylation on 3 hierarchical levels built around the multiple tissue sampling. We fit the model to the CRC data using a Monte Carlo Markov Chain algorithm (Stan). Based on the posterior parameter distributions of the fits, we defined a conservation score to indicate reduced within-tumor variation of methylation relative to between-patient normal variation, thereby quantifying preferential methylation conservation at single CpG sites, individual genes, and molecular pathways. Across gene regulatory regions, preferential conservation was highest in the vicinity of gene transcription start sites and lowest at exon boundaries. Genes belonging to CRC gene sets exhibited increased preferential conservation, suggesting the model's ability to identify functionally relevant regions based on methylation conservation. A pathway analysis of significantly preferentially conserved genes implicated several CRC relevant pathways and pathways related to immune evasion.

Kathleen Wilkie

Ryerson University

"Chemotherapy Induced Cachexia: Insights from a Mathematical Model"

Cachexia is the loss of muscle and adipose tissues that directly correlates with patient energy levels, strength, and general quality of life. Chemotherapy is a standard cancer treatment with notorious side effects including nausea, diarrhea, anorexia, and fatigue. Unfortunately, chemotherapy can also induce severe muscle loss. Cancer presence itself can induce cachexia, leading to a double-barrelled attack on healthy lean mass, and thus patient life quality.In this work, we develop a novel mathematical framework to investigate the response of muscle tissue to 5-FU chemotherapy. We model the role of stem cells in tissue maintenance and use the model to examine potential mechanisms of chemotherapy induced muscle loss, including disruption of the differentiation pathway. We confront our model to various treatment doses and dose schedules in an attempt to understand several qualitative features of chemotherapy-induced cachexia. In this talk I will review the model mechanisms we used to capture the qualitative features of the experimental data and discuss some of the computational challenges including parameterization of this dynamic process.