Wednesday, June 16 at 02:15pm (PDT)Wednesday, June 16 at 10:15pm (BST)Thursday, June 17 06:15am (KST)
SMB2021 FollowWednesday (Thursday) during the "CT07" time block.
Anna Claudia Mello de Resende
Laboratório Nacional de Computação Científica (LNCC)
"Integrating Image-Driven Deformation with Tumor Growth Models"
As a solid tumor evolves, compressive stresses accumulate within the tumor due to growth. These stresses play important roles on tumor cells phenotype differentiation and tumor microenvironment conditions. Many mathematical models have been developed to represent tumor growth under deformation. From a continuum mechanics point of view, they are usually built by performing a kinematic decomposition, applying the momentum balance equation, and adding constitutive relations. This framework involves a series of assumptions that ultimately impact the prediction of the tumor deformation. A different framework can be pursued by using in vivo data to recover the tumor deformation. Here we investigate the use of a classical optical flow methodology known as the Lucas-Kanade technique to track tumor deformation in a synthetic experimental breast cancer setting. We also perform a model-free sensitivity analysis to study the impact of parameter uncertainties on the tumor evolution in the proposed modeling framework. We focus on the identification of the set of influential parameters with respect to the tumor area evolution, recognized as a meaningful quantity of interest. We show that optical flow techniques may capture deformations appearing in breast cancers, being a useful alternative to integrate in vivo deformation data to mathematical tumor models.
Department of Applied Mathematics, University of Waterloo, Waterloo, Canada
"In-vitro and In-silico Study on a 3D-Bioprinted Breast Cancer Tumor Model"
3D culture methods, by incorporating significant properties of cellular habitat, such as heterogeneous microenvironment, complex interactions of cells with their neighbor cells as well as local extracellular matrix, and complicated diffusion processes of nutrients and oxygen, provide a closer prediction of the real system. One of the most recent 3D biofabrication methods is 3D bioprinting which has contributed dramatically to the development of three-dimensionality and heterogeneity of the tumor microenvironment adequately to replicate characteristics of cancer tumor in vivo. Although 3D bioprinting is rapidly progressing in cancer-related studies, there is still a need to gain a better insight into cell growth mechanism post printing. Computational tools, such as cellular Automata Modelling can be a good complement to the In-vitro experiments that assists to simulate the breast cancer cells activity and growth while cells are encapsulated within porous hydrogel-based construct fabricated using extrusion-based 3D bioprinting technique.
Moffitt Cancer Center
"Tipping cancer cells over the edge; the context-dependent cost of DNA content variation"
The tip-over hypothesis of DNA damage therapy sensitivity proposes that cytotoxic therapy is eﬀective if it pushes a cancer cell's somatic copy number alteration (SCNA) load above a tipping point. We present evidence that the tipping point is accounted for not by elevated SCNA load alone, but by an inability of the tissue micro-environment (TME) to provide the necessary resources. The energetic costs of DNA content levels required for high SCNA loads do not, in the absence of cytotoxic therapy, justify the masking benefits they bring. We investigate Oxygen, Phosphate and Glucose as candidate rate-limiting substrates of dNTP synthesis of cancer cells with variable DNA contents. Hereby we focus on stomach and brain tumors as two representative cancer types whose TME can “aﬀord” diﬀerent amounts of DNA. Our results point to the potential of tumor cell DNA content and dNTP substrate availability to predict a tumor's vulnerability to increasing SCNA rate.
Department of Integrated Mathematical Oncology, Moffitt Cancer Center
"An integrated computational model of multiple myeloma-bone dynamics under treatment"
Multiple myeloma is a largely incurable cancer characterized by the expansion of plasma cells in the bone marrow. Osteolytic lesions occur as a result of a “vicious cycle” between myeloma cells and trabecular bone that tips the balance of normal bone remodeling in favor of bone resorption. Standard of care treatments include bisphosphonates to slow down bone loss, and bortezomib, an anticancer therapy. Understanding how the composition of the bone microenvironment impacts the success of treatments using in vitro and in vivo methods alone remains a challenge. However, integration of biology and computational modeling allows a unique insight into the spatiotemporal aspects of myeloma progression and how treatments impact the disease.To explore these dynamics, we developed a hybrid agent-based model that incorporates key cell types that drive normal bone remodeling, including osteoclasts and osteoblasts, and use published data as well as our own to calibrate parameters such as the dose-dependent responses of treatments on myeloma and bone cells. We simulate the progression of myeloma growth and bone disease, starting from bone homeostasis, and explore how the “vicious cycle” is modified in the presence of treatments. This computational model has the potential to provide insight into better treatment strategies.