Tuesday, June 15 at 02:15pm (PDT)Tuesday, June 15 at 10:15pm (BST)Wednesday, June 16 06:15am (KST)
SMB2021 FollowTuesday (Wednesday) during the "CT04" time block.
Moffitt Cancer Center
We develop a novel paradigm of cancer therapy based on the 'anti-fragility' of cancer cell lines. Anti-fragility is a situation where the dose response function is convex. Treatment schedules with high variance of dose delivered result in maximum cell kill. For example, if the curvature is convex near a dose of 'x', continuous administration of x may have a less efficacious response compared to a regimen that switches equally between 120% and 80% of x, even though both regimens use the same total drug. We advocate for the need to disentangle first- and second-order treatment effects.Recent advances in personalized treatment scheduling known as 'adaptive' therapies typically result in a high level of variance in dose delivered in patients, similar to the theory behind anti-fragility. In this work we develop mathematical models of tumor pharmacodynamics (PKPD) and treatment resistance (Lotka-Volterra) to improve personalized dose protocols using principles from anti-fragile theory. PKPD dynamics are parameterized using in vitro dose response of H3122 non-small cell lung cancer cell lines confronted to ALK inhibitors. Competition between subpopulations (sensitive and resistant subclones) is the key determinant of optimal dose variance for individual patients. This work has implications for cancer therapy, antibiotics, and beyond.
Moffitt Cancer Center
"Simulating tumor-immune ecosystem evolution during cancer radiotherapy"
Radiotherapy efficacy is the result of radiation-mediated cytotoxicity coupled with stimulation of anti-tumor immune responses. We developed an in silico three-dimensional agent-based model of diverse tumor-immune ecosystems (TIES) represented as anti- or pro-tumor immune phenotypes. We validate the model in 10,469 patients by demonstrating clinically-detected tumors have pro-tumor TIES. We then quantify the likelihood radiation induces anti-tumor TIES shifts by developing the individual Radiation Immune Sensitivity (iRIS), a novel biomarker. We show iRIS distribution across 31 tumor types is consistent with the clinical effectiveness of radiotherapy and predicts for local control and survival in a separate cohort of 59 lung cancer patients. This is the first clinically and biologically-validated model to represent the perturbation of the TIES by radiotherapy.
Emanuelle Arantes Paixão
Laboratório Nacional de Computação Científica
"CARTmath: an in silico laboratory to simulate CAR-T immunotherapy in preclinical models"
CAR-T cell immunotherapy has been obtaining expressive results in therapies against hematological cancers. Different antineoplastic targets are under investigation as well as therapy combinations with immune checkpoint blockade drugs, minimum effective CAR-T cell dose, memory pool formation, patient specificity, among others. Many of these studies require a preclinical proof-of-concept experiment using immunodeficient mouse models. Aiming at minimizing and optimizing in vivo experiments, we developed an open-source software in a Shiny R-based platform, named CARTmath. It allows simulating a three population mathematical model that represents the dynamics of tumor cells and effector and memory CAR-T cells in immunodeficient mouse models. Designed to be a friendly platform, even researchers unfamiliar with mathematical modeling can investigate the effects of different CAR-T cell immunotherapy protocols, types of tumors, immunosuppressive mechanisms, to mention a few, hopefully reducing in vivo experiments. CARTmath is available at github.com/tmglncc/CARTmath or directly on the webpage cartmath.lncc.br.
António Sergio Dias Morais
Universidade de Coimbra
"Role of prostate gland network structure in early stage prostate cancer"
Prostate cancer (PCa) is the second most frequent cancer in men. The limited individualization of the clinical management beyond risk-group definition leads to significant overtreatment/undertreatment rate. PCa is a paradigmatic condition in which an individualized predictive technology could make a difference in treatment.Mathematical modeling and simulation highlight the mechanisms behind disease progression. The prostate is a small organ with a structure composed by a network of glands within smooth muscle connectivity tissue. To explore the prostate structure in PCa growth we developed 2 mathematical models. The first, a 2D cellular Potts model (CPM), simulates the interactions between the different types of cells and the deformation of the glands in time. The second is a 3D phase-field model with tumor growth, prostate gland dynamics and nutrient consumption.The CPM gives important clues: how the cells and the glands rearrange locally in tumor growth. We import these insights to the 3D phase-field model to study how the adaptation of the grand morphology influences the lesion morphology.We conclude that the ramified structure of the prostate has a determinant impact in the tumor growth. The model parameter range that creates a ramified tumor phenotype is dramatically extended when prostate glands are considered.