Monday, June 14 at 03:15pm (PDT)Monday, June 14 at 11:15pm (BST)Tuesday, June 15 07:15am (KST)
SMB2021 FollowMonday (Tuesday) during the "CT01" time block.
Andrei S Rodin
City of Hope
"Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data"
Cancer immunotherapy, specifically immune checkpoint blockade, has been found to be effec-tive in the treatment of metastatic cancers. However, only a subset of patients achieve clinical responses. Elucidating pretreatment biomarkers predictive of sustained clinical response is a major research priority. Another research priority is evaluating changes in the immune system before and after treatment in responders vs. nonresponders. Our group has been studying im-mune networks as an accurate reflection of the global immune state. Flow cytometry (FACS, fluorescence-activated cell sorting) data characterizing immune cell panels in peripheral blood mononuclear cells (PBMC) from gastroesophageal adenocarcinoma (GEA) patients were used to analyze changes in immune networks in this setting. Here, we describe a novel computational pipeline to perform secondary analyses of FACS data using systems biology/machine learning techniques and concepts. The pipeline is centered around comparative Bayesian network anal-yses of immune networks and is capable of detecting strong signals that conventional methods (such as FlowJo manual gating) might miss. Future studies are planned to validate and follow up the immune biomarkers (and combinations/interactions thereof) associated with clinical re-sponses identified with this computational pipeline.
Moffitt Cancer Center & Research Institute
"Integrating Spatial Point Pattern Analysis and Agent Based Modeling for Studies of Stroma Sheltering Effects on Tumor"
Over the past two decades, the advancement in visualization and analysis of molecular and cellular data led to the development of highly efficient, targeted cancer drugs. However, cancer relapse occurs frequently, indicating that the tumor microenvironment plays a crucial role in treatment response potentially sheltering tumor cells during drug administration. We studied the eco-evolutionary dynamics of stroma-proliferating tumor cells interaction via point pattern analysis methods and spatial agent-based modeling (ABM). We characterized the spatial extent and amplitude of stroma shielding in the presence and absence of treatment using various pairwise distance methods adapted from ecology and geology. The spatial distributions of tumor cells, their proliferation rates, and the identified stroma protective effects were used to parametrize the ABM and simulate the spatio-temporal dynamics of tumor growth. Our preliminary results show that stroma-proliferating tumor cell clustering is considerably higher under treatment than in control samples. Moreover, stroma's protective effect during treatment is limited to cells that are either in direct contact with stromal cells or in their immediate proximity, suggesting a paracrine mediate effect. We expect our results to lead to novel therapeutic interventions that aim to shift eco-evolutionary dynamics rather than maximize short-term tumor cell killing efficiency.
City, University of London
"Explaining modes of tumour evolution"
Understanding the mode of tumour evolution is important for accurate prognosis and designing effective treatment strategies. Whereas selective sweeps are prevalent during early tumour growth, later stages exhibit either sparse branching or effectively neutral evolution. The causes of these different patterns remain poorly understood. I will present a new model for determining the probability of selective sweeps versus clonal interference in one-, two- and three-dimensional expanding populations. The solutions of this model are surprisingly simple mathematical expressions that are independent of mutation rate. Given parameter values obtained with human cancer data, the model offers to explain why selective sweeps are rare except when tumours are relatively very small. I will discuss these results in the context of additional computational modelling and new indices for classifying modes of tumour evolution that I and my coauthors have developed.
Texas Tech University Mathematics and Statistics
"Diffusion Tensor Imaging (DTI) Based Drug Diffusion - Population Model for Solid Tumors"
In this work, we study the effect of drug distribution on tumor cell death when the drug is internally injected in the tumorous tissue. We derive a full 3-dimensional inhomogeneous – anisotropic diffusion model. To capture the anisotropic nature of the diffusion process in the model, we use an MRI data of a 35-year old patient diagnosedwith Glioblastoma multiform(GBM) which is the most common and most aggressive primary brain tumor. Afterpreprocessing the data with a medical image processing software, we employ finite element method in MPI-basedparallel setting to numerically simulate the full model and produce dose-response curves. We then illustrate theapoptosis (cell death) fractions in the tumor region over the course of simulation and proposed several ways toimprove the drug efficacy. Our model also allows us to visually examine the toxicity. Since the model is builtdirectly on the top of a patient-specific data, we hope that this study will contribute to the individualized cancertreatment efforts from a computational bio-mechanics viewpoint.