Increasing the efficacy of dendritic cell vaccines in melanoma and lymphoma
Dr. Doron Levy, Department of Mathematics, University of Maryland
Dr. Noriko Sato, Center for Cancer Research, National Cancer Institute
Dr. Peter L. Choyke, Center for Cancer Research, National Cancer Institute
Zuping Wang, Program in Applied Mathematics (AMSC), University of Maryland
Dendritic cell (DC) vaccines are a strategy for improving the efficacy of the immune system in fighting certain cancers. Preliminary unpublished data from the Sato lab suggests that the location of vaccination has an unexpected impact on the efficacy of the vaccine in mouse models. While certain vaccine locations [intravenous (i.v.) and intraperitoneal (i.p.)] result with an immediate activation of effector T cells better than subcutaneous (s.c.) and intradermal (i.d.) vaccines, upon challenge with melanoma tumors 8 weeks later, better survival is observed with vaccines that were administered i.p. and i.d. The goal of this project is to develop a methodology to improve survival rate in cancers after a DC vaccine using tumor specific antigens. The approach will be highly integrative combing mathematical modeling, flow cytometry analysis, and experimental data that is collected through state-of-the-art imaging techniques. With a validated mathematical model, we will be able to predict the magnitude of T cell activation, optimize the dosage (number of DCs), the location of vaccination, the combinations of locations, and the timing of boosting after priming. The project will lead to experiments of combination injection sites using the input from the mathematical modelling.
Studying immune evasion in lung cancer carcinogenesis as a basis for predicting response to immune checkpoint blockade
Dr. Max Leiserson Computer Science Department, University of Maryland
Dr. Eytan Ruppin, Center for Cancer Research, National Cancer Institute
Dr. Bríd Ryan, Center for Cancer Research, National Cancer Institute
Sanna Madan, Computer Science Department, University of Maryland
Lung cancer is the leading cause of death among all cancer types in the United States. Recent studies have zeroed on the key involvement of immune processes in early carcinogenesis of non-small cell lung cancer (NSCLC), determining the progression or spontaneous regression of precursor lesions. We hypothesize that understanding these early events, an interesting task on its own, may importantly facilitate our ability to build better predictors of patients’ response to immune checkpoint inhibitors (ICI), which have recently revolutionized cancer therapeutics but still benefit a small number of NSCLC patients. Addressing these challenges by studying a host of pertaining publicly available transcriptomics datasets, we seek, from a basic science perspective, to chart the immune cell landscape across the stages of evolution of lung cancer, and from a translational perspective, to build novel machine-learning based predictors of ICI clinical response in NSCLC, by applying computational deconvolution approaches to elucidate cell-type specific gene expression from bulk transcriptomics data, allowing us to gain a high-resolution understanding of immune activity and evasion in NSCLC evolution. This will enable us to glean insights for its early detection, identify potentially new drug targets and treatment opportunities, and construct robust biomarkers of ICI clinical response that is currently an important unmet need in lung cancer.
Cytosol-specific delivery of microRNA for enhanced therapy of glioma
Dr. Xiaoming He, Fischell Department of Bioengineering, University of Maryland
Dr. Shuo Gu, Center for Cancer Research, National Cancer Institute
James Shamul, Fischell Department of Bioengineering, University of Maryland
To overcome major barriers of delivering therapeutic ribonucleic acids (RNAs) to the brain including the blood-brain barrier (BBB), intracellular entry, and enzymatic degradation, we will design a BBB-penetrating, biodegradable, and non-toxic nanoparticles that will precisely deliver therapeutic RNAs inside brain cancer cells. These nanoparticles will selectively explode during their journey inside cancer cells so that their contents will arrive efficiently. After the RNAs are expelled, they will lower the activity of pro-cancerous proteins and diminish growth and survival of cancer cells. This treatment holds promise for providing long-term killing of brain cancer without requiring repeated intracranial surgeries, helping to reduce the unfavorable outcomes of conventional brain cancer therapies.
A zebrafish model to study blood-brain barrier opening by photo-activable nanocrystals
Dr. Huang Chiao Huang, Fischell Department of Bioengineering, University of Maryland
Dr. Michael M. Gottesman, Center for Cancer Research, National Cancer Institute
Dr. Robert W. Robey, Center for Cancer Research, National Cancer Institute
Collin T. Inglut, Fischell Department of Bioengineering, University of Maryland
The blood-brain barrier (BBB) is comprised of endothelial cells that form tight junctions to limit the transport of compounds from the bloodstream into the brain. ATP-binding cassette (ABC) transporters form a second line of barrier by pumping compounds back into the bloodstream, thus protecting the brain. Accordingly, a strategy that simultaneously opens the tight junctions and degrades ABC transporters would represent a transformative advance for drug delivery to brain cancer.
Computational modeling of antibody repertoire evolution in response to tumor progression
Dr. Uzi Vishkin, Institute for Advanced Computer Studies, University of Maryland
Dr. Cenk Sahinalp, Center for Cancer Research, National Cancer Institute
Ananth Hari, Electrical and Computer Engineering, University of Maryland
Abstract: The goal of the project is to develop combinatorial algorithms and their high performance implementations for simultaneously modeling B-cell receptors (immunoglobulins) and in particular the V segments through hyper mutation in response to tumor progression, by the use of single cell sequencing. In particular we aim to collect and analyze single-cell smart-seq data from which mutations, copy number, expression and methylation information will be derived for tumor cells to infer a tumor-phylogeny while simultaneously building the corresponding immunoglobulin repertoire phylogeny. The ultimate goal is to explore the impact of the germ line immunoglobulin heavy chain region on the adaptive immune system response to tumor progression. The project will first formulate the simultaneous phylogeny construction problem as an integer linear program and will then focus on custom-built highly parallelizable branch-and-bound algorithms with improved running time.