3D extracellular matrix scaffold models of nanoparticle transport in the tumor microenvironment
PRINCIPAL INVESTIGATORS:
Gregg Duncan, Fischell Department of Bioengineering, University of Maryland
Matthew Wolf, Center for Cancer Research, National Cancer Institute
PH.D. STUDENT:
Devorah Cahn, Fischell Department of Bioengineering, University of Maryland
Nanoparticle systems used for cancer drug delivery must overcome multiple biological barriers to avoid clearance and effectively deliver anti-cancer treatments. Upon arrival at the tumor site, the extracellular matrix presents a significant physical obstacle to widespread distribution of nanoparticles within the tumor microenvironment. In order to design nanoparticles with the ability to transport within this microenvironment, we will systematically evaluate the effect of nanoparticle size, shape, and surface chemistry on their transport within normal and tumor-derived extracellular matrices. We will then perform proof-of-concept studies to examine nanoparticle spread in vivo following direct intratumoral injection.
Epigenomic Tumor Evolution Modeling with Single-cell Methylation Data Profiling and Applications in Human Glioma
PRINCIPAL INVESTIGATORS:
Mihai Pop, Department of Computer Science, University of Maryland
S. Cenk Sahinalp, Center for Cancer Research, National Cancer Institute
PH.D. STUDENT:
Yuelin Liu, Department of Computer Science, University of Maryland
DNA methylation, the addition of a methyl group to the C5 position of the cytosine at a CpG site along the DNA, is a key mechanism for epigenetic regulation; its pervasiveness in the genome provides rich signals for studying tumor evolution. With novel sequencing technologies, it is now possible to obtain transcriptomic and methylation profiles of individual cells within a tumor lesion. Here we propose to develop a computational framework to construct phylogenies from single-cell methylation profiles (Figure 1), mitigating the effect of noise and uncertainty due to single-cell data sparsity through a novel probabilistic model, leveraging copy number calls from matched transcriptomic data when available. Successful reconstruction of single-cell methylation phylogenies will facilitate the identification of key methylation events in tumor evolution and help discover novel methylation markers for overall survival in cancer patients. We will apply our framework to a novel single-cell glioma data set, furthering our understanding of this common type of brain tumor.
Polymer-assisted intratumoral delivery of ethanol: Preclinical investigation of safety, efficacy, and immune effects in a hepatocellular carcinoma model
PRINCIPAL INVESTIGATORS:
Jenna Mueller, Fischell Department of Bioengineering, University of Maryland
Bradford J. Wood, Center for Cancer Research, National Cancer Institute
Andrew S. Mikhail, Center for Cancer Research, National Cancer Institute
PH.D. STUDENT:
Jeffrey Yang, Fischell Department of Bioengineering, University of Maryland
Hepatocellular carcinoma (HCC) is the third most common cause of cancer-related deaths worldwide and has poor prognosis for under-privileged patients especially in low and middle-income countries (LMICs) with limited resources to manage HCC. Percutaneous ethanol injection (PEI), which consists of direct injection of absolute ethanol resulting in destruction of tumor cells, is a low-cost therapy that could be used to treat HCCs; however, efficacy is limited due to early leakage of liquid ethanol from the tumor, which can also cause off-target damage and risk. To reduce leakage, we have added the polymer ethyl cellulose (EC) to the injectate, which forms a cohesive local gel when injected into tissue and substantially improves efficacy in a preclinical model (Fig. 1); however, translation to human tumors requires evaluation of EC-ethanol gel distribution to ensure complete tumor coverage in an in vivo HCC model. An expert team of a CCR/CC interventional radiologist (Wood), bioengineer (Mueller), bioengineering Ph.D. student (Yang), and a CCR/CC drug delivery scientist (Mikhail) will design and optimize delivery and tissue distribution of ultrasound-visible EC-ethanol gels to treat HCC and evaluate safety, efficacy, and immune effects in an immunocompetent woodchuck HCC model.
Cancer stem cells-targeted high throughput combinatorial drug screening for inhibiting cancer metastasis
PRINCIPAL INVESTIGATORS:
Xiaoming He, Fischell Department of Bioengineering, University of Maryland
Kandice Tanner, Center for Cancer Research, National Cancer Institute
PH.D. STUDENT:
Hyeyeon Gong, Fischell Department of Bioengineering, University of Maryland
To address cancer metastasis that is a major cause of mortality and morbidity to most cancer patients, we will develop a high-throughput single-cell microencapsulation-based drug screening approach for identifying effective drug combinations to target cancer stem cells (CSCs) that cause cancer metastasis. This platform will enable effective isolation and growth of metastatic CSCs as well as deliver various drug solutions in a combinatorial and temporal manner. Furthermore, the zebrafish model will be used to ascertain the efficacy of the discovered drug combinations in inhibiting cancer metastasis into specific organs like the brain and liver where triple-negative breast cancer (TNBC that is to be studied in this project) tends to metastasize. Our approach holds great promise for effective drug discovery to target CSCs, with the ultimate goal of eliminating cancer metastasis and relapse from the root to reduce cancer mortality and morbidity.
Serological responses to human virome as a platform for early detection of liver cancer
PRINCIPAL INVESTIGATORS:
Doron Levy, Department of Mathematics, University of Maryland
Xin Wei Wang, Center for Cancer Research, National Cancer Institute
PH.D. STUDENT:
Yue Dong, Program in Biostatistics/Bioinformatics, University of Maryland
Liver cancer is an aggressive malignancy with its global incidence and mortality rate continuing to rise. Early detection of liver cancer may provide a chance for patients to receive curative therapy thereby improving their outcomes. However, the current strategies for surveillance/diagnosis of liver cancer are inadequate in detecting cancer at an early stage or in providing survival benefit, since a majority of liver cancer patients are still diagnosed at an advanced stage, which precludes their chance to receive potentially durative therapies. There is an unmet need to identify an effective biomarker-guided surveillance program for early liver cancer diagnosis. As cancer may be due to a failure of immunosurveillance, we hypothesize that a history of viral exposure reflecting from virus-host interaction may reflect the status of host immunity and thus serve as stable biomarkers for early onset of liver cancer. Recently, we have used a phage immunoprecipitation sequencing technology, i.e, VirScan, to determine serological responses to the human virome and have identified a viral exposure signature to be associated with early onset of liver cancer 2. VirScan is an emerging technique that allows comprehensive profiling of individual’s serological response memory of antibody repertoire left by viral exposure history, namely by any of all the known more than 200 species, 1,000 strains of human pathogenic and non-pathogenic viruses (virome) 3. To further validate our initial results, we are conducting multiple large size cohort studies by profiling serum samples from patients with different ethnicities, etiologies enrolled from different geographic locations (Figure 1). These include patients from the U.S., South Korea, Taiwan, Thailand and mainland China. The aims of this study are to explore various mathematical/computational models by combining serological responses to individual’s history of viral exposures with other clinical variables, and to develop a robust and stable algorithm for liver cancer early detection. The ultimate goal of our proposed research plan is to implement these models in the liver cancer surveillance program with the aim to detect liver cancer at an early stage and provide survival benefit to patients with liver cancer.
