Photodynamic targeting of drug efflux transporters in peritoneal carcinomatosis
PRINCIPAL INVESTIGATORS:
Huang Chiao Huang, Fischell Department of Bioengineering, University of Maryland
Suresh V. Ambudkar, Center for Cancer Research, National Cancer Institute
PH.D. STUDENT:
Idrisa Rahman, Fischell Department of Bioengineering, University of Maryland
Advanced stage ovarian cancer often spreads along currents of abdominal fluid and remains the leading cause of deaths from gynecologic tumors. These currents confer fluid shear stress (FSS) on ovarian cancer metastases, which changes the molecular characteristics of the tumors and makes them more resistant to chemotherapy. This proposal will develop photodynamic therapy to modulate ATP-binding cassette (ABC) drug transporters and overcome FSS-induced chemoresistance in ovarian cancer (Figure 1). An expert team of cancer biologist (Ambudkar), biomedical engineer (Huang), and bioengineering Ph.D. student (Rahman) will study the mechanism by which photodynamic therapy can be exploited to shut down ABC transporters in ovarian cancer cells.
Modeling structure, thermodynamics and kinetics of T cell activation by antigens
PRINCIPAL INVESTIGATORS:
Pratyush Tiwary, Department of Chemistry and Biochemistry and IPST, University of Maryland
Grégoire Altan-Bonnet, Laboratory of Integrative Cancer Immunology, National Cancer Institute
PH.D. STUDENT:
Akashnathan Aranganathan, Biophysics Program, University of Maryland
Four sentences describing the project in layman's terms plus a picture/figure Predicting how T cells interact with tumor cells to trigger an immune response is a key challenge in cancer immunotherapy. We propose to develop computational models of T cell receptor (TCR) and chimeric antigen receptors (CAR) interacting with tumor antigens. We will validate our theoretical predictions using robotic experimental measurements on ex vivo models of T cell activation.
Harnessing methylation changes to understand tumor heterogeneity and diversity
PRINCIPAL INVESTIGATORS:
Stephen M. Mount, Department of Cell Biology and Molecular Genetics, University of Maryland
S. Cenk Sahinalp, Center for Cancer Research, National Cancer Institute
PH.D. STUDENT:
Xuan Cindy Li, Program in Computational Biology, Bioinformatics, and Genomics, University of Maryland
The fast-changing nature of cancer imposes major challenges on the prospect of personalized diagnosis, treatment, and prognostics, thus motivating the investigation of tumor heterogeneity and diversity on a molecular level. While heritable molecular markers like mutation are often employed to understand tumor progression, DNA methylation, the addition of a methyl group to the DNA molecule and most commonly at cytosine, has also been proven stably inherited with far richer signals but remains overlooked in current research. Here, we propose to understand the complex dynamics of tumors with pioneering, multidisciplinary strategies of harnessing methylation data, including (i) developing a pipeline that concomitantly infers tumor lineages and identifies stably inherited CpG methylation changes from single-cell methylation data, (ii) applying the pipeline on real patient single-cell data, and (iii) performing targeted methylation sequencing informed by the pipeline to pinpoint key CpG sites of diagnostic values. With our preliminary research already demonstrating initial success (Figure 1), we expect the outcomes of our proposed work to bring forth important insights into the understanding of tumor heterogeneity and diversity, as well as significant impacts on the improvement of non-invasive cancer management.
Spatial Single-cell Dissection of Tumor-immune Interactions in Liver Cancer
PRINCIPAL INVESTIGATORS:
Takumi Saegusa, Department of Mathematics, University of Maryland
Lichun Ma, Center for Cancer Research, National Cancer Institute
PH.D. STUDENT:
Dmitrii Gudin, Program in Mathematical Statistics, University of Maryland
Intratumor heterogeneity may result from the evolution of tumor cells and their continuous interactions with the tumor microenvironment which collectively drives tumorigenesis. Defining the interactions of tumor and immune/stromal cells may, thus, represent unique fingerprints stable for its tumor biology. Using liver cancer patient derived single-cell and spatial profiles, we aim to identify tumor-immune interactions in a tumor ecosystem by developing a novel machine learning based computational model. The uncovered molecular interactions network will be validated for patient stratification and clinical outcome prediction in multiple data cohorts with more than 1000 liver cancer patients, which may further open a path for therapeutic exploration.
Quantifying human RNA targetability for small molecule drug discovery
PRINCIPAL INVESTIGATORS:
Pratyush Tiwary, Department of Chemistry and Biochemistry, University of Maryland
John Schneekloth, Jr, Center for Cancer Research, National Cancer Institute
PH.D. STUDENT:
Shams Mehdi, Program in Biophysics, University of Maryland
Although many different genes are known to drive cancer, a majority of these genes encode proteins that are considered “undruggable” at the protein level. One strategy to target so-called “undruggable” oncogenes is to develop small molecules that directly target the RNA that encodes these proteins. Our work aims to develop an integrated computational and experimental pipeline to evaluate human RNAs as targets for small molecules, and develop small molecules that target them. By using a combination of generative artificial intelligence (AI)-informed molecular dynamics studies in the Tiwary lab coupled with high throughput screening and chemical/biophysical approaches in the Schneekloth lab, we aim to develop a strategy for the first examples of small molecules that target clinically important mRNAs for human oncogenes. A central aspect of this work will be the adaptation of generative AI models from image processing and other arenas for generating structures of RNA conformations. Specifically, denoising diffusion probabilistic models (DDPMs) will be used that work through converting data to noise to back to more meaningful data.
Methodologies for serological profiling in a multi-omics cope within 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:
Wing Yan “Becky” Yuen, Program in Biostatistics/Bioinformatics, University of Maryland
VirScan is a novel technology using phage immunoprecipitation to examine the serological profiles of serum samples and test for exposure to >1200 viral organisms, information of high utility for cancer research as markers or modifiers of disease risk. However, phage immunoprecipitation experiments have several unique properties which make the data normalization and bioinformatic processing of the data a challenge which has yet to be addressed in the literature. Using liver cancer as a model, we propose to focus on bioinformatic methods development and application to identify best strategies for measuring presence and breadth of serological response as compared to gold standard measurements (Aim 1) and subsequently, we will focus on development of data integration strategies to examine viral exposures in the scope of a systems biology approach with multi-omic data (Aim 2 and 3). Ultimately, this research will serve as a fundamental methodology for future studies examining viral exposures in the context of cancer research.
