Mahesh Prasad Bekal PhD

Affiliation: School of Biomedical Informatics, UTHealth Houston

Appointed: May 1, 2023 – April 30, 2024

Final Degree: Ph.D

Personal Statement: My research aims to uncover the intricate relationship between the gut microbiome and brain function, specifically in response to radiation therapy. By investigating the impact of gut microbiome and their metabolites on radiation-induced cognitive dysfunction, I hope to identify potential therapeutic strategies that can mitigate cognitive dysfunction in patients with brain cancer. My work involves studying how radiation therapy affects the gut microbiome and metabolites, and how changes in the gut-brain connection can be used to improve outcomes for these patients. Ultimately, this research could identify new targets for therapeutic intervention and improve the quality of life for brain cancer patients undergoing radiation therapy.

Primary Mentor: Dr. Yoshua Esquenazi Levy

Project Title: "Impact of fecal microbiome and metabolites on radiation-induced cognitive dysfunction.”

Brief Introduction of The Project:
Radiation therapy (RT) is a crucial treatment option for patients with brain tumors, but it can lead to radiation-induced cognitive dysfunction (RICD) in up to 90% of cases, significantly impacting their quality of life. Recent research has highlighted the crucial role of the gut microbiome and its metabolites in regulating central nervous system function, with potential implications for neurodegenerative disorders. Our preliminary data show that cranial RT induces gut microbiome changes and cognitive dysfunction in mice, and we aim to investigate whether fecal microbiome transplantation (FMT) or supplementation with SCFA-producing bacteria can prevent or alleviate RICD by modulating the host's microbial, metabolomics, and immunologic profiles. Our findings may provide translational strategies for improving the quality of life of brain tumor patients undergoing cranial RT.

Le Chang, PhD

Affiliation: McWilliams School of Biomedical Informatics at UTHealth Houston

Appointed: January 1, 2024 – December 31, 2024

Personal Statement: I am a Postdoctoral Research Fellow at UTHealth's Center for Precision Health. I am interested in transforming big data into biological insights and have developed multiple applications for analyzing and visualizing complex omics datasets. My current research focuses on exploring the role of viral transcriptional regulators in cancer. The CPRIT BIG-TCR Postdoctoral Fellowship has been pivotal in my career, offering extensive training and networking opportunities that align with my passion for advancing precision health through innovative bioinformatics. .

Primary Mentor: Dr. Zhongming Zhao

Project Title: Decoding Tissue and Cell Type Specificity of Viral Transcriptional Regulators (vTRs) and Interactions in Cancer

Brief Introduction of The Project:
Our project focuses on understanding the mechanisms of viral transcriptional regulators (vTRs) in human cancers. By leveraging a novel computational framework, we aim to identify and annotate vTRs, analyze their interactions with the host genome, and examine their influence on cellular processes across various tissues and cell types. The research will integrate both manual curation and computational methods to curate a comprehensive list of vTRs from existing literature and databases, using advanced techniques like deep learning for detection. This will be complemented by an analysis of transcriptomic and epigenomic data to elucidate the functional consequences of vTR interactions in cancer. Additionally, we plan to develop a user-friendly computational platform to facilitate the exploration of vTR-host interactions, making our findings accessible to the broader cancer research community. This project promises to provide critical insights into the role of vTRs in virus-induced cancers, potentially leading to innovative anticancer therapeutic strategies, and to bridge the gap between basic science and clinical application, marking a significant advancement in the field.

Shin-Fu Chen, PhD

Affiliation: McGovern Medical School at UTHealth Houston

Appointed: January 1, 2023 – December 31, 2024

Personal Statement: My research interest is to study molecular mechanisms of macromolecular complexes involved in essential cellular processes and cancer development by utilizing structural and biochemical approaches. Currently, I am studying how uterine leiomyomas, cardiovascular disease (CVD), and various types of cancers mutations occurred on the Mediator, a 1.5 MDa transcriptional complex consisting of a large Core and a dissociable CDK8 Kinase module (CKM), affect transcriptional regulation. I expect to develop new therapeutic strategies for cancer prevention and treatment by better understanding the regulatory mechanisms of transcriptional machinery.

Primary mentor: Dr. Kuang-Lei Tsai

Project title: Investigating CDK8-mediated interaction within the transcriptional machinery

Brief introduction of the project:
Human Mediator is a large and evolutionarily conserved protein complex that conveys regulatory signals from activators to the RNA polymerase II transcription machinery. The CDK8 kinase module (CKM), a dissociable subcomplex of the Mediator, consisting of MED13, MED12, and a CDK8/CycC pair, influences gene expression through its kinase-dependent function. Dissociation of CKM from Mediator is a critical step for access of RNAPII to Mediator prior to transcription activation. Interestingly, even though numerous pathogenic mutations causative for neurodevelopmental disorders and cancer congregate in CKM, how these mutations influence the interaction between MED and CKM remains poorly understood. In this project, by combining structural, biochemical and cancer research methods, we aim to investigate the molecular mechanism that regulates the MED-CKM interaction and understand the role of CDK8 in regulation of gene expression. This project will provide functional and structural insights into regulation of Mediator-CKM interaction through CDK8 kinase activity and may contribute to development of novel anti-cancer drugs.

Iona Hill PhD

Affiliation: Department of Neurosurgery, UTHealth Houston

Appointed: January 1, 2023 – December 31, 2024

Personal Statement: I have always been driven by research with a real-life application, particularly in the field of medicine. My background is in chemistry but during my PhD I expanded my knowledge of medicine, biology, cancer therapy and nanotechnology through my highly interdisciplinary project. This project investigated the use of metallic nanoparticles for applications in cancer therapy, for example as radiosensitising agents, and I have continued this research in my postdoctoral position. Studying combined cancer therapy allows me to implement my interdisciplinary skills by including many different aspects of science in the approach. I look forward to continuing this research theme as I progress in my career.

Primary Mentor: Dr Sunil Krishnan

Project Title: Targeting PI3K Akt Axis to Radiosensitise Pancreatic Cancer

Brief Introduction of The Project:
Pancreatic cancer is a common malignancy, however it is difficult to diagnose at an early stage when most curable, it is therefore a leading cause of cancer related mortality across the world. Developing combined approaches to pancreatic cancer treatment would lead to more patient-specific treatment and consequently greater treatment outcomes. Finding agents which are radiosensitisers for treatment of pancreatic cancer would enhance the dose effect of radiation therapy. Our previous work has focused on an unbiased throughput screening to identify potential radiosensitising drugs for pancreatic cancer. This screen identified a number of potential drugs, with PI3K inhibitors and mammalian target of rapamycin (mTOR) inhibitors being the most potent sensitisers of pancreatic cancers to radiation therapy in vitro and in vivo. This project aims to validate potential radiosensitising drugs for treatment of pancreatic cancer by evaluating their mechanisms of radiosensitisation. In addition, we plan to evaluate the immune response of these drugs by a combination of flow cytometry and single cell sequencing. Thereby developing big data workflows to integrate this immune response.

Xiaomin Liang, MS

Affiliation: McWilliams School of Biomedical Informatics at UTHealth Houston

Appointed: June 1, 2024 – May 31, 2025

Personal Statement:
My academic and professional journey has convinced me that a career in cancer research offers immense diversity and fulfillment. I am fully aware of the importance of cancer screening, diagnostic, and treatment accuracy based on medical imaging. One of my projects for inverted papilloma attachment segmentation aims to reduce the probability of receiving wider resection, chemotherapy, or radiotherapy, which means patients could have a better prognosis. My ambition is to work within clinical institutions, contributing to the development of innovative therapies and enhancing the quality of life for cancer patients. I am enthusiastic about embracing innovative ideas from various domains and collaborating on cancer-related projects.

Primary Mentor: Dr. Luca Giancardo

Project Title: Automatic Segmentation Methods with Weak and Missing Labels in Cancer Medical Images

Brief Introduction of The Project:
Our goal is to devise a versatile deep-learning technique to address the lack of precise voxel-level labels and pave the way for more effective image segmentation in cancer medical imaging that can be readily deployed in clinical settings. Advanced machine learning segmentation algorithms can only be trained with the precise labeling of voxels for each image, a task known to be exceedingly challenging due to its demands on expertise, time, and cost. This challenge becomes particularly pronounced when one considers that voxel-level manual labeling is not a standard practice in clinical settings for cancer treatment, except for cases related to radiation therapy treatment planning. However, many clinical datasets are equipped with weak voxel labels, characterized by bounding boxes and image-level descriptions. Consequently, an urgent imperative exists for the development of automatic segmentation methods capable of learning from uncertain voxel and image-level labels and handling missing data. This approach holds significant promise for cancer care applications such as precise cancer surgery guidance.

Ko-Hong Lin, MS

Affiliation: McWilliams School of Biomedical Informatics at UTHealth Houston

Appointed: January 1, 2023 – December 31, 2024

Personal Statement: My study is to apply graph neural network (GNN) models in drug discovery in complex diseases. GNNs can learn multimodal relationships and predict hidden interactions from biological networks. Since pathological mechanisms of cancer have been extensively investigated, existing evidence of biomedical interactions in cancer etiology is comprehensive and sophisticated. Consequently, utilizing GNNs in cancer drug discovery could be a feasible method to identify repurposable drugs. For my long-term career goal, I am interested in applying deep learning to address challenges in disease prevention and novel therapeutic development, such as predicting genomic and immunogenomic profiles that lead to differential treatment responses.

Primary Mentor: Dr. Xiaoqian Jiang

Project Title: A Comprehensive Drug Repurposing Study for Glioblastoma Multiforme

Brief Introduction of The Project:
The goal of this study is to develop a pipeline for drug repurposing for Glioblastoma Multiforme (GBM). Our goal is to identify effective drugs or drug combinations that can improve GBM patient outcomes. We propose three aims that can be integrated as an end-to-end pipeline. The first aim is to use a multi-task GNN model to learn existing knowledge of the biomedical interactome and chemical features. The second aim is to leverage a large-scale claims database to identify treatment outcomes with statistical correction of confounding effects. The third aim is to apply GBM cell lines and human-induced pluripotent stem cells (hiPSC) as a drug screening platform to validate drug efficacy. This pipeline will accelerate drug selection and identify drug candidates that can be promising treatment strategies for GBM patients.

Sseu-Pei Hwang, MS

Affiliation: The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences

Appointed: January 1, 2023 – December 31, 2024

Personal Statement: I couldn’t move my eye away from science, attracted by its complexity and elegancy. I realized that working as a scientist is not only challenging and exciting but also meaningful, which is the desirable career I’m looking forward to. My professional goal is to be a well-qualified research scientist to make a breakthrough in the study of disease and help human beings overcome the suffering. I joined Dr. Denicourt’s lab to expand my passion for resolving the beauty of delicate regulations in molecular mechanisms and working on a study that is meaningful and helpful to all humans.

Primary Mentor: Dr. Catherine Denicourt

Project Title: Mechanisms of specialized onco-ribosome production in cancer

Brief Introduction of The Project:
In my project, I plan to apply several bioinformatic approaches to answer the big questions of how ribosome biogenesis and ribosome heterogeneity are involved in cancer development. I wish to analyze the epitranscriptomic regulation of rRNA during ribosome biogenesis, in which a RiboMeth-seq approach will be perfect for providing a comprehensive view of the modification pattern of the rRNAs. Furthermore, since ribosomes are critical for translational regulation in the cells, I will study the translatomic changes contributed by ribosome heterogeneity that involved the epitranscriptomics regulation of rRNA. With this study, we will be able to elucidate how onco-ribosomes are produced and identify potential targets for developing treatments for cancers.

Shraddha Subramanian, MS

Affiliation: The University of Texas MD Anderson UTHealth Houston Graduate School of Biomedical Sciences

Appointed: January 1, 2024 – December 31, 2024

Personal Statement: I am a 4th year student pursuing my PhD at GSBS. My motivation to pursue a career in oncology research stems from the desire to identify cancer’s one flaw and bring us a step closer to making cancer history. Research in my lab is focused on investigating and therapeutically targeting the molecular players implicated in CRC progression. My long-term career goal is to become an independent investigator who will leverage innovative academic science and computational excellence to advance novel therapeutic concepts from the bench to the bedside.

Primary Mentor: Dr. Kendra S. Carmon

Project Title: Targeting Cancer Stem Cell Plasticity to Overcome Colorectal Cancer Resistance and Relapse

Brief Introduction of The Project:
A significant hurdle in colorectal cancer (CRC) treatment is the relapse of residual disease, which can be attributed to cancer stem cells (CSCs). CSCs are an immortal cell population that exhibit plasticity, allowing cells to alter their phenotype in response to environmental cues that bolster inherent drug resistance. As a bona fide marker of functional CSCs, the Leucine-rich repeat-containing G protein-coupled receptor 5 (LGR5) is a promising drug target. Our previous attempts at eliminating CSCs using antibody-drug conjugates (ADCs) against LGR5 resulted in an initial tumor regression that was reversed following treatment termination. Follow-up studies indicate that the recurrent tumors evade therapy by converting into a LGR5 negative (LGR5-) state. Moreover, these LGR5- cancer cells rely, at least in part, on the MET-STAT3 pathway to bolster their metastatic potential. Our proposed project aims to identify the mechanism underlying LGR5+ CSC plasticity and develop a therapeutic strategy to abrogate drug-resistant CRC tumors. Mechanistically, we anticipate that CSC-driven tumor heterogeneity depends on the LGR5/MET feedback loop. We will employ RNA sequencing to identify drivers of MET-STAT3 activation and resistance in LGR5- CRC cells. Therapeutically, we will evaluate efficacy of an ADC combination treatment co-targeting LGR5 and MET. This project will reveal novel genetic and molecular regulators of CSC-driven plasticity and a unique therapeutic strategy to potentially overcome colorectal tumor resistance and relapse.

Wendao Liu, BS

Affiliation: The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences

Appointed: January 1, 2023 – December 31, 2024

Personal Statement: I am a PhD student in GSBS quantitative science program. With interdisciplinary training in biology, statistics and informatics, I am motivated to harness them in biomedical research. My research interest is to investigate how advanced sequencing technologies and bioinformatics could increase the understanding of human diseases. As a GSBS student, making cancer history is my long-term research goal. I aim to leverage this cancer research training program and participate in cancer research with the emerging precision medicine paradigm.

Primary Mentor: Dr. Zhongming Zhao

Project Title: Investigating the mutual implications of cancer and COVID-19 by single-cell omics approaches

Brief Introduction of The Project:
Cancer and COVID-19 are leading threats to human health and patients may develop both diseases at the same time. Cancer patients are more vulnerable to COVID-19 and generally have worse outcomes. Understanding how Cancer and COVID-19 have implications for each other is critical for proper prevention and treatment. I aim to analyze the underlying molecular mechanisms of mutual implications of cancer and COVID-19 using single-cell omics approaches. The main aims of the project include: characterizing the common immunological features in cancer and COVID-19; Evaluating and predicting the effect of potential anticancer therapies to COVID-19; 3. Understanding the potential implications of COVID-19 for cancer. My findings will provide insights into why cancer patients are more vulnerable to COVID-19 and whether COVID-19 may promote cancer development or progression.

Trinh T. T. Phan, PhD

Affiliation: McGovern Medical School at UTHealth Houston

Appointed: January 1, 2024 – December 31, 2024

Personal Statement:
Early correction of germline mutations in tumor suppressor genes holds the potential to significantly delay or even prevent the onset of tumors, thereby extending patients' survival and saving lives. With my solid and comprehensive background in p53 biology acquired during my PhD studies and an unwavering passion for cancer research, I joined Dr. Dung-Fang Lee's lab to focus on developing therapeutic strategies for treating and preventing malignancies in Li-Fraumeni syndrome, a genetic disorder characterized by a germline p53 mutation. The UTHealth BIG-TCR Postdoctoral Training Program in Cancer Research offers an excellent opportunity for me to augment my research skills, expand my expertise across diverse and multidisciplinary domains, and support me in achieving my career goal of becoming an independent investigator in cancer research.

Primary Mentor: Dr. Dung-Fang Lee

Project Title: Therapeutically targeted base editing to correct p53 mutations and prevent malignancies

Brief Introduction of The Project:
Osteosarcoma (OS) is the most common malignant bone tumor. Mutations on the p53 tumor suppressor gene are one of the most frequent alternations in almost every type of cancer, including OS. Despite progress in treatments, survival rates continue to be discouraging when p53 missense mutations are present. These p53 mutations play a significant role in tumor progression and the development of resistance to therapy. Recently, the advent of base editing, a novel genome editing tool, provides a potential strategy for precisely correcting such mutations in vivo, which could transform the landscape of clinical practice. Unlike traditional CRISPR-Cas9, this tool offers a potential avenue for correcting pathogenic point mutations efficiently and precisely. Notably, base editing has demonstrated success in preclinical models for a number of genetic diseases, such as progeria, muscular dystrophy, and beta-thalassemia, paving the way for translation to the clinic. However, the application of this technology in treating cancers and/or cancer-prone genetic disorders remains largely unexplored. This project investigates the therapeutic potential of base editing to target p53 missense mutations in OS. The successful accomplishment of this project not only advances p53 understanding but also offers a transformative therapeutic approach, holding promise for revolutionizing cancer treatment strategies.

Wang Yichun, PhD

Affiliation: McGovern Medical School at UTHealth Houston

Appointed: January 1, 2024 – December 31, 2024

Personal Statement:
I am committed to studying the functional role and mechanism of eRNAs (enhancer RNAs) in human breast cancers. I will focus on identifying the enhancer programs and eRNA landscapes differently expressed between triple negative breast cancer (TNBC) cells and their lung metastasis counterpart cell lines. I will select top deregulated eRNAs, applying novel tools such as CRISPR-Cas13, ASO and O-Map to study their functions in metastasis gene program and the underlying mechanism. I am also excited to learn bioinformatics technology, to understand the mechanisms behind and guiding research direction. I aim to explore the function of eRNAs in various cancers to solve clinically important questions related to cancer development and metastasis, the underlying epigenetic mechanisms and potential therapeutic intervention.

Primary Mentor: Dr. Wenbo Li

Project Title: The function of Enhancer RNAs in Triple Negative Breast Cancer lung metastasis

Brief Introduction of The Project:
Triple negative breast cancer (TNBC) tends to be more aggressive and has fewer treatment options compared to other subtypes. TNBC lung metastasis refers to the spread of triple negative cancer cells to the lungs. Treating and preventing metastasis of TNBC is a primary challenge. One important feature of metastasis as compared to the primary tumor is a dramatic transcriptional reprogramming of the cancer cells. Underlying these, enhancers are often the key genetic elements that orchestrate such transcriptional reprogramming in cancers. Enhancer RNA (eRNA) refers to a class of non-coding RNAs that are transcribed from enhancer regions. I aim to seize this opportunity to address the enhancer RNA functions in TNBC metastasis with two aims. First, I will use leading edge epigenomic methods to elucidate the enhancer and eRNA landscapes in cell line models of TNBC. I will validate the clinical relevance of these eRNAs by analyzing published RNA-seq datasets from TNBC patients with paired primary tumor and lung metastasis. In Aim-2, I will select the top deregulated eRNAs and/or these close to key metastasis genes for functional perturbation. The expected results of this proposal will not only elucidate basic biological mechanisms of metastasis gene reprogramming, but also provide potential new avenues to target eRNAs to potentially overcome cancer metastasis.

Zening Wang, PhD

Affiliation: John P. and Kathrine G. McGovern Medical School, UTHealth Houston

Appointed: June 1, 2024 - May 31, 2025

Personal Statement:
I am a Postdoctoral Research Fellow in Dr. Xin Ge’s Lab at IMM. I spearhead the development of mammalian display platform technologies for various applications, e.g., massively parallel reporter assay, Fc engineering and CAR/TCR engineering. Our platforms, surpassing existing approaches, enable the construction of mammalian cell libraries with ideal characteristics, e.g., high stability, uniform transcription level, single variant per cell, large cargo capacity, and large diversity. This leads to unprecedently rapid identification of therapeutic leads with better developability..

Primary Mentor: Dr. Xin Ge

Project Title: “Functional Discovery of TCRm-CAR Targeting Cancer Germline Antigens”

Brief Introduction of The Project:
Achieving remarkable clinical successes for leukemia and lymphoma, CAR-T has revolutionized cancer treatment and is considered as the 'third pillar' of medicine1. However, current CAR-T technology has at least three limitations: (1) Only targeting surface antigens. Therefore, many solid tumors, which have intracellular antigens, are not targetable by CAR. (2) Current CARs (scFv as the binding module) discovery methods, such as phage / yeast surface display are binding-based assays. Therefore, isolated CARs often fail to demonstrate T cell activation function when produced in T cells. (3) Toxicity due to cross reaction to normal tissues, which can be devastating and lead to patient death. To address these issues, we will perform function-based selection and counter-selection for the discovery of CAR mimicking T cell receptor (TCR) on intracellular antigen recognition without cross reaction to normal cells. We will apply recombinase-mediated approach for the construction of high-fidelity, large diversity monoclonal CAR libraries in Jurkat T cells, and incorporate machine learning for the optimization of CAR’s functionality and specificity.

Aman Kaushik, PhD

Current position: Assistant Professor, Parul University, Vadodara, Gujarat (India)

Appointed: December 1, 2021 - May, 2022 (Grant Year 01)

Affiliation: McWilliams School of Biomedical Informatics at UTHealth Houston

Personal Statement: My research focuses on developing computational methods to drive biological discoveries and medical innovations that will benefit public health by analyzing and modeling large-scale biomedical data, especially cancer genomics data.

Primary Mentor: Dr. Zhongming Zhao

Project Title: Exploring potential therapeutic effects of drugs in triple-negative breast cancer (TNBC) through functional genomics and machine learning approaches

Brief Introduction of The Project:
The novel anti-cancer technique of potential drugs is propitious, which has indicated selective anti-cancer capability in diverse cancer cell lines. Breast cancer is heterogeneous, with the triple-negative subtypes as the most aggressive ones and lack effective therapy. Here, we proposed to further exploring drugs curative ability and understanding the procedure of action involved in it. We will also test the possible drugs from DrugBank and CCLE databases to offer a potent drug to cure cancer using advanced Machine Learning approaches. Besides, drug-target interactions (DTIs) have an essential role in target-specific drug encounters and design.

An Xu, PhD

Affiliation: McGovern Medical School at UTHealth Houston

Appointed: Dec.1, 2021 – Nov.30, 2022 (Grant year 01)

Personal Statement: My long-term career goal is to develop new strategies for cancer prevention and treatment based on a better molecular understanding of the causes of cancer. Now having worked in cancer research labs for more than 10 years since beginning undergraduate, I remain as enthusiastic for pursuing this career as ever. Currently, I am interested in understanding how the well-known tumor suppressor p53, the most frequently mutated protein in various human cancers, mediates tumor formation through its complex set of interacting factors and epigenetic regulation.

Primary Mentor: Dr. Dung-Fang Lee

Project Title: Systematical analysis of mutant p53 altered m6A epitranscriptomics in gliomagenesis

Brief Introduction of The Project:
N6-methyladenosine (m6A), one of the most prevalent mRNA modifications in eukaryotes, plays a critical role in modulating both biological and pathological processes. However, it remains nebulous if dysregulation of m6A epitranscriptomic networks contributes to mutant p53’s neomorphic oncogenic functions. In our study, we are applying Li-Fraumeni syndrome (LFS) human induced pluripotent stem cell (hiPSC)-derived astrocytes, the cell-of-origin of gliomas, to investigate the function of mutant p53 in regulating m6A during gliomas formation. Our study will reveal how mutant p53 hijacks epigenetic and epitranscriptomic machinery to initiate gliomagenesis and suggests potential treatment strategies for LFS gliomas.

Avisha Das, PhD

Affiliation: McWilliams School of Biomedical Informatics at UTHealth Houston

Appointed: May 1, 2022 – April 30, 2023 (Grant year 01)

Final Degree: Ph.D in Computer Science (Fall 2020)

Personal Statement: During my doctoral research, I have focused extensively on exploring and studying the techniques to understand and provide solutions related to natural language-based tasks. A major portion of my research effort was concentrated towards automated generative modeling of coherent textual content – both long (e.g., stories) and short (e.g., Tweets) form. My current research interest involves developing an exhaustive knowledge resource for biomedical topics through a multi-faceted approach through biomedical literature mining. My long-term objective is to be able to a build a comprehensive and automated knowledge-based data discovery tool through mining the various avenues and resources of biomedical literature.

Primary Mentor: Dr. Wenjin Jim Zheng, Professor, McWilliams School of Biomedical Informatics at UTHealth Houston

Project Title: Building an Automated Tool for Knowledge and Data Discovery for Cancer Research: A Multi-Faceted Approach by Biomedical Literature Mining

Brief Introduction of The Project:
The volume of cancer related scientific materials has proliferated at an unprecedented rate in the past few decades. Given a particular topic on cancer, finding a relevant set of knowledge base and resources therefore become indispensable. Although, there exist multiple keyword-based search engines and software, but their outputs are massive in volume, the manual filtering of which is both time consuming and tedious. The primary goal of this proposal is to build a systematic and automated pipeline for knowledge and data discovery by combining existing multi-faceted recommendation systems and networks. Such an automated pipeline will result in a much distilled and accurate collection of relevant resources for a query-based search. We aim to determine the effectiveness and scope of existing approaches like content- and network-based methods through an experimental task-based evaluation trial to measure the effectiveness and correctness of the pipeline combining the standalone research discovery tools.

Benxia Hu, PhD

Affiliation: Department of Biochemistry and Molecular Biology, McGovern Medical School at UTHealth Houston

Appointed: December 1, 2021 – November 30, 2022 (Grant year 01)

Personal Statement: I am very interested in 3D genome architecture and bioinformatics

Primary Mentor: Dr. Wenbo Li

Project Title: Dissecting the enhancer and promoter recognition code in cancer genome

Brief Introduction of The Project:
Genetic changes of the cancer genome play important roles in gene deregulation and cancer progression. A large portion of the genetic changes take place in the non-coding part of the genome. However, mechanistic insights to understand that how genetic changes contribute to cancer development remain limited. Our recent results uncovered an ‘enhancer release and retargeting’ process (ERR), in which functional loss of gene promoters can aberrantly activate adjacent genes in the genome to modulate human disease risk. However, the commonality of ERR in cancer genome remains unknown. In this project, I plan to integrate omics and functional experiments to fully dissect the commonality of ERR in human cancer genome that can lead to oncogene deregulation. We expect that our work would provide insights into mechanisms underlying roles of non-coding genetic mutations in cancer, paving way for new diagnostic and therapeutic strategies.

Tanjida Kabir, MS

Affiliation: McWilliams School of Biomedical Informatics at UTHealth Houston

Appointed: December 1, 2021 – November 30, 2022

Final Degree: MS

Personal Statement: I am a Ph.D. student at SBMI, and my long-term career goal is to establish myself as an independent scientist specialized in applying machine learning to biomedical datasets and discovering novel insights about diagnosis and treatment. I have always been interested in utilizing my expertise to benefit human health and quality of life. My research aims at performing impactful analysis on complex human diseases like cancer by integrating mathematics, machine learning, and computer vision. I believe my machine learning and big data analyses skills will help me gain valuable insight into cancer research.

Primary Mentor: Dr. Xiaoqian Jiang

Project Title: An Automated MRI Analysis Tool to Measure the Tumor Volume and Assess the Treatment Response for Glioblastoma

Brief Introduction of The Project: Glioblastoma is the most common and aggressive grade IV malignant glioma brain tumor. The median survival rate of Glioblastoma patients is 11 months. Newly diagnosed Glioblastoma patients often receive surgical resection, but complete tumor removal is often impossible due to its structure and position. Furthermore, surgical resection changes the structure of brain and tumor. Therefore, the residual contrast-enhancing tumor region becomes the survival predictor for patients. This proposal aims at designing an artificial intelligence framework to (i) quantify the residual volume of contrast-enhancing tumor region using the postoperative MRIs considering brain and tumor structural changes; (ii) assess treatment response for Glioblastoma utilizing molecular, imaging, and clinical features. The proposed framework will potentially estimate the tumor volume by identifying the surgical and radiation-induced changes and overcoming human-induced bias. Besides, it may lead to the discovery of new combined biomarkers (combination of molecular biomarkers, MRI features, clinical parameters) to assess treatment efficacy.

Fangfang Yan, MS

Affiliation: McWilliams School of Biomedical Informatics at UTHealth Houston

Appointed: December 1, 2021 – November 30, 2022

Personal Statement: I’m a fourth year PhD student in bioinformatics and have been a GRA in Dr. Zhongming Zhao’s lab in the Center for Precision Health at SBMI since Fall 2018. Resistance to therapy is a major challenge in the cancer research area and caused by multiple determinants, such as heterogeneity, novel mutations, and tumor microenvironment. Applying bioinformatics approaches to cancer patients’ data will help unfold such determinants and greatly advance our understanding of underlying mechanisms and thus improve patients' outcomes.

Primary Mentor: Dr. Zhongming Zhao

Project Title: Dynamic Reprogramming and Evolution Associated with Sequential Resistance to Ibrutinib and CAR T therapy in Mantle Cell Lymphoma

Brief Introduction of The Project:
Mantle cell lymphoma (MCL) is a heterogeneous B-cell lymphoma. Therapeutic relapse is a major medical challenge. Single-cell RNA sequencing (scRNA-seq) has revolutionized biology and enable molecular profiling of individual cells, including tumor B cells and other immune cells in the tumor microenvironment. In this proposal, we propose to adapt and apply a random effect mixed model to deal with patient heterogeneity and batch effect issues. It will allow us to discover the differentially expressed genes and pathways across therapeutic sensitivity rather than among patients, as well as early drivers that resulting in therapeutic resistance.

Kimberly Rivera Caraballo, BS

Affiliation: UTHealth Houston Department of Neurosurgery Cancer Biology Program; Translational Track

Appointed: December 1, 2021 – November 30, 2022
(Grant year 01)

Personal Statement: At GSBS, I am training to become an independent translational investigator, focused on the development of innovative cancer therapeutics to target and regulate the tumor microenvironment and reduce tumor development. I am highly motivated to make a difference in the way we detect and treat cancer today and am also driven to mentor and guide young minds through the process of graduate school applications, finding research opportunities, and generating a career development plan. I envision myself leading a research laboratory, performing cutting-edge science at a leading institution, and as a leader that ensures inclusive professional growth of minorities and women.

Primary Mentor: Dr. Balveen Kaur

Project Title: Oncolytic Herpes Virus Armed with a blocking antibody to treat Glioblastoma

Brief Introduction of The Project:
My project aims to target a protein that promotes tumor growth and is highly expressed in glioblastoma, the most aggressive brain tumor with a median survival of 16 months with the current standard of care. Since oncolytic viruses such as herpes simplex virus type-1 (oHSV; Imlygic) has been FDA approved to treat melanoma, we use an attenuated HSV-1 to potentially treat glioblastoma. oHSV relies on the defective immune response of cancer cells against viral pathogens, to selectively propagate in and kill tumor cells while sparing healthy cells. Insertion of a sequence of interest into the viral DNA, enables the virus to create a protein that can block pro-tumorigenic targets in tumor cells and reduce tumor progression. Thus, the generated oHSV serves as a targeted therapy against tumor cells: generates and delivers the inhibitory antibodies into these cells, reduces their viability, and can potentially improve the life expectancy of glioblastoma patients.

Toshiyuki Itai MD, PhD

Affiliation: McWilliams School of Biomedical Informatics at UTHealth Houston

Appointed: January 1, 2023 – December 31, 2023

Personal Statement: I am interested in analyzing complex traits, from congenital diseases (e.g., orofacial clefts and heart defects) to cancer, by combining genetic variants and single-cell RNA-sequencing data. I also have clinical experience in obstetrics, gynecology, including prenatal diagnosis and genetic counseling for hereditary cancer syndrome. My long-term career goal is to become a physician-scientist that can lead translational research projects bridging clinical and research fields in obstetrics/gynecology.

Primary Mentor: Dr. Zhongming Zhao

Project Title: Integrating expression profiles and genetic variants of high-grade serous ovarian cancer (HGSOC) and developing a deep learning model to predict the prognosis of HGSOC

Brief Introduction of The Project:
Ovarian cancer is the fifth most common cancer death among women, and HGSOC is the most common subtype and has the worst prognosis. Although it is known that several genes and copy number variations are related to HGSOC, we still need to learn more to enable tailored clinical management. Several genetic studies and scRNA-seq analyses have been published. However, these results have not been integrated, so it is not easily accessible. To address the issue, we propose to integrate expression profiles and genetic variants of HGSOC, which will be stored in a database. We will integrate whole-exome/genome sequencing, literature-based knowledge, and scRNA-seq data of HGSOC. With the integrated data, we will extract gene modules that are specific to each cancer status and develop a deep-learning model to predict prognosis from expression profiles or genetic variants from patient data.

Surabhi Datta, MS

Affiliation: McWilliams School of Biomedical Informatics at UTHealth Houston

Appointed: December 1, 2021 – November 30, 2022 (Grant year 01)

Personal Statement: My research goals broadly fall in the area of clinical natural language processing (NLP) with particular focus on using NLP for extracting important information from free-text radiology reports. My work primarily focuses on developing NLP methods to extract useful clinical information hidden in the radiology reports, including cancer-related findings (e.g., tumor), their corresponding anatomical locations, and characteristics (e.g., size). The granular information extracted from the reports can be used for various clinical informatics applications including cancer applications such as automated image labeling for risk prediction of cancers, cohort retrieval for cancer trials, and automated tracking of the same cancer findings.

Primary Mentor: Dr. Kirk Roberts

Project Title: Automated tracking of cancer findings and medical devices across radiology reports over time

Brief Introduction of The Project:
Unstructured EHR data such as radiology reports contain important clinical information about a patient’s cancer progress. My proposed project aims to track cancer-related findings (e.g., tumor, lesion) as well as medical devices over time across radiology reports of a patient. This includes identifying the same findings as well as their change in characteristics (e.g., size, spread, etc.) over time. For this, the main goals include – 1. curating a dataset of reports at patient-level to track the same cancer-related findings and devices, 2. developing natural language processing (NLP) methods to extract fine-grained spatial and other contextual information of cancer findings, and 3. developing NLP-based automated tracking method that involves identifying cross-document co-references for the cancer findings/devices. Automated tracking holds potential to reduce physicians’ burden in making patient-related decisions and can also help access cancer outcomes that can benefit cancer treatment research.