Trainees

MinHye Noh, PhD

Affiliation: McGovern Medical School at UTHealth Houston

Appointed: January 1, 2025 – December 31, 2025

Personal Statement:
My academic journey began with a deep dive into the field of tumor immunology, driven by a passion for understanding the molecular mechanisms underlying cancer development and progression. Currently, my research focuses on elucidating how tumors and tumor microenvironments respond to oncolytic virus (OV) therapy and developing novel therapeutic OVs to enhance their therapeutic efficacy. My ultimate career goal is to become an independent principal investigator specializing in cancer-focused translational research. In the near term, I aim to gain expertise in utilizing OVs to treat glioblastoma (GBM), one of the deadliest brain tumors.

Primary Mentor: Dr. Ji Young Yoo

Project Title: Targeted Modulation of miRNA-155 for Enhanced Viro-immunotherapy

Brief Introduction of The Project:
Despite the promising anti-tumor potential of oncolytic virus (OV) therapy, only a small subset of glioblastoma (GBM) patients experiences survival benefits, largely due to both intrinsic and extrinsic (i.e., the tumor microenvironment (TME)) resistance mechanisms. This project aims to investigate the role of microRNA-155 (miR-155) in mediating this resistance. We aim to elucidate the mechanistic role of endogenous miR-155 in the anti-viral response to oncolytic herpes simplex virus-1 (oHSV) therapy within GBM cells and exploit the therapeutic potential of miR-155 inhibition in GBM therapy. By identifying the miR-155-mediated antiviral resistance mechanisms, we seek to evaluate the preclinical potential of targeted miR-155 inhibition. We anticipate that targeted inhibition of miRNA-155 will reprogram the tumor and TME, enhancing the efficacy of viro-immunotherapy and ultimately improving clinical outcomes for GBM patients.

Trinh Thi Tuyet Phan, PhD

Affiliation: Department of Integrative Biology and Pharmacology, McGovern Medical School, The University of Texas Health Science Center at Houston

Appointed: January 1, 2025 – December 31, 2025

Personal Statement:
My research journey has been driven by a profound passion for cancer research, particularly the multifaceted roles of p53 in tumor development and progression. I am especially interested in uncovering the molecular drivers of cancer and applying these insights to develop effective therapies. The UTHealth BIG-TCR Postdoctoral Training Program in Cancer Research provides a valuable opportunity to expand my scientific knowledge, refine my research skills, and build essential professional competencies. This training is preparing me with the expertise and tools necessary to pursue a productive career in cancer research.

Primary Mentor: Dr. Dung-Fang Lee

Project Title: Integrated m6A and senescence targeting therapy for p53-mutant osteosarcoma

Brief Introduction of The Project:
My research investigates the role of the m6A reader YTHDF2 in p53-mutant osteosarcoma, a highly aggressive bone cancer. Mutations in the tumor suppressor p53 are prevalent in osteosarcoma and drive cancer progression through dysregulated signaling pathways. As mutant p53 itself is not a druggable target, exploring its downstream oncogenic signaling offers an important therapeutic strategy. Reportedly, mutant p53 hijacks the epitranscriptome to drive tumor development by transcriptionally upregulating YTHDF2. However, the full extent of YTHDF2’s role in regulating oncogenic signaling and its therapeutic implications in p53-mutant osteosarcoma remains poorly understood. My work aims to identify key molecular targets regulated by YTHDF2, linking their functions to cellular senescence in p53-mutant osteosarcoma, and propose a synergistic therapeutic strategy for this challenging cancer. By integrating basic and translational cancer research with biomedical informatics and genomics, this study seeks to advance therapeutic approaches for osteosarcoma and other cancers driven by p53 mutations.

Gang Qu, PhD

Affiliation: McWilliams School of Biomedical Informatics at UTHealth Houston

Appointed: January 1, 2025 – December 31, 2025

Personal Statement:
I am committed to advancing cancer research by integrating artificial intelligence with multi-omics to improve personalized medicine. My research includes developing computational models to understand complex disease mechanisms and creating AI-driven tools for cancer diagnosis and treatment. Through the BIG-TCR Postdoctoral Training Program, I aim to further develop my computational skills and establish myself as an independent leader in translational cancer research, focusing on lung cancer's genetic and behavioral risk factors.

Primary Mentor: Dr. Zhongming Zhao

Project Title: An AI-based Framework for Integrating Genetic and Behavioral Factors to Identify and Validate Key Contributors in Lung Cancer

Brief Introduction of The Project:
Lung cancer, a leading cause of cancer-related death, is influenced by genetic and environmental factors like smoking and radon exposure. Our project proposes a novel AI framework, MIC-PGS Rank, which integrates multimodal data to identify and validate key contributors to lung cancer. Using advanced algorithms and experimental validation, this approach aims to prioritize and validate genetic and behavioral factors, improving prevention and treatment strategies. The project will enhance understanding of lung cancer through data-driven insights, offering new avenues for targeted interventions and contributing to both the BIG and TCR areas by bridging computational innovations with clinical applications, aiming to transform cancer diagnostics and therapeutics.

Rachel Shoemaker, BS

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

Appointed: January 1, 2025 – December 31, 2025

Personal Statement:
Tumor suppressor p53 is the most commonly mutated gene in all human cancers, including in about 95% of osteosarcoma tumors. These mutations disrupt normal cell function to promote tumor initiation and progression. One mechanism which can be disrupted in cancer cells is alternative splicing, which changes the sequence of mRNA transcripts to produce unique proteins in a cell. However, little is known about how missense mutations in p53 impact alternative splicing. For my research, I plan to elucidate how p53 mutations promote alternative splicing dysregulation to enhance osteosarcoma tumor initiation and progression, which will identify new targets for treatment.

Primary Mentor: Dr. Dung-Fang Lee

Project Title: Exploring the functional consequences of mutant p53-dysregulated alternative splicing in osteosarcomagenesis

Brief Introduction of The Project:
Missense mutations in p53 are found in over half of all cancers and promote new characteristics in cellular pathways to drive cancer development. mRNA alternative splicing, a process affecting all mRNA transcripts, is often dysregulated in cancers. Although p53 can indirectly influence alternative splicing, the direct impact of its missense mutations on this process remains unclear. Using 3D protein modeling, sequencing analysis, and functional studies, I will study whether different p53 missense mutations dysregulate alternative splicing in osteosarcoma cells by altering its interaction with SF3A2, a critical spliceosome component. This research will enhance our understanding of osteosarcoma genesis and provide insights into the mechanisms behind mutant p53 variants, with implications for other mutant p53-driven cancers. It will also identify mutant p53-induced alternative mRNA transcripts that can be targeted by current FDA-approved compounds.

Arnav Solanki, PhD

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

Appointed: January 1, 2025 – December 31, 2025

Personal Statement:
Problem-solving has always fascinated me. During my PhD at the University of Minnesota, I trained in computer engineering and molecular biology, working on various omics data science projects. Now, as a postdoc at UTHealth, I aim to develop novel data processing algorithms for cancer therapy. My objectives include improving drug candidate decisions, modeling cancer mechanisms, and predicting cancer risk. For my upcoming project, I will focus on developing interpretable AI methods for cancer therapy.

Primary Mentor: Dr. Wenjin Jim Zheng

Project Title: The Use of Emerging Artificial Intelligence Models to Interpret Cancer Bioinformatics Data

Brief Introduction of The Project:
Cancer remains an unsolved disease, demanding multidisciplinary expertise to fight against. In recent years, vast amounts of transcriptomic data have accumulated across numerous cancer studies, ideal for big data methods such as Machine Learning (ML) models. While many such models exist, a mechanistic model that understands the biological system’s underlying mechanics is still missing. This fellowship proposal aims to develop Artificial Intelligence (AI) models with high prediction accuracy and interpretability, focusing on cancer-related pathways like PI3K/AKT/mTOR. These predictions will be tested through experimental means such as knockout and drug assays to validate the models.

Yichun Wang, PhD

Affiliation: McGovern Medical School at UTHealth Houston

Appointed: January 1, 2025 – December 31, 2025

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 and apply novel tools such as CRISPR-Cas13, ASO, and O-Map to study their functions in metastasis gene programs.

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. One important feature of metastasis is a dramatic transcriptional reprogramming of the cancer cells, often orchestrated by enhancers. Enhancer RNAs (eRNAs), transcribed from enhancer regions, are thought to play key roles in metastasis gene regulation. This project will use epigenomic methods to map eRNA landscapes in TNBC models and functionally validate their role in metastasis, aiming to identify new therapeutic targets.

Mariana Najjar, MS, PharmD

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

Appointed: January 1, 2025 – December 31, 2025

Personal Statement:
I am a second year PhD student at GSBS with a passion for cancer biology and therapy development. My research focuses on investigating the specific processes that drive breast cancer growth and metastasis to the brain, identifying druggable targets, and developing therapies that effectively penetrate the blood-brain barrier and the blood-tumor barrier. I am dedicated to advancing cancer research by creating novel therapeutics to treat breast cancer brain metastases and significantly reduce breast cancer mortality in women. My goal is to uncover novel treatments that improve patient outcomes and save lives, striving to make a lasting impact in oncology.

Primary Mentor: Dr. Hui-Wen Lo

Project Title: A novel BBB-permeable agent for therapeutic efficacy in breast cancer brain metastases

Brief Introduction of The Project:
Breast cancer brain metastasis (BCBM) is associated with poor prognoses due to the limited understanding of its underlying mechanisms and the scarcity of effective therapies capable of crossing both the blood-brain barrier and blood-tumor barrier. My project focuses on validating WF-229A as a novel pharmacological inhibitor of tGLI1, a promoter of BCBM, and identifying its mechanisms of action and novel targets. Additionally, the aim of my project is to examine WF-229A’s ability to offer therapeutic benefits which include the prevention and/or suppression of BCBM in vivo. Furthermore, this work lays the groundwork for potential novel combination therapy modalities through assessing drug synergism between WF-229A and FDA-approved inhibitors for better treatment of BCBM.

Jihyun Park, MS

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

Appointed: January 1, 2025 – December 31, 2025

Personal Statement:
My goal is to investigate the underlying mechanisms of the immune system within tumors and further develop immunotherapeutic strategies. During my master's, I took my first step toward becoming a cancer researcher. To pursue my interest in science with therapeutic implications, I developed an antibody-cytokine fusion protein and identified its clinical feasibility.

My current research focus is on developing novel immunotherapies to target cold tumors, such as glioblastoma. I am particularly interested in leveraging bioinformatics to advance my understanding of translational science and determine the molecular mechanisms of action for novel immunotherapies.

Primary Mentor: Dr. Zhiqiang An

Project Title: Therapeutic strategies to treat glioblastoma targeting TREM2

Brief Introduction of The Project:
Glioblastoma (GBM) is the most prevalent and lethal type among primary brain tumors. The tumor microenvironment (TME) is highly immunosuppressive, with myeloid cells constituting up to 50% of the total tumor mass, playing a crucial role in immune evasion. Despite the severity, only four drugs have been approved by the FDA for glioblastoma treatment. To address this unmet medical need, our primary objective is to develop novel immunotherapy by leveraging our expertise in translational cancer research and single-cell transcriptomics. Through this interdisciplinary approach, we aim to elucidate the intricate mechanisms of immune remodeling within the GBM microenvironment at various stages of disease progression, paving the way for more effective and personalized treatment strategies.

Mahesh Prasad Bekal, PhD

Affiliation: Vivian L. Smith Department of Neurosurgery, UTHealth Houston

Appointed: May 1, 2023 – April 30, 2025

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.

Avisha Das, PhD

Affiliation: McWilliams School of Biomedical Informatics at UTHealth Houston

Appointed: May 1 2023 – April 30 2024 (Grant year 02)

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 toward 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 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

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

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 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 the way for new diagnostic and therapeutic strategies.

Tanjida Kabir, MS

Affiliation: McWilliams School of Biomedical Informatics at UTHealth Houston

Appointed: May 1 2023 – April 30 2024

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 analysis 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 quantify the residual tumor volume, assess treatment response, and potentially discover new combined biomarkers. This may help overcome human bias and lead to improved Glioblastoma treatment strategies.

Jiajinlong Kang, MS

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

Appointed: January 1 2023 – December 31 2023 (Grant year 01)

Personal Statement: I am a second-year PhD student in the Quantitative Science program and have been a GRA in Dr. Zhongming Zhao’s Bioinformatics and Systems Medicine lab in the Center for Precision Health since 2022. My long-term career goal is to become an established investigator in academia specializing in the study of major brain diseases using bioinformatic approaches. Currently, my research is centered around dissecting major brain diseases through integrating genetic, epigenetic and transcriptomic approaches, with an emphasis on systematic interrogation of the shared and unique signatures between glioma and Alzheimer’s disease.

Primary Mentor: Dr. Zhongming Zhao

Project Title: Spatially resolved clonal evolution in glioblastoma and its comparison with Alzheimer’s disease

Brief Introduction of The Project: Glioblastoma (GBM) is an aggressive brain malignancy. Single-cell sequencing and spatial transcriptomic sequencing have provided valuable insights into its transcriptional landscape, spatial organization, and evolutionary trajectory. However, it remains elusive if evolutionary GBM clones can be organized in alignment with underlying transcriptomic programs. It is also unclear if Alzheimer’s disease (AD), which converges with GBM on connections with both aging and inflammation, shares certain transcriptional programs. This project aims to develop a computational pipeline to construct a spatially resolved evolutionary tree in GBM and interpret the subclones using transcriptional programs, while exploring the shared dynamics between GBM and AD for potential combined drug discovery.

Aman Kaushik, PhD

Affiliation: McWilliams School of Biomedical Informatics

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

Final Degree: PhD

Personal Statement: TBD

Primary Mentor: TBD

Project Title: TBD

Brief Introduction of The Project: TBD

Fangfang Yan, MS

Affiliation: McWilliams School of Biomedical Informatics at UTHealth Houston

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

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 enabled 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 result 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 (Grant Year 01)

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 prognosis of HGSOC

Brief Introduction of The Project: Ovarian cancer is the fifth most common cause of cancer-related deaths among women, with HGSOC being the most aggressive. This project integrates sequencing and scRNA-seq data into a database, extracts gene modules, and develops a deep-learning model to predict prognosis and improve clinical management of HGSOC patients.

Surabhi Datta, MS

Affiliation: McWilliams School of Biomedical Informatics at UTHealth Houston

Appointed: December 1, 2021 – November 30, 2022

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. This work includes cancer-related findings, their locations, and characteristics, to support cancer risk prediction and automated tracking across time.

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: My project aims to identify tumors and medical devices from radiology reports and track their characteristics (size, spread, etc.) across time for a given patient. This will allow clinicians to monitor cancer progression and device status more effectively.

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. My research focuses on analyzing and visualizing complex omics datasets, with a specific interest in the role of viral transcriptional regulators in cancer. I aim to develop computational tools that decode viral and host interactions.

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: The project leverages computational frameworks to identify vTRs, analyze their host genome interactions, and evaluate their effects across tissues. This contributes to understanding the mechanisms of vTRs in cancers and informs potential therapeutic strategies.

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 investigating how mutations in transcriptional machinery contribute to cancers and cardiovascular disease.

Primary Mentor: Dr. Kuang-Lei Tsai

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

Brief Introduction of The Project: This project focuses on the Mediator-CDK8 kinase module complex and its regulation of transcription. By integrating structural biology, biochemistry, and cancer research methods, I aim to uncover mechanisms underlying disease-associated mutations in Mediator-CKM interactions and provide insights for novel therapeutic strategies.

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 into biology, cancer therapy, and nanotechnology. I focus on using metallic nanoparticles in cancer therapy as radiosensitizing agents and continue this research as a postdoctoral fellow.

Primary Mentor: Dr. Sunil Krishnan

Project Title: Targeting PI3K/Akt Axis to Radiosensitize Pancreatic Cancer

Brief Introduction of The Project: Pancreatic cancer is difficult to diagnose early and has poor survival outcomes. My project investigates agents that radiosensitize pancreatic tumors to enhance radiation therapy effectiveness. Using high-throughput screening, PI3K and mTOR inhibitors were identified as strong candidates. This work integrates drug validation and immune response profiling to improve treatment outcomes.

Ko-Hong Lin, MS

Affiliation: McWilliams School of Biomedical Informatics at UTHealth Houston

Appointed: January 1, 2023 – December 31, 2024

Personal Statement: My research applies graph neural network (GNN) models to drug discovery for complex diseases. GNNs can integrate multimodal biological data to reveal hidden interactions and predict effective drug candidates. My long-term goal is to use deep learning to develop precision therapeutic strategies for cancer.

Primary Mentor: Dr. Xiaoqian Jiang

Project Title: A Comprehensive Drug Repurposing Study for Glioblastoma Multiforme

Brief Introduction of The Project: Glioblastoma Multiforme (GBM) is one of the most aggressive brain cancers. This project develops a computational pipeline to identify repurposable drugs for GBM using GNN models, claims data, and validation in GBM cell lines and hiPSC models. The approach integrates AI and biomedical data to accelerate discovery of novel treatment strategies.