Medical Genetics Commons^™

Integrative Multi-Omics Profiling Identifies Infiltrative Hepatocellular Carcinoma As An Immunotherapy-Resistant Subtype With Distinct Molecular Features, Won Suk Lee, Seonjeong Woo, Sung Hwan Lee, Gae Hoon Jo, Ilhwan Kim, Hyeyeong Kim, Chansik An, Sanghoon Jung, Gwangil Kim, Haeyoun Kang, Beodeul Kang, Jung Sun Kim, Ho Yeong Lim, Incheon Kang, Hannah Yang, So Jung Kong, Dahyeon Son, Dong Jun Shin, Woo Young Kwon, Da-Yeon Lee, Ju-Seog Lee, Junho Park, Youngsoo Kim, Sohyun Hwang, Chan Kim, Hong Jae Chon

Faculty, Staff and Student Publications

Background/aims: Hepatocellular carcinoma (HCC) exhibits substantial morphological and biological heterogeneity. Clinical and molecular relevance of the infiltrative subtype remains poorly defined in the context of cancer immunotherapy. We aimed to evaluate the prognostic impact and molecular features of infiltrative HCC in patients treated with first-line atezolizumab plus bevacizumab (Ate/Bev).

Methods: We included 307 patients with advanced HCC treated with Ate/Bev and classified them into four gross morphological types based on imaging. Multi-omics profiling was conducted on tumor samples. Type IV infiltrative signature was derived and externally validated using five independent HCC cohorts, including IMbrave150.

Results: Infiltrative morphology, encompassing pure and …

Evolving Molecular Subtypes Of Gastric Cancer: From Past Classifications To Present Consensus And Future Directions For Precision Therapy, Ju-Seog Lee

Faculty, Staff and Student Publications

Gastric cancer is a biologically heterogeneous disease. The advent of high-throughput multi-omic technologies has revolutionized our understanding of gastric cancer by deconstructing this heterogeneous entity into distinct and more homogeneous molecular subtypes. Early classifications based on gene expression, methylation, and histology have laid the groundwork for multi-omic frameworks proposed by The Cancer Genome Atlas and Asian Cancer Research Group, which established the foundation of modern molecular taxonomy. Subsequent integrative efforts, particularly the Consensus Genomic Subtypes (Super 6) model, have unified this collected information into clinically relevant subtypes that bridge prognostic stratification with treatment strategies. Established biomarkers such as human epidermal …

Bop2-Te: Bayesian Optimal Phase 2 Design For Jointly Monitoring Efficacy And Toxicity With Application To Dose Optimization, Kai Chen, Heng Zhou, J Jack Lee, Ying Yuan

Faculty, Staff and Student Publications

We propose a Bayesian optimal phase 2 design for jointly monitoring efficacy and toxicity, referred to as BOP2-TE, to improve the operating characteristics of the BOP2 design proposed by Zhou et al. (2017). BOP2-TE utilizes a Dirichlet-multinomial model to jointly model the distribution of toxicity and efficacy endpoints, making go/no-go decisions based on the posterior probability of toxicity and futility. In comparison to the original BOP2 and other existing designs, BOP2-TE offers the advantage of providing rigorous type I error control in cases where the treatment is toxic and futile, effective but toxic, or safe but futile, while optimizing …

Engineering Mutation Clones In Mammalian Cells With Crispr/Cas9, Zijun Huo, Jian Tu, Rachel Shoemaker, Dung-Fang Lee, Ruiying Zhao

Faculty, Staff and Student Publications

CRISPR, Clustered Regularly Interspaced Short Palindromic Repeat, as a powerful genome engineering system, has been widely accepted and employed in gene editing of a vast range of cell types. Compared to zinc finger nucleases (ZFNs) or transcription activator-like effector nucleases (TALENs), CRISPR shows a less complicated process and higher efficiency. With the development of different CRISPR systems, it can be used not only to knock out a gene but also to make precise modifications, activate or repress target genes with epigenetic modifications, and even for genome wide screening. Here we will describe the procedure of generating a stable cell line …

A Combinatorial Approach To Synthetic Data Generation For Machine Learning, Krishna Khadka, Jaganmohan Chandrasekaran, Yu Lei, Raghu Kacker, D Richard Kuhn

Faculty, Staff and Student Publications

Datasets used in machine learning often contain sensitive information, including personally identifiable health and financial details. A common challenge faced by organizations and researchers is the risk of privacy breaches when using real-world data. Synthetic data can be used as an alternative to the real-world data. In existing synthetic data generation techniques, an encoder processes the real-world data to map it into a lower-dimensional latent space. Random sampling is then performed in this latent space. Subsequently, a decoder network is utilized to generate synthetic data from these sampled points in the latent space. Such approaches typically require generating a large …

Functional, Pharmacogenomic, And Immune Landscapes Of Long Non-Coding Rnas In Cancer, Runhao Wang, Mei Luo, Yuan Liu, Jingwen Yang, Yamei Chen, Chengxuan Chen, Lifei Ma, Stephanie Ding, James Wengler, Yong Zang, Bora Lim, Wenbo Li, Liuqing Yang, Chunru Lin, Lixia Diao, Leng Han

Faculty, Staff and Student Publications

Long non-coding RNAs (lncRNAs) are emerging as key regulators in cancer, with significant potential as diagnostic, prognostic, and therapeutic targets. Here, this work systematically analyzes lncRNA associations with targeted therapies and immunotherapies across 33 cancer types using The Cancer Genome Atlas (TCGA) and real-world datasets. This work identifies 53,173 lncRNA-pathway associations, millions of lncRNA-drug response associations (via CancerRxTissue and VAEN), and extensive correlations with immune checkpoints and infiltration. This work further identifies 69 lncRNAs associated with immunotherapy response and 2,611 differentially expressed lncRNAs between low and high objective response rate (ORR) groups. Additionally, two lncRNAs are correlated with immune-related adverse …

Bop2-Comb: Bayesian Optimal Phase Ii Design For Optimizing Doses And Assessing Contribution Of Components In Drug Combinations, Xiaohan Chi, Ying Yuan, Ruitao Lin

Faculty, Staff and Student Publications

Background: Personalized cancer treatment using combination therapies offers substantial therapeutic benefits over single-agent treatments in most cancers. However, unmet clinical needs and increasing market competition pressure drug developers to quickly optimize combination doses and clearly demonstrate the contribution of each component when developing and evaluating new combination treatments.

Methods: We propose a Bayesian optimal phase II drug-combination (BOP2-Comb) design that optimizes the combination dose and evaluates the proof-of-concept as well as the contribution of each component in two seamless stages. Our optimal calibration scheme minimizes the total trial sample size while controlling incorrect decision rates at nominal levels. This calibration …

Peripheral Cd200r Signaling: A Critical Regulator Of Post-Stroke Inflammation In Aged Mice, Conelius Ngwa, Afzal Misrani, Yan Xu, Jingjing Wang, Rodney Ritzel, Fudong Liu

Faculty, Staff and Student Publications

The immune responses to ischemic stroke are subjected to endogenous inhibitory pathways that delimitate the post-stroke inflammation. Among them, the interaction between CD200 and its receptor (CD200R) is increasingly recognized for its role in regulating neuroinflammation across various central nervous system (CNS) disorders. In the present study, we have examined the role of central (brain) vs. peripheral CD200R signaling in acute ischemic stroke using aged bone marrow chimeric (BMC) mice (16-19 months old). These chimeras were generated by transplanting bone marrow from CD200R knockout (KO), green fluorescent protein (GFP), or wild-type (WT) donor mice into irradiated recipient mice, and then …

The Legacy Of A Gentleman Scientist: Pierre Hainaut, Christophe Arnoult, Laura D Attardi, Kerem Batsheva, Giovanni Blandino, Kathleen H Burns, Giannino Del Sal, David G Kirsch, David P Lane, Arnold J Levine, Guillermina Lozano, David Malkin, Gerry Melino, Moshe Oren, Carol Prives, Daniel Schramek

Faculty, Staff and Student Publications

No abstract provided.

Contemporary Opportunities And Potential Of Auger Electron-Emitting Theranostics, Seok-Yong Lee, H Charles Manning

Faculty, Staff and Student Publications

Recent breakthroughs in radiopharmaceutical (RP) therapy have emerged interest in employing Auger electron (AE)-emitting radionuclides as potential agents for precise theranostics. AE provides energy with exceptional localization due to their short tissue penetration range (TPR, < 10 nm), rendering them particularly effective for targeting nuclear DNA in tumor cells. In this context, AE-emitting radionuclide therapy (AE-emitting RLT) enables the targeted destruction of tumor cells while reducing harm to adjacent healthy tissue, a significant challenge in this field. Preclinical and early clinical investigations reveal the efficacy of AE-emitting RLTs in the theranostics of diverse malignancies, such as glioblastoma, prostate cancer, and neuroendocrine tumors. Notwithstanding these developments, challenges and limitations persist regarding dosimetry, delivery efficiency, and the treatment of radiotoxicity. A new paradigm is being developed to tackle the obstacles encountered by integrating molecular target markers (e.g., PARP) that function near the nucleus to improve the intranuclear delivery efficiency of AE-emitting radionuclides. Novel radiochemical methods such as these have facilitated the more stable and efficient labeling of biomolecules with AE-emitting radionuclides. Also, recent advances in DNA-molecular targeting, nanoparticles, nucleic acid/protein engineering, click- or bioorthogonal conjugation chemistry, and artificial intelligence (AI)-based structure modeling present concrete opportunities to overcome these limitations. Moreover, the integration of diagnostic imaging companion platforms employing theranostic radioisotope pairings facilitates real-time assessment of therapeutic efficacy and biodistribution, resulting in the formulation of enhanced treatment regimens. This review summarizes the prior development, recent advancements, barriers in clinical implementation, and future perspective of AE-emitting RLTs.

Nsd2 Inhibitors Rewire Chromatin To Treat Lung And Pancreatic Cancers, Jinho Jeong, Simone Hausmann, Hanyang Dong, Kacper Szczepski, Natasha M Flores, Andy Garcia Gonzalez, Liyang Shi, Xiaoyin Lu, Joanna Lempiäinen, Moritz Jakab, Liyong Zeng, Tourkian Chasan, Eric Bareke, Rui Dong, Emma Carlson, Reinnier Padilla, Dylan Husmann, Julia Thompson, Gerry A Shipman, Emily Zahn, Courtney A Barnes, Laiba F Khan, Liz Marie Albertorio-Sáez, Eva Brill, Vishnu Udayakumar Sunita Kumary, Matthew R Marunde, Danielle N Maryanski, Cheryl C Szany, Bryan J Venters, Carolina Lin Windham, Michal Eligiusz Nowakowski, Iwona Czaban, Mariusz Jaremko, Michael-Christopher Keogh, Kang Le, Michael J Soth, Benjamin A Garcia, Łukasz Jaremko, Jacek Majewski, Pawel K Mazur, Or Gozani

Faculty, Staff and Student Publications

NSD2 catalyses the epigenetic modification H3K36me2 (refs. 1,2) and is a candidate convergent downstream effector of oncogenic signalling in diverse malignancies3–5. However, it remains unclear whether the enzymatic activity of NSD2 is therapeutically targetable. Here we characterize a series of clinical-grade small-molecule catalytic NSD2 inhibitors (NSD2i) and show that the pharmacological targeting of NSD2 constitutes an epigenetic dependency with broad therapeutic efficacy in KRAS-driven preclinical cancer models. NSD2i inhibits NSD2 with single-digit nanomolar half-maximal inhibitory concentration potency and high selectivity over related methyltransferases. Structural analyses reveal that the specificity of NSD2i for NSD2 …

Host Modulation Therapy In Periodontitis: From Established Therapies To Emerging Technologies, Aonjittra Phanrungsuwan, Jiaqi Huang, Neeraja Dharmaraj, Alejandra Cobos Perez, Omid Veiseh, Simon Young, Chun-Teh Lee

Faculty, Staff and Student Publications

Periodontitis is a biofilm-induced chronic inflammatory disease, characterized by gingival inflammation and alveolar bone loss. According to a national survey, approximately half of the U.S. adults are affected by periodontal disease. To effectively prevent and treat periodontitis, it is essential to address its underlying causes. The primary etiological factors include polymicrobial synergy and dysbiosis of the oral microbiota, and a dysregulated immune response. The standard therapeutic approach, mechanical removal of biofilm through debridement, sometimes demonstrates limited efficacy, particularly in cases of severe periodontitis, which may require adjunctive or additional therapy. Emerging evidence indicates that periodontal tissue destruction is initiated by …

Management Of Retropharyngeal Lymph Node Metastases In Thyroid Cancer, Isabelle Fournier, Ashwini Venkata Bandi, Sarah Hamidi, Priyanka Iyer, Naifa L Busaidy, Maria E Cabanillas, Ramona Dadu, Mimi I Hu, Steven G Waguespack, Ugur Nur Basmaci, Michelle D Williams, Mena Mansour, Maria Gule Monroe, Neil D Gross, Ryan P Goepfert, Nancy D Perrier, Victoria Banuchi, Jennifer R Wang, Anastasios Maniakas, Mark E Zafereo

Faculty, Staff and Student Publications

Background: Retropharyngeal lymph node (RPLN) metastases in thyroid cancer are rare, with optimal management underreported.

Methods: Retrospective study of consecutive thyroid cancer patients with RPLN metastases treated at MD Anderson Cancer Center between 2000 and 2024.

Results: One hundred and sixty-seven patients (75% differentiated, 21% medullary, 4% poorly differentiated thyroid cancer) were divided into three groups: active surveillance (AS) (13%), surgery (56%), and nonsurgical treatment (31%). In the AS group (median follow-up 2.3 years), RPLN metastases grew a median 3% (range: 0-56) or 0.02 cm (range: 0-0.7) per year. Surgical therapy included transcervical (73%), transoral robotic (14%), transoral (12%), and …

Knowledge Distillation And Dataset Distillation Of Large Language Models: Emerging Trends, Challenges, And Future Directions, Luyang Fang, Xiaowei Yu, Jiazhang Cai, Yongkai Chen, Shushan Wu, Zhengliang Liu, Zhenyuan Yang, Haoran Lu, Xilin Gong, Yufang Liu, Terry Ma, Wei Ruan, Ali Abbasi, Jing Zhang, Tao Wang, Ehsan Latif, Wei Liu, Wei Zhang, Soheil Kolouri, Xiaoming Zhai, Dajiang Zhu, Wenxuan Zhong, Tianming Liu, Ping Ma

Faculty, Staff and Student Publications

The exponential growth of Large Language Models (LLMs) continues to highlight the need for efficient strategies to meet ever-expanding computational and data demands. This survey provides a comprehensive analysis of two complementary paradigms: Knowledge Distillation (KD) and Dataset Distillation (DD), both aimed at compressing LLMs while preserving their advanced reasoning capabilities and linguistic diversity. We first examine key methodologies in KD, such as task-specific alignment, rationale-based training, and multi-teacher frameworks, alongside DD techniques that synthesize compact, high-impact datasets through optimization-based gradient matching, latent space regularization, and generative synthesis. Building on these foundations, we explore how integrating KD and DD can …

Blood Flow Regulates Metabolism In Hematopoietic Development, Pamela L Wenzel

Faculty, Staff and Student Publications

Blood flow modifies oxygen availability and biomechanical forces within the vasculature of the embryo as the hematopoietic system develops. The aorta-gonad-mesonephros (AGM) envelops the largest artery in the body and is a critical site for the emergence of hematopoietic stem cells (HSCs). Herein, I discuss the role of hypoxia-inducible factors (HIFs) and force as determinants of metabolism and fate determination. To address the effects of blood flow on hematopoietic development, I employ mouse embryo models and biomimetic culture. Real-time cell metabolic analyses show that oxygen consumption rates (OCR) and extracellular acidification rates (ECAR) are altered by flow in cultures of …

Intratumoral Bacteria Are Immunosuppressive And Promote Immunotherapy Resistance In Head And Neck Squamous Cell Carcinoma, Natalie L Silver, Jin Dai, Travis D Kerr, Jessica Altemus, Rekha Garg, Hannah Simmons, Tyler Alban, Laura Noel-Romas, Vladimir Makarov, David J H Shih, Shwetha V Kumar, Akeem Santos, Rehan Akbani, Adam Burgener, Mohammed Dwidar, Neil Gross, Andrew G Sikora, Elias J Sayour, Apollo Stacy, Christian Jobin, Timothy A Chan, Renata Ferrarotto, Daniel J Mcgrail

Faculty, Staff and Student Publications

Despite the promise of immune checkpoint blockade (ICB) in head and neck squamous cell carcinoma (HNSCC), mediators of response are poorly understood. To address this, here we analyzed oropharyngeal HNSCCs treated with neoadjuvant durvalumab (anti-PDL1) alone or in combination with tremelimumab (anti-CTLA4) from the CIAO clinical trial ( NCT03144778 ). We found that only the total abundance of intratumoral bacteria predicted ICB response, which was validated in multiple independent cohorts. High intratumoral bacteria abundance was associated with an immunosuppressive tumor microenvironment, characterized by an accumulation of neutrophils coupled with depletion of T cells and other adaptive immune cells. Experimental elevation …

The Effects Of Measurement Errors On Minimum Ablative Margins After Thermal Ablation Of Liver Tumors: A Simulation Study, Iwan Paolucci, Jessica Albuquerque, Noreen S Siddiqi, A Kyle Jones, Kristy K Brock, Bruno C Odisio

Faculty, Staff and Student Publications

Purpose: To develop a mathematical framework to estimate the in silico A0 threshold based on the technical specifications of a specific ablation confirmation software package for thermal ablation of liver tumors that can then be used to identify the impact of different sources of error.

Methods: To estimate in silico A0 thresholds, we developed a simulation framework incorporating technical parameters and biological effects. Technical parameters were segmentation error, registration error, and slice thickness, and biological effects were tissue shrinkage and microscopic satellite lesions; these parameters and effects were all modeled using statistical distributions. For each permutation of parameters, a logistic …

Association Between Three-Dimensional Margin Assessment And Local Tumor Control In Percutaneous Lung Cryoablation Of Colorectal Metastases, Frank Mohn, Koustav Pal, Caleb O'Connor, Iwan Paolucci, Austin Castelo, Kamran Ahrar, Steven Yevich, Joshua D Kuban, Bruno C Odisio, Alda Lui Tam, Rahul A Sheth, Kristy K Brock

Faculty, Staff and Student Publications

Purpose

To evaluate the association of two minimum ablation margin (MAM) estimation methods: manual three-dimensional segmentation and biomechanical deformable image registration (DIR) with local tumor progression-free survival (LTPFS) after percutaneous lung cryoablation of colorectal pulmonary metastases.

Materials and Methods

This retrospective single-institution study included patients who underwent percutaneous lung cryoablation between May 2012 and March 2023 (median follow-up, 3.6 years; range, 0.8–9.6 years). Manual three-dimensional MAM was calculated for all tumors, and DIR-based MAM was estimated in a subset with intraprocedural full-thorax imaging. Cox proportional hazards regression assessed associations between MAM and LTPFS. The primary outcome was 2-year LTPFS, with …

Dosimetric Effect Of Abdominal Compression In Online Adaptive Planning For Abdominal Cancers Treated With A 15 Tesla Magnetic Resonance-Guided Linear Accelerator, Moghadaseh Khaleghibizaki, Angela Sobremonte, Luis Perles, Surendra Prajapati, Ergys Subashi, Yao Ding, Kristy Brock, Roya Barati, Eugene Koay, Chad Tang, Jinzhong Yang

Faculty, Staff and Student Publications

Compression belts (CBs) are sometimes used to reduce respiratory motion during stereotactic body radiotherapy of abdominal cancers with magnetic resonance (MR)-guided online adaptive planning. This study evaluated the dosimetric effects of overriding the relative electron density (ED) value of CBs in creating synthetic computed tomography (CT) scans for MR-guided adaptive planning. We evaluated plans for 12 patients with abdominal cancer and identified that ED values between 0.2 and 0.3 achieved the best approximation of CB ED in dose calculation. Our study presented an approach to estimate appropriate ED overrides for CBs in MR-guided online adaptive planning.

Uncertainty-Guided Test-Time Optimization For Personalizing Segmentation Models In Longitudinal Medical Imaging, Jaehee Chun, Austin Castelo, Mckell Woodland, Caleb O'Connor, Mais Al Taie, Mohamed Eltaher, Aashish Gupta, Bilel Daoud, Shanli Ding, Jeddy Bennett, Anirban Maitra, Matthew A Firpo, Kimberly Kirkwood, Eugene J Koay, Kristy K Brock

Faculty, Staff and Student Publications

Background: Accurate and consistent image segmentation across longitudinal scans is essential in many clinical applications, including surveillance, treatment monitoring, and adaptive interventions. While personalized model adaptation using patient-specific prior scans has shown promise, current approaches typically rely on fixed training durations and lack mechanisms to determine optimal stopping points on a per-patient basis, particularly in the absence of validation labels.

Purpose: We propose an uncertainty-guided test-time optimization (TTO) framework that dynamically adjusts the personalization duration for each patient using a validation-free stopping criterion based on predictive uncertainty.

Methods: Our framework personalizes a generalized segmentation model using patient-specific prior imaging and …