AI driven 3D Molecular Tumor Modeling
Research Interests
We believe that understanding diseases in their natural 3D and 4D states—integrating both spatial and temporal molecular information—provides the most comprehensive insights into disease initiation, progression, metastasis, and treatment response. Our lab develops computational and experimental approaches to generate and analyze 3D/4D models of cells, tissues, and organs, enabling us to study complex biological systems with unprecedented precision. By leveraging AI, machine learning, spatial and single-cell biology, we aim to decode complex interactions within the tumor immune microenvironment (TIME) to identify novel biomarkers, therapeutic targets, and treatment strategies.
Our lab processes cells, organoids, and tissues, generating single-cell, spatial multimodal, and 3D/4D molecular imaging data in-house. We are equipped with a sequencer (Singular Genomics G4), spatial instruments (e.g., 10x Genomics Xenium, Cytassist), Holotomography (TOMOCUBE's HT-X1), and Spatial Sorter (Meteor Biotechnology CosmoSort), and have access to additional cutting-edge instruments (Akoya PhenoCycler, Ultra tims TOFs), enabling innovative and rigorous data generation. The data we generate are fed into custom algorithms to drive new discoveries.
Our interdisciplinary approach integrates single-cell and subcellular spatial analysis, digital pathology, spatial microbiome research, and 3D spatial multimodal techniques, alongside the development of novel immunotherapies and cellular therapies. By studying diseases in their natural 3D/4D context, we generate insights that drive the development of personalized therapeutic interventions.
The followings are the areas that our group are actively working:
AI and Machine Learning-driven 3D Molecular Tumor Modeling: We are advancing AI-driven 3D molecular tumor models to study pre-cancer stages and their progression. This approach maps the multidimensional evolution of tumors, providing critical insights into cancer development and treatment response.
Subcellular-Resolution 3D and 4D Atlas Models: We develop subcellular-resolution 3D and 4D atlas models, powered by AI and machine learning with a combination of holotomography and light sheet microscopy tehcnologies, to investigate how individual cells, their suborganelles, and even full 3D models of organs and organisms contribute to disease processes. Using live 3D and 4D holotomography combined with molecular data, we track cellular dynamics in real time, uncovering novel insights into disease initiation, progression, metastasis, and therapeutic response.
Spatial Microbiome in the Tumor Immune Microenvironment: Using Spatial Sorting technology, we isolate individual microorganisms along with their surrounding cellular and non-cellular components from a tumor. This allows us to study how these microorganisms impact tumor progression, immune modulation, and treatment response. This approach could also be applied to investigate how fungi, viruses, and other pathogens influence the tumor immune microenvironment.
3D Spatial Multimodal Approaches: We combine spatial sorting and holotomography to isolate individual cells and suborganelles at the tissue level and generate multimodal data (e.g., DNA, RNA, protein, and methylation) simultaneously. This integrated 3D spatial multimodal approach enables us to comprehensively profile the tumor microenvironment and uncover complex molecular interactions that drive disease progression and treatment response.
Real-Time Drug Delivery and Response Using Organotypic Models: We employ organotypic models to study drug delivery and therapeutic response in real time, focusing on therapies such as ADCs, CAR-T cells, and mRNA therapeutics. Using hybrid holotomography and light sheet microscopy, we track drug interactions within the tumor immune microenvironment to optimize precision treatment strategies.
By integrating cutting-edge AI, machine learning, and experimental methodologies, the Hwang Lab aims to uncover the mechanisms driving health and disease, with a particular emphasis on the tumor immune microenvironment, ultimately advancing precision care and therapeutic innovation.
Announcement:
We are always looking for talented individuals to join our journey to end cancer. We welcome applicants at all levels, including undergraduates, graduate students, PhDs, MDs, and MD/PhDs. Our lab is open to both dry and wet lab researchers from fields such as biology, immunology, bioengineering, statistics, mathematics, physics, computer science, and data science.
Requirements:
Strong academic background and/or relevant experience in the specified fields.
A passion for cancer research and a commitment to contributing to innovative scientific advancements.
For wet lab positions: experience with molecular biology techniques, tissue culture, biomedical engineering or immunoassays is preferred.
For dry lab positions: experience with machine learning, bioinformatics, or computational biology is preferred.
Excellent problem-solving skills and the ability to work both independently and as part of an interdisciplinary team.
Interested candidates should submit a CV, cover letter, and references for consideration via email.
Talks!
News!
09/14/2024: We are excited to share that we will lead Human Tumor Atlas Pre Gastric Cancer Atlas (GAME3D). Our group will lead AI driven 3D Molecular Tumor Modeling.
10/17/2024: Our work on AI-driven approach combining hematoxylin and eosin (H&E) staining with immunohistochemistry (IHC) to identify distinct immune subtypes that can predict outcomes in HPV-related oropharyngeal cancer is published in Communications Medicine “Integrative analysis of H&E and IHC identifies prognostic immune subtypes in HPV related oropharyngeal cancer”.
03/15/2023: Our work made Clinical Cancer Research’s March 15th edition COVER image “ACTA2 expression predicts survival and is associated with response to immune checkpoint inhibitors in gastric cancer”
03/07/2023: We are honored to receive a fund from Eric and Wendy Schmidt Foundation to improve immunotherapy efficacy in Gastric Cancer. This is a great accomplishment and a testament to the importance and impact of our lab’s research. Big congrats to Hwang lab members!
03/06/2023: Our collaborative work with Dr. Koga from Mayo Clinic to identify neurodegenerative disorders “Diagnosis of Alzheimer’s Disease and Tauopathies on Whole Slide Histopathology Images Using a Weakly Supervised Deep Learning Algorithm” is accepted in Laboratory Investigation.
02/08/2023: Our work to identify biomarkers that predict overall survival (OS) and response to immune checkpoint inhibitors (ICI) for patients with gastric cancer “ACTA2 expression predicts survival and is associated with response to immune checkpoint inhibitors in gastric cancer” is accepted in Clinical Cancer Research.
02/02/2023: [AACR 2023] Thrilled to share that six abstracts are accepted at AACR 2023 this April!
07/16/2022: Tae Hyun Hwang, PhD is invited to serve National Cancer Institute Gastric and Esophageal Cancers Subgroup on Multi-Omics Technology. He will provide insights into what is needed to advance Gastric and Esophageal Cancer prevention, diagnosis, and treatment, in particular the role of multi-comics technology in GE cancer, and help to identify the most promising translational research opportunities to ultimately help patients with GE cancers.
06/16/2022: Our work to identify TIGIT as predictive biomarker and potential therapeutic target to improve CD19 CAR-T cell therapy efficacy “Sequential Single-Cell Transcriptional and Protein Marker Profiling Reveals TIGIT as a Marker of CD19 CAR-T Cell Dysfunction in Patients with Non-Hodgkin Lymphoma” is accepted in Cancer Discovery. This is the first study show how TIGIT drives CAR-T cell dysfunction in Lymphoma and could be used as a therapeutic target to improve the efficacy of CD19 CAR-T cell treatment. We used large scale single cell sequencing from pre and post-infusion CAR-T cells empowered our own computational apporaches to investigate mechanisms of resistance to CD19 CAR-T cell therapy in Lymphoma.
05/21/2022: Our work to study spatial heterogeneity and organization of Tumor Mutation Burden (TMB) and Tumor Infiltrate Lymphocytes (TILs) as prognostic biomarker in bladder cancer “Spatial heterogeneity and organization of tumor mutation burden with immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer” is accepted in Journal of Pathology Informatics. This is the first study to explore spatial heterogeneity of TMB and their co-localization with TILs within tumors. Our findings suggest that tumors with less spatial TMB heterogeneity with high TILs show better overall survival. This indicates that not just patient level TMB and/or TILs important but also spatial TMB and TILs co-organization could further stratify patients with distinct survival outcome in Bladder Cancer.
04/28/2022: Our work about spatial analysis of Tumor Infiltrate Lymphocytes (TILs) based on H&E images to develop AI-based immune score “Spatial analysis of tumor-infiltrating lymphocytes in histological sections using deep learning techniques predicts survival in colorectal carcinoma” is accepted in the Journal of Pathology: Clinical Research. This is the first study to develop AI-based immune score based on H&E images in Colorectal Cancer. Our findings show that AI-based immune score combined with human TILs quantification further improve patient’s prognostication in Colorectal Cancer.
03/07/2022: Our collaborative work with Dr. Sam Wang from U of Texas Southwestern Medical Center “Lenvatinib inhibits the growth of gastric cancer patient-derived xenografts generated from a heterogeneous population” is accepted in Journal of Translational Medicine. This work shows the use of Patient-derived xenografts (PDX) by engrafting human tumors into immunodeficient mice to evaluate an efficacy of lenvatinib monotherapy.
03/05/2022: Our work to study microbiome and host-gene interaction in tumor immune microenvironment in stomach cancer “Multi-omics Reveals Microbiome, Host Gene Expression, and Immune Landscape in Gastric Carcinogenesis” is accepted in iScience. This is the first study how micro-organisms in addition to Helicobacter pylori could impact tumor immune microenvironment to develop gastritis and gastric cancer. Our findings indicate that micro-organisms in stomach could play important roles for immune invasion and development of gastric cancer.
02/28/2022: [AACR 2022] Happy to share that one oral presentation and four posters of AI and machine learning approaches utilizing digital pathology, single cell, microbiome, tissue immune microenvironment to predict patients’ prognosis and clinical outcome including immunotherapy respsone in blood and solid tumor are accepted at AACR 2022!
02/08/2022: Our machine learning approaches to predict chemotherapy and immunotherapy response in stomach cancer, “Development and validation of a prognostic and predictive 32-gene signature for gastric cancer" is published in Nature Communication 2022. This is the first signature to select chemotherapy and immunotherapy response and resistant patients as well as risk stratification in stomach cancer.
08/03/2021: Our mini review article “Machine Learning and Artificial Intelligence–driven Spatial Analysis of the Tumor Immune Microenvironment in Pathology Slides” is published inEuropean Urology Focus.
07/13/2021: Our collaborative research sponsored by NASA Specialized Center of Research on Radiation, “Associations between lipids in selected brain regions, plasma miRNA, and behavioral and cognitive measures following 28Si ion irradiation“, published in Scientific Report.
07/09/2021: Our collaborative single cell research with David Wald’s lab at Case Western Reserve University, “Single cell RNA sequencing of AML initiating cells reveals RNA-based evolution during disease progression” is published in Leukemia journal.
05/28/2021: Our ongoing projects for 1) Machine Learning and Deep Learning driven Spatial Tumor Immune Microenvironment analysis in Bladder Cancer and 2) Single cell approaches for AML biomarker discovery are selected as a long oral presentation (20mins) and poster presentation at ISMB/ECCB 2021 conference! Big congrats to Jean Clemenceau and Robert Schauner, our PhD students at CCLCM and Case Western Reserve University!
01/01/2021: Our post-doc researcher Hongming Xu PhD accepted an associate professor position in the department of Biomedical Engineering at Dalian University of Technology in China. Big congrats to Hongming and look forward to continue to work together!
12/03/2020: Our research to study spatial tumor and immune microenvironment using H&E image and spatial transcriptome for biomarker discovery related to immune checkpoint inhibitor in gastric cancer presented at 7th Digital Pathology & AI congress: USA and Europe!
11/06/2020: Our very talented the first year Cleveland College Lerner College of Medicine MD program student, Monica Nair, is featured at Cold Spring Harbor Laboratory Biodata 2020 meeting. Big congratulations Monica and very proud of you! https://currentexchange.cshl.edu/blog/2020/11/visitor-of-the-week-186
7/16/2020: Hwang team won NanoString GeoMX Cancer Transcriptome Atlas Grant to study tumor and immune microenvironment using spatial transcriptome to identify biomarker and build a predictive model for immune checkpoint inhibitor in gastric cancer.
4/24/2020: Our collaborative work with Ting Lab “DNA Methylation Regulates Alternative Polyadenylation via CTCF and the Cohesin Complex” is published in Molecular Cell.
4/16/2020: Our collaborative work with Lathia Lab “Myeloid-derived suppressor cell subsets drive glioblastoma growth in a sex-specific manner” is published in Cancer Discovery.
4/09/2020: Our paper “Hispanic/Latino gastric adenocarcinoma patients have distinct molecular profiles including a high rate of germline CDHI mutations” is published in Cancer Research.
3/17/2020: Our Department Of Defense Translational Team Science Award with Omar Mian, Shilpa Gupta, Lily Wang, Marcela Diaz to discover biomarker and combinatory synergies to improve #BladderCancer immunotherapy is funded! Our group will develop novel machine learning and AI algorithms to integrate image/genomics/single cell and clinical data to identify predictive biomarker for bladder immunotherapy response.
3/01/2020: Hwang lab has six posters accepted at @AACR this April.
2/22/2020: Our collaborative Department Of Defense grant with Shilpa Gupta to identify biomarker for #BladderCancer immunotherapy is funded!
1/04/2020: Our new algorithm, Bayesian Semi-nonnegative Matrix Tri-factorization, is accepted to publish in Pacific Symposium on Biocomputing (PSB) 2020.
06/14/2023: We are selected to present two oral presentation at Immunotherapy Scientific Program at the 15th International Gastric Cancer Congress.
05/22/2023: Tae Hyun Hwang will give an invited talk at HIMA Imaging Science Session and serve as a panelist at Pathology Informatics Summit 2023
05/14/2023: Tae Hyun Hwang will give a talk at Representation Learning and serve as a panelist at AI and Genomics at the 2023 Great Lakes Bioinformatics Conference
05/05/2023: 12th Annual Individualizing Medicine Conference: Direct-to-Patient Omics-Based Clinical Trials Tae Hyun Hwang will give a talk
04/14/2023: Our group will present 6 posters about AI, Machine Learning, Deep Learning based approaches utilizing single cell, spatial biology, and image data at AACR 2023 meeting.
11/31/2021: Tae Hyun Hwang gave a talk about machine learning and AI approaches developing clinically actionable biomarker for chemotherapy and immunotherapy in gastric cancer at TargetCancer Foundation.
04/21/2021: Tae Hyun Hwang gave an invited webinar about “Computational driven Spatial Transcriptome Analysis to Investigate Molecular Mechanisms present in Tumor Immune Microenvironment Associated With Immune Checkpoint Inhibitor Response in Gastric Cancer” at NanoString Webinar Series “Total Transcriptome Takeover”.
04/20/2021: Tae Hyun Hwang gave an invited talk about “Machine Learning driven Digital Pathology and Spatial Transcriptome Analyses to Predict Immune Directed Therapy Response in Bladder and Gastric Cancer” at Brigham and Women’s Hospital Computational Digital Pathology Symposium with >400 participants.
02/27/2021: Tae Hyun Hwang gave a seminar about our ongoing research "Spatial transcriptome, single cell and digital pathology approaches to understand mechanisms of response and resistance to Immunotherapy and cell therapy: Opportunities and challenges for machine learning and AI scientists" in the department of computational biology at Carnegie Melon University on March 5th.
1/13/2020: Tae Hyung Hwang gave a seminar at Cleveland Clinic-Yonsei Severance Joint AI and Data Science conference.
8/04/2019: Tae Hyun Hwang gave a keynote regarding AI in Healthcare at 24th ACM SIGKDD conference on Knowledge Discovery and Data Mining.