1
|
Saldarriaga OA, Wanninger TG, Arroyave E, Gosnell J, Krishnan S, Oneka M, Bao D, Millian DE, Kueht ML, Moghe A, Jiao J, Sanchez JI, Spratt H, Beretta L, Rao A, Burks JK, Stevenson HL. Heterogeneity in intrahepatic macrophage populations and druggable target expression in patients with steatotic liver disease-related fibrosis. JHEP Rep 2024; 6:100958. [PMID: 38162144 PMCID: PMC10757256 DOI: 10.1016/j.jhepr.2023.100958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 08/18/2023] [Accepted: 09/25/2023] [Indexed: 01/03/2024] Open
Abstract
Background & Aims Clinical trials for reducing fibrosis in steatotic liver disease (SLD) have targeted macrophages with variable results. We evaluated intrahepatic macrophages in patients with SLD to determine if activity scores or fibrosis stages influenced phenotypes and expression of druggable targets, such as CCR2 and galectin-3. Methods Liver biopsies from controls or patients with minimal or advanced fibrosis were subject to gene expression analysis using nCounter to determine differences in macrophage-related genes (n = 30). To investigate variability among individual patients, we compared additional biopsies by staining them with multiplex antibody panels (CD68/CD14/CD16/CD163/Mac387 or CD163/CCR2/galectin-3/Mac387) followed by spectral imaging and spatial analysis. Algorithms that utilize deep learning/artificial intelligence were applied to create cell cluster plots, phenotype profile maps, and to determine levels of protein expression (n = 34). Results Several genes known to be pro-fibrotic (e.g. CD206, TREM2, CD163, and ARG1) showed either no significant differences or significantly decreased with advanced fibrosis. Although marked variability in gene expression was observed in individual patients with cirrhosis, several druggable targets and their ligands (e.g. CCR2, CCR5, CCL2, CCL5, and LGALS3) were significantly increased when compared to patients with minimal fibrosis. Antibody panels identified populations that were significantly increased (e.g. Mac387+), decreased (e.g. CD14+), or enriched (e.g. interactions of Mac387) in patients that had progression of disease or advanced fibrosis. Despite heterogeneity in patients with SLD, several macrophage phenotypes and druggable targets showed a positive correlation with increasing NAFLD activity scores and fibrosis stages. Conclusions Patients with SLD have markedly varied macrophage- and druggable target-related gene and protein expression in their livers. Several patients had relatively high expression, while others were like controls. Overall, patients with more advanced disease had significantly higher expression of CCR2 and galectin-3 at both the gene and protein levels. Impact and implications Appreciating individual differences within the hepatic microenvironment of patients with SLD may be paramount to developing effective treatments. These results may explain why such a small percentage of patients have responded to macrophage-targeting therapies and provide additional support for precision medicine-guided treatment of chronic liver diseases.
Collapse
Affiliation(s)
- Omar A. Saldarriaga
- Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA
| | - Timothy G. Wanninger
- Department of Microbiology and Immunology, University of Texas Medical Branch, Galveston, TX, USA
| | - Esteban Arroyave
- Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA
| | - Joseph Gosnell
- Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA
| | - Santhoshi Krishnan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
| | - Morgan Oneka
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Daniel Bao
- School of Medicine, University of Texas Medical Branch, Galveston, TX, USA
| | - Daniel E. Millian
- Department of Pathology, University of Texas Medical Branch, Galveston, TX, USA
| | - Michael L. Kueht
- Department of Surgery, University of Texas Medical Branch, Galveston, TX, USA
| | - Akshata Moghe
- Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX, USA
| | - Jingjing Jiao
- Department of Molecular and Cellular Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jessica I. Sanchez
- Department of Molecular and Cellular Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Heidi Spratt
- Department of Biostatistics and Data Science, University of Texas Medical Branch, Galveston, TX, USA
| | - Laura Beretta
- Department of Molecular and Cellular Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Arvind Rao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, USA
- Departmen of Radiation Oncology, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, Rice University, Ann Arbor, MI, USA
| | - Jared K. Burks
- Department of Leukemia, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | |
Collapse
|
2
|
Saldarriaga OA, Krishnan S, Wanninger TG, Oneka M, Rao A, Bao D, Arroyave E, Gosnell J, Kueht M, Moghe A, Millian D, Jiao J, Sanchez JI, Spratt H, Beretta L, Stevenson HL. Patients with fibrosis from non-alcoholic steatohepatitis have heterogeneous intrahepatic macrophages and therapeutic targets. medRxiv 2023:2023.02.16.23285924. [PMID: 36865099 PMCID: PMC9980226 DOI: 10.1101/2023.02.16.23285924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
Background and Aims In clinical trials for reducing fibrosis in NASH patients, therapeutics that target macrophages have had variable results. We evaluated intrahepatic macrophages in patients with non-alcoholic steatohepatitis to determine if fibrosis influenced phenotypes and expression of CCR2 and Galectin-3. Approach & Results We used nCounter to analyze liver biopsies from well-matched patients with minimal (n=12) or advanced (n=12) fibrosis to determine which macrophage-related genes would be significantly different. Known therapy targets (e.g., CCR2 and Galectin-3) were significantly increased in patients with cirrhosis.However, several genes (e.g., CD68, CD16, and CD14) did not show significant differences, and CD163, a marker of pro-fibrotic macrophages was significantly decreased with cirrhosis. Next, we analyzed patients with minimal (n=6) or advanced fibrosis (n=5) using approaches that preserved hepatic architecture by multiplex-staining with anti-CD68, Mac387, CD163, CD14, and CD16. Spectral data were analyzed using deep learning/artificial intelligence to determine percentages and spatial relationships. This approach showed patients with advanced fibrosis had increased CD68+, CD16+, Mac387+, CD163+, and CD16+CD163+ populations. Interaction of CD68+ and Mac387+ populations was significantly increased in patients with cirrhosis and enrichment of these same phenotypes in individuals with minimal fibrosis correlated with poor outcomes. Evaluation of a final set of patients (n=4) also showed heterogenous expression of CD163, CCR2, Galectin-3, and Mac387, and significant differences were not dependent on fibrosis stage or NAFLD activity. Conclusions Approaches that leave hepatic architecture intact, like multispectral imaging, may be paramount to developing effective treatments for NASH. In addition, understanding individual differences in patients may be required for optimal responses to macrophage-targeting therapies.
Collapse
|
3
|
Al-Holou WN, Wang H, Ravikumar V, Shankar S, Oneka M, Fehmi Z, Verhaak RG, Kim H, Pratt D, Camelo-Piragua S, Speers C, Wahl DR, Hollon T, Sagher O, Heth JA, Muraszko KM, Lawrence TS, de Carvalho AC, Mikkelsen T, Rao A, Rehemtulla A. Subclonal evolution and expansion of spatially distinct THY1-positive cells is associated with recurrence in glioblastoma. Neoplasia 2023; 36:100872. [PMID: 36621024 PMCID: PMC9841165 DOI: 10.1016/j.neo.2022.100872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 12/22/2022] [Indexed: 01/07/2023]
Abstract
PURPOSE Glioblastoma(GBM) is a lethal disease characterized by inevitable recurrence. Here we investigate the molecular pathways mediating resistance, with the goal of identifying novel therapeutic opportunities. EXPERIMENTAL DESIGN We developed a longitudinal in vivo recurrence model utilizing patient-derived explants to produce paired specimens(pre- and post-recurrence) following temozolomide(TMZ) and radiation(IR). These specimens were evaluated for treatment response and to identify gene expression pathways driving treatment resistance. Findings were clinically validated using spatial transcriptomics of human GBMs. RESULTS These studies reveal in replicate cohorts, a gene expression profile characterized by upregulation of mesenchymal and stem-like genes at recurrence. Analyses of clinical databases revealed significant association of this transcriptional profile with worse overall survival and upregulation at recurrence. Notably, gene expression analyses identified upregulation of TGFβ signaling, and more than one-hundred-fold increase in THY1 levels at recurrence. Furthermore, THY1-positive cells represented <10% of cells in treatment-naïve tumors, compared to 75-96% in recurrent tumors. We then isolated THY1-positive cells from treatment-naïve patient samples and determined that they were inherently resistant to chemoradiation in orthotopic models. Additionally, using image-guided biopsies from treatment-naïve human GBM, we conducted spatial transcriptomic analyses. This revealed rare THY1+ regions characterized by mesenchymal/stem-like gene expression, analogous to our recurrent mouse model, which co-localized with macrophages within the perivascular niche. We then inhibited TGFBRI activity in vivo which decreased mesenchymal/stem-like protein levels, including THY1, and restored sensitivity to TMZ/IR in recurrent tumors. CONCLUSIONS These findings reveal that GBM recurrence may result from tumor repopulation by pre-existing, therapy-resistant, THY1-positive, mesenchymal cells within the perivascular niche.
Collapse
Affiliation(s)
- Wajd N Al-Holou
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48109, United States
| | - Hanxiao Wang
- Department of Radiation Oncology, University of Michigan, NCRC 520, Room 1342, Ann Arbor, MI 48105, United States; AstraZeneca, United States
| | - Visweswaran Ravikumar
- Department of Computational Medicine & Bioinformatics, The University of Michigan Medical School, Ann Arbor, MI 48109, United States
| | - Sunita Shankar
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48109, United States
| | - Morgan Oneka
- Department of Computational Medicine & Bioinformatics, The University of Michigan Medical School, Ann Arbor, MI 48109, United States
| | - Ziad Fehmi
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48109, United States
| | | | - Hoon Kim
- The Jackson Laboratory, Farmington, CT 06032, United States; Department of Biopharmaceutical Convergence, Sungkyunkwan University, South Korea
| | - Drew Pratt
- Department of Pathology, University of Michigan, United States
| | | | - Corey Speers
- Department of Radiation Oncology, University of Michigan, NCRC 520, Room 1342, Ann Arbor, MI 48105, United States
| | - Daniel R Wahl
- Department of Radiation Oncology, University of Michigan, NCRC 520, Room 1342, Ann Arbor, MI 48105, United States
| | - Todd Hollon
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48109, United States
| | - Oren Sagher
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48109, United States
| | - Jason A Heth
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48109, United States
| | - Karin M Muraszko
- Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48109, United States
| | - Theodore S Lawrence
- Department of Radiation Oncology, University of Michigan, NCRC 520, Room 1342, Ann Arbor, MI 48105, United States
| | - Ana C de Carvalho
- Department of Neurosurgery, Henry Ford Hospital, Detroit, MI 48202, United States
| | - Tom Mikkelsen
- Department of Neurosurgery, Henry Ford Hospital, Detroit, MI 48202, United States
| | - Arvind Rao
- Department of Radiation Oncology, University of Michigan, NCRC 520, Room 1342, Ann Arbor, MI 48105, United States; Department of Computational Medicine & Bioinformatics, The University of Michigan Medical School, Ann Arbor, MI 48109, United States
| | - Alnawaz Rehemtulla
- Department of Radiation Oncology, University of Michigan, NCRC 520, Room 1342, Ann Arbor, MI 48105, United States.
| |
Collapse
|
4
|
Al-Holou WN, Wang H, Ravikumar V, Oneka M, Verhaak RG, Kim H, Pratt D, Camelo-Piragua S, Speers C, Wahl D, Shankar S, Hollon T, Sagher O, Heth J, Muraszko KM, Lawrence TS, deCarvalho AC, Mikkelsen T, Rao A, Rehemtulla A. Abstract LB013: Subclonal evolution and expansion of spatially distinct THY1-positive cells is associated with recurrence in glioblastoma. Cancer Res 2022. [DOI: 10.1158/1538-7445.am2022-lb013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Purpose: Glioblastoma (GBM) is a lethal disease characterized by inevitable recurrence. Here we investigate the molecular pathways mediating resistance, with the goal of identifying therapeutic opportunities to target this tumor.
Experimental Design: We developed a longitudinal in vivo recurrence model utilizing patient-derived explants to produce paired specimens (pre- and post-recurrence) following temozolomide(TMZ) and radiation(IR). These specimens were evaluated for treatment response and to identify gene expression pathways driving treatment resistance. Findings were clinically validated using spatial transcriptomics of human GBMs.
Results: These studies reveal in replicate cohorts, a gene expression profile characterized by upregulation of mesenchymal and stem-like genes at recurrence. Analyses of clinical databases revealed increased expression of this transcriptional profile to be significantly associated with worse median overall survival (248 days vs 430 days, p=0.0004), and upregulation of this profile at recurrence. Most notably, we identified upregulation of TGFβ signaling, and more than one-hundred-fold increase in THY1 levels at recurrence. Utilizing cell sorting, we observed that THY1-positive cells represented <10% of cells in the treatment-naïve tumors and 75-96% in the recurrent tumors. We then isolated THY1-positive cells from treatment-naïve patient samples and determined that they were inherently resistant to chemoradiation in orthotopic models. Additionally, using image-guided biopsies from treatment-naïve human GBM, we conducted spatial transcriptomic analyses. This revealed rare THY1+ regions characterized by mesenchymal/stem-like gene expression, analogous to our recurrent mouse model samples, which co-localized with macrophages within the perivascular niche. Since TGFβ signaling contributes to a mesenchymal/stem-like phenotype, we inhibited TGFβRI activity in vivo which resulted in decreased mesenchymal/stem-like protein levels, including THY1, and restored sensitivity to TMZ/IR in recurrent tumors.
Conclusions: These findings reveal that GBM recurrence may result from tumor repopulation by pre-existing, therapy-resistant, THY1-positive, mesenchymal/stem-like cells within the perivascular niche. Furthermore, our data demonstrate the promise of targeting upregulated pathways in resistant subclones as a novel mechanism to achieve therapeutic response, and specifically that THY1 expression may represent a biomarker of response to TGFβ inhibition.
Citation Format: Wajd N. Al-Holou, Hanxiao Wang, Visweswaran Ravikumar, Morgan Oneka, Roel G. Verhaak, Hoon Kim, Drew Pratt, Sandra Camelo-Piragua, Corey Speers, Daniel Wahl, Sunita Shankar, Todd Hollon, Oren Sagher, Jason Heth, Karin M. Muraszko, Theodore S. Lawrence, Ana C. deCarvalho, Tom Mikkelsen, Arvind Rao, Alnawaz Rehemtulla. Subclonal evolution and expansion of spatially distinct THY1-positive cells is associated with recurrence in glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB013.
Collapse
Affiliation(s)
| | | | | | | | | | - Hoon Kim
- 2Jackson Laboratory, Farmington, CT
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
5
|
Stanley TR, Guisbert KSK, Perez SM, Oneka M, Kernin I, Higgins NR, Lobo A, Subasi MM, Carroll DJ, Turingan RG, Guisbert E. Stress response gene family expansions correlate with invasive potential in teleost fish. J Exp Biol 2022; 225:274389. [PMID: 35258619 PMCID: PMC8987736 DOI: 10.1242/jeb.243263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 01/24/2022] [Indexed: 11/20/2022]
Abstract
The bluegill sunfish Lepomis macrochirus and the closely related redear sunfish Lepomis microlophus have important ecological and recreational value and are widely used for research and aquaculture. While both species have been introduced outside of their native ranges, only the bluegill is considered invasive. Here, we report de novo transcriptome assemblies for these fish as a resource for sunfish biology. Comparative analyses of the transcriptomes revealed an unexpected, bluegill-specific expansion in the HSP70 and HSP90 molecular chaperone gene families. These expansions were not unique to the bluegill as expansions in HSP70s and HSP90s were identified in the genomes of other teleost fish using the NCBI RefSeq database. To determine whether gene family expansions are specific for thermal stress responses, GST and SOD gene families that are associated with oxidative stress responses were also analyzed. Species-specific expansions were also observed for these gene families in distinct fish species. Validating our approach, previously described expansions in the MHC gene family were also identified. Intriguingly, the number of HSP70 paralogs was positively correlated with thermotolerance range for each species, suggesting that these expansions can impact organismal physiology. Furthermore, fish that are considered invasive contained a higher average number of HSP70 paralogs than non-invasive fish. Invasive fish also had higher average numbers of HSP90, MHC and GST paralogs, but not SOD paralogs. Taken together, we propose that expansions in key cellular stress response gene families represent novel genetic signatures that correlate with invasive potential.
Collapse
Affiliation(s)
- Taylor R Stanley
- Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology, Melbourne, FL 32937, USA
| | - Karen S Kim Guisbert
- Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology, Melbourne, FL 32937, USA
| | - Sabrina M Perez
- Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology, Melbourne, FL 32937, USA
| | - Morgan Oneka
- Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology, Melbourne, FL 32937, USA
| | - Isabela Kernin
- Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology, Melbourne, FL 32937, USA
| | - Nicole R Higgins
- Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology, Melbourne, FL 32937, USA
| | - Alexandra Lobo
- Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology, Melbourne, FL 32937, USA
| | - Munevver M Subasi
- Department of Mathematical Sciences, Florida Institute of Technology, Melbourne, FL 32937, USA
| | - David J Carroll
- Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology, Melbourne, FL 32937, USA
| | - Ralph G Turingan
- Department of Ocean Engineering and Marine Sciences, Florida Institute of Technology, Melbourne, FL 32937, USA
| | - Eric Guisbert
- Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology, Melbourne, FL 32937, USA
| |
Collapse
|
6
|
Lapp Z, Sovacool KL, Lesniak N, King D, Barnier C, Flickinger M, Krüger J, Armour CR, Lapp MM, Tallant J, Diao R, Oneka M, Tomkovich S, Anderson JM, Lucas SK, Schloss PD. Developing and deploying an integrated workshop curriculum teaching computational skills for reproducible research. JOSE 2022; 5. [PMID: 35224460 PMCID: PMC8872090 DOI: 10.21105/jose.00144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Inspired by well-established material and pedagogy provided by The Carpentries (Wilson, 2016), we developed a two-day workshop curriculum that teaches introductory R programming for managing, analyzing, plotting and reporting data using packages from the tidyverse (Wickham et al., 2019), the Unix shell, version control with git, and GitHub. While the official Software Carpentry curriculum is comprehensive, we found that it contains too much content for a two-day workshop. We also felt that the independent nature of the lessons left learners confused about how to integrate the newly acquired programming skills in their own work. Thus, we developed a new curriculum that aims to teach novices how to implement reproducible research principles in their own data analysis. The curriculum integrates live coding lessons with individual-level and group-based practice exercises, and also serves as a succinct resource that learners can reference both during and after the workshop. Moreover, it lowers the entry barrier for new instructors as they do not have to develop their own teaching materials or sift through extensive content. We developed this curriculum during a two-day sprint, successfully used it to host a two-day virtual workshop with almost 40 participants, and updated the material based on instructor and learner feedback. We hope that our new curriculum will prove useful to future instructors interested in teaching workshops with similar learning objectives.
Collapse
Affiliation(s)
- Zena Lapp
- Department of Computational Medicine & Bioinformatics, University of Michigan
| | - Kelly L Sovacool
- Department of Computational Medicine & Bioinformatics, University of Michigan
| | - Nick Lesniak
- Department of Microbiology & Immunology, University of Michigan
| | - Dana King
- BRCF Bioinformatics Core, University of Michigan
| | - Catherine Barnier
- Department of Computational Medicine & Bioinformatics, University of Michigan
| | | | - Jule Krüger
- Center for Political Studies, Institute for Social Research, University of Michigan
| | | | | | | | - Rucheng Diao
- Department of Computational Medicine & Bioinformatics, University of Michigan
| | - Morgan Oneka
- Department of Computational Medicine & Bioinformatics, University of Michigan
| | - Sarah Tomkovich
- Department of Microbiology & Immunology, University of Michigan
| | | | - Sarah K Lucas
- Department of Microbiology & Immunology, University of Michigan
| | | |
Collapse
|
7
|
Duda M, Sovacool KL, Farzaneh N, Nguyen VK, Haynes SE, Falk H, Furman KL, Walker LA, Diao R, Oneka M, Drotos AC, Woloshin A, Dotson GA, Kriebel A, Meng L, Thiede SN, Lapp Z, Wolford BN. Teaching Python for Data Science: Collaborative development of a modular interactive curriculum. JOSE 2021; 4. [PMID: 35187422 PMCID: PMC8851894 DOI: 10.21105/jose.00138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Marlena Duda
- Department of Computational Medicine & Bioinformatics, University of Michigan
| | - Kelly L Sovacool
- Department of Computational Medicine & Bioinformatics, University of Michigan
| | - Negar Farzaneh
- Department of Computational Medicine & Bioinformatics, University of Michigan
- Michigan Center for Integrative Research in Critical Care, University of Michigan
| | - Vy Kim Nguyen
- Department of Computational Medicine & Bioinformatics, University of Michigan
- Department of Environmental Health Sciences, University of Michigan
| | | | - Hayley Falk
- Department of Computational Medicine & Bioinformatics, University of Michigan
| | - Katherine L Furman
- Neuroscience Graduate Program, University of Michigan
- Michigan Neuroscience Institute, University of Michigan
| | - Logan A Walker
- Biophysics Graduate Program, University of Michigan
- Department of Computational Medicine & Bioinformatics, University of Michigan
| | - Rucheng Diao
- Department of Computational Medicine & Bioinformatics, University of Michigan
| | - Morgan Oneka
- Department of Computational Medicine & Bioinformatics, University of Michigan
| | - Audrey C Drotos
- Kresge Hearing Research Institute, Department of Otolaryngology-Head and Neck Surgery, University of Michigan
| | | | - Gabrielle A Dotson
- Department of Computational Medicine & Bioinformatics, University of Michigan
| | - April Kriebel
- Department of Computational Medicine & Bioinformatics, University of Michigan
| | - Lucy Meng
- Department of Electrical Engineering & Computer Sciences, University of California, Berkeley
| | | | - Zena Lapp
- Department of Computational Medicine & Bioinformatics, University of Michigan
| | - Brooke N Wolford
- Department of Computational Medicine & Bioinformatics, University of Michigan
| |
Collapse
|
8
|
Baranwal M, Krishnan S, Oneka M, Frankel T, Rao A. CGAT: Cell Graph ATtention Network for Grading of Pancreatic Disease Histology Images. Front Immunol 2021; 12:727610. [PMID: 34671349 PMCID: PMC8522581 DOI: 10.3389/fimmu.2021.727610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2021] [Accepted: 09/03/2021] [Indexed: 11/13/2022] Open
Abstract
Early detection of Pancreatic Ductal Adenocarcinoma (PDAC), one of the most aggressive malignancies of the pancreas, is crucial to avoid metastatic spread to other body regions. Detection of pancreatic cancer is typically carried out by assessing the distribution and arrangement of tumor and immune cells in histology images. This is further complicated due to morphological similarities with chronic pancreatitis (CP), and the co-occurrence of precursor lesions in the same tissue. Most of the current automated methods for grading pancreatic cancers rely on extensive feature engineering involving accurate identification of cell features or utilising single number spatially informed indices for grading purposes. Moreover, sophisticated methods involving black-box approaches, such as neural networks, do not offer insights into the model's ability to accurately identify the correct disease grade. In this paper, we develop a novel cell-graph based Cell-Graph Attention (CGAT) network for the precise classification of pancreatic cancer and its precursors from multiplexed immunofluorescence histology images into the six different types of pancreatic diseases. The issue of class imbalance is addressed through bootstrapping multiple CGAT-nets, while the self-attention mechanism facilitates visualization of cell-cell features that are likely responsible for the predictive capabilities of the model. It is also shown that the model significantly outperforms the decision tree classifiers built using spatially informed metric, such as the Morisita-Horn (MH) indices.
Collapse
Affiliation(s)
- Mayank Baranwal
- Division of Data & Decision Sciences, Tata Consultancy Services Research, Mumbai, India.,Department of Systems and Control Engineering, Indian Institute of Technology, Bombay, India
| | - Santhoshi Krishnan
- Department of Electrical & Computer Engineering, Rice University, Houston, TX, United States.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Morgan Oneka
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Timothy Frankel
- Department of Surgery, University of Michigan, Ann Arbor, MI, United States
| | - Arvind Rao
- Department of Electrical & Computer Engineering, Rice University, Houston, TX, United States.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, United States.,Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States.,Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| |
Collapse
|
9
|
Zhou W, Brumpton B, Kabil O, Gudmundsson J, Thorleifsson G, Weinstock J, Zawistowski M, Nielsen JB, Chaker L, Medici M, Teumer A, Naitza S, Sanna S, Schultheiss UT, Cappola A, Karjalainen J, Kurki M, Oneka M, Taylor P, Fritsche LG, Graham SE, Wolford BN, Overton W, Rasheed H, Haug EB, Gabrielsen ME, Skogholt AH, Surakka I, Davey Smith G, Pandit A, Roychowdhury T, Hornsby WE, Jonasson JG, Senter L, Liyanarachchi S, Ringel MD, Xu L, Kiemeney LA, He H, Netea-Maier RT, Mayordomo JI, Plantinga TS, Hrafnkelsson J, Hjartarson H, Sturgis EM, Palotie A, Daly M, Citterio CE, Arvan P, Brummett CM, Boehnke M, de la Chapelle A, Stefansson K, Hveem K, Willer CJ, Åsvold BO. GWAS of thyroid stimulating hormone highlights pleiotropic effects and inverse association with thyroid cancer. Nat Commun 2020; 11:3981. [PMID: 32769997 PMCID: PMC7414135 DOI: 10.1038/s41467-020-17718-z] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 07/08/2020] [Indexed: 12/11/2022] Open
Abstract
Thyroid stimulating hormone (TSH) is critical for normal development and metabolism. To better understand the genetic contribution to TSH levels, we conduct a GWAS meta-analysis at 22.4 million genetic markers in up to 119,715 individuals and identify 74 genome-wide significant loci for TSH, of which 28 are previously unreported. Functional experiments show that the thyroglobulin protein-altering variants P118L and G67S impact thyroglobulin secretion. Phenome-wide association analysis in the UK Biobank demonstrates the pleiotropic effects of TSH-associated variants and a polygenic score for higher TSH levels is associated with a reduced risk of thyroid cancer in the UK Biobank and three other independent studies. Two-sample Mendelian randomization using TSH index variants as instrumental variables suggests a protective effect of higher TSH levels (indicating lower thyroid function) on risk of thyroid cancer and goiter. Our findings highlight the pleiotropic effects of TSH-associated variants on thyroid function and growth of malignant and benign thyroid tumors.
Collapse
Affiliation(s)
- Wei Zhou
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.
| | - Ben Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Thoracic Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Omer Kabil
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, Michigan, USA
- Division of Metabolism Endocrinology and Diabetes, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | | | | | - Josh Weinstock
- Center for Statistical Genetics and Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Matthew Zawistowski
- Center for Statistical Genetics and Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Jonas B Nielsen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Internal Medicine, Division of Cardiology, University of Michigan Medical School, Ann Arbor, Michigan, USA
- Department of Epidemiology Research, Statens Serum Institute, Copenhagen, Denmark
| | - Layal Chaker
- Erasmus MC Academic Center for Thyroid Diseases, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Marco Medici
- Erasmus MC Academic Center for Thyroid Diseases, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
- Division of Endocrinology, Department of Internal Medicine, Radboud University Medical Centre, Radboud Institute for Molecular Life Sciences, 6500HB, Nijmegen, The Netherlands
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Greifswald, Greifswald, Germany
| | - Silvia Naitza
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche Monserrato, Monserrato, Italy
| | - Serena Sanna
- Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche Monserrato, Monserrato, Italy
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ulla T Schultheiss
- Faculty of Medicine and Medical Center, Institute of Genetic Epidemiology, University of Freiburg, Freiburg, Germany
- Faculty of Medicine and Medical Center, Department of Medicine IV-Nephrology and Primary Care, University of Freiburg, Freiburg, Germany
| | - Anne Cappola
- Division of Endocrinology, Diabetes, and Metabolism, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA
| | - Juha Karjalainen
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, 00014, Finland
| | - Mitja Kurki
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, 00014, Finland
| | - Morgan Oneka
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Peter Taylor
- Thyroid Research Group, Systems Immunity Research Institute, Cardiff University School of Medicine, Cardiff, UK
| | - Lars G Fritsche
- Center for Statistical Genetics and Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Sarah E Graham
- Department of Internal Medicine, Division of Cardiology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Brooke N Wolford
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
- Center for Statistical Genetics and Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - William Overton
- Center for Statistical Genetics and Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Humaira Rasheed
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Eirin B Haug
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Maiken E Gabrielsen
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
| | - Anne Heidi Skogholt
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- Faculty of Medicine and Health Sciences, Department of Public Health and Nursing, Norwegian University of Science and Technology, NTNU, Trondheim, Norway
| | - Ida Surakka
- Department of Internal Medicine, Division of Cardiology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - George Davey Smith
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Anita Pandit
- Center for Statistical Genetics and Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Tanmoy Roychowdhury
- Department of Internal Medicine, Division of Cardiology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Whitney E Hornsby
- Department of Internal Medicine, Division of Cardiology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Jon G Jonasson
- Landspitali-University Hospital, 101, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
- The Icelandic Cancer Registry, 105, Reykjavik, Iceland
| | - Leigha Senter
- Division of Human Genetics, Ohio State University Comprehensive Cancer Center, Columbus, Ohio, 43210, USA
| | - Sandya Liyanarachchi
- Department of Cancer Biology and Genetics, Ohio State University Comprehensive Cancer Center, Columbus, Ohio, 43210, USA
| | - Matthew D Ringel
- Division of Endocrinology, Diabetes, and Metabolism, The Ohio State University, Columbus, Ohio, 43210, USA
| | - Li Xu
- Department of Head and Neck Surgery, and Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, 77030, USA
| | - Lambertus A Kiemeney
- Radboud University Medical Centre, Radboud Institute for Health Sciences, 6500HB, Nijmegen, The Netherlands
| | - Huiling He
- Department of Cancer Biology and Genetics, Ohio State University Comprehensive Cancer Center, Columbus, Ohio, 43210, USA
| | - Romana T Netea-Maier
- Division of Endocrinology, Department of Internal Medicine, Radboud University Medical Centre, Radboud Institute for Molecular Life Sciences, 6500HB, Nijmegen, The Netherlands
| | | | - Theo S Plantinga
- Department of Pathology, Radboud University Medical Center, Radboud Institute for Molecular Life Sciences, 6500HB, Nijmegen, The Netherlands
| | | | | | - Erich M Sturgis
- Department of Head and Neck Surgery, and Department of Epidemiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, 77030, USA
| | - Aarno Palotie
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, 00014, Finland
| | - Mark Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Sciences, University of Helsinki, Helsinki, 00014, Finland
| | - Cintia E Citterio
- Division of Metabolism Endocrinology and Diabetes, University of Michigan Medical School, Ann Arbor, Michigan, USA
- Universidad de Buenos Aires, Facultad de Farmacia y Bioquímica, Departamento de Microbiología, Inmunología y Biotecnología/Cátedra de Genética, Buenos Aires, C1113AAD, Argentina
- CONICET-Universidad de Buenos Aires, Instituto de Inmunología, Genética y Metabolismo (INIGEM), C1120AAR, Buenos Aires, Argentina
| | - Peter Arvan
- Division of Metabolism Endocrinology and Diabetes, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Chad M Brummett
- Division of Pain Medicine, Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Michael Boehnke
- Center for Statistical Genetics and Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA
| | - Albert de la Chapelle
- Department of Cancer Biology and Genetics, Ohio State University Comprehensive Cancer Center, Columbus, Ohio, 43210, USA
| | - Kari Stefansson
- deCODE genetics/AMGEN, 101, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, 7600, Norway
- Department of Medicine, Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, 7600, Norway
| | - Cristen J Willer
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
- Department of Internal Medicine, Division of Cardiology, University of Michigan Medical School, Ann Arbor, Michigan, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, USA
| | - Bjørn Olav Åsvold
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway.
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger, 7600, Norway.
- Department of Endocrinology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
| |
Collapse
|