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Bakr S, Brennan K, Mukherjee P, Argemi J, Hernaez M, Gevaert O. Identifying key multifunctional components shared by critical cancer and normal liver pathways via SparseGMM. CELL REPORTS METHODS 2023; 3:100392. [PMID: 36814838 PMCID: PMC9939431 DOI: 10.1016/j.crmeth.2022.100392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 09/16/2022] [Accepted: 12/21/2022] [Indexed: 01/19/2023]
Abstract
Despite the abundance of multimodal data, suitable statistical models that can improve our understanding of diseases with genetic underpinnings are challenging to develop. Here, we present SparseGMM, a statistical approach for gene regulatory network discovery. SparseGMM uses latent variable modeling with sparsity constraints to learn Gaussian mixtures from multiomic data. By combining coexpression patterns with a Bayesian framework, SparseGMM quantitatively measures confidence in regulators and uncertainty in target gene assignment by computing gene entropy. We apply SparseGMM to liver cancer and normal liver tissue data and evaluate discovered gene modules in an independent single-cell RNA sequencing (scRNA-seq) dataset. SparseGMM identifies PROCR as a regulator of angiogenesis and PDCD1LG2 and HNF4A as regulators of immune response and blood coagulation in cancer. Furthermore, we show that more genes have significantly higher entropy in cancer compared with normal liver. Among high-entropy genes are key multifunctional components shared by critical pathways, including p53 and estrogen signaling.
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Affiliation(s)
- Shaimaa Bakr
- Department of Electrical Engineering, Stanford University, Stanford, CA 94305, USA
- Stanford Center for Biomedical Informatics Research, Department of Medicine and Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
- Department of Radiology, Stanford University, Stanford, CA 94305, USA
| | - Kevin Brennan
- Stanford Center for Biomedical Informatics Research, Department of Medicine and Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Pritam Mukherjee
- Stanford Center for Biomedical Informatics Research, Department of Medicine and Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Josepmaria Argemi
- Liver Unit, Clinica Universidad de Navarra, Hepatology Program, Center for Applied Medical Research, 31008 Pamplona, Navarra, Spain
| | - Mikel Hernaez
- Center for Applied Medical Research, University of Navarra, 31009 Pamplona, Navarra, Spain
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research, Department of Medicine and Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
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Gevaert O, Nabian M, Bakr S, Everaert C, Shinde J, Manukyan A, Liefeld T, Tabor T, Xu J, Lupberger J, Haas BJ, Baumert TF, Hernaez M, Reich M, Quintana FJ, Uhlmann EJ, Krichevsky AM, Mesirov JP, Carey V, Pochet N. Imaging-AMARETTO: An Imaging Genomics Software Tool to Interrogate Multiomics Networks for Relevance to Radiography and Histopathology Imaging Biomarkers of Clinical Outcomes. JCO Clin Cancer Inform 2020; 4:421-435. [PMID: 32383980 PMCID: PMC7265792 DOI: 10.1200/cci.19.00125] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/16/2020] [Indexed: 12/18/2022] Open
Abstract
PURPOSE The availability of increasing volumes of multiomics, imaging, and clinical data in complex diseases such as cancer opens opportunities for the formulation and development of computational imaging genomics methods that can link multiomics, imaging, and clinical data. METHODS Here, we present the Imaging-AMARETTO algorithms and software tools to systematically interrogate regulatory networks derived from multiomics data within and across related patient studies for their relevance to radiography and histopathology imaging features predicting clinical outcomes. RESULTS To demonstrate its utility, we applied Imaging-AMARETTO to integrate three patient studies of brain tumors, specifically, multiomics with radiography imaging data from The Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and low-grade glioma (LGG) cohorts and transcriptomics with histopathology imaging data from the Ivy Glioblastoma Atlas Project (IvyGAP) GBM cohort. Our results show that Imaging-AMARETTO recapitulates known key drivers of tumor-associated microglia and macrophage mechanisms, mediated by STAT3, AHR, and CCR2, and neurodevelopmental and stemness mechanisms, mediated by OLIG2. Imaging-AMARETTO provides interpretation of their underlying molecular mechanisms in light of imaging biomarkers of clinical outcomes and uncovers novel master drivers, THBS1 and MAP2, that establish relationships across these distinct mechanisms. CONCLUSION Our network-based imaging genomics tools serve as hypothesis generators that facilitate the interrogation of known and uncovering of novel hypotheses for follow-up with experimental validation studies. We anticipate that our Imaging-AMARETTO imaging genomics tools will be useful to the community of biomedical researchers for applications to similar studies of cancer and other complex diseases with available multiomics, imaging, and clinical data.
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Affiliation(s)
- Olivier Gevaert
- Stanford Center for Biomedical Informatics Research, Department of Medicine and Biomedical Data Science, Stanford University, Stanford, CA
- Cell Circuits Program, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Mohsen Nabian
- Cell Circuits Program, Broad Institute of MIT and Harvard, Cambridge, MA
- Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Shaimaa Bakr
- Stanford Center for Biomedical Informatics Research, Department of Medicine and Biomedical Data Science, Stanford University, Stanford, CA
| | - Celine Everaert
- Cell Circuits Program, Broad Institute of MIT and Harvard, Cambridge, MA
- Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Jayendra Shinde
- Stanford Center for Biomedical Informatics Research, Department of Medicine and Biomedical Data Science, Stanford University, Stanford, CA
| | - Artur Manukyan
- Cell Circuits Program, Broad Institute of MIT and Harvard, Cambridge, MA
- Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Ted Liefeld
- Department of Medicine, University of California, San Diego, San Diego, CA
| | - Thorin Tabor
- Department of Medicine, University of California, San Diego, San Diego, CA
| | - Jishu Xu
- Cell Circuits Program, Broad Institute of MIT and Harvard, Cambridge, MA
- Rush University Medical Center, Chicago, IL
| | - Joachim Lupberger
- INSERM, U1110, Institut de Recherche sur les Maladies Virales et Hépatiques, Université de Strasbourg, Institut Hopitalo-Universitaire, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Brian J. Haas
- Cell Circuits Program, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Thomas F. Baumert
- INSERM, U1110, Institut de Recherche sur les Maladies Virales et Hépatiques, Université de Strasbourg, Institut Hopitalo-Universitaire, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Mikel Hernaez
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL
| | - Michael Reich
- Department of Medicine, University of California, San Diego, San Diego, CA
| | - Francisco J. Quintana
- Cell Circuits Program, Broad Institute of MIT and Harvard, Cambridge, MA
- Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Erik J. Uhlmann
- Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Anna M. Krichevsky
- Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Jill P. Mesirov
- Department of Medicine, University of California, San Diego, San Diego, CA
| | - Vincent Carey
- Cell Circuits Program, Broad Institute of MIT and Harvard, Cambridge, MA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Nathalie Pochet
- Cell Circuits Program, Broad Institute of MIT and Harvard, Cambridge, MA
- Ann Romney Center for Neurologic Diseases, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
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