Hydra: A mixture modeling framework for subtyping pediatric cancer cohorts using multimodal gene expression signatures
| Autor: | Phuong T. Dinh, Holly C. Beale, Isabel Bjork, Alana S. Weinstein, Stanley G. Leung, E. Alejandro Sweet-Cordero, David Haussler, Ioannis N. Anastopoulos, Jacob Pfeil, Avanthi Tayi Shah, W. Patrick Devine, Yuanqing Xue, Lauren Sanders, Alex G. Lee, Sofie R. Salama, Olena M. Vaske, Marcus R. Breese, A. Geoffrey Lyle, Andrew Blair |
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| Přispěvatelé: | Markowetz, Florian, Pfeil, Jacob [0000-0002-8773-8520], Anastopoulos, Ioannis [0000-0002-6279-0648], Lyle, A Geoffrey [0000-0002-3435-526X], Weinstein, Alana S [0000-0002-1563-9072], Xue, Yuanqing [0000-0003-1892-6787], Beale, Holly C [0000-0003-4091-538X], Dinh, Phuong T [0000-0002-0273-1603], Devine, W Patrick [0000-0003-4634-8830], Salama, Sofie R [0000-0001-6999-7193], Sweet-Cordero, E Alejandro [0000-0002-9787-9351], Vaske, Olena Morozova [0000-0002-1677-417X], Apollo - University of Cambridge Repository |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Hydra Gene Expression Pathology and Laboratory Medicine Pediatrics Mathematical Sciences Transcriptome Neuroblastoma 0302 clinical medicine Animal Cells Models Neoplasms Gene expression Medicine and Health Sciences Tumor Microenvironment Blastomas Cluster Analysis Biology (General) Precision Medicine Child Immune Response Cancer Regulation of gene expression Pediatric Osteosarcoma Tumor Ecology Sarcomas Eukaryota Animal Models Statistical Biological Sciences Synovial sarcoma Gene Expression Regulation Neoplastic Computational Theory and Mathematics Experimental Organism Systems Oncology Modeling and Simulation Lernaean Hydra Cellular Types Research Article Biotechnology Pediatric Research Initiative QH301-705.5 Pediatric Cancer Bioinformatics Immune Cells Ewing Sarcoma Immunology Computational biology Biology Research and Analysis Methods 03 medical and health sciences Cellular and Molecular Neuroscience Cnidaria Signs and Symptoms Rare Diseases Diagnostic Medicine Information and Computing Sciences medicine Biomarkers Tumor Genetics Animals Humans Molecular Biology Ecology Evolution Behavior and Systematics ATRX Inflammation Tumor microenvironment Neoplastic Models Statistical Gene Expression Profiling Organisms Neurosciences Biology and Life Sciences Cancers and Neoplasms Computational Biology Cell Biology medicine.disease Pediatric cancer Invertebrates 030104 developmental biology Orphan Drug Good Health and Well Being Gene Expression Regulation Animal Studies 030217 neurology & neurosurgery Biomarkers |
| Zdroj: | PLoS computational biology, vol 16, iss 4 PLoS Computational Biology PLoS Computational Biology, Vol 16, Iss 4, p e1007753 (2020) |
| Popis: | Precision oncology has primarily relied on coding mutations as biomarkers of response to therapies. While transcriptome analysis can provide valuable information, incorporation into workflows has been difficult. For example, the relative rather than absolute gene expression level needs to be considered, requiring differential expression analysis across samples. However, expression programs related to the cell-of-origin and tumor microenvironment effects confound the search for cancer-specific expression changes. To address these challenges, we developed an unsupervised clustering approach for discovering differential pathway expression within cancer cohorts using gene expression measurements. The hydra approach uses a Dirichlet process mixture model to automatically detect multimodally distributed genes and expression signatures without the need for matched normal tissue. We demonstrate that the hydra approach is more sensitive than widely-used gene set enrichment approaches for detecting multimodal expression signatures. Application of the hydra analysis framework to small blue round cell tumors (including rhabdomyosarcoma, synovial sarcoma, neuroblastoma, Ewing sarcoma, and osteosarcoma) identified expression signatures associated with changes in the tumor microenvironment. The hydra approach also identified an association between ATRX deletions and elevated immune marker expression in high-risk neuroblastoma. Notably, hydra analysis of all small blue round cell tumors revealed similar subtypes, characterized by changes to infiltrating immune and stromal expression signatures. Author summary Pediatric cancers generally have few somatic mutations. To increase the number of actionable treatment leads, precision pediatric oncology initiatives also analyze tumor gene expression patterns. However, currently available approaches for gene expression data analysis in the clinical setting often use arbitrary thresholds for assessing overexpression and assume gene expression is normally distributed. These methods also rely on reference distributions of related cancer types or normal samples for assessing expression distributions. Often adequate normal samples are not available, and comparing matched cancer cohorts without accounting for subtype expression overestimates the uncertainty in the analysis. We developed a computational framework to automatically detect multimodal expression distributions within well-defined disease populations. Our analysis of small blue round cell tumors (including rhabdomyosarcoma, synovial sarcoma, neuroblastoma, Ewing sarcoma and osteosarcoma) discovered a significant number of multimodally expressed genes. Multimodally expressed genes were associated with proliferative signaling, extracellular matrix organization, and immune signaling pathways across cancer types. Expression signatures correlated with differences in patient outcomes for MYCN non-amplified neuroblastoma, osteosarcoma, and synovial sarcoma. The low mutation rate in pediatric cancers has led some to suggest that pediatric cancers are less immunogenic. However, our analysis suggests that immune infiltration can be identified across small blue round cell tumors. Thus, further research into modulating immune cells for patient benefit may be warranted. |
| Databáze: | OpenAIRE |
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