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dc.contributor.authorPfeil, Jacob
dc.contributor.authorSanders, Lauren M
dc.contributor.authorAnastopoulos, Ioannis
dc.contributor.authorLyle, A Geoffrey
dc.contributor.authorWeinstein, Alana S
dc.contributor.authorXue, Yuanqing
dc.contributor.authorBlair, Andrew
dc.contributor.authorBeale, Holly C
dc.contributor.authorLee, Alex
dc.contributor.authorLeung, Stanley G
dc.contributor.authorDinh, Phuong T
dc.contributor.authorShah, Avanthi Tayi
dc.contributor.authorBreese, Marcus R
dc.contributor.authorDevine, W Patrick
dc.contributor.authorBjork, Isabel
dc.contributor.authorSalama, Sofie R
dc.contributor.authorSweet-Cordero, E Alejandro
dc.contributor.authorHaussler, David
dc.contributor.authorVaske, Olena Morozova
dc.date.accessioned2020-04-23T03:06:44Z
dc.date.available2020-04-23T03:06:44Z
dc.date.issued2020-04
dc.date.submitted2019-11-02
dc.identifier.issn1553-734X
dc.identifier.otherpcompbiol-d-19-01924
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/304631
dc.description.abstractPrecision 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.
dc.languageen
dc.publisherPublic Library of Science (PLoS)
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectResearch Article
dc.subjectBiology and life sciences
dc.subjectResearch and analysis methods
dc.subjectMedicine and health sciences
dc.titleHydra: A mixture modeling framework for subtyping pediatric cancer cohorts using multimodal gene expression signatures.
dc.typeArticle
dc.date.updated2020-04-23T03:06:32Z
prism.issueIdentifier4
prism.publicationNamePLoS Comput Biol
prism.volume16
dc.identifier.doi10.17863/CAM.51712
dcterms.dateAccepted2020-02-28
rioxxterms.versionofrecord10.1371/journal.pcbi.1007753
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
datacite.contributor.supervisoreditor: Markowetz, Florian
dc.contributor.orcidPfeil, Jacob [0000-0002-8773-8520]
dc.contributor.orcidAnastopoulos, Ioannis [0000-0002-6279-0648]
dc.contributor.orcidLyle, A Geoffrey [0000-0002-3435-526X]
dc.contributor.orcidWeinstein, Alana S [0000-0002-1563-9072]
dc.contributor.orcidXue, Yuanqing [0000-0003-1892-6787]
dc.contributor.orcidBeale, Holly C [0000-0003-4091-538X]
dc.contributor.orcidDinh, Phuong T [0000-0002-0273-1603]
dc.contributor.orcidDevine, W Patrick [0000-0003-4634-8830]
dc.contributor.orcidSalama, Sofie R [0000-0001-6999-7193]
dc.contributor.orcidSweet-Cordero, E Alejandro [0000-0002-9787-9351]
dc.contributor.orcidVaske, Olena Morozova [0000-0002-1677-417X]
dc.identifier.eissn1553-7358
cam.issuedOnline2020-04-10


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)