Show simple item record

dc.contributor.authorNyamundanda, G
dc.contributor.authorEason, K
dc.contributor.authorGuinney, J
dc.contributor.authorLord, CJ
dc.contributor.authorSadanandam, A
dc.date.accessioned2020-11-04T11:57:13Z
dc.date.issued2020-09-30
dc.identifier.citationCancers, 2020, 12 (10)
dc.identifier.issn2072-6694
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/4214
dc.identifier.eissn2072-6694en_US
dc.identifier.doi10.3390/cancers12102811en_US
dc.description.abstractOne of the major challenges in defining clinically-relevant and less heterogeneous tumor subtypes is assigning biological and/or clinical interpretations to etiological (intrinsic) subtypes. Conventional clustering/subtyping approaches often fail to define such subtypes, as they involve several discrete steps. Here we demonstrate a unique machine-learning method, phenotype mapping (<i>PhenMap</i>), which jointly integrates single omics data with phenotypic information using three published breast cancer datasets (n = 2045). The <i>PhenMap</i> framework uses a modified factor analysis method that is governed by a key assumption that, features from different omics data types are correlated due to specific "hidden/mapping" variables (context-specific mapping variables (CMV)). These variables can be simultaneously modeled with phenotypic data as covariates to yield functional subtypes and their associated features (e.g., genes) and phenotypes. In one example, we demonstrate the identification and validation of six novel "functional" (discrete) subtypes with differential responses to a cyclin-dependent kinase (CDK)4/6 inhibitor and etoposide by jointly integrating transcriptome profiles with four different drug response data from 37 breast cancer cell lines. These robust subtypes are also present in patient breast tumors with different prognosis. In another example, we modeled patient gene expression profiles and clinical covariates together to identify continuous subtypes with clinical/biological implications. Overall, this genome-phenome machine-learning integration tool, <i>PhenMap</i> identifies functional and phenotype-integrated discrete or continuous subtypes with clinical translational potential.
dc.formatElectronic
dc.languageeng
dc.language.isoeng
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleA Machine-Learning Tool Concurrently Models Single Omics and Phenome Data for Functional Subtyping and Personalized Cancer Medicine.
dc.typeJournal Article
dcterms.dateAccepted2020-09-25
rioxxterms.versionofrecord10.3390/cancers12102811
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2020-09-30en_US
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfCancers
pubs.issue10
pubs.notesNot known
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research/Gene Function
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Gene Function
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Systems and Precision Cancer Medicine
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research/Gene Function
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Gene Function
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Systems and Precision Cancer Medicine
pubs.publication-statusPublished
pubs.volume12en_US
pubs.embargo.termsNot known
icr.researchteamGene Functionen_US
icr.researchteamSystems and Precision Cancer Medicineen_US
dc.contributor.icrauthorSadanandam, Angurajen
dc.contributor.icrauthorLord, Christopheren


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

http://creativecommons.org/licenses/by/4.0/
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/