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dc.contributor.authorMaley, CC
dc.contributor.authorKoelble, K
dc.contributor.authorNatrajan, R
dc.contributor.authorAktipis, A
dc.contributor.authorYuan, Y
dc.date.accessioned2020-07-24T14:59:06Z
dc.date.issued2015-09-22
dc.identifier.citationBreast cancer research : BCR, 2015, 17 (1), pp. 131 - ?
dc.identifier.issn1465-5411
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/3863
dc.identifier.eissn1465-542X
dc.identifier.doi10.1186/s13058-015-0638-4
dc.description.abstractIntroduction Abundance of immune cells has been shown to have prognostic and predictive significance in many tumor types. Beyond abundance, the spatial organization of immune cells in relation to cancer cells may also have significant functional and clinical implications. However there is a lack of systematic methods to quantify spatial associations between immune and cancer cells.Methods We applied ecological measures of species interactions to digital pathology images for investigating the spatial associations of immune and cancer cells in breast cancer. We used the Morisita-Horn similarity index, an ecological measure of community structure and predator-prey interactions, to quantify the extent to which cancer cells and immune cells colocalize in whole-tumor histology sections. We related this index to disease-specific survival of 486 women with breast cancer and validated our findings in a set of 516 patients from different hospitals.Results Colocalization of immune cells with cancer cells was significantly associated with a disease-specific survival benefit for all breast cancers combined. In HER2-positive subtypes, the prognostic value of immune-cancer cell colocalization was highly significant and exceeded those of known clinical variables. Furthermore, colocalization was a significant predictive factor for long-term outcome following chemotherapy and radiotherapy in HER2 and Luminal A subtypes, independent of and stronger than all known clinical variables.Conclusions Our study demonstrates how ecological methods applied to the tumor microenvironment using routine histology can provide reproducible, quantitative biomarkers for identifying high-risk breast cancer patients. We found that the clinical value of immune-cancer interaction patterns is highly subtype-specific but substantial and independent to known clinicopathologic variables that mostly focused on cancer itself. Our approach can be developed into computer-assisted prediction based on histology samples that are already routinely collected.
dc.formatElectronic
dc.format.extent131 - ?
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectLymphocytes, Tumor-Infiltrating
dc.subjectHumans
dc.subjectBreast Neoplasms
dc.subjectReceptor, erbB-2
dc.subjectPrognosis
dc.subjectMultivariate Analysis
dc.subjectProportional Hazards Models
dc.subjectEcosystem
dc.subjectModels, Biological
dc.subjectAdult
dc.subjectAged
dc.subjectAged, 80 and over
dc.subjectMiddle Aged
dc.subjectFemale
dc.subjectYoung Adult
dc.subjectKaplan-Meier Estimate
dc.titleAn ecological measure of immune-cancer colocalization as a prognostic factor for breast cancer.
dc.typeJournal Article
dcterms.dateAccepted2015-09-07
rioxxterms.versionofrecord10.1186/s13058-015-0638-4
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2015-09-22
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfBreast cancer research : BCR
pubs.issue1
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/Functional Genomics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Computational Pathology & Integrated Genomics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Functional Genomics
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/Functional Genomics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Computational Pathology & Integrated Genomics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Functional Genomics
pubs.publication-statusPublished
pubs.volume17
pubs.embargo.termsNot known
icr.researchteamComputational Pathology & Integrated Genomicsen_US
icr.researchteamFunctional Genomicsen_US
dc.contributor.icrauthorYuan, Yinyinen
dc.contributor.icrauthorNatrajan, Rachaelen


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