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dc.contributor.authorNatrajan, Ren_US
dc.contributor.authorSailem, Hen_US
dc.contributor.authorMardakheh, FKen_US
dc.contributor.authorArias Garcia, Men_US
dc.contributor.authorTape, CJen_US
dc.contributor.authorDowsett, Men_US
dc.contributor.authorBakal, Cen_US
dc.contributor.authorYuan, Yen_US
dc.identifier.citationPLoS medicine, 2016, 13 (2), pp. e1001961 - ?en_US
dc.description.abstract<h4>Background</h4>The intra-tumor diversity of cancer cells is under intense investigation; however, little is known about the heterogeneity of the tumor microenvironment that is key to cancer progression and evolution. We aimed to assess the degree of microenvironmental heterogeneity in breast cancer and correlate this with genomic and clinical parameters.<h4>Methods and findings</h4>We developed a quantitative measure of microenvironmental heterogeneity along three spatial dimensions (3-D) in solid tumors, termed the tumor ecosystem diversity index (EDI), using fully automated histology image analysis coupled with statistical measures commonly used in ecology. This measure was compared with disease-specific survival, key mutations, genome-wide copy number, and expression profiling data in a retrospective study of 510 breast cancer patients as a test set and 516 breast cancer patients as an independent validation set. In high-grade (grade 3) breast cancers, we uncovered a striking link between high microenvironmental heterogeneity measured by EDI and a poor prognosis that cannot be explained by tumor size, genomics, or any other data types. However, this association was not observed in low-grade (grade 1 and 2) breast cancers. The prognostic value of EDI was superior to known prognostic factors and was enhanced with the addition of TP53 mutation status (multivariate analysis test set, p = 9 × 10-4, hazard ratio = 1.47, 95% CI 1.17-1.84; validation set, p = 0.0011, hazard ratio = 1.78, 95% CI 1.26-2.52). Integration with genome-wide profiling data identified losses of specific genes on 4p14 and 5q13 that were enriched in grade 3 tumors with high microenvironmental diversity that also substratified patients into poor prognostic groups. Limitations of this study include the number of cell types included in the model, that EDI has prognostic value only in grade 3 tumors, and that our spatial heterogeneity measure was dependent on spatial scale and tumor size.<h4>Conclusions</h4>To our knowledge, this is the first study to couple unbiased measures of microenvironmental heterogeneity with genomic alterations to predict breast cancer clinical outcome. We propose a clinically relevant role of microenvironmental heterogeneity for advanced breast tumors, and highlight that ecological statistics can be translated into medical advances for identifying a new type of biomarker and, furthermore, for understanding the synergistic interplay of microenvironmental heterogeneity with genomic alterations in cancer cells.en_US
dc.format.extente1001961 - ?en_US
dc.subjectBreast Neoplasmsen_US
dc.subjectDisease Progressionen_US
dc.subjectDNA, Neoplasmen_US
dc.subjectNeoplasm Stagingen_US
dc.subjectRetrospective Studiesen_US
dc.subjectGene Expression Profilingen_US
dc.subjectAged, 80 and overen_US
dc.subjectMiddle Ageden_US
dc.subjectYoung Adulten_US
dc.subjectBiomarkers, Tumoren_US
dc.titleMicroenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology-Genomic Integration Analysis.en_US
dc.typeJournal Article
rioxxterms.typeJournal Article/Reviewen_US
dc.relation.isPartOfPLoS medicineen_US
pubs.notesNot knownen_US
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/Endocrinology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Breast Cancer Research/Functional Genomics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Biology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Cancer Biology/Dynamical Cell Systems
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Closed research teams
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Closed research teams/Oncogene
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/Endocrinology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Endocrinology/Endocrinology (hon.)
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Functional Genomics
pubs.embargo.termsNot knownen_US
icr.researchteamDynamical Cell Systemsen_US
icr.researchteamComputational Pathology & Integrated Genomicsen_US
icr.researchteamFunctional Genomicsen_US
dc.contributor.icrauthorNatrajan, Rachaelen_US
dc.contributor.icrauthorBakal, Christopheren_US
dc.contributor.icrauthorDowsett, Mitchen_US
dc.contributor.icrauthorTape, Christopheren_US
dc.contributor.icrauthorYuan, Yinyinen_US
dc.contributor.icrauthorSailem, Hebaen_US

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