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dc.contributor.authorFailmezger, H
dc.contributor.authorMuralidhar, S
dc.contributor.authorRullan, A
dc.contributor.authorde Andrea, CE
dc.contributor.authorSahai, E
dc.contributor.authorYuan, Y
dc.date.accessioned2020-04-02T14:30:27Z
dc.date.issued2020-03-01
dc.identifier.citationCancer research, 2020, 80 (5), pp. 1199 - 1209
dc.identifier.issn0008-5472
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/3575
dc.identifier.eissn1538-7445
dc.identifier.doi10.1158/0008-5472.can-19-2268
dc.description.abstractDespite the advent of immunotherapy, metastatic melanoma represents an aggressive tumor type with a poor survival outcome. The successful application of immunotherapy requires in-depth understanding of the biological basis and immunosuppressive mechanisms within the tumor microenvironment. In this study, we conducted spatially explicit analyses of the stromal-immune interface across 400 melanoma hematoxylin and eosin (H&E) specimens from The Cancer Genome Atlas. A computational pathology pipeline (CRImage) was used to classify cells in the H&E specimen into stromal, immune, or cancer cells. The estimated proportions of these cell types were validated by independent measures of tumor purity, pathologists' estimate of lymphocyte density, imputed immune cell subtypes, and pathway analyses. Spatial interactions between these cell types were computed using a graph-based algorithm (topological tumor graphs, TTG). This approach identified two stromal features, namely stromal clustering and stromal barrier, which represented the melanoma stromal microenvironment. Tumors with increased stromal clustering and barrier were associated with reduced intratumoral lymphocyte distribution and poor overall survival independent of existing prognostic factors. To explore the genomic basis of these TTG-derived stromal phenotypes, we used a deep learning approach integrating genomic (copy number) and transcriptomic data, thereby inferring a compressed representation of copy number-driven alterations in gene expression. This integrative analysis revealed that tumors with high stromal clustering and barrier had reduced expression of pathways involved in naïve CD4 signaling, MAPK, and PI3K signaling. Taken together, our findings support the immunosuppressive role of stromal cells and T-cell exclusion within the vicinity of melanoma cells. SIGNIFICANCE: Computational histology-based stromal phenotypes within the tumor microenvironment are significantly associated with prognosis and immune exclusion in melanoma.
dc.formatPrint-Electronic
dc.format.extent1199 - 1209
dc.languageeng
dc.language.isoeng
dc.publisherAMER ASSOC CANCER RESEARCH
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectT-Lymphocytes
dc.subjectLymphocytes, Tumor-Infiltrating
dc.subjectStromal Cells
dc.subjectSkin
dc.subjectHumans
dc.subjectMelanoma
dc.subjectSkin Neoplasms
dc.subjectImage Interpretation, Computer-Assisted
dc.subjectBiopsy
dc.subjectPrognosis
dc.subjectCohort Studies
dc.subjectFollow-Up Studies
dc.subjectTumor Escape
dc.subjectGene Expression Regulation, Neoplastic
dc.subjectDrug Resistance, Neoplasm
dc.subjectModels, Biological
dc.subjectAdult
dc.subjectAged
dc.subjectAged, 80 and over
dc.subjectMiddle Aged
dc.subjectDNA Copy Number Variations
dc.subjectKaplan-Meier Estimate
dc.subjectTumor Microenvironment
dc.subjectSpatial Analysis
dc.subjectBiomarkers, Tumor
dc.subjectAntineoplastic Agents, Immunological
dc.subjectDeep Learning
dc.subjectRNA-Seq
dc.titleTopological Tumor Graphs: A Graph-Based Spatial Model to Infer Stromal Recruitment for Immunosuppression in Melanoma Histology.
dc.typeJournal Article
dcterms.dateAccepted2019-12-10
rioxxterms.versionofrecord10.1158/0008-5472.can-19-2268
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2020-03
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfCancer research
pubs.issue5
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/Molecular Pathology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Molecular Pathology/Computational Pathology & Integrated Genomics
pubs.organisational-group/ICR/Students
pubs.organisational-group/ICR/Students/PhD and MPhil
pubs.organisational-group/ICR/Students/PhD and MPhil/17/18 Starting Cohort
pubs.publication-statusPublished
pubs.volume80
pubs.embargo.termsNot known
icr.researchteamComputational Pathology & Integrated Genomics
dc.contributor.icrauthorYuan, Yinyin


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