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dc.contributor.authorSalvucci, M
dc.contributor.authorRahman, A
dc.contributor.authorResler, AJ
dc.contributor.authorUdupi, GM
dc.contributor.authorMcNamara, DA
dc.contributor.authorKay, EW
dc.contributor.authorLaurent-Puig, P
dc.contributor.authorLongley, DB
dc.contributor.authorJohnston, PG
dc.contributor.authorLawler, M
dc.contributor.authorWilson, R
dc.contributor.authorSalto-Tellez, M
dc.contributor.authorVan Schaeybroeck, S
dc.contributor.authorRafferty, M
dc.contributor.authorGallagher, WM
dc.contributor.authorRehm, M
dc.contributor.authorPrehn, JHM
dc.date.accessioned2020-08-28T09:43:59Z
dc.date.issued2019-04
dc.identifier.citationJCO clinical cancer informatics, 2019, 3 pp. 1 - 17
dc.identifier.issn2473-4276
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/4051
dc.identifier.eissn2473-4276
dc.identifier.doi10.1200/cci.18.00056
dc.description.abstractPurpose Dynamic network models predict clinical prognosis and inform therapeutic intervention by elucidating disease-driven aberrations at the systems level. However, the personalization of model predictions requires the profiling of multiple model inputs, which hampers clinical translation.Patients and methods We applied APOPTO-CELL, a prognostic model of apoptosis signaling, to showcase the establishment of computational platforms that require a reduced set of inputs. We designed two distinct and complementary pipelines: a probabilistic approach to exploit a consistent subpanel of inputs across the whole cohort (Ensemble) and a machine learning approach to identify a reduced protein set tailored for individual patients (Tree). Development was performed on a virtual cohort of 3,200,000 patients, with inputs estimated from clinically relevant protein profiles. Validation was carried out in an in-house stage III colorectal cancer cohort, with inputs profiled in surgical resections by reverse phase protein array (n = 120) and/or immunohistochemistry (n = 117).Results Ensemble and Tree reproduced APOPTO-CELL predictions in the virtual patient cohort with 92% and 99% accuracy while decreasing the number of inputs to a consistent subset of three proteins (40% reduction) or a personalized subset of 2.7 proteins on average (46% reduction), respectively. Ensemble and Tree retained prognostic utility in the in-house colorectal cancer cohort. The association between the Ensemble accuracy and prognostic value (Spearman ρ = 0.43; P = .02) provided a rationale to optimize the input composition for specific clinical settings. Comparison between profiling by reverse phase protein array (gold standard) and immunohistochemistry (clinical routine) revealed that the latter is a suitable technology to quantify model inputs.Conclusion This study provides a generalizable framework to optimize the development of network-based prognostic assays and, ultimately, to facilitate their integration in the routine clinical workflow.
dc.formatPrint
dc.format.extent1 - 17
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectHumans
dc.subjectColorectal Neoplasms
dc.subjectNeoplasm Staging
dc.subjectPrognosis
dc.subjectReproducibility of Results
dc.subjectComputational Biology
dc.subjectApoptosis
dc.subjectAlgorithms
dc.subjectDecision Trees
dc.subjectModels, Biological
dc.subjectDecision Support Systems, Clinical
dc.subjectMachine Learning
dc.subjectBiomarkers, Tumor
dc.titleA Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic.
dc.typeJournal Article
rioxxterms.versionofrecord10.1200/cci.18.00056
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2019-04
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfJCO clinical cancer informatics
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/Integrated Pathology
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/Integrated Pathology
pubs.publication-statusPublished
pubs.volume3
pubs.embargo.termsNot known
icr.researchteamIntegrated Pathologyen_US
dc.contributor.icrauthorSalto-Tellez, Manuelen


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