dc.contributor.author | Salvucci, M | |
dc.contributor.author | Rahman, A | |
dc.contributor.author | Resler, AJ | |
dc.contributor.author | Udupi, GM | |
dc.contributor.author | McNamara, DA | |
dc.contributor.author | Kay, EW | |
dc.contributor.author | Laurent-Puig, P | |
dc.contributor.author | Longley, DB | |
dc.contributor.author | Johnston, PG | |
dc.contributor.author | Lawler, M | |
dc.contributor.author | Wilson, R | |
dc.contributor.author | Salto-Tellez, M | |
dc.contributor.author | Van Schaeybroeck, S | |
dc.contributor.author | Rafferty, M | |
dc.contributor.author | Gallagher, WM | |
dc.contributor.author | Rehm, M | |
dc.contributor.author | Prehn, JHM | |
dc.date.accessioned | 2020-08-28T09:43:59Z | |
dc.date.issued | 2019-04-17 | |
dc.identifier.citation | JCO clinical cancer informatics, 2019, 3 pp. 1 - 17 | |
dc.identifier.issn | 2473-4276 | |
dc.identifier.uri | https://repository.icr.ac.uk/handle/internal/4051 | |
dc.identifier.eissn | 2473-4276 | |
dc.identifier.doi | 10.1200/cci.18.00056 | |
dc.description.abstract | PURPOSE: 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.format | Print | |
dc.format.extent | 1 - 17 | |
dc.language | eng | |
dc.language.iso | eng | |
dc.publisher | AMER SOC CLINICAL ONCOLOGY | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | |
dc.subject | Humans | |
dc.subject | Colorectal Neoplasms | |
dc.subject | Neoplasm Staging | |
dc.subject | Prognosis | |
dc.subject | Reproducibility of Results | |
dc.subject | Computational Biology | |
dc.subject | Apoptosis | |
dc.subject | Algorithms | |
dc.subject | Decision Trees | |
dc.subject | Models, Biological | |
dc.subject | Decision Support Systems, Clinical | |
dc.subject | Machine Learning | |
dc.subject | Biomarkers, Tumor | |
dc.title | A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic. | |
dc.type | Journal Article | |
rioxxterms.versionofrecord | 10.1200/cci.18.00056 | |
rioxxterms.licenseref.uri | https://creativecommons.org/licenses/by/4.0 | |
rioxxterms.licenseref.startdate | 2019-04 | |
rioxxterms.type | Journal Article/Review | |
dc.relation.isPartOf | JCO clinical cancer informatics | |
pubs.notes | Not 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-status | Published | |
pubs.volume | 3 | |
pubs.embargo.terms | Not known | |
icr.researchteam | Integrated Pathology | |
dc.contributor.icrauthor | Salto-Tellez, Manuel | |