A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic.
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Embargo End Date
ICR Authors
Authors
Salvucci, M
Rahman, A
Resler, AJ
Udupi, GM
McNamara, DA
Kay, EW
Laurent-Puig, P
Longley, DB
Johnston, PG
Lawler, M
Wilson, R
Salto-Tellez, M
Van Schaeybroeck, S
Rafferty, M
Gallagher, WM
Rehm, M
Prehn, JHM
Rahman, A
Resler, AJ
Udupi, GM
McNamara, DA
Kay, EW
Laurent-Puig, P
Longley, DB
Johnston, PG
Lawler, M
Wilson, R
Salto-Tellez, M
Van Schaeybroeck, S
Rafferty, M
Gallagher, WM
Rehm, M
Prehn, JHM
Document Type
Journal Article
Date
2019-04-17
Date Accepted
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.
Citation
JCO clinical cancer informatics, 2019, 3 pp. 1 - 17
Source Title
Publisher
LIPPINCOTT WILLIAMS & WILKINS
ISSN
2473-4276
eISSN
2473-4276
Collections
Research Team
Integrated Pathology