A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic

Manuela Salvucci, Arman Rahman, Alexa J Resler, Girish M Udupi, Deborah A McNamara, Elaine W Kay, Pierre Laurent-Puig, Daniel B Longley, Patrick G Johnston, Mark Lawler, Richard Wilson, Manuel Salto-Tellez, Sandra Van Schaeybroeck, Mairin Rafferty, William M Gallagher, Markus Rehm, Jochen H M Prehn

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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.

Original languageEnglish
Number of pages17
JournalJCO Clinical Cancer Informatics
DOIs
Publication statusPublished - 17 Apr 2019

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Salvucci, M., Rahman, A., Resler, A. J., Udupi, G. M., McNamara, D. A., Kay, E. W., ... Prehn, J. H. M. (2019). A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic. JCO Clinical Cancer Informatics. https://doi.org/10.1200/CCI.18.00056