A machine learning platform to optimize the translation of personalized network models to the clinic

Salvucci, M. et al. (2019) A machine learning platform to optimize the translation of personalized network models to the clinic. JCO Clinical Cancer Informatics(3), pp. 1-17. (doi: 10.1200/CCI.18.00056) (PMID:30995124)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Wilson, Professor Richard
• Authors: Salvucci, M., Rahman, A., Resler, A. J., Udupi, G. M., McNamara, D. A., Kay, E. W., Laurent-Puig, P., Longley, D. B., Johnston, P. G., Lawler, M., Wilson, R., Salto-Tellez, M., Van Schaeybroeck, S., Rafferty, M., Gallagher, W. M., Rehm, M., and Prehn, J. H.M.
• College/School: College of Medical Veterinary and Life Sciences > School of Cancer Sciences
• Journal Name: JCO Clinical Cancer Informatics
• Publisher: American Society of Clinical Oncology
• ISSN: 2473-4276
• ISSN (Online): 2473-4276
• Copyright Holders: Copyright © 2020 American Society of Clinical Oncology
• First Published: First published in JCO Clinical Cancer Informatics 3:1-17
• Publisher Policy: Reproduced under a Creative Commons Licence