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