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JCO Clin Cancer Inform. 2019 Apr;3:1-17. doi: 10.1200/CCI.18.00056.

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

Author information

1
Royal College of Surgeons in Ireland, Dublin, Ireland.
2
OncoMark, Dublin, Ireland.
3
Beaumont Hospital, Dublin, Ireland.
4
Université Paris Descartes, Paris, France.
5
Queen's University Belfast, Belfast, United Kingdom.
6
University of Stuttgart, Stuttgart, Germany.

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.

PMID:
30995124
DOI:
10.1200/CCI.18.00056
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