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

doi:10.1200/CCI.18.00056

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

School of Medicine, Dentistry and Biomedical Sciences

Research output: Contribution to journal › Article › peer-review

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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 language	English
Number of pages	17
Journal	JCO Clinical Cancer Informatics
DOIs	https://doi.org/10.1200/CCI.18.00056
Publication status	Published - 17 Apr 2019

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10.1200/CCI.18.00056Licence: CC BY

A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic
© 2019 by American Society of Clinical Oncology. This is an open access article published under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Final published version, 7.14 MBLicence: CC BY

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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., & 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

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title = "A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic",

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.",

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

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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 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

TY - JOUR

T1 - A Machine Learning Platform to Optimize the Translation of Personalized Network Models to the Clinic

AU - Salvucci, Manuela

AU - Rahman, Arman

AU - Resler, Alexa J

AU - Udupi, Girish M

AU - McNamara, Deborah A

AU - Kay, Elaine W

AU - Laurent-Puig, Pierre

AU - Longley, Daniel B

AU - Johnston, Patrick G

AU - Lawler, Mark

AU - Wilson, Richard

AU - Salto-Tellez, Manuel

AU - Van Schaeybroeck, Sandra

AU - Rafferty, Mairin

AU - Gallagher, William M

AU - Rehm, Markus

AU - Prehn, Jochen H M

PY - 2019/4/17

Y1 - 2019/4/17

N2 - 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.

AB - 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.

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DO - 10.1200/CCI.18.00056

M3 - Article

C2 - 30995124

SN - 2473-4276

JO - JCO Clinical Cancer Informatics

JF - JCO Clinical Cancer Informatics

ER -

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

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