Systems biology informed neural networks (SBINN) predict response and novel combinations for PD-1 checkpoint blockade

Michelle Przedborski,Mohammad Kohandel,Saravanan Thiyagarajan,Aaron Goldman,Munisha Smalley

doi:10.1038/s42003-021-02393-7

Abstract

Anti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressive cancers. However, variability and unpredictability in treatment outcome have been observed, and are thought to be driven by patient-specific biology and interactions of the patient’s immune system with the tumor. Here we develop an integrative systems biology and machine learning approach, built around clinical data, to predict patient response to anti-PD-1 immunotherapy and to improve the response rate. Using this approach, we determine biomarkers of patient response and identify potential mechanisms of drug resistance. We develop systems biology informed neural networks (SBINN) to calculate patient-specific kinetic parameter values and to predict clinical outcome. We show how transfer learning can be leveraged with simulated clinical data to significantly improve the response prediction accuracy of the SBINN. Further, we identify novel drug combinations and optimize the treatment protocol for triple combination therapy consisting of IL-6 inhibition, recombinant IL-12, and anti-PD-1 immunotherapy in order to maximize patient response. We also find unexpected differences in protein expression levels between response phenotypes which complement recent clinical findings. Our approach has the potential to aid in the development of targeted experiments for patient drug screening as well as identify novel therapeutic targets.

Highlights

Anti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressive cancers
Anti-PD-1 immunotherapy is emerging as a targeted treatment strategy that has recently shown promise for several aggressive cancers, including melanoma, non-small-cell lung cancer (NSCLC), bladder, and head and neck cancers
We leverage interdisciplinary science, integrating systems biology and machine learning approaches, to make a number of interesting discoveries that have the potential to substantially improve anti-PD-1 immunotherapy, including: empirical features that lead to response variability, mechanisms of drug resistance, novel drug combinations and optimal treatment protocols, and unexpected differences in protein expression between response phenotypes

Summary

Introduction

Anti-PD-1 immunotherapy has recently shown tremendous success for the treatment of several aggressive cancers. The mathematical model was grounded on results from experiments of PD-1 blockade in a live ex-vivo human system, and the multi-disciplinary approach enabled the analysis and interpretation of the response dynamics of PD-1 blockade This field of study is still missing an integrative computational approach for making discoveries in clinically relevant data sets that could help to identify biomarkers of response and to improve the patient response rate. We leverage interdisciplinary science, integrating systems biology and machine learning approaches, to make a number of interesting discoveries that have the potential to substantially improve anti-PD-1 immunotherapy, including: empirical features that lead to response variability, mechanisms of drug resistance, novel drug combinations and optimal treatment protocols, and unexpected differences in protein expression between response phenotypes. Using systems biology informed neural networks (SBINN), we calculate patient-specific parameter values and predict clinical outcome

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