Pharmacodynamic model of PARP1 inhibition and global sensitivity analyses can lead to cancer biomarker discovery

Abstract

AbstractPharmacodynamic models provide inroads to understanding key mechanisms of action and may significantly improve patient outcomes in cancer with improved ability to determine therapeutic benefit. Additionally, these models may also lead to insights into potential biomarkers that can be utilized for prediction in prognosis and therapeutic decisions. As an example of this potential, here we present an advanced computational Ordinary Differential Equation (ODE) model of PARP1 signalling and downstream effects due to its inhibition. The model has been validated experimentally and further evaluated through a global sensitivity analysis. The sensitivity analysis uncovered two model parameters related to protein synthesis and degradation rates that were also found to contribute the most variability to the therapeutic prediction. Because this variability may define cancer patient subpopulations, we interrogated genomic, transcriptomic, and clinical databases, to uncover a biomarker that may correspond to patient outcomes in the model. In particular, GSPT2, a GTPase with translation function, was discovered and if mutations serve to alter catalytic activity, its presence may explain the variability in the model’s parameters. This work offers an analysis of ODE models, inclusive of model development, sensitivity analysis, and ensuing experimental data analysis, and demonstrates the utility of this methodology in uncovering biomarkers in cancer.Author summaryBecause biochemical reaction networks are complex, dynamic, and typically provide output that results from non-linear interactions, mathematical models of such offer insight into cell function. In the clinic, models including drug action further their usefulness in that they may predict therapeutic outcome and other useful markers such as those for prognosis. In this study, we report a model of drug action that targets a critical protein, that when inhibited, promotes tumor cell death and documented remissions. Because all patients do not respond to the described treatment, a means to find cancer patient subpopulations that might benefit continues to be a challenge. Therefore, we analyzed the pharmacodynamic model by defining the parameters of the greatest variability and interrogated genomic, transcriptomic, and clinical cohort databases with this information and discovered a novel biomarker associated with prognosis in some ovarian and uterine cancer patients and separately, associated with the potential to respond to treatment.