Cross-Scale Sensitivity Analysis of a Non-Small Cell Lung Cancer Model: Linking Molecular Signaling Properties to Cellular Behavior

TL;DR

This study introduces a cross-scale sensitivity analysis method for a multiscale NSCLC model, linking molecular signaling to cellular behavior, identifying key pathway components affecting tumor expansion, and demonstrating model robustness.

Contribution

It presents a novel cross-scale sensitivity analysis approach that links molecular parameters to cellular tumor behavior in a multiscale cancer model.

Findings

PKC, MEK, and ERK are critical pathway components.

Most parameters showed robustness over large variations.

Sensitive parameters had a pattern of diminishing influence with larger changes.

Abstract

Sensitivity analysis is an effective tool for systematically identifying specific perturbations in parameters that have significant effects on the behavior of a given biosystem, at the scale investigated. In this work, using a two-dimensional, multiscale non-small cell lung cancer (NSCLC) model, we examine the effects of perturbations in system parameters which span both molecular and cellular levels, i.e. across scales of interest. This is achieved by first linking molecular and cellular activities and then assessing the influence of parameters at the molecular level on the tumor's spatio-temporal expansion rate, which serves as the output behavior at the cellular level. Overall, the algorithm operated reliably over relatively large variations of most parameters, hence confirming the robustness of the model. However, three pathway components (proteins PKC, MEK, and ERK) and eleven reaction steps were determined to be of critical importance by employing a sensitivity coefficient as an evaluation index. Each of these sensitive parameters exhibited a similar changing pattern in that a relatively larger increase or decrease in its value resulted in a lesser influence on the system's cellular performance. This study provides a novel cross-scaled approach to analyzing sensitivities of computational model parameters and proposes its application to interdisciplinary biomarker studies.