Data Driven Modeling of Pseudopalisade Pattern Formation

TL;DR

This paper introduces a data-driven approach to model, analyze, and control pseudopalisade pattern formation in glioblastoma, enabling targeted disruption and synthesis of complex tumor structures through optimal parameter identification.

Contribution

It presents a novel methodology combining optimal control and linear parameter synthesis to understand, generate, and disrupt pseudopalisade patterns in glioblastoma models.

Findings

Optimal model parameters can replicate observed pseudopalisade patterns.

Counteracting strategies can impair pattern formation.

Complex patterns can be synthesized by linear combinations of simple pattern parameters.

Abstract

In this paper we propose a data-driven methodology to gain insight into the formation of different types of pseudopalisade structures. To this end, we start from a state of the art macroscopic model for the dynamics of GBM, that is coupled with the dynamics of extracellular pH, and formulate a terminal value optimal control problem. Thus, given a specific, observed pseudopalisade pattern, we determine the evolution of parameters (bio-mechanisms) that are responsible for its emergence. Random histological images exhibiting pseudopalisade-like structures are chosen to serve as target pattern. Having identified the optimal model parameters that generate the specified target pattern, we then formulate two different types of pattern counteracting ansatzes in order to determine possible ways to impair or obstruct the process of pseudopalisade formation. This provides the basis for designing active or live control of malignant GBM. Furthermore, we also provide a simple, yet insightful, mechanism to synthesize new pseudopalisade patterns by linearly combining the optimal model parameters responsible for generating different known target patterns. This particularly provides a hint that complex pseudopalisade patterns could be synthesized by a linear combination of parameters responsible for generating simple patterns. Going even further, we ask ourselves if complex therapy approaches can be conceived by linearly combining such that are able to reverse or disrupt simple pseudopalisade patterns, which is then positively answered with the help of numerical simulations.