Pattern Detection on Glioblastoma's Waddington landscape via Generative Adversarial Networks

TL;DR

This paper introduces a novel approach using generative adversarial networks and complex systems analysis to identify attractor dynamics in glioblastoma, revealing a Rossler-like strange attractor in patient data.

Contribution

It presents a new methodology combining GANs and fractal analysis to uncover developmental trajectories and attractor patterns in glioblastoma gene expression data.

Findings

Identification of a Rossler-like strange attractor with fractal dimension ~1.7

Reconstruction of Waddington landscape for GBM

Evidence of complex attractor dynamics in tumor progression

Abstract

Glioblastoma (GBM) is a highly morbid and lethal disease with poor prognosis. Their emergent properties such as cellular heterogeneity, therapy resistance, and self-renewal are largely attributed to the interactions between a subset of their population known as glioblastoma-derived stem cells (GSCs) and their microenvironment. Identifying causal patterns in the developmental trajectories between GSCs and the mature, well-differentiated GBM phenotypes remains a challenging problem in oncology. The paper presents a blueprint of complex systems approaches to infer attractor dynamics from the single-cell gene expression datasets of pediatric GBM and adult GSCs. These algorithms include Waddington landscape reconstruction, Generative Adversarial Networks, and fractal dimension analysis. Here I show, a Rossler-like strange attractor with a fractal dimension of roughly 1.7 emerged in the GAN-reconstructed patterns of all twelve patients. The findings suggest a strange attractor may be driving the complex dynamics and adaptive behaviors of GBM in signaling state-space.