A Quantitative Analysis of Localized Robustness of MYCN in Neuroblastoma

TL;DR

This paper uses network curvature to quantify the robustness of MYCN in neuroblastoma, revealing that MYCN amplification correlates with higher robustness, which may inform indirect targeting strategies for this undruggable gene.

Contribution

It introduces the application of network curvature from network science to assess the biological robustness of MYCN in neuroblastoma, providing new insights beyond traditional differential expression analysis.

Findings

Amplified MYCN shows higher network curvature in Stage IV neuroblastoma.

Network curvature captures robustness not evident through differential expression.

Results suggest potential indirect targeting strategies for MYCN.

Abstract

The amplification of the gene MYCN (V-myc myelocytomatosis viral-valeted oncogene, neuroblastoma derived) has been a well-documented indicator for poor prognosis in neuroblastoma, a childhood cancer. Unfortunately, there has been limited success in understanding MYCN functionality in the landscape of neuroblastoma and more importantly, given that MYCN has been deemed undruggable, the need to potentially illuminate key opportunities that indirectly target MYCN is of great interest. To this end, this work employs an emerging quantitative technique from network science, namely network curvature, to quantify the biological robustness of MYCN and its surrounding neighborhood. In particular, when amplified in Stage IV cancer, MYCN exhibits higher curvature (more robust) than those samples with under expressed MYCN levels. When examining the surrounding neighborhood, the above argument still holds for network curvature, but is lost when only analyzing differential expression - a common technique amongst oncologists and computational/molecular biologists. This finding points to the problem (and possible solution) of drug targeting in the context of complexity and indirect cell signaling affects that have often been obfuscated through traditional techniques.