  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.
