Sara Green (University of Copenhagen)
This paper explores whether mechanistic research can improve predictions based on correlation-based cancer biomarkers. I examine a case where a model based on the best-characterized genetic marker for neuroblastoma was improved through connections to a mechanistic model of a cell death-promoting signaling pathway. Inclusion of mechanistic information on an experimentally resolved feedback loop was shown to improve the ability to stratify patients according to treatment outcomes. The case illustrates how a dynamic approach to biomarkers can be facilitated through research on signaling pathways and network dynamics. Moreover, it illustrates important complementary benefits and limitations of mechanistic heuristics and machine-learning strategies.
