Researchers from the Kulesa Lab at the Stowers Institute for Medical Research set out to construct a logic-based model incorporating information about developmental signaling pathways implicated in Neuroblastoma. The study was conducted in collaboration with the researchers at the University of Michigan and Oxford University. The findings of the study were published in journal Biophysical Chemistry.
Neuroblastoma is a rare, particularly deadly childhood cancer of the sympathetic nervous system. This is because it is difficult to detect and generally develops much before one could begin with the treatment. Embryonic neural crest cells that fail to properly migrate or differentiate usually cause the development of the disease. However, the particular details about how exactly these cells go off track are unclear yet.
The scientists have been motivated by a desire to better understand the molecular circuitry underlying Neuroblastoma. Moreover, the limitations of current methods for predicting disease progression have caused the researchers to carry out this advanced research.
The current methods used to predict the disease are based on gene expression information from human patient samples. However, they do not provide much insight into the interaction of molecules participating in the disease progression. The research team sought to test whether their model could predict disease outcomes more effectively or not.
What did the model comprise of?
The scientists used a 6-gene input logic model and simulated a molecular network of developmental genes. Moreover, they down streamed signals predicting a favorable or unfavorable disease outcome. It was based on the outcome of four cell states related to tumor development which included,
- Cell differentiation
The six genes of the model included three receptor tyrosine kinases (RTKs) and their three ligands. The RTKs were involved in the development of the sympathetic nervous system and implicated in Neuroblastoma. The respective RTKs includes were trkA, trkB, and ALK along with their three ligands.
How did the research team evaluate the model?
The researchers of the study used an aggressively growing human neuroblastoma cell line for the six input genes. Using that information to predict cell states and disease outcome, the researchers checked the predictive value of their model. The next step was to determine the relevance of the model to the human disease population.
The model was tested against a gene expression database of human neuroblastoma patients with known outcomes. The model predicted 91% accurate outcomes in children less than 2 years old. The accuracy of gene lists, the current approach for predicting outcomes, ranges from 75% to 80%.
Importance of the study
The findings of the study highlighted the predictive strength of a logic-based model based on developmental genes. Furthermore, the study offers a better comprehension of the molecular network interactions occurring in neuroblastoma. The study is built upon research published by the Kulesa Lab in Nature Communications. It suggested the role of trkB and BDNF signaling in sympathetic neuron development.
This advanced logic-based model allows a better understanding of the mechanics of the molecular network of receptor tyrosine kinase signaling. Plus it explains the interplay between these genes and signals, leading to a favorable or unfavorable outcome in neuroblastoma patients. According to the research experts, the model could quickly simulate the over-expression or underexpression of a particular gene within a day. Thus, we can start to make predictions about how these genes play together.