GSE115166
Predicting Neuroblastoma Using Developmental Signals and a Logic-Based Model
Genomic information from human patient samples of pediatric neuroblastoma cancers and known outcomes have led to specific gene lists put forward as high risk for disease progression. However, the reliance on gene expression correlations rather than mechanistic insight has shown limited potential and suggests a critical need for molecular network models that better predict neuroblastoma progression. In this study, we construct and simulate a molecular network of developmental genes and downstream signals in a 6-gene input logic model that predicts a favorable/unfavorable outcome based on the outcome of the four cell states including cell differentiation, proliferation, apoptosis, and angiogenesis. We simulate the mis-expression of the tyrosine receptor kinases, trkA and trkB, two prognostic indicators of neuroblastoma, and find differences in the number and probability distribution of steady state outcomes. We validate the mechanistic model assumptions using RNAseq of the SHSY5Y human neuroblastoma cell line to define the input states and confirm the predicted outcome with antibody staining. Lastly, we apply input gene signatures from 77 published human patient samples and show that our model makes more accurate disease outcome predictions for early stage disease than any current neuroblastoma gene list. These findings highlight the predictive strength of a logic-based model based on developmental genes and offer a better understanding of the molecular network interactions during neuroblastoma disease progression. Overall design: The human SHSY5Y neuroblastoma cell line was obtained from ATCC (CRL-2266) and maintained in 1:1 mixture of Eagles's Minimum Essential Medium (ATCC, 30-2003) supplemented with 10% FBS (VWR, 97068-085). Cells were plated in 8-well chamber slides (ThermoFisher, 177402) for Immunohistochemistry. All cultures were determined to be free of mycoplasma contamination using a polymerase chain reaction-based detection system (Roche, 0518424001). Total RNA was isolated from three, separate T25 flasks of adherent SHSY5Y cells at approximately 60% confluency using Sigma's GenElute mammalian Total RNA miniprep kit (Millipore Sigma, RTN70) per the manufacturer's recommendations. Total RNA was quantified on an Agilent Bioanalyzer 2100 using a Eukaryote Total RNA Nano chip (Agilent, 5067-1511) as well as a Nanodrop spectrophotometer (ThermoFisher Scientific, ND-1000). Libraries were made from 500ng of total RNA according to the manufacturer's directions for the TruSeq Stranded mRNA LT– set A and B (Illumina,  Cat. No. RS-122-2101 and RS-122-2102) kit. Resulting short fragment libraries were checked for quality and quantity using the Bioanalyzer High Sensitivity DNA assay (Agilent; 5067-4626) and Qubit Fluorometer (Life Technologies). Libraries were pooled, re-quantified and sequenced as 50 base pair, single reads on the Illumina HiSeq 2500 instrument to a depth of at least 20 million reads per sample using HiSeq Control Software 2.2.58.