Systems-level network modeling of Small Cell Lung Cancer subtypes identifies master regulators and destabilizers
Fig 7
TF network simulations reproduce subtypes as attractors.
A. Regulatory network of differentially expressed TFs from each of the 11 co-expressed gene modules in Fig 2B. Colors indicate which phenotype each TF is upregulated in. Red edges indicate inhibition (on average), and green activation (on average). B. Probabilistic Boolean rule fits for ASCL1. The target gene is a function of all the genes along the binary tree at the top, while expression of the target is shown on the left. Each row represents one cell line, each column represents one possible input state, and the bottom shows the inferred function F for every possible input state. Color ranges from 0 = blue (highly confident the TF is off), to 0.5 = white, to 1 = red (highly confident the TF is on). Rows are organized by subtype (top to bottom: NE, NEv1, NEv2, non-NE). C. Attractors found with asynchronous updates of Boolean network. 10 attractors were found, and each correlates highly with one of the four defined subtypes (represented by stars). Hamming distance between intra-subtype attractors and inter-subtype attractors are shown. The average distance between intra-subtype attractors was around 2.5, while the average distance between subtype attractors was around 16, signifying that the variation between subtypes is much greater that that within a single subtype. Specifics of the probabilistic simulation are described in Results.