Topological signatures in regulatory network enable phenotypic heterogeneity in small cell lung cancer
- Centre for BioSystems Science and Engineering, Indian Institute of Science, India
- Undergraduate Programme, Indian Institute of Science, India
Figures
Figure 1
Dynamic simulations of SCLC network.
(A) SCLC regulatory network, in which green nodes denote activation and red nodes denote inhibitory interaction. (B) Steady states achieved from asynchronous Boolean update, using Ising model and 220…
Figure 2
Identification of two ‘teams’.
(A) (i) Pearson’s correlation matrix for Boolean simulations of WT SCLC network. Each node represents the correlation coefficient for pairwise correlations, as shown in adjacent colormap. (ii) …
Figure 3 with 2 supplements
Topological features underlying the two ‘teams’ in SCLC network.
(A) (i) (Left) Pearson’s correlation matrix P for RACIPE simulations of WT SCLC network (Pij = Pji). (Right) Influence matrix Inf for WT SCLC network for path length = 10. Heatmap denotes Pij and …
Figure 3—figure supplement 1
Influence matrix based on Boolean simulations for states with various frustration levels Influence matrix in Figure 3A,i is based on simulations from RACIPE.
Influence matrices based on Boolean simulation results were also derived. Instead of taking all steady-state solutions together, we classified them into three categories – states 1–4 in Supplementary…
Figure 3—figure supplement 2
Topological features.
(A) Scatter plot of R1 and R2 values obtained for varying path lengths, when influence matrix values/coefficients (for varying path lengths) for WT SCLC network were regressed against correlation …
Figure 4 with 3 supplements
Classification of SCLC phenotypes based on ASCL1 and NEUROD1.
(A) (i) Summary of classification of SCLC subtypes in the existing literature (adapted from Rudin et al., 2019). (ii) Density-based scatter plot for ASCL1 and NEUROD1 levels, as obtained via RACIPE …
Figure 4—figure supplement 1
Analysis for GSE73160 using ASCL1 and NEUROD1.
(A) (i) Hierarchical clustering of GSE73160 dataset across ASCL1 and NEUROD1. (ii) Scatter plot of normalized gene expression for NEUROD1 vs ASCL1 of GSE73160 dataset, labeled by n = 4 clusters …
Figure 4—figure supplement 2
Gene expression of nodes involved in graph after clustering: Red bars indicate a node belonging to group A, and blue bar indicates belonging to group B.
ASCL1, NEUROD1, YAP1, and POU2F3 have different colors for better comparison (A) (i–iv) Hierarchical clustering of CCLE dataset across ASCL1 and NEUROD1, with individual panels depicting individual …
Figure 4—figure supplement 3
Clustering efficiency of other node pairs.
(A) (i) Pie-chart depicting the frequency of cases for different optimal k-values, as identified by average silhouette score analysis, for all combinations of two nodes taken at a time (33C2). (ii) …
Figure 5 with 1 supplement
Classification of SCLC phenotypes based on ASCL1, NEUROD1, YAP1, and POU2F3.
(A) (i) Hierarchical clustering of CCLE samples using ASCL1, NEUROD1, YAP1, and POU2F3. (ii) Scatter plot of normalized gene expression data for CCLE samples for NEUROD1 and ASCL1. Color coding in …
Figure 5—figure supplement 1
Analysis for GSE73160 using ASCL1, NEUROD1, POU2F3, and YAP1.
(A) (i) Hierarchical clustering of GSE73160 dataset across ASCL1, NEUROD1, YAP1, and NPOU2F3. (ii) Scatter plot of normalized gene expression for NEUROD1 vs ASCL1 of GSE73160 dataset, labeled by n = …
Additional files
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Supplementary file 1
Frequency distributions for SCLC network.
(a) Frequency distribution for asynchronous Boolean update of WT SCLC network using Ising update with 220 and 225 initial conditions over three replicates. (b) Steady-state frequency distribution for top 20 states of binarized RACIPE simulation of the network. (c) Node summary of single-edge perturbation of wild-type SCLC network.
- https://cdn.elifesciences.org/articles/64522/elife-64522-supp1-v2.xlsx
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Supplementary file 2
Cell line classification using ASCL1, NEUROD1, YAP1 and POU2F3.
(a) Cell line classification of CCLE dataset using different cluster values for k-means and hierarchical algorithm over four genes of interest (ASCL1, NEUROD1, YAP1, and POU2F3). Also contains the classification as given by Wooten et al., 2019. (b) Cell line classification of GSE73160 dataset using different cluster values for k-means and hierarchical algorithm over four genes of interest (ASCL1, NEUROD1, YAP1, and POU2F3). Also contains the classification as given by Wooten et al., 2019 (for the cell lines included in both GSE73160 and CCLE).
- https://cdn.elifesciences.org/articles/64522/elife-64522-supp2-v2.xlsx
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Supplementary file 3
Frequency distributions for reduced SCLC network.
(a) Steady-state frequency distribution for asynchronous Boolean update of network for genes corresponding to GROUP A and ELF3. (b) Steady-state frequency distribution for asynchronous Boolean update of network for genes corresponding to GROUP A only. (c) Steady-state frequency distribution for asynchronous Boolean update of network for genes corresponding to GROUP B only. (b) Steady-state frequency distribution for asynchronous Boolean update of network for genes corresponding to GROUP A only. Steady-state frequency distribution for asynchronous Boolean update of network for genes corresponding to GROUP B only. Steady-state frequency distribution for asynchronous Boolean update of network for genes corresponding to GROUP B and ELF3.
- https://cdn.elifesciences.org/articles/64522/elife-64522-supp3-v2.xlsx
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Transparent reporting form
- https://cdn.elifesciences.org/articles/64522/elife-64522-transrepform-v2.docx
