Contextual Refinement of Regulatory Targets Reveals Effects on Breast Cancer Prognosis of the Regulome

<div><p>Gene expression regulators, such as transcription factors (TFs) and microRNAs (miRNAs), have varying regulatory targets based on the tissue and physiological state (context) within which they are expressed. While the emergence of regulator-characterizing experiments has inferred the target genes of many regulators across many contexts, methods for transferring regulator target genes across contexts are lacking. Further, regulator target gene lists frequently are not curated or have permissive inclusion criteria, impairing their use. Here, we present a method called iterative Contextual Transcriptional Activity Inference of Regulators (icTAIR) to resolve these issues. icTAIR takes a regulator’s previously-identified target gene list and combines it with gene expression data from a context, quantifying that regulator’s activity for that context. It then calculates the correlation between each listed target gene’s expression and the quantitative score of regulatory activity, removes the uncorrelated genes from the list, and iterates the process until it derives a stable list of refined target genes. To validate and demonstrate icTAIR’s power, we use it to refine the MSigDB c3 database of TF, miRNA and unclassified motif target gene lists for breast cancer. We then use its output for survival analysis with clinicopathological multivariable adjustment in 7 independent breast cancer datasets covering 3,430 patients. We uncover many novel prognostic regulators that were obscured prior to refinement, in particular NFY, and offer a detailed look at the composition and relationships among the breast cancer prognostic regulome. We anticipate icTAIR will be of general use in contextually refining regulator target genes for discoveries across many contexts. The icTAIR algorithm can be downloaded from <a href="https://github.com/icTAIR" target="_blank">https://github.com/icTAIR</a>.</p></div

Network analysis of prognostic regulators.

<p>For all 59 significant prognostic regulatory motifs, each associated icTAIR-refined target gene list was searched for the presence of other regulators and used to construct a relational map (regulator-to-target) of the prognostic MSigDB c3 regulome. (A) Network map of prognostic regulators, all regulators significant, all regulatees (significant and non-significant) allowed. Size of circle indicates relative number of target regulatees. (B) Network map of prognostic regulators, all regulators and regulatees significantly prognostic. Of note, the unknown regulatory motifs are necessarily excluded here. For both maps: blue dots, HR < 1 of associated regulatory motifs; purple dots, HR > 1 of associated regulatory motifs. Each dot is a composite of regulatory motifs for the same regulator. Arrow directionality -> signifies a regulator -> regulatee relationship. Auto-regulatory relationships are indicated with dotted lines.</p

Pan-dataset univariate prognostic regulatory motifs that survive pan-dataset clinicopathological multivariate-adjusted significance testing.

<p>Pan-dataset univariate prognostic regulatory motifs that survive pan-dataset clinicopathological multivariate-adjusted significance testing.</p

Validation of icTAIR refinement using V$ER_Q6_01.

<p>V$ER_Q6_01 iRASs (reflecting ER regulatory activity) were dichotomized around 0 and used to construct Kaplan-Meier survival curves for patient samples stratified by ER staining status (positive versus negative) across all datasets. V$ER_Q6_01 regulatory motif activity confers a favorable prognosis in ER-positive samples across all datasets (A, B, and C, all p-values < 0.05, log-rank test) and has no significant prognostic effect in ER-negative samples (D, E, and F, all p-values > 0.05, log-rank test). Sample numbers for each curve are given in the bottom left and log-rank p-value significance test results in the top right of each panel. Vertical hashes indicate right-censored data points. Survival times are disease-specific for METABRIC, relapse-free for Ur-Rehman, and overall for Vijver, respectively.</p

icTAIR used for MSigDB c3 contextual refinement with its output applied to breast cancer survival analysis.

<p>(A) Venn diagrammatic breakdown of significant regulators across the datasets. All regulators that passed Q-value significance thresholds (1e-06 for METABRIC and 1e-03 for Vijver and Ur-Rehman) have been tabulated, with the numbers indicating the total amount of regulators for each dataset indicated. 200 significant regulators in METABRIC, 234 in Ur-Rehman, and 129 in Vijver were found, with 59 significant across all three datasets. (B) Volcano plot of the regulators’ Cox PH results in METABRIC Dataset. The x-axis indicates the Hazard Ratio (HR) and the y-axis the FDR-corrected degree of significance (Q-value), scaled by a–log10 transformation. Dots that are higher are more statistically significant, while those that are more polarized to the left or right have a larger survival effect size. The horizontal line indicates a Q-value cutoff of 1e-06. Q-values less than 1e-15 have been censored to that value. (C) Pair-wise analysis of survival effect size concordance between datasets. Left: Ur-Rehmann vs. METABRIC; middle: Vijver vs. METABRIC; right: Vijver vs. Ur-Rehman. Each dot represents a HR coordinate (HR in first dataset, HR in second dataset) for each statistically significant regulator shared between the indicated datasets (<i>e</i>.<i>g</i>., the left panel includes 100 (41 + 59) regulators (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005340#pcbi.1005340.g003" target="_blank">Fig 3A</a>)). Perfect directional (> or < 1) concordance of HRs between all datasets is seen. Dot coloring is by type: green, TF; red, miRNA; cyan, unclassified regulator motif.</p

The network topologies of Case 9 after concatenating 1 to 3 adjacent Delaunay triangles.

<p>(A) Concatenation of 1 Delaunay triangle. (B) Concatenation of 2 Delaunay triangles. (C) Concatenation of 3 Delaunay triangles.</p

The ESMT topology for Case 15 by GeoSteiner.

<p>The ESMT topology for Case 15 by GeoSteiner.</p

The relative error percentage: .

<p>The relative error percentage: .</p

SIF result for random network after concatenating 1 and 2 adjacent Delaunay triangles, when <i>N</i> = 9.

<p>(A) Concatenation of 1 Delaunay triangle. (B) Concatenation of 2 Delaunay triangles.</p

MST topology by Matlab for Butterfly network.

<p>MST topology by Matlab for Butterfly network.</p