Comprehensive Molecular Characterization of the Hippo Signaling Pathway in Cancer

Abstract

Hippo signaling has been recognized as a key tumor suppressor pathway. Here, we perform a comprehensive molecular characterization of 19 Hippo core genes in 9,125 tumor samples across 33 cancer types using multidimensional "omic" data from The Cancer Genome Atlas. We identify somatic drivers among Hippo genes and the related microRNA (miRNA) regulators, and using functional genomic approaches, we experimentally characterize YAP and TAZ mutation effects and miR-590 and miR-200a regulation for TAZ. Hippo pathway activity is best characterized by a YAP/TAZ transcriptional target signature of 22 genes, which shows robust prognostic power across cancer types. Our elastic-net integrated modeling further reveals cancer-type-specific pathway regulators and associated cancer drivers. Our results highlight the importance of Hippo signaling in squamous cell cancers, characterized by frequent amplification of YAP/TAZ, high expression heterogeneity, and significant prognostic patterns. This study represents a systems-biology approach to characterizing key cancer signaling pathways in the post-genomic era.

Keywords: TAZ; TCGA; YAP; driver mutation; miRNA regulation; pan-cancer analysis; pathway activity; prognostic power; tumor subtype.

Figure 1.. Somatic Alteration Landscape of the Hippo Pathway

(A) Diagram of 19 Hippo pathway core genes. Red depicts oncogene, and blue depicts tumor suppressor. (B) Waterfall plots of gene mutation and copy number alteration of 19 Hippo core genes. Each row represents a gene, and each column represents a sample. Genes are ranked from high to low somatic alteration frequency. Oncogenes are highlighted in red. (C) Significant amplification peaks of oncogenes and deletion peaks of tumor suppressors in each cancer type identified by GISTIC2 (q < 0.25). Dot size shows level of significance; color depicts peak status (red: amplification peak; blue: deletion peak). Oncogenes are highlighted in red. (D) Mutation frequency heatmap of Hippo pathway core genes in each cancer type. Color depicts mutation frequency, with higher mutation frequency in a cancer type indicated by darker color, and significantly mutated genes identified by MutSigCV (q < 0.25) are highlighted in black boxes. (E) Hippo pathway DNA alteration load relative to the genomic background of each cancer type. The bar plots show the top percentile of the observed mutation load in the distribution of 1,000 random sample sets with the same number and same gene length of Hippo pathway genes. See also Figures S1 and S2 and Tables S1 and S2.

Figure 2.. YAP1/TAZ Amplification and Mutation Patterns

(A) The combined amplification frequency of YAP1/TAZ in different cancer types. Squamous cell cancer types are labeled in red. (B) Significant mutually exclusive pattern of YAP1 and TAZ in squamous cell cancer. Each bar represents a gene, and each column is the copy number status of a patient sample. Red depicts high-level amplification, pink depicts low-level amplification, light blue depicts heterozygous deletion, and dark blue depicts homozygous deletion. p values of each pair are shown. (C) Functional annotation of YAP1 somatic mutations based on cell viability assays. Red depicts moderately activating, orange depicts weakly activating, yellow depicts no difference from wild-type, and blue depicts inactivating. The relative mutation positions are shown at the top. (D) Functional annotation of TAZ somatic mutations. (E) Cell images of three functional YAP1 mutants compared with wild-type in 4 (upper row) and 11 days (lower row). (F) Cell images of three functional TAZ mutants compared with wild-type in 4 (upper row) and 11 days (lower row).

Figure 3.. Gene Expression Pattern of Hippo Pathway Genes

(A) Unsupervised consensus clustering of Hippo component gene expression showing five distinct clusters, each marked by a different color in the top row bar. (B) Sample distribution in the five clusters. Each row is a cancer type, and each column represents a cluster. The number in each cell shows the number of samples classified in the corresponding cluster; red intensity shows the percentage of samples falling into one cluster. (C) Comparison of clustering pattern of Hippo pathway with the other nine major biological pathways. y axis shows the fraction of the second largest cluster in a given cancertype. The middle line in the box is the median, and the bottom and top of the box are the first and third quartiles, and the whiskers extend to 1.5× interquartile range of the lower quartile and the upper quartile, respectively. Only the Hippo pathway shows a statistically significant higher ratio for squamous cell-involved cancers than for non-squamous cell cancers, indicating the uniqueness of the observed clustering pattern of Hippo pathway. Wilcoxon rank-sum test p value and q are shown for significant comparisons. See also Figure S3.

Figure 4.. miRNA Regulation of the Hippo Pathway

(A) High-confidence miRNA-Hippo pathway gene interaction network. miRNA-Hippo gene regulation is identified by integrating sequence information and co-expression pattern. Significant pairs recurring in at least three cancer types are shown (q < 10⁻⁵ and Rs < −0.5). (B) TAZ 3′ UTR binding sites with miR-200a-3p and miR-590-3p. Red depicts the binding regions for miR-590-3p; purple depicts the binding regions for miR-200a-3p. Detailed sequences are magnified for each binding motif. (C) Normalized TAZ gene expression level after miR-590-3p transfection in HCT116 and KM12 cell lines (**p < 0.01; ***p < 0.001). (D) Normalized TAZ gene expression level after miR-200a-3p transfection in HCT116 and KM12 cell lines. (C and D) Data are represented as mean ± SD. (E) Relative protein expression level of TAZ upon overexpression of miR-590-3p in HCT116 and KM12 cell lines. Western blot gel (upper panel) and quantification (lower panel) are shown. (F) Relative protein expression level of TAZ upon overexpression of miR-200a-3p in HCT116 and KM12 cell lines. Western blot gel (upper panel) and quantification (lower panel) are shown.

Figure 5.. Prognostic Power of the YAP/TAZ Target Gene Signature

(A) Flow diagram of the curation of the YAP/TAZ target score. Venn diagram of shared genes from published YAP/TAZ chromatin immunoprecipitation (ChIP)-sequencing data (Galli et al., 2015; Stein et al., 2015; Zanconato et al., 2015). Gene list was then filtered for significantly upregulated or downregulated genes (≥2-fold) in YAP/TAZ knockdown or YAP overexpression or activation transcriptomic data (Elbediwy et al., 2016; Galli et al., 2015; Kim et al., 2015; Stein et al., 2015; Zanconato et al., 2015; Zhang et al., 2008), following selection by significant correlations with YAP and TAZ protein expression in 891 cell lines from the Cancer Cell Line Encyclopedia (CCLE; p < 10⁻⁴) and manual curation leading to a 22-gene score. (B) Summary of correlations of (1) mRNA-based 22-gene YAP/TAZ target score; (2) mRNA-based 22-gene YAP/TAZ target score after adjusting for tumor purity; (3) YAP1 mRNA; (4) YAP protein; (5) TAZ mRNA; (6) TAZ protein; (7) protein-based Hippo pathway gene score; and (8) mRNA-based Hippo pathway gene score with patient overall survival in different cancer types. Circle size represents statistical significance; color represents direction. Overall, the YAP/TAZ target score shows the most consistent and extensive significant correlations with patient survival times, thereby representing the best indicator for Hippo pathway activity. (C) Kaplan-Meier survival plots of patients grouped by the YAP/TAZ target score in individual cancer types. Both p values for log rank and Cox tests are shown. See also Figure S4.

Figure 6.. Biological Pathways and Tumor-Infiltrating Immune Cell Abundance Associated with the YAP/TAZ Target Signature

(A) Bar plots showing the most consistent hallmark pathways correlated with the YAP/TAZ target score (q < 0.05). Color indicates the correlation direction. (B) Heatmap showing the correlation of significant tumor-infiltrating immune cell abundance with YAP/TAZ target scores (q < 0.05 and |corr| > 0.3). Red depicts positive correlations, and blue depicts negative correlations. See also Figure S5.

Figure 7.. Integrative Modeling of Cancer-Type-Specific Hippo Regulatory Networks

(A) Workflow of integration modeling based on elastic net regression. For each cancertype, somatic mutation, SCNA, mRNA expression, DNA methylation, and RPPA protein expression data of 19 Hippo core genes, and cancer-specific predicted miRNA regulators were used as features to build an elastic net regression model to predict YAP/TAZ target score. In parallel, the modeling for cancer-type-specific significantly mutated genes and SCNAs were carried out. After 100 rounds of 10-fold cross-validation, the best model yielding the lowest mean squared error was selected, and the top five weighted predictors were used to construct the Hippo regulatory network. (B) Hippo regulatory networks of the nine cancer types with YAP/TAZ target scores that showed significant survival correlations. Each color depicts a molecular data type, and each shape depicts a molecular type. Blue line depicts negative regulations, and red line depicts positive regulations. See also Figures S6 and S7.