Abstract
The disruption of the Hippo pathway occurs in many cancer types and is associated with cancer progression. Herein, we investigated the impact of 32 Hippo genes on overall survival (OS) of cancer patients, by both analysing data from The Cancer Genome Atlas (TCGA) and reviewing the related literature. mRNA and protein expression data of all solid tumors except pure sarcomas were downloaded from TCGA database. Thirty-two Hippo genes were considered; for each gene, patients were dichotomized based on median expression value. Survival analyses were performed to identify independent predictors, taking into account the main clinical-pathological features affecting OS. Finally, independent predictors were correlated with YAP1 oncoprotein expression. At least one of the Hippo genes is an independent prognostic factor in 12 out of 13 considered tumor datasets. mRNA levels of the independent predictors coherently correlate with YAP1 in glioma, kidney renal clear cell, head and neck, and bladder cancer. Moreover, literature data revealed the association between YAP1 levels and OS in gastric, colorectal, hepatocellular, pancreatic, and lung cancer. Herein, we identified cancers in which Hippo pathway affects OS; these cancers should be candidates for YAP1 inhibitors development and testing.
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Introduction
Since its discovery in Drosophila Melanogaster1, Hippo pathway has gained ever-increasing attention. Nowadays, the involvement of Hippo pathway in cancer development and progression is well recognised. However, the different and sometimes controversial roles that it may play rise the scientific interest about this pathway. The main example is the enhanced immune response against the tumor after depletion of the LATS1-2 oncosuppressors observed in immune-competent mice2. Nevertheless, the canonical oncosuppressor role is the widely accepted one3,4. In this view, the kinases axis, represented by STK3-4/LATS1-2, works as a brake, controlling cell cycle, apoptosis and cell patterning, thus avoiding uncontrolled proliferation and loss of epithelial-like features. LATS kinases can be activated by a great variety of stimuli through different groups of kinases, such as MAP4Ks and TAOKs3. The activity of these kinases depends on the presence of co-activators, among which SAV1, NF2 and FRMD6 represents the first to be discovered1,5.
The final outcome of Hippo pathway is the LATS-mediated phosphorylation of YAP1, mainly at the residue S127, leading to its cytoplasmic retention and eventually degradation6. Unphosphorylated YAP1, together with WWTR1, activates the TEAD1-4-mediated transcription in the nucleus, representing the cancer progression accelerator. Finally, VGLL4 is a peptide acting as an oncosuppressor by competing with YAP1-WWTR1 complex to TEADs binding3 (Fig. 1). The presence of natural YAP1 competitor uncovered a new scenario to counterbalance the insufficient Hippo pathway oncosuppressor activity. Several molecules are capable to interfere with YAP1 activity by both mimicking VGLL4 function and preventing YAP1-WWTR1 interaction7. Among YAP1 inhibitors, the photosensitizer verteporfin, already approved by the Food and Drug Administration for the macular degeneration treatment, showed excellent results both in vitro and in mice, with no or limited side effects8,9. Verteporfin is then one of the main candidate to move a step forward as a therapeutic agent for YAP1 inhibition. In the present study, we conducted a data analysis of all solid tumor datasets of The Cancer Genome Atlas (TCGA) except pure sarcomas, and a review of literature to investigate the impact of the Hippo pathway dysregulation on survival of cancer patients, providing food for thought and data-driven proposals for approaching future Hippo-directed therapies.
Results
Power analysis and definitive datasets
Thirteen of the twenty-nine downloaded TCGA datasets had β above 0.8 with the set parameters and were selected for further analyses. Details and covariates for each dataset were reported in Table 1.
Survival analyses
Univariate and multivariate results were summarized in Table 2, p values of univariate and multivariate analyses were reported in Supplementary Tables S1 and S2 respectively. Briefly, univariate analyses showed that 12 out of 13 cancer models had at least one Hippo gene associated with patients prognosis and ten datasets had 3 or more significant genes. Brain lower grade glioma and kidney renal clear cell carcinoma had the higher number of Hippo genes associated with patients’ survival, 16 and 15 respectively, whereas liver hepatocellular carcinoma was the only dataset with no significant genes. With regard to genes, TEAD4 and LATS2 were the most frequently associated with patients’ survival, in 6 and 5 out of 13 datasets respectively. Genes and clinical-pathological parameters resulting associated with prognosis after univariate analyses were then used in the multivariate cox regression. Again, 12 out of 13 datasets had at least one Hippo gene as independent survival predictor, and TEAD4 resulted an independent prognostic factor in 3 different datasets. Survival curves of the independent predictors are reported in Fig. 2 and in Supplementary Figure S1.
mRNA-protein correlation
Genes resulted as independent predictors were correlated with the expression of YAP1 and YAP1pS127 proteins. YAP1 and YAP1pS127 expression levels were always highly correlated, whereas a significant correlation between mRNA levels of Hippo genes and at least one of YAP1 or YAP1pS127 proteins was found in 7 datasets. Further details were reported in Table 3 and Supplementary Figure S2.
Review of literature
Seventy-two original articles associated 17 of the 32 Hippo genes with patients’ survival in more than 20 human cancers. Gastric and colorectal cancers were the most frequently tumors reporting association of Hippo genes with patients’ prognosis; whereas the most represented gene was YAP1, reported as prognostic factor in 29 different studies in 14 cancer models. The majority of these 29 studies were conducted on a protein level and, in all but 2, patients with a high expression level of YAP1 had a lower survival rate. In addition, more than 10 studies associated only nuclear and not cytoplasm staining with patients’ prognosis. Table 4 summarizes the review of literature, and Fig. 3 sums up the overall results.
Discussion
Genetic alterations affecting the Hippo pathway components are generally rare events in the cancer biology landscape, except for malignant pleural mesothelioma and some tumors of the nervous system, such as neurofibromas, meningiomas and shwannomas4,10,11. However, the disruption of this pathway was reported in several human cancers. Epigenetic events, post-transcriptional and post-translational modifications can all play a crucial effect on this pathway12, and simultaneously monitoring all these alterations is impracticable. If a positive aspect can exist in this scenario, it is the converging effect of a great variety of dysregulation on a single protein expression and/or phosphorylation, YAP1. Herein, we investigated the effect of mRNA and protein levels of the Hippo pathway components on survival of cancer patients by both analysing TCGA data and reviewing the literature.
In the large majority of analysed datasets, the mRNA levels of the Hippo pathway components were associated with patients’ survival, and most importantly, in almost all cancer models taken into account at least one of the considered genes was an independent predictor (Table 2). We then decided to move another step forward, on a protein level, to understand if the predictors correlated with the effector, YAP1 protein and its phosphorylation status.
The protein levels from TCGA were obtained by standard reverse phase protein lysate microarray, a technique that allows to reliably estimate protein levels and post-translational modifications, without considering the initial compartmentalization13. As a consequence, we always found a very high direct correlation between YAP1 and YAP1pS127 that theoretically should determine a very different output: TEAD-mediated transcription and YAP1 inactivation respectively. Considering that this incongruence should be overcome by other techniques such as immunohistochemistry (IHC), we found that 7 of the 19 predictors were correlated with high levels of YAP1 protein (Table 3). Interestingly, MAP4Ks never correlated with YAP1 protein, and, when they were independent predictors, very often the expression levels associated with a worse prognosis were not justified by their theoretical role within Hippo pathway. Nevertheless, this is in agreement with other well-known functions of MAP4Ks14 and with 8 out of 9 previous studies that associated high MAP4Ks levels with a worse prognosis (Table 4). Assuming that MAP4Ks should not play a pivotal role in the regulation of Hippo pathway, more than half (7 out of 12) of the other independent predictors were correlated with YAP1. In addition, because of mRNA levels were compared with survival of patients, some incongruence should be accounted for feedback mechanisms such as in the case of LATS2. In fact, LATS2 is a direct transcriptional target of activated YAP1-WWTR1-TEADs15, thus explaining high LATS2 mRNA levels associated with poor prognosis.
Yet, more than half of Hippo genes were already associated with patients’ prognosis in different independent studies in several human cancers (Table 4). High expression levels of YAP1 were repeatedly reported as a poor prognostic factor, especially in gastric, colorectal, hepatocellular, pancreatic and lung cancer. These cancer types should then really benefit from treatment with YAP1 inhibitors, as well as kidney renal clear cell carcinoma, head and neck carcinoma, bladder cancer and lower grade glioma, in which we found not only at least one Hippo gene as an independent prognostic factor, but also a correlation between the predictors and YAP1 protein levels, coherently with their role within Hippo pathway.
In conclusion, the independent impact of YAP1 activation on patients’ survival was repeatedly proven by several independent studies and in a large variety of human cancers. Several molecules can disrupt YAP1 activation, and showed very promising results both in vitro and in mice. Some of these molecules directly bind to YAP1 thus allowing to use its expression levels as a potential predictive biomarker. Moreover, YAP1 evaluation by IHC would provide not only the direct quantification of the protein levels, but also the visualization of its compartmentalization: this is a relevant point because nuclear YAP1 is the real biological effector and strongly correlated with patients prognosis. Indeed, YAP1 quantification by IHC needs to be uniformly assessed because of the wide interpretation criteria that still exist.
Finally, Kary Mullis truly said that the majority of the scientific studies are correlation and not cause-effect, but when a great number of independent studies point in the same direction, maybe the time is ripe to move a step forward.
Methods
Selection of genes and datasets
Thirty-two genes belonging to the core Hippo pathway were considered in the present study (Table 5). Level 3 RNA Seq, level 3 reverse phase protein lysate microarray and clinical data of all solid tumor datasets of TCGA except pure sarcomas were downloaded from cBioPortal (www.cbioportal.org). In order to select datasets for further investigation, power analysis for survival data was performed with the powerSurvEpi R package version 0.0.9. In detail, two hypothetical groups with the same number of patients and the same probability of death were considered. Moreover, postulated risk ratio of 2.3 and alpha of 0.05 were set to assess the statistical power of each dataset. Datasets with β above 0.8 were selected for further analyses.
Survival and correlation analyses
For each dataset, clinical-pathological features mainly affecting patients’ survival according to the eighth edition of the American Joint Committee on Cancer16 were taken into account as covariates. In order to directly compare the effect of genes and covariates, patients with missing values for any of the selected clinical-pathological parameters were removed from the analyses. For each gene, patients were divided into two groups, high and low expression levels, based on the median value. Also for age, the median was used to dichotomize patients. Survival curves were estimated with the Kaplan-Meier method and compared using the log-rank test. Multivariate Cox proportional hazard modelling of genes and covariates identified as potential prognostic factors in the univariate analyses was then used to determine their independent impact on patients’ survival, and to estimate the corresponding hazard ratio, setting high expression as reference group. All survival analyses were performed with the survival R package version 2.41-3. All p values below 0.05 were considered to be statistically significant.
All genes identified as independent prognostic factors were correlated with YAP1 and YAP1pS127 protein expression levels using Pearson’s correlation, following the procedures of Hmisc R package version 4.1-1. The flow chart of data analyses is reported in Fig. 4.
Review of literature
PubMed database (www.ncbi.nlm.nih.gov/pubmed) was used to search papers investigating Hippo genes and survival of cancer patients. All aliases provided by HUGO nomenclature (www.genenames.org) were used. Only English-written original articles were selected, and only papers containing original data and concerning protein or mRNA levels were considered.
Data availability
The datasets analysed during the current study are available at www.cbioportal.org.
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Acknowledgements
This work was supported by Associazione Italiana per la Ricerca sul Cancro (AIRC, grant number IG_10316_2010); and Progetti di Rilevante Interesse Nazionale (PRIN, grant number 2015HPMLFY).
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A.M.P., L.T. and F.B. designed the study, A.M.P. performed statistical analyses, F.B. and G.F. supervised the study, A.M.P. and R.B. write the manuscript. All authors reviewed the manuscript.
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Poma, A.M., Torregrossa, L., Bruno, R. et al. Hippo pathway affects survival of cancer patients: extensive analysis of TCGA data and review of literature. Sci Rep 8, 10623 (2018). https://doi.org/10.1038/s41598-018-28928-3
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DOI: https://doi.org/10.1038/s41598-018-28928-3
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