Protein phosphatase 2A (PP2A) critically regulates cell signaling and is a human tumor suppressor. PP2A complexes are modulated by proteins such as cancerous inhibitor of protein phosphatase 2A (CIP2A), protein phosphatase methylesterase 1 (PME-1), and SET nuclear proto-oncogene (SET) that often are deregulated in cancers. However, how they impact cellular phosphorylation and how redundant they are in cellular regulation is poorly understood. Here, we conducted a systematic phosphoproteomics screen for phosphotargets modulated by siRNA-mediated depletion of CIP2A, PME-1, and SET (to reactivate PP2A) or the scaffolding A-subunit of PP2A (PPP2R1A) (to inhibit PP2A) in HeLa cells. We identified PP2A-modulated targets in diverse cellular pathways, including kinase signaling, cytoskeleton, RNA splicing, DNA repair, and nuclear lamina. The results indicate nonredundancy among CIP2A, PME-1, and SET in phosphotarget regulation. Notably, PP2A inhibition or reactivation affected largely distinct phosphopeptides, introducing a concept of nonoverlapping phosphatase inhibition- and activation-responsive sites (PIRS and PARS, respectively). This phenomenon is explained by the PPP2R1A inhibition impacting primarily dephosphorylated threonines, whereas PP2A reactivation results in dephosphorylation of clustered and acidophilic sites. Using comprehensive drug-sensitivity screening in PP2A-modulated cells to evaluate the functional impact of PP2A across diverse cellular pathways targeted by these drugs, we found that consistent with global phosphoproteome effects, PP2A modulations broadly affect responses to more than 200 drugs inhibiting a broad spectrum of cancer-relevant targets. These findings advance our understanding of the phosphoproteins, pharmacological responses, and cellular processes regulated by PP2A modulation and may enable the development of combination therapies.
A comprehensive catalogue of the mutations that drive tumorigenesis and progression is essential to understanding tumor biology and developing therapies. Protein-coding driver mutations have been well-characterized by large exome-sequencing studies, however many tumors have no mutations in protein-coding driver genes. Non-coding mutations are thought to explain many of these cases, however few non-coding drivers besides TERT promoter are known. To fill this gap, we analyzed 150,000 cis-regulatory regions in 1,844 whole cancer genomes from the ICGC-TCGA PCAWG project. Using our new method, ActiveDriverWGS, we found 41 frequently mutated regulatory elements (FMREs) enriched in non-coding SNVs and indels (FDR<0.05) characterized by aging-associated mutation signatures and frequent structural variants. Most FMREs are distal from genes, reported here for the first time and also recovered by additional driver discovery methods. FMREs were enriched in super-enhancers, H3K27ac enhancer marks of primary tumors and long-range chromatin interactions, suggesting that the mutations drive cancer by distally controlling gene expression through threedimensional genome organization. In support of this hypothesis, the chromatin interaction network of FMREs and target genes revealed associations of mutations and differential gene expression of known and novel cancer genes (e.g., CNNB1IP1, RCC1), activation of immune response pathways and altered enhancer marks. Thus distal genomic regions may include additional, infrequently mutated drivers that act on target genes via chromatin loops. Our study is an important step towards finding such regulatory regions and deciphering the somatic mutation landscape of the non-coding genome..
Systemic understanding of protein phosphatase 2A (PP2A)-regulated cellular processes is still at infancy. Here, we present mass-spectrometry analysis of phospho-targets (dephosphorylome) regulated by PP2A modulation. In addition to PP2A-regulated processes and targets, the data reveal important general concepts and rules related to PP2A-mediated phosphoregulation.These include the unidirectionality paradigm of regulation of phosphorylation, and differential spatial distribution of kinase-and phosphatase-dominated phosphotargets. Data also present first systemic analysis of targets of PP2Amodulating oncoproteins, CIP2A, PME-1, and SET; including targets via which PP2A may coordinately regulate activities of cancer drivers and tumor suppressors such as MYC or TP53. To validate functional utility of this dataset, PP2A dephosphorylome activity was correlated with cancer cell responses to over 300 drugs. Notably, we find that cancer therapy responses can be broadly classified based on PP2A dephosphorylome activity, both in quantitative and qualitative manner. In summary, our data characterize rules by which PP2A coordinate cancer cell phosphosignaling and drug responses. The results also may also direct the use of emerging pharmacological approaches for PP2A activity modulation in human diseases.
Supplementary data are available at Bioinformatics online.
Motivation: An important property of a valid method for testing for differential expression is that the false positive rate should at least roughly correspond to the p-value cutoff, so that if 10,000 genes are tested at a p-value cutoff of 10 −4 , and if all the null hypotheses are true, then there should be only about 1 gene declared to be significantly differentially expressed. We tested this by resampling from existing RNA-Seq data sets and also by matched negative binomial simulations.Results: Methods we examined, which rely strongly on a negative binomial model, such as edgeR, DESeq, and DESeq2, show large numbers of false positives in both the resampled real-data case and in the simulated negative binomial case. This also occurs with a negative binomial generalized linear model function in R. Methods that use only the variance function, such as limma-voom, do not show excessive false positives, as is also the case with a variance stabilizing transformation followed by linear model analysis with limma. The excess false positives are likely caused by apparently small biases in estimation of negative binomial dispersion and, perhaps surprisingly, occur mostly when the mean and/or the dispersion is high, rather than for low-count genes. Contact:dmrocke@ucdavis.edu, lruan@ucdavis.edu, yilzhang@ucdavis.edu, gt4636b@gatech.edu, bpdurbin@ucdavis.edu, saviran@ucdavis.edu.Supplementary Information: The computational tools developed for this study are freely available via our website http://dmrocke.ucdavis.edu/software.html. They can be downloaded as R code or run directly through an interactive web-based shiny application to reproduce the analysis presented here per a user's choice of dataset and the methods to be evaluated.
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