Distinct resistance mechanisms arise to allosteric vs. ATP-competitive AKT inhibitors.

The AKT kinases have emerged as promising therapeutic targets in oncology and both allosteric and ATP-competitive AKT inhibitors have entered clinical investigation. However, long-term efficacy of such inhibitors will likely be challenged by the development of resistance. We have established prostate cancer models of acquired resistance to the allosteric inhibitor MK-2206 or the ATP-competitive inhibitor ipatasertib following prolonged exposure. While alterations in AKT are associated with acquired resistance to MK-2206, ipatasertib resistance is driven by rewired compensatory activity of parallel signaling pathways. Importantly, MK-2206 resistance can be overcome by treatment with ipatasertib, while ipatasertib resistance can be reversed by co-treatment with inhibitors of pathways including PIM signaling. These findings demonstrate that distinct resistance mechanisms arise to the two classes of AKT inhibitors and that combination approaches may reverse resistance to ATP-competitive inhibition.

NRF2 Activation Promotes Aggressive Lung Cancer and Associates with Poor Clinical Outcomes.

PURPOSE: Stabilization of the transcription factor NRF2 through genomic alterations in KEAP1 and NFE2L2 occurs in a quarter of patients with lung adenocarcinoma and a third of patients with lung squamous cell carcinoma. In lung adenocarcinoma, KEAP1 loss often co-occurs with STK11 loss and KRAS-activating alterations. Despite its prevalence, the impact of NRF2 activation on tumor progression and patient outcomes is not fully defined. EXPERIMENTAL DESIGN: We model NRF2 activation, STK11 loss, and KRAS activation in vivo using novel genetically engineered mouse models. Furthermore, we derive a NRF2 activation signature from human non-small cell lung tumors that we use to dissect how these genomic events impact outcomes and immune contexture of participants in the OAK and IMpower131 immunotherapy trials. RESULTS: Our in vivo data reveal roles for NRF2 activation in (i) promoting rapid-onset, multifocal intrabronchiolar carcinomas, leading to lethal pulmonary dysfunction, and (ii) decreasing elevated redox stress in KRAS-mutant, STK11-null tumors. In patients with nonsquamous tumors, the NRF2 signature is negatively prognostic independently of STK11 loss. Patients with lung squamous cell carcinoma with low NRF2 signature survive longer when receiving anti-PD-L1 treatment. CONCLUSIONS: Our in vivo modeling establishes NRF2 activation as a critical oncogenic driver, cooperating with STK11 loss and KRAS activation to promote aggressive lung adenocarcinoma. In patients, oncogenic events alter the tumor immune contexture, possibly having an impact on treatment responses. Importantly, patients with NRF2-activated nonsquamous or squamous tumors have poor prognosis and show limited response to anti-PD-L1 treatment.

Applying graph database technology for analyzing perturbed co-expression networks in cancer.

Graph representations provide an elegant solution to capture and analyze complex molecular mechanisms in the cell. Co-expression networks are undirected graph representations of transcriptional co-behavior indicating (co-)regulations, functional modules or even physical interactions between the corresponding gene products. The growing avalanche of available RNA sequencing (RNAseq) data fuels the construction of such networks, which are usually stored in relational databases like most other biological data. Inferring linkage by recursive multiple-join statements, however, is computationally expensive and complex to design in relational databases. In contrast, graph databases store and represent complex interconnected data as nodes, edges and properties, making it fast and intuitive to query and analyze relationships. While graph-based database technologies are on their way from a fringe domain to going mainstream, there are only a few studies reporting their application to biological data. We used the graph database management system Neo4j to store and analyze co-expression networks derived from RNAseq data from The Cancer Genome Atlas. Comparing co-expression in tumors versus healthy tissues in six cancer types revealed significant perturbation tracing back to erroneous or rewired gene regulation. Applying centrality, community detection and pathfinding graph algorithms uncovered the destruction or creation of central nodes, modules and relationships in co-expression networks of tumors. Given the speed, accuracy and straightforwardness of managing these densely connected networks, we conclude that graph databases are ready for entering the arena of biological data.

ERBB3 and IGF1R Signaling Are Required for Nrf2-Dependent Growth in KEAP1-Mutant Lung Cancer.

Mutations in KEAP1 and NFE2L2 (encoding the protein Nrf2) are prevalent in both adeno and squamous subtypes of non-small cell lung cancer, as well as additional tumor indications. The consequence of these mutations is stabilized Nrf2 and chronic induction of a battery of Nrf2 target genes. We show that knockdown of Nrf2 caused modest growth inhibition of cells growing in two-dimension, which was more pronounced in cell lines expressing mutant KEAP1. In contrast, Nrf2 knockdown caused almost complete regression of established KEAP1-mutant tumors in mice, with little effect on wild-type (WT) KEAP1 tumors. The strong dependency on Nrf2 could be recapitulated in certain anchorage-independent growth environments and was not prevented by excess extracellular glutathione. A CRISPR screen was used to investigate the mechanism(s) underlying this dependence. We identified alternative pathways critical for Nrf2-dependent growth in KEAP1-mutant cell lines, including the redox proteins thioredoxin and peroxiredoxin, as well as the growth factor receptors IGF1R and ERBB3. IGF1R inhibition was effective in KEAP1-mutant cells compared with WT, especially under conditions of anchorage-independent growth. These results point to addiction of KEAP1-mutant tumor cells to Nrf2 and suggest that inhibition of Nrf2 or discrete druggable Nrf2 target genes such as IGF1R could be an effective therapeutic strategy for disabling these tumors. SIGNIFICANCE: This study identifies pathways activated by Nrf2 that are important for the proliferation and tumorigenicity of KEAP1-mutant non-small cell lung cancer.

Systematic analysis of the intersection of disease mutations with protein modifications.

BACKGROUND: Perturbed posttranslational modification (PTM) landscapes commonly cause pathological phenotypes. The Cancer Genome Atlas (TCGA) project profiles thousands of tumors allowing the identification of spontaneous cancer-driving mutations, while Uniprot and dbSNP manage genetic disease-associated variants in the human population. PhosphoSitePlus (PSP) is the most comprehensive resource for studying experimentally observed PTM sites and the only repository with daily updates on functional annotations for many of these sites. To elucidate altered PTM landscapes on a large scale, we integrated disease-associated mutations from TCGA, Uniprot, and dbSNP with PTM sites from PhosphoSitePlus. We characterized each dataset individually, compared somatic with germline mutations, and analyzed PTM sites intersecting directly with disease variants. To assess the impact of mutations in the flanking regions of phosphosites, we developed DeltaScansite, a pipeline that compares Scansite predictions on wild type versus mutated sequences. Disease mutations are also visualized in PhosphoSitePlus. RESULTS: Characterization of somatic variants revealed oncoprotein-like mutation profiles of U2AF1, PGM5, and several other proteins, showing alteration patterns similar to germline mutations. The union of all datasets uncovered previously unknown losses and gains of PTM events in diseases unevenly distributed across different PTM types. Focusing on phosphorylation, our DeltaScansite workflow predicted perturbed signaling networks consistent with calculations by the machine learning method MIMP. CONCLUSIONS: We discovered oncoprotein-like profiles in TCGA and mutations that presumably modify protein function by impacting PTM sites directly or by rewiring upstream regulation. The resulting datasets are enriched with functional annotations from PhosphoSitePlus and present a unique resource for potential biomarkers or disease drivers.

NRG1 is a critical regulator of differentiation in TP63-driven squamous cell carcinoma.

Squamous cell carcinomas (SCCs) account for the majority of cancer mortalities. Although TP63 is an established lineage-survival oncogene in SCCs, therapeutic strategies have not been developed to target TP63 or it's downstream effectors. In this study we demonstrate that TP63 directly regulates NRG1 expression in human SCC cell lines and that NRG1 is a critical component of the TP63 transcriptional program. Notably, we show that squamous tumors are dependent NRG1 signaling in vivo, in both genetically engineered mouse models and human xenograft models, and demonstrate that inhibition of NRG1 induces keratinization and terminal squamous differentiation of tumor cells, blocking proliferation and inhibiting tumor growth. Together, our findings identify a lineage-specific function of NRG1 in SCCs of diverse anatomic origin.

The Case for Proteomics and Phospho-Proteomics in Personalized Cancer Medicine.

The concept of personalized medicine is predominantly been pursued through genomic and transcriptomic technologies, leading to the identification of multiple mutations in a large variety of cancers. However, it has proven challenging to distinguish driver and passenger mutations and to deal with tumor heterogeneity and resistant clonal populations. More generally, these heterogeneous mutation patterns do not in themselves predict the tumor phenotype. Analysis of the expressed proteins in a tumor and their modification states reveals if and how these mutations are translated to the functional level. It is already known that proteomic changes including posttranslational modifications are crucial drivers of oncogenesis, but proteomics technology has only recently become comparable in depth and accuracy to RNAseq. These advances also allow the rapid and highly sensitive analysis of formalin-fixed and paraffin-embedded biobank tissues, on both the proteome and phosphoproteome levels. In this perspective, pioneering mass spectrometry-based proteomic studies are highlighted that pave the way toward clinical implementation. It is argued that proteomics and phosphoproteomics could provide the missing link to make omics analysis actionable in the clinic.

15 years of PhosphoSitePlus®: integrating post-translationally modified sites, disease variants and isoforms.

For 15 years the mission of PhosphoSitePlus® (PSP, https://www.phosphosite.org) has been to provide comprehensive information and tools for the study of mammalian post-translational modifications (PTMs). The number of unique PTMs in PSP is now more than 450 000 from over 22 000 articles and thousands of MS datasets. The most important areas of growth in PSP are in disease and isoform informatics. Germline mutations associated with inherited diseases and somatic cancer mutations have been added to the database and can now be viewed along with PTMs and associated quantitative information on novel 'lollipop' plots. These plots enable researchers to interactively visualize the overlap between disease variants and PTMs, and to identify mutations that may alter phenotypes by rewiring signaling networks. We are expanding the sequence space to include over 30 000 human and mouse isoforms to enable researchers to explore the important but understudied biology of isoforms. This represents a necessary expansion of sequence space to accommodate the growing precision and depth of coverage enabled by ongoing advances in mass spectrometry. Isoforms are aligned using a new algorithm. Exploring the worlds of PTMs and disease mutations in the entire isoform space will hopefully lead to new biomarkers, therapeutic targets, and insights into isoform biology.

iProteinDB: An Integrative Database of Drosophila Post-translational Modifications.

Post-translational modification (PTM) serves as a regulatory mechanism for protein function, influencing their stability, interactions, activity and localization, and is critical in many signaling pathways. The best characterized PTM is phosphorylation, whereby a phosphate is added to an acceptor residue, most commonly serine, threonine and tyrosine in metazoans. As proteins are often phosphorylated at multiple sites, identifying those sites that are important for function is a challenging problem. Considering that any given phosphorylation site might be non-functional, prioritizing evolutionarily conserved phosphosites provides a general strategy to identify the putative functional sites. To facilitate the identification of conserved phosphosites, we generated a large-scale phosphoproteomics dataset from Drosophila embryos collected from six closely-related species. We built iProteinDB (https://www.flyrnai.org/tools/iproteindb/), a resource integrating these data with other high-throughput PTM datasets, including vertebrates, and manually curated information for Drosophila At iProteinDB, scientists can view the PTM landscape for any Drosophila protein and identify predicted functional phosphosites based on a comparative analysis of data from closely-related Drosophila species. Further, iProteinDB enables comparison of PTM data from Drosophila to that of orthologous proteins from other model organisms, including human, mouse, rat, Xenopus tropicalis, Danio rerio, and Caenorhabditis elegans.

Kras mutant genetically engineered mouse models of human cancers are genomically heterogeneous.

KRAS mutant tumors are largely recalcitrant to targeted therapies. Genetically engineered mouse models (GEMMs) of Kras mutant cancer recapitulate critical aspects of this disease and are widely used for preclinical validation of targets and therapies. Through comprehensive profiling of exomes and matched transcriptomes of >200 KrasG12D-initiated GEMM tumors from one lung and two pancreatic cancer models, we discover that significant intratumoral and intertumoral genomic heterogeneity evolves during tumorigenesis. Known oncogenes and tumor suppressor genes, beyond those engineered, are mutated, amplified, and deleted. Unlike human tumors, the GEMM genomic landscapes are dominated by copy number alterations, while protein-altering mutations are rare. However, interspecies comparative analyses of the genomic landscapes demonstrate fidelity between genes altered in KRAS mutant human and murine tumors. Genes that are spontaneously altered during murine tumorigenesis are also among the most prevalent found in human indications. Using targeted therapies, we also demonstrate that this inherent tumor heterogeneity can be exploited preclinically to discover cancer-specific and genotype-specific therapeutic vulnerabilities. Focusing on Kras allelic imbalance, a feature shared by all three models, we discover that MAPK pathway inhibition impinges uniquely on this event, indicating distinct susceptibility and fitness advantage of Kras-mutant cells. These data reveal previously unknown genomic diversity among KrasG12D-initiated GEMM tumors, places them in context of human patients, and demonstrates how to exploit this inherent tumor heterogeneity to discover therapeutic vulnerabilities.

USP7 small-molecule inhibitors interfere with ubiquitin binding.

The ubiquitin system regulates essential cellular processes in eukaryotes. Ubiquitin is ligated to substrate proteins as monomers or chains and the topology of ubiquitin modifications regulates substrate interactions with specific proteins. Thus ubiquitination directs a variety of substrate fates including proteasomal degradation. Deubiquitinase enzymes cleave ubiquitin from substrates and are implicated in disease; for example, ubiquitin-specific protease-7 (USP7) regulates stability of the p53 tumour suppressor and other proteins critical for tumour cell survival. However, developing selective deubiquitinase inhibitors has been challenging and no co-crystal structures have been solved with small-molecule inhibitors. Here, using nuclear magnetic resonance-based screening and structure-based design, we describe the development of selective USP7 inhibitors GNE-6640 and GNE-6776. These compounds induce tumour cell death and enhance cytotoxicity with chemotherapeutic agents and targeted compounds, including PIM kinase inhibitors. Structural studies reveal that GNE-6640 and GNE-6776 non-covalently target USP7 12 Å distant from the catalytic cysteine. The compounds attenuate ubiquitin binding and thus inhibit USP7 deubiquitinase activity. GNE-6640 and GNE-6776 interact with acidic residues that mediate hydrogen-bond interactions with the ubiquitin Lys48 side chain, suggesting that USP7 preferentially interacts with and cleaves ubiquitin moieties that have free Lys48 side chains. We investigated this idea by engineering di-ubiquitin chains containing differential proximal and distal isotopic labels and measuring USP7 binding by nuclear magnetic resonance. This preferential binding protracted the depolymerization kinetics of Lys48-linked ubiquitin chains relative to Lys63-linked chains. In summary, engineering compounds that inhibit USP7 activity by attenuating ubiquitin binding suggests opportunities for developing other deubiquitinase inhibitors and may be a strategy more broadly applicable to inhibiting proteins that require ubiquitin binding for full functional activity.

Role of the E3 ubiquitin ligase RNF157 as a novel downstream effector linking PI3K and MAPK signaling pathways to the cell cycle.

The interconnected PI3K and MAPK signaling pathways are commonly perturbed in cancer. Dual inhibition of these pathways by the small-molecule PI3K inhibitor pictilisib (GDC-0941) and the MEK inhibitor cobimetinib (GDC-0973) suppresses cell proliferation and induces cell death better than either single agent in several preclinical models. Using mass spectrometry-based phosphoproteomics, we have identified the RING finger E3 ubiquitin ligase RNF157 as a target at the intersection of PI3K and MAPK signaling. We demonstrate that RNF157 phosphorylation downstream of the PI3K and MAPK pathways influences the ubiquitination and stability of RNF157 during the cell cycle in an anaphase-promoting complex/cyclosome-CDH1-dependent manner. Deletion of these phosphorylation-targeted residues on RNF157 disrupts binding to CDH1 and protects RNF157 from ubiquitination and degradation. Expression of the cyclin-dependent kinase 2 (CDK2), itself a downstream target of PI3K/MAPK signaling, leads to increased phosphorylation of RNF157 on the same residues modulated by PI3K and MAPK signaling. Inhibition of PI3K and MEK in combination or of CDK2 by their respective small-molecule inhibitors reduces RNF157 phosphorylation at these residues and attenuates RNF157 interaction with CDH1 and its subsequent degradation. Knockdown of endogenous RNF157 in melanoma cells leads to late S phase and G2/M arrest and induces apoptosis, the latter further potentiated by concurrent PI3K/MEK inhibition, consistent with a role for RNF157 in the cell cycle. We propose that RNF157 serves as a novel node integrating oncogenic signaling pathways with the cell cycle machinery and promoting optimal cell cycle progression in transformed cells.

Recurrent Loss of NFE2L2 Exon 2 Is a Mechanism for Nrf2 Pathway Activation in Human Cancers.

The Nrf2 pathway is frequently activated in human cancers through mutations in Nrf2 or its negative regulator KEAP1. Using a cell-line-derived gene signature for Nrf2 pathway activation, we found that some tumors show high Nrf2 activity in the absence of known mutations in the pathway. An analysis of splice variants in oncogenes revealed that such tumors express abnormal transcript variants from the NFE2L2 gene (encoding Nrf2) that lack exon 2, or exons 2 and 3, and encode Nrf2 protein isoforms missing the KEAP1 interaction domain. The Nrf2 alterations result in the loss of interaction with KEAP1, Nrf2 stabilization, induction of a Nrf2 transcriptional response, and Nrf2 pathway dependence. In all analyzed cases, transcript variants were the result of heterozygous genomic microdeletions. Thus, we identify an alternative mechanism for Nrf2 pathway activation in human tumors and elucidate its functional consequences.

Cellular Interrogation: Exploiting Cell-to-Cell Variability to Discriminate Regulatory Mechanisms in Oscillatory Signalling.

The molecular complexity within a cell may be seen as an evolutionary response to the external complexity of the cell's environment. This suggests that the external environment may be harnessed to interrogate the cell's internal molecular architecture. Cells, however, are not only nonlinear and non-stationary, but also exhibit heterogeneous responses within a clonal, isogenic population. In effect, each cell undertakes its own experiment. Here, we develop a method of cellular interrogation using programmable microfluidic devices which exploits the additional information present in cell-to-cell variation, without requiring model parameters to be fitted to data. We focussed on Ca2+ signalling in response to hormone stimulation, which exhibits oscillatory spiking in many cell types and chose eight models of Ca2+ signalling networks which exhibit similar behaviour in simulation. We developed a nonlinear frequency analysis for non-stationary responses, which could classify models into groups under parameter variation, but found that this question alone was unable to distinguish critical feedback loops. We further developed a nonlinear amplitude analysis and found that the combination of both questions ruled out six of the models as inconsistent with the experimentally-observed dynamics and heterogeneity. The two models that survived the double interrogation were mathematically different but schematically identical and yielded the same unexpected predictions that we confirmed experimentally. Further analysis showed that subtle mathematical details can markedly influence non-stationary responses under parameter variation, emphasising the difficulty of finding a "correct" model. By developing questions for the pathway being studied, and designing more versatile microfluidics, cellular interrogation holds promise as a systematic strategy that can complement direct intervention by genetics or pharmacology.

Quantitative phosphoproteomic analysis of the PI3K-regulated signaling network.

The PI3K pathway is commonly activated in cancer. Only a few studies have attempted to explore the spectrum of phosphorylation signaling downstream of the PI3K cascade. Such insight, however, is imperative to understand the mechanisms responsible for oncogenic phenotypes. By applying MS-based phosphoproteomics, we mapped 2509 phosphorylation sites on 1096 proteins, and quantified their responses to activation or inhibition of PIK3CA using isogenic knock-in derivatives and a series of targeted inhibitors. We uncovered phosphorylation changes in a wide variety of proteins involved in cell growth and proliferation, many of which have not been previously associated with PI3K signaling. A significant update of the posttranslational modification database PHOSIDA (http://www.phosida.com) allows efficient use of the data. All MS data have been deposited in the ProteomeXchange with identifier PXD003899 (http://proteomecentral.proteomexchange.org/dataset/PXD003899).

Phosphoproteome analysis of the MAPK pathway reveals previously undetected feedback mechanisms.

The RAS-RAF-MEK-ERK (MAPK) pathway is prevalently perturbed in cancer. Recent large-scale sequencing initiatives profiled thousands of tumors providing insight into alterations at the DNA and RNA levels. These efforts confirmed that key nodes of the MAPK pathway, in particular KRAS and BRAF, are among the most frequently altered proteins in cancer. The establishment of targeted therapies, however, has proven difficult. To decipher the underlying challenges, it is essential to decrypt the phosphorylation network spanned by the MAPK core axis. Using mass spectrometry we identified 2241 phosphorylation sites on 1020 proteins, and measured their responses to inhibition of MEK or ERK. Multiple phosphorylation patterns revealed previously undetected feedback, as upstream signaling nodes, including receptor kinases, showed changes at the phosphorylation level. We provide a dataset rich in potential therapeutic targets downstream of the MAPK cascade. By integrating TCGA (The Cancer Genome Atlas) data, we highlight some downstream phosphoproteins that are frequently altered in cancer. All MS data have been deposited in the ProteomeXchange with identifier PXD003908 (http://proteomecentral.proteomexchange.org/dataset/PXD003908).

Uncovering a Dual Regulatory Role for Caspases During Endoplasmic Reticulum Stress-induced Cell Death.

Many diseases are associated with endoplasmic reticulum (ER) stress, which results from an accumulation of misfolded proteins. This triggers an adaptive response called the "unfolded protein response" (UPR), and prolonged exposure to ER stress leads to cell death. Caspases are reported to play a critical role in ER stress-induced cell death but the underlying mechanisms by which they exert their effect continue to remain elusive. To understand the role caspases play during ER stress, a systems level approach integrating analysis of the transcriptome, proteome, and proteolytic substrate profile was employed. This quantitative analysis revealed transcriptional profiles for most human genes, provided information on protein abundance for 4476 proteins, and identified 445 caspase substrates. Based on these data sets many caspase substrates were shown to be downregulated at the protein level during ER stress suggesting caspase activity inhibits their cellular function. Additionally, RNA sequencing revealed a role for caspases in regulation of ER stress-induced transcriptional pathways and gene set enrichment analysis showed expression of multiple gene targets of essential transcription factors to be upregulated during ER stress upon inhibition of caspases. Furthermore, these transcription factors were degraded in a caspase-dependent manner during ER stress. These results indicate that caspases play a dual role in regulating the cellular response to ER stress through both post-translational and transcriptional regulatory mechanisms. Moreover, this study provides unique insight into progression of the unfolded protein response into cell death, which may help identify therapeutic strategies to treat ER stress-related diseases.

Comprehensive genomic analysis of malignant pleural mesothelioma identifies recurrent mutations, gene fusions and splicing alterations.

We analyzed transcriptomes (n = 211), whole exomes (n = 99) and targeted exomes (n = 103) from 216 malignant pleural mesothelioma (MPM) tumors. Using RNA-seq data, we identified four distinct molecular subtypes: sarcomatoid, epithelioid, biphasic-epithelioid (biphasic-E) and biphasic-sarcomatoid (biphasic-S). Through exome analysis, we found BAP1, NF2, TP53, SETD2, DDX3X, ULK2, RYR2, CFAP45, SETDB1 and DDX51 to be significantly mutated (q-score ≥ 0.8) in MPMs. We identified recurrent mutations in several genes, including SF3B1 (∼2%; 4/216) and TRAF7 (∼2%; 5/216). SF3B1-mutant samples showed a splicing profile distinct from that of wild-type tumors. TRAF7 alterations occurred primarily in the WD40 domain and were, except in one case, mutually exclusive with NF2 alterations. We found recurrent gene fusions and splice alterations to be frequent mechanisms for inactivation of NF2, BAP1 and SETD2. Through integrated analyses, we identified alterations in Hippo, mTOR, histone methylation, RNA helicase and p53 signaling pathways in MPMs.

CCAT1 is an enhancer-templated RNA that predicts BET sensitivity in colorectal cancer.

Colon tumors arise in a stepwise fashion from either discrete genetic perturbations or epigenetic dysregulation. To uncover the key epigenetic regulators that drive colon cancer growth, we used a CRISPR loss-of-function screen and identified a number of essential genes, including the bromodomain and extraterminal (BET) protein BRD4. We found that BRD4 is critical for colon cancer proliferation, and its knockdown led to differentiation effects in vivo. JQ1, a BET inhibitor, preferentially reduced growth in a subset of epigenetically dysregulated colon cancers characterized by the CpG island methylator phenotype (CIMP). Integrated transcriptomic and genomic analyses defined a distinct superenhancer in CIMP+ colon cancers that regulates cMYC transcription. We found that the long noncoding RNA colon cancer-associated transcript 1 (CCAT1) is transcribed from this superenhancer and is exquisitely sensitive to BET inhibition. Concordantly, cMYC transcription and cell growth were tightly correlated with the presence of CCAT1 RNA in a variety of tumor types. Taken together, we propose that CCAT1 is a clinically tractable biomarker for identifying patients who are likely to benefit from BET inhibitors.

Bioinformatics analysis of thousands of TCGA tumors to determine the involvement of epigenetic regulators in human cancer.

BACKGROUND: Many cancer cells show distorted epigenetic landscapes. The Cancer Genome Atlas (TCGA) project profiles thousands of tumors, allowing the discovery of somatic alterations in the epigenetic machinery and the identification of potential cancer drivers among members of epigenetic protein families. METHODS: We integrated mutation, expression, and copy number data from 5943 tumors from 13 cancer types to train a classification model that predicts the likelihood of being an oncogene (OG), tumor suppressor (TSG) or neutral gene (NG). We applied this predictor to epigenetic regulator genes (ERGs), and used differential expression and correlation network analysis to identify dysregulated ERGs along with co-expressed cancer genes. Furthermore, we quantified global proteomic changes by mass spectrometry after EZH2 inhibition. RESULTS: Mutation-based classifiers uncovered the OG-like profile of DNMT3A and TSG-like profiles for several ERGs. Differential gene expression and correlation network analyses revealed that EZH2 is the most significantly over-expressed ERG in cancer and is co-regulated with a cell cycle network. Proteomic analysis showed that EZH2 inhibition induced down-regulation of cell cycle regulators in lymphoma cells. CONCLUSIONS: Using classical driver genes to train an OG/TSG predictor, we determined the most predictive features at the gene level. Our predictor uncovered one OG and several TSGs among ERGs. Expression analyses elucidated multiple dysregulated ERGs including EZH2 as member of a co-expressed cell cycle network.