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Showing papers by "Chris Sander published in 2011"


Journal ArticleDOI
Debra A. Bell1, Andrew Berchuck2, Michael J. Birrer3, Jeremy Chien1  +282 moreInstitutions (35)
30 Jun 2011-Nature
TL;DR: It is reported that high-grade serous ovarian cancer is characterized by TP53 mutations in almost all tumours (96%); low prevalence but statistically recurrent somatic mutations in nine further genes including NF1, BRCA1,BRCA2, RB1 and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes.
Abstract: A catalogue of molecular aberrations that cause ovarian cancer is critical for developing and deploying therapies that will improve patients' lives. The Cancer Genome Atlas project has analysed messenger RNA expression, microRNA expression, promoter methylation and DNA copy number in 489 high-grade serous ovarian adenocarcinomas and the DNA sequences of exons from coding genes in 316 of these tumours. Here we report that high-grade serous ovarian cancer is characterized by TP53 mutations in almost all tumours (96%); low prevalence but statistically recurrent somatic mutations in nine further genes including NF1, BRCA1, BRCA2, RB1 and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes. Analyses delineated four ovarian cancer transcriptional subtypes, three microRNA subtypes, four promoter methylation subtypes and a transcriptional signature associated with survival duration, and shed new light on the impact that tumours with BRCA1/2 (BRCA1 or BRCA2) and CCNE1 aberrations have on survival. Pathway analyses suggested that homologous recombination is defective in about half of the tumours analysed, and that NOTCH and FOXM1 signalling are involved in serous ovarian cancer pathophysiology.

5,878 citations


01 Jun 2011
TL;DR: The Cancer Genome Atlas project has analyzed messenger RNA expression, microRNA expression, promoter methylation and DNA copy number in 489 high-grade serous ovarian adenocarcinomas and the DNA sequences of exons from coding genes in 316 of these tumours as mentioned in this paper.
Abstract: A catalogue of molecular aberrations that cause ovarian cancer is critical for developing and deploying therapies that will improve patients’ lives. The Cancer Genome Atlas project has analysed messenger RNA expression, microRNA expression, promoter methylation and DNA copy number in 489 high-grade serous ovarian adenocarcinomas and the DNA sequences of exons from coding genes in 316 of these tumours. Here we report that high-grade serous ovarian cancer is characterized by TP53 mutations in almost all tumours (96%); low prevalence but statistically recurrent somatic mutations in nine further genes including NF1, BRCA1, BRCA2, RB1 and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes. Analyses delineated four ovarian cancer transcriptional subtypes, three microRNA subtypes, four promoter methylation subtypes and a transcriptional signature associated with survival duration, and shed new light on the impact that tumours with BRCA1/2 (BRCA1 or BRCA2) and CCNE1 aberrations have on survival. Pathway analyses suggested that homologous recombination is defective in about half of the tumours analysed, and that NOTCH and FOXM1 signalling are involved in serous ovarian cancer pathophysiology.

5,609 citations


Journal ArticleDOI
TL;DR: A new functional impact score (FIS) for amino acid residue changes using evolutionary conservation patterns is introduced, estimating that at least 5% of cancer-relevant mutations involve switch of function, rather than simply loss or gain of function.
Abstract: As large-scale re-sequencing of genomes reveals many protein mutations, especially in human cancer tissues, prediction of their likely functional impact becomes important practical goal. Here, we introduce a new functional impact score (FIS) for amino acid residue changes using evolutionary conservation patterns. The information in these patterns is derived from aligned families and sub-families of sequence homologs within and between species using combinatorial entropy formalism. The score performs well on a large set of human protein mutations in separating disease-associated variants (∼19 200), assumed to be strongly functional, from common polymorphisms (∼35 600), assumed to be weakly functional (area under the receiver operating characteristic curve of ∼0.86). In cancer, using recurrence, multiplicity and annotation for ∼10 000 mutations in the COSMIC database, the method does well in assigning higher scores to more likely functional mutations (‘drivers’). To guide experimental prioritization, we report a list of about 1000 top human cancer genes frequently mutated in one or more cancer types ranked by likely functional impact; and, an additional 1000 candidate cancer genes with rare but likely functional mutations. In addition, we estimate that at least 5% of cancer-relevant mutations involve switch of function, rather than simply loss or gain of function.

1,715 citations


Journal ArticleDOI
TL;DR: The findings suggest that contacts predicted by DCA can be used as a reliable guide to facilitate computational predictions of alternative protein conformations, protein complex formation, and even the de novo prediction of protein domain structures, contingent on the existence of a large number of homologous sequences which are being rapidly made available due to advances in genome sequencing.
Abstract: The similarity in the three-dimensional structures of homologous proteins imposes strong constraints on their sequence variability. It has long been suggested that the resulting correlations among amino acid compositions at different sequence positions can be exploited to infer spatial contacts within the tertiary protein structure. Crucial to this inference is the ability to disentangle direct and indirect correlations, as accomplished by the recently introduced direct-coupling analysis (DCA). Here we develop a computationally efficient implementation of DCA, which allows us to evaluate the accuracy of contact prediction by DCA for a large number of protein domains, based purely on sequence information. DCA is shown to yield a large number of correctly predicted contacts, recapitulating the global structure of the contact map for the majority of the protein domains examined. Furthermore, our analysis captures clear signals beyond intradomain residue contacts, arising, e.g., from alternative protein conformations, ligand-mediated residue couplings, and interdomain interactions in protein oligomers. Our findings suggest that contacts predicted by DCA can be used as a reliable guide to facilitate computational predictions of alternative protein conformations, protein complex formation, and even the de novo prediction of protein domain structures, contingent on the existence of a large number of homologous sequences which are being rapidly made available due to advances in genome sequencing.

1,319 citations


Journal ArticleDOI
07 Dec 2011-PLOS ONE
TL;DR: Surprisingly, it is found that the strength of these inferred couplings is an excellent predictor of residue-residue proximity in folded structures, and the top-scoring residue couplings are sufficiently accurate and well-distributed to define the 3D protein fold with remarkable accuracy.
Abstract: The evolutionary trajectory of a protein through sequence space is constrained by its function. Collections of sequence homologs record the outcomes of millions of evolutionary experiments in which the protein evolves according to these constraints. Deciphering the evolutionary record held in these sequences and exploiting it for predictive and engineering purposes presents a formidable challenge. The potential benefit of solving this challenge is amplified by the advent of inexpensive high-throughput genomic sequencing. In this paper we ask whether we can infer evolutionary constraints from a set of sequence homologs of a protein. The challenge is to distinguish true co-evolution couplings from the noisy set of observed correlations. We address this challenge using a maximum entropy model of the protein sequence, constrained by the statistics of the multiple sequence alignment, to infer residue pair couplings. Surprisingly, we find that the strength of these inferred couplings is an excellent predictor of residue-residue proximity in folded structures. Indeed, the top-scoring residue couplings are sufficiently accurate and well-distributed to define the 3D protein fold with remarkable accuracy. We quantify this observation by computing, from sequence alone, all-atom 3D structures of fifteen test proteins from different fold classes, ranging in size from 50 to 260 residues., including a G-protein coupled receptor. These blinded inferences are de novo, i.e., they do not use homology modeling or sequence-similar fragments from known structures. The co-evolution signals provide sufficient information to determine accurate 3D protein structure to 2.7–4.8 A Cα-RMSD error relative to the observed structure, over at least two-thirds of the protein (method called EVfold, details at http://EVfold.org). This discovery provides insight into essential interactions constraining protein evolution and will facilitate a comprehensive survey of the universe of protein structures, new strategies in protein and drug design, and the identification of functional genetic variants in normal and disease genomes.

1,125 citations


Journal ArticleDOI
TL;DR: A web-based interface that enables biologists to browse and search a comprehensive collection of pathways from multiple sources represented in a common language, a download site that provides integrated bulk sets of pathway information in standard or convenient formats and a web service that software developers can use to conveniently query and access all data.
Abstract: Pathway Commons (http://www.pathwaycommons.org) is a collection of publicly available pathway data from multiple organisms. Pathway Commons provides a web-based interface that enables biologists to browse and search a comprehensive collection of pathways from multiple sources represented in a common language, a download site that provides integrated bulk sets of pathway information in standard or convenient formats and a web service that software developers can use to conveniently query and access all data. Database providers can share their pathway data via a common repository. Pathways include biochemical reactions, complex assembly, transport and catalysis events and physical interactions involving proteins, DNA, RNA, small molecules and complexes. Pathway Commons aims to collect and integrate all public pathway data available in standard formats. Pathway Commons currently contains data from nine databases with over 1400 pathways and 687,000 interactions and will be continually expanded and updated.

1,095 citations


Journal ArticleDOI
TL;DR: A series of databases that run parallel to the Protein Data Bank, used for the analysis of properties of protein structures in areas ranging from structural genomics, to cancer biology and protein design, are presented.
Abstract: The Protein Data Bank (PDB) is the world-wide repository of macromolecular structure information. We present a series of databases that run parallel to the PDB. Each database holds one entry, if possible, for each PDB entry. DSSP holds the secondary structure of the proteins. PDBREPORT holds reports on the structure quality and lists errors. HSSP holds a multiple sequence alignment for all proteins. The PDBFINDER holds easy to parse summaries of the PDB file content, augmented with essentials from the other systems. PDB_REDO holds re-refined, and often improved, copies of all structures solved by X-ray. WHY_NOT summarizes why certain files could not be produced. All these systems are updated weekly. The data sets can be used for the analysis of properties of protein structures in areas ranging from structural genomics, to cancer biology and protein design.

1,024 citations


Journal ArticleDOI
TL;DR: It is shown that BAP1 knockdown in MPM cell lines affects E2F and Polycomb target genes, which implicate transcriptional deregulation in the pathogenesis of MPM.
Abstract: Malignant pleural mesotheliomas (MPMs) often show CDKN2A and NF2 inactivation, but other highly recurrent mutations have not been described. To identify additional driver genes, we used an integrated genomic analysis of 53 MPM tumor samples to guide a focused sequencing effort that uncovered somatic inactivating mutations in BAP1 in 23% of MPMs. The BAP1 nuclear deubiquitinase is known to target histones (together with ASXL1 as a Polycomb repressor subunit) and the HCF1 transcriptional co-factor, and we show that BAP1 knockdown in MPM cell lines affects E2F and Polycomb target genes. These findings implicate transcriptional deregulation in the pathogenesis of MPM.

608 citations


Journal ArticleDOI
TL;DR: Using PAR-CLIP, global RNA targets for all human FET proteins and two ALS-causing human FUS mutants were defined and FET members showed similar binding profiles, whereas F US mutants showed a drastically altered binding pattern, consistent with changes in subcellular localization.
Abstract: FUS, EWSR1 and TAF15, constituting the FET protein family, are abundant, highly conserved RNA-binding proteins with important roles in oncogenesis and neuronal disease, yet their RNA targets and recognition elements are unknown. Using PAR-CLIP, we defined global RNA targets for all human FET proteins and two ALS-causing human FUS mutants. FET members showed similar binding profiles, whereas FUS mutants showed a drastically altered binding pattern, consistent with changes in subcellular localization.

319 citations


Journal ArticleDOI
TL;DR: It is shown that in addition to its role in TGF-β signaling, miR-302/367 promotes bone morphogenetic protein (BMP) signaling by targeting BMP inhibitors TOB2, DAZAP2, and SLAIN1.
Abstract: MicroRNAs are important regulators in many cellular processes, including stem cell self-renewal. Recent studies demonstrated their function as pluripotency factors with the capacity for somatic cell reprogramming. However, their role in human embryonic stem (ES) cells (hESCs) remains poorly understood, partially due to the lack of genome-wide strategies to identify their targets. Here, we performed comprehensive microRNA profiling in hESCs and in purified neural and mesenchymal derivatives. Using a combination of AGO cross-linking and microRNA perturbation experiments, together with computational prediction, we identified the targets of the miR-302/367 cluster, the most abundant microRNAs in hESCs. Functional studies identified novel roles of miR-302/367 in maintaining pluripotency and regulating hESC differentiation. We show that in addition to its role in TGF-β signaling, miR-302/367 promotes bone morphogenetic protein (BMP) signaling by targeting BMP inhibitors TOB2, DAZAP2, and SLAIN1. This study broadens our understanding of microRNA function in hESCs and is a valuable resource for future studies in this area.

183 citations



Journal ArticleDOI
TL;DR: The results suggest that SOD1 is an LCS-1–binding protein that may act in concert with mutant proteins, such as EGFR and KRAS, to promote cell growth, providing a therapeutic target for compounds likeLCS-1.
Abstract: We previously described four small molecules that reduced the growth of lung adenocarcinoma cell lines with either epidermal growth factor receptor (EGFR) or KRAS mutations in a high-throughout chemical screen. By combining affinity proteomics and gene expression analysis, we now propose superoxide dismutase 1 (SOD1) as the most likely target of one of these small molecules, referred to as lung cancer screen 1 (LCS-1). siRNAs against SOD1 slowed the growth of LCS-1 sensitive cell lines; conversely, expression of a SOD1 cDNA increased proliferation of H358 cells and reduced sensitivity of these cells to LCS-1. In addition, SOD1 enzymatic activity was inhibited in vitro by LCS-1 and two closely related analogs. These results suggest that SOD1 is an LCS-1–binding protein that may act in concert with mutant proteins, such as EGFR and KRAS, to promote cell growth, providing a therapeutic target for compounds like LCS-1.

Journal ArticleDOI
TL;DR: Clinical observations of glucose uptake with a pathologic and molecular subtype of human breast cancer are linked and related approaches to derive molecular determinants of radiotracer retention for other PET-imaging probes are suggested.
Abstract: In contrast to normal cells, cancer cells avidly take up glucose and metabolize it to lactate even when oxygen is abundant, a phenomenon referred to as the Warburg effect. This fundamental alteration in glucose metabolism in cancer cells enables their specific detection by positron emission tomography (PET) following i.v. injection of the glucose analogue (18)F-fluorodeoxy-glucose ((18)FDG). However, this useful imaging technique is limited by the fact that not all cancers avidly take up FDG. To identify molecular determinants of (18)FDG retention, we interrogated the transcriptomes of human-cancer cell lines and primary tumors for metabolic pathways associated with (18)FDG radiotracer uptake. From ninety-five metabolic pathways that were interrogated, the glycolysis, and several glycolysis-related pathways (pentose phosphate, carbon fixation, aminoacyl-tRNA biosynthesis, one-carbon-pool by folate) showed the greatest transcriptional enrichment. This "FDG signature" predicted FDG uptake in breast cancer cell lines and overlapped with established gene expression signatures for the "basal-like" breast cancer subtype and MYC-induced tumorigenesis in mice. Human breast cancers with nuclear MYC staining and high RNA expression of MYC target genes showed high (18)FDG-PET uptake (P < 0.005). Presence of the FDG signature was similarly associated with MYC gene copy gain, increased MYC transcript levels, and elevated expression of metabolic MYC target genes in a human breast cancer genomic dataset. Together, our findings link clinical observations of glucose uptake with a pathologic and molecular subtype of human breast cancer. Furthermore, they suggest related approaches to derive molecular determinants of radiotracer retention for other PET-imaging probes.

Journal ArticleDOI
TL;DR: Re-expression vectors or selective agents directed at miR-143 or its targets may have therapeutic value in dedifferentiated liposarcoma, and treatment with a PLK1 inhibitor potently induced G(2)-M growth arrest and apoptosis in liposARcoma cells.
Abstract: Liposarcoma remains the most common mesenchymal cancer, with a mortality rate of 60% among patients with this disease. To address the present lack of therapeutic options, we embarked upon a study of microRNA (miRNA) expression alterations associated with liposarcomagenesis with the goal of exploiting differentially expressed miRNAs and the gene products they regulate as potential therapeutic targets. MicroRNA expression was profiled in samples of normal adipose tissue, well-differentiated liposarcoma, and dedifferentiated liposarcoma by both deep sequencing of small RNA libraries and hybridization-based Agilent microarrays. The expression profiles discriminated liposarcoma from normal adipose tissue and well-differentiated from dedifferentiated disease. We defined over 40 miRNAs that were dysregulated in dedifferentiated liposarcomas in both the sequencing and the microarray analysis. The upregulated miRNAs included two cancer-associated species (miR-21, miR-26a), and the downregulated miRNAs included two species that were highly abundant in adipose tissue (miR-143, miR-145). Restoring miR-143 expression in dedifferentiated liposarcoma cells inhibited proliferation, induced apoptosis, and decreased expression of BCL2, TOP2A, PRC1, and PLK1. The downregulation of PRC1 and its docking partner PLK1 suggests that miR-143 inhibits cytokinesis in these cells. In support of this idea, treatment with a PLK1 inhibitor potently induced G2/M growth arrest and apoptosis in liposarcoma cells. Taken together, our findings suggest that miR-143 re-expression vectors or selective agents directed at miR-143 or its targets may have therapeutic value in dedifferentiated liposarcoma.

Journal ArticleDOI
03 Nov 2011-PLOS ONE
TL;DR: A prediction tool that inputs genomic profiles of primary surgical samples and generates patient-specific predictions for the time to recurrence and survival, along with outcome risk predictions and a prognostic index for patient risk stratification is provided.
Abstract: Background: Serous ovarian cancer (SeOvCa) is an aggressive disease with differential and often inadequate therapeutic outcome after standard treatment. The Cancer Genome Atlas (TCGA) has provided rich molecular and genetic profiles from hundreds of primary surgical samples. These profiles confirm mutations of TP53 in ,100% of patients and an extraordinarily complex profile of DNA copy number changes with considerable patient-to-patient diversity. This raises the joint challenge of exploiting all new available datasets and reducing their confounding complexity for the purpose of predicting clinical outcomes and identifying disease relevant pathway alterations. We therefore set out to use multi-data type genomic profiles (mRNA, DNA methylation, DNA copy-number alteration and microRNA) available from TCGA to identify prognostic signatures for the prediction of progression-free survival (PFS) and overall survival (OS). Methodology/Principal Findings: We implemented a multivariate Cox Lasso model and median time-to-event prediction algorithm and applied it to two datasets integrated from the four genomic data types. We (1) selected features through cross-validation; (2) generated a prognostic index for patient risk stratification; and (3) directly predicted continuous clinical outcome measures, that is, the time to recurrence and survival time. We used Kaplan-Meier p-values, hazard ratios (HR), and concordance probability estimates (CPE) to assess prediction performance, comparing separate and integrated datasets. Data integration resulted in the best PFS signature (withheld data: p-value=0.008; HR=2.83; CPE=0.72). Conclusions/Significance: We provide a prediction tool that inputs genomic profiles of primary surgical samples and generates patient-specific predictions for the time to recurrence and survival, along with outcome risk predictions. Using integrated genomic profiles resulted in information gain for prediction of outcomes. Pathway analysis provided potential insights into functional changes affecting disease progression. The prognostic signatures, if prospectively validated, may be useful for interpreting therapeutic outcomes for clinical trials that aim to improve the therapy for SeOvCa patients.

Journal ArticleDOI
TL;DR: Multimodality sequence analysis of DLPS revealed recurrent mutations and epigenetic abnormalities critical to liposarcomagenesis and to the suppression of adipocyte differentiation, revealing an unanticipated role for methylation abnormalities in DLPS tumors and suggesting demethylating agents as potential therapeutics.
Abstract: We explored diverse alterations contributing to liposarcomagenesis by sequencing the genome, exome, transcriptome, and cytosine methylome of a primary and recurrent dedifferentiated liposarcoma (DLPS) from distinct chemotherapy/radiotherapy-naive patients. The liposarcoma genomes had complex structural rearrangements, but in different patterns, and with varied effects on the structure and expression of affected genes. While the point mutation rate was modest, integrative analyses and additional screening identified somatic mutations in HDAC1 in 8.3% of DLPS. Liposarcoma methylomes revealed alterations in differentiation pathway genes, including CEBPA methylation in 24% of DLPS. Treatment with demethylating agents, which restored CEBPA expression in DLPS cells, was antiproliferative and proapoptotic in vitro and reduced tumor growth in vivo . Both genetic and epigenetic abnormalities established a role for small RNAs in liposarcomagenesis, typified by methylation-induced silencing of microRNA-193b in DLPS but not its well-differentiated counterpart. These findings reveal an unanticipated role for epigenetic abnormalities in DLPS tumors and suggest demethylating agents as potential therapeutics. Significance: Multimodality sequence analysis of DLPS revealed recurrent mutations and epigenetic abnormalities critical to liposarcomagenesis and to the suppression of adipocyte differentiation. Pharmacologic inhibition of DNA methylation promoted apoptosis and differentiated DLPS cells in vitro and inhibited tumor growth in vivo , providing a rationale for investigating methylation inhibitors in this disease. Cancer Discovery; 1(7) ; 587–97. ©2011 AACR . Read the Commentary on this article by Meltzer and Helman, [p. 555][1] This article is highlighted in the In This Issue feature, [p. 539][2] [1]: /lookup/volpage/1/555?iss=7 [2]: /lookup/volpage/1/539?iss=7

Journal ArticleDOI
TL;DR: A modeling framework to detect disease‐driving CNAs and their effect on target mRNA expression, and to stratify cancer patients into long‐ and short‐term survivors is developed, concluding that large‐scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer.
Abstract: DNA copy number aberrations (CNAs) are a hallmark of cancer genomes. However, little is known about how such changes affect global gene expression. We develop a modeling framework, EPoC (Endogenous Perturbation analysis of Cancer), to (1) detect disease-driving CNAs and their effect on target mRNA expression, and to (2) stratify cancer patients into long- and short-term survivors. Our method constructs causal network models of gene expression by combining genome-wide DNA- and RNA-level data. Prognostic scores are obtained from a singular value decomposition of the networks. By applying EPoC to glioblastoma data from The Cancer Genome Atlas consortium, we demonstrate that the resulting network models contain known disease-relevant hub genes, reveal interesting candidate hubs, and uncover predictors of patient survival. Targeted validations in four glioblastoma cell lines support selected predictions, and implicate the p53-interacting protein Necdin in suppressing glioblastoma cell growth. We conclude that large-scale network modeling of the effects of CNAs on gene expression may provide insights into the biology of human cancer. Free software in MATLAB and R is provided.

Journal ArticleDOI
14 Mar 2011-Silence
TL;DR: It is suggested that the type II TGF-β receptor is regulated by multiple miRNAs, and the risk of obtaining misleading results in siRNA screens using large libraries with single-assay readout is substantial.
Abstract: RNA interference (RNAi) screens have been used to identify novel components of signal-transduction pathways in a variety of organisms. We performed a small interfering (si)RNA screen for novel members of the transforming growth factor (TGF)-β pathway in a human keratinocyte cell line. The TGF-β pathway is integral to mammalian cell proliferation and survival, and aberrant TGF-β responses have been strongly implicated in cancer. We assayed how strongly single siRNAs targeting each of 6,000 genes affect the nuclear translocation of a green fluorescent protein (GFP)-SMAD2 reporter fusion protein. Surprisingly, we found no novel TGF-β pathway members, but we did find dominant off-target effects. All siRNA hits, whatever their intended direct target, reduced the mRNA levels of two known upstream pathway components, the TGF-β receptors 1 and 2 (TGFBR1 and TGFBR2), via micro (mi)RNA-like off-target effects. The scale of these off-target effects was remarkable, with at least 1% of the sequences in the unbiased siRNA library having measurable off-target effects on one of these two genes. It seems that relatively minor reductions of message levels via off-target effects can have dominant effects on an assay, if the pathway output is very dose-sensitive to levels of particular pathway components. In search of mechanistic details, we identified multiple miRNA-like sequence characteristics that correlated with the off-target effects. Based on these results, we identified miR-20a, miR-34a and miR-373 as miRNAs that inhibit TGFBR2 expression. Our findings point to potential improvements for miRNA/siRNA target prediction methods, and suggest that the type II TGF-β receptor is regulated by multiple miRNAs. We also conclude that the risk of obtaining misleading results in siRNA screens using large libraries with single-assay readout is substantial. Control and rescue experiments are essential in the interpretation of such screens, and improvements to the methods to reduce or predict RNAi off-target effects would be beneficial.

Journal ArticleDOI
04 Mar 2011-PLOS ONE
TL;DR: Bigenic mice generated in which both activated human AKT1 and human MYC are expressed in the prostate showed reduced sensitivity to mTOR inhibition, suggesting that additional genetic events may dampen mTOR dependence, and these data have implications for treatment of human prostate cancers with PI3K-pathway alterations using mTOR inhibitors.
Abstract: MYC and phosphoinositide 3-kinase (PI3K)-pathway deregulation are common in human prostate cancer. Through examination of 194 human prostate tumors, we observed statistically significant co-occurrence of MYC amplification and PI3K-pathway alteration, raising the possibility that these two lesions cooperate in prostate cancer progression. To investigate this, we generated bigenic mice in which both activated human AKT1 and human MYC are expressed in the prostate (MPAKT/Hi-MYC model). In contrast to mice expressing AKT1 alone (MPAKT model) or MYC alone (Hi-MYC model), the bigenic phenotype demonstrates accelerated progression of mouse prostate intraepithelial neoplasia (mPIN) to microinvasive disease with disruption of basement membrane, significant stromal remodeling and infiltration of macrophages, B- and T-lymphocytes, similar to inflammation observed in human prostate tumors. In contrast to the reversibility of mPIN lesions in young MPAKT mice after treatment with mTOR inhibitors, Hi-MYC and bigenic MPAKT/Hi-MYC mice were resistant. Additionally, older MPAKT mice showed reduced sensitivity to mTOR inhibition, suggesting that additional genetic events may dampen mTOR dependence. Since increased MYC expression is an early feature of many human prostate cancers, these data have implications for treatment of human prostate cancers with PI3K-pathway alterations using mTOR inhibitors.

Posted Content
TL;DR: It is shown that co-variation of residue pairs, observed in a large protein family, provides sufficient information to determine 3D protein structure, which opens the door to a comprehensive survey of protein 3D structures, including many not currently accessible to the experimental methods of structural genomics.
Abstract: The evolutionary trajectory of a protein through sequence space is constrained by function and three-dimensional (3D) structure. Residues in spatial proximity tend to co-evolve, yet attempts to invert the evolutionary record to identify these constraints and use them to computationally fold proteins have so far been unsuccessful. Here, we show that co-variation of residue pairs, observed in a large protein family, provides sufficient information to determine 3D protein structure. Using a data-constrained maximum entropy model of the multiple sequence alignment, we identify pairs of statistically coupled residue positions which are expected to be close in the protein fold, termed contacts inferred from evolutionary information (EICs). To assess the amount of information about the protein fold contained in these coupled pairs, we evaluate the accuracy of predicted 3D structures for proteins of 50-260 residues, from 15 diverse protein families, including a G-protein coupled receptor. These structure predictions are de novo, i.e., they do not use homology modeling or sequence-similar fragments from known structures. The resulting low C{\alpha}-RMSD error range of 2.7-5.1A, over at least 75% of the protein, indicates the potential for predicting essentially correct 3D structures for the thousands of protein families that have no known structure, provided they include a sufficiently large number of divergent sample sequences. With the current enormous growth in sequence information based on new sequencing technology, this opens the door to a comprehensive survey of protein 3D structures, including many not currently accessible to the experimental methods of structural genomics. This advance has potential applications in many biological contexts, such as synthetic biology, identification of functional sites in proteins and interpretation of the functional impact of genetic variants.

Proceedings ArticleDOI
TL;DR: It is demonstrated how the p53-response can antagonise co-deletion of PTEN and PHLPP to form a barrier against prostate cancer progression to emphasise the need for careful evaluation of PI 3-Kinase target therapy effects in prostate cancer and highlight the value of genetically engineered mouse models.
Abstract: Hyper-activation of the PI 3-Kinase/ AKT pathway is common in many cancer types. Tumourigenesis through this pathway is prevented by concerted action of multiple tumour suppressor genes. Most notably, PTEN reverts PI 3-Kinase activity whereas excessive pathway activation triggers the p53-mediated senescence arrest. However, it remains ill defined if and at what stage this response acts in human prostate cancer. Here we identify the AKT-inactivating phosphatase PHLPP as a tumour suppressor and demonstrate how the p53-response can antagonise co-deletion of PTEN and PHLPP to form a barrier against prostate cancer progression. We show that Phlpp-loss causes neoplasia and upon partial Pten-loss, carcinoma in mouse prostate. In this setting, Phlpp-deficiency triggers growth arrest via mTorC1-dependent activation of p53 and we find that co-deletion of Pten and Phlpp selects for spontaneous inactivation of p53 in prostate. Validating this conditional gene inactivation scheme in a comprehensive genomic patient data set we find that co-deletion of PTEN and PHLPP is almost exclusively observed in metastatic prostate cancer and tightly correlated to deletion of TP53. Furthermore, PTEN/ PHLPP expression can be used to predict disease outcome in these patients, comparable to the standard histology based method, but adding actionable information on pathway status. Finally, we show that both known PHLPP isoforms compensate for PTEN-suppression in a novel pathway feedback explaining their co-deletion with PTEN in the metastatic samples. Surprisingly, we find that the feedback surge of these genes is sensitive to some pharmacological inhibitors of the PI 3-Kinase pathway. Collectively, our findings emphasise the need for careful evaluation of PI 3-Kinase target therapy effects in prostate cancer and highlight the value of genetically engineered mouse models in this process. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2405. doi:10.1158/1538-7445.AM2011-2405