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Kundu et al, Supplementary Figure 2

Kundu et al, Supplementary Figure 1

Kundu et al, Supplementary Figure 4

Malignant glioma continues to be fatal, despite improved insight into its underlying molecular mechanisms. The most malignant form, glioblastoma (GBM), is characterized by aberrant activation of receptor tyrosine kinases (RTK) and infiltrative growth. Heparan sulfate proteoglycans (HSPG), integral components of the extracellular matrix of brain tumors, can regulate activation of many RTK pathways. This prompted us to investigate heparanase (HPSE), which cleaves HSPGs, for its role in glioma. This hypothesis was evaluated using tissue microarrays, GBM cells derived from patients, murine in vitro and in vivo models of glioma, and public databases. Downregulation of HPSE attenuated glioma cell proliferation, whereas addition of HPSE stimulated growth and activated ERK and AKT signaling. Using HPSE transgenic and knockout mice, it was demonstrated that tumor development in vivo was positively correlated to HPSE levels in the brain. HPSE also modified the tumor microenvironment, influencing reactive astrocytes, microglia/monocytes, and tumor angiogenesis. Furthermore, inhibition of HPSE reduces tumor cell numbers, both in vitro and in vivo. HPSE was highly expressed in human glioma and GBM cell lines, compared with normal brain tissue. Indeed, a correlation was observed between high levels of HPSE and shorter survival of patients with high-grade glioma. In conclusion, these data provide proof-of-concept for anti-HPSE treatment of malignant glioma, as well as novel insights for the development of HPSE as a therapeutic target.Implications: This study aims to target both the malignant brain tumor cells per se and their microenvironment by changing the level of an enzyme, HPSE, that breaks down modified sugar chains on cell surfaces and in the extracellular space. Mol Cancer Res; 14(12); 1243–53. ©2016 AACR.

Kundu et al, Supplementary Figure 3

File contains list of GISTIC Defined Recurrent SCNA genes that overlap between Humans and Dogs.

Small molecule screen data

Supplementary Figure 1 - Workflow for Somatic Point and Indel Mutation detection. Supplementary Figure 2 - Heatmap counts of samples with recurrent focal SCNAs per chromosome. Supplementary Figure 3 - oaCGH profiles penetrance plots and GISTIC analysis. Supplementary Figure 4 - Venn diagrams of shared and unique somatic point mutations and somatic indel mutations across the three breeds. Supplementary Figure 5 - Scatter plot shows age of diagnosis is positively correlated to the mutation counts across all breeds. Supplementary Figure 6 - Mutational signature distribution per breed shows enrichment for the novel signature in Golden Retriever. Supplementary Figure 7 - Overview and functional classes for the mutations in histone modifier genes across the three breeds. Supplementary Figure 8 - Overview of germ-line candidate mutation across three breeds and their functional impacts.

Supplementary Figure Legends and Materials and Methods

File contains genes that have focal somatic copy number deletions.

File contains supplementary tables and figures for the main article. Supplementary Table 1 - Source for tools and databases used in the methods for analysis. Supplementary Table 2 - Summary of Somatic Point and Indel Mutation across the three breeds. Supplementary Table 3 - Summary of GISTIC-defined recurrent large-scale SCNAs and gene frequency. Supplementary Table 4 - List of GISTIC classified amplification genes. See attached excel spreadsheet. Supplementary Table 5 - List of GISTIC classified deletion genes. See attached excel spreadsheet. Supplementary Table 6 - List of GISTIC genes and their predicted roles in cancer. Supplementary Table 7 - Significantly over-represented pathways for GISTIC- and MuSiC-defined genes. Supplementary Table 8 - Significantly mutated genes as identified by MuSiC and the proportion of samples per breed that show mutation in them. Supplementary Table 9 - Putative driver genes as detected by MutSigCV algorithm Supplementary Table 10 - Putative driver genes assessed by VEP high-impact mutations and their variant allele frequency. Supplementary Table 11 - Mutations and their effects on p53 protein. Supplementary Table 12 - Mutations and their effects on SETD2 protein. Supplementary Table 13 - Count of non-silent mutations per breed/per germline for OSA susceptibility genes Supplementary Table 14 - Overlap of GISTIC-defined recurrent SCNA genes between humans and dogs. See attached excel spreadsheet.

Proteomics analysis

File contains genes that have focal somatic copy number amplifications

Glioma-initiating cells (GIC) are considered the underlying cause of recurrences of aggressive glioblastomas, replenishing the tumor population and undermining the efficacy of conventional chemotherapy. Here we report the discovery that inhibiting T-type voltage-gated Ca2+ and KCa channels can effectively induce selective cell death of GIC and increase host survival in an orthotopic mouse model of human glioma. At present, the precise cellular pathways affected by the drugs affecting these channels are unknown. However, using cell-based assays and integrated proteomics, phosphoproteomics, and transcriptomics analyses, we identified the downstream signaling events these drugs affect. Changes in plasma membrane depolarization and elevated intracellular Na+, which compromised Na+-dependent nutrient transport, were documented. Deficits in nutrient deficit acted in turn to trigger the unfolded protein response and the amino acid response, leading ultimately to nutrient starvation and GIC cell death. Our results suggest new therapeutic targets to attack aggressive gliomas. Cancer Res; 77(7); 1741–52. ©2017 AACR.

Osteosarcoma is a debilitating bone cancer that affects humans, especially children and adolescents. A homologous form of osteosarcoma spontaneously occurs in dogs, and its differential incidence observed across breeds allows for the investigation of tumor mutations in the context of multiple genetic backgrounds. Using whole-exome sequencing and dogs from three susceptible breeds (22 golden retrievers, 21 Rottweilers, and 23 greyhounds), we found that osteosarcoma tumors show a high frequency of somatic copy-number alterations (SCNA), affecting key oncogenes and tumor-suppressor genes. The across-breed results are similar to what has been observed for human osteosarcoma, but the disease frequency and somatic mutation counts vary in the three breeds. For all breeds, three mutational signatures (one of which has not been previously reported) and 11 significantly mutated genes were identified. TP53 was the most frequently altered gene (83% of dogs have either mutations or SCNA in TP53), recapitulating observations in human osteosarcoma. The second most frequently mutated gene, histone methyltransferase SETD2, has known roles in multiple cancers, but has not previously been strongly implicated in osteosarcoma. This study points to the likely importance of histone modifications in osteosarcoma and highlights the strong genetic similarities between human and dog osteosarcoma, suggesting that canine osteosarcoma may serve as an excellent model for developing treatment strategies in both species.Significance: Canine osteosarcoma genomics identify SETD2 as a possible oncogenic driver of osteosarcoma, and findings establish the canine model as a useful comparative model for the corresponding human disease. Cancer Res; 78(13); 3421–31. ©2018 AACR.

File contains additional information regarding methods for identifying putative driver genes with MutSigCV and high-impact mutations based analysis. Also methods for predicting impacts of TP53 and SETD2 genes. And further information about the oaCGH analysis for somatic copy number aberrations across a subset of 22 dogs.

Dogs represent a unique spontaneous cancer model. Osteosarcoma (OSA) is the most common primary bone tumor in dogs (OMIA 001441-9615), and strongly resembles human forms of OSA. Several large- to giant-sized dog breeds, including the Leonberger, have a greatly increased risk of developing OSA. We performed genome-wide association analysis with high-density imputed SNP genotype data from 273 Leonberger cases with a median age of 8.1 [3.1–13.5] years and 365 controls older than eight years. This analysis revealed significant associations at the CDKN2A/B gene locus on canine chromosome 11, mirroring previous findings in other dog breeds, such as the greyhound, that also show an elevated risk for OSA. Heritability (h2SNP) was determined to be 20.6% (SE = 0.08; p-value = 5.7 × 10−4) based on a breed prevalence of 20%. The 2563 SNPs across the genome accounted for nearly all the h2SNP of OSA, with 2183 SNPs of small effect, 316 SNPs of moderate effect, and 64 SNPs of large effect. As with many other cancers it is likely that regulatory, non-coding variants underlie the increased risk for cancer development. Our findings confirm a complex genetic basis of OSA, moderate heritability, and the crucial role of the CDKN2A/B locus leading to strong cancer predisposition in dogs. It will ultimately be interesting to study and compare the known genetic loci associated with canine OSA in human OSA.

Glioblastoma (GBM) is the most common and lethal primary malignant brain tumor which lacks efficient treatment and predictive biomarkers. Expression of the epithelial stem cell marker Leucine-rich repeat-containing G-protein coupled receptor 5 (LGR5) has been described in GBM, but its functional role has not been conclusively elucidated. Here, we have investigated the role of LGR5 in a large repository of patient-derived GBM stem cell (GSC) cultures. The consequences of LGR5 overexpression or depletion have been analyzed using in vitro and in vivo methods, which showed that, among those with highest LGR5 expression (LGR5high ), there were two phenotypically distinct groups: one that was dependent on LGR5 for its malignant properties and another that was unaffected by changes in LGR5 expression. The LGR5-responding cultures could be identified by their significantly higher self-renewal capacity as measured by extreme limiting dilution assay (ELDA), and these LGR5high -ELDAhigh cultures were also significantly more malignant and invasive compared to the LGR5high -ELDAlow cultures. This showed that LGR5 expression alone would not be a strict marker of LGR5 responsiveness. In a search for additional biomarkers, we identified LPAR4, CCND2, and OLIG2 that were significantly upregulated in LGR5-responsive GSC cultures, and we found that OLIG2 together with LGR5 were predictive of GSC radiation and drug response. Overall, we show that LGR5 regulates the malignant phenotype in a subset of patient-derived GSC cultures, which supports its potential as a predictive GBM biomarker. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Breast cancer (BC) is a genetically heterogeneous disease with high prevalence in Northern Europe. However, there has been no detailed investigation into the Scandinavian somatic landscape. Here, in a homogeneous Swedish cohort, we describe the somatic events underlying BC, leveraging a targeted next-generation sequencing approach. We designed a 20.5 Mb array targeting coding and regulatory regions of genes with a known role in BC (n = 765). The selected genes were either from human BC studies (n = 294) or from within canine mammary tumor associated regions (n = 471). A set of predominantly estrogen receptor positive tumors (ER + 85%) and their normal tissue counterparts (n = 61) were sequenced to ~ 140 × and 85 × mean target coverage, respectively. MuTect2 and VarScan2 were employed to detect single nucleotide variants (SNVs) and copy number aberrations (CNAs), while MutSigCV (SNVs) and GISTIC (CNAs) algorithms estimated the significance of recurrent somatic events. The significantly mutated genes (q ≤ 0.01) were PIK3CA (28% of patients), TP53 (21%) and CDH1 (11%). However, histone modifying genes contained the largest number of variants (KMT2C and ARID1A, together 28%). Mutations in KMT2C were mutually exclusive with PI3KCA mutations (p ≤ 0. 001) and half of these affect the formation of a functional PHD domain. The tumor suppressor CDK10 was deleted in 80% of the cohort while the oncogene MDM4 was amplified. Mutational signature analyses pointed towards APOBEC deaminase activity (COSMIC signature 2) and DNA mismatch repair (COSMIC signature 6). We noticed two significantly distinct patterns related to patient age; TP53 being more mutated in the younger group (29% vs 9% of patients) and CDH23 mutations were absent from the older group. The increased somatic mutation prevalence in the histone modifying genes KMT2C and ARID1A distinguishes the Swedish cohort from previous studies. KMT2C regulates enhancer activation and assists tumor proliferation in a hormone-rich environment, possibly pointing to a role in ER + BC, especially in older cases. Finally, age of onset appears to affect the mutational landscape suggesting that a larger age-diverse population incorporating more molecular subtypes should be studied to elucidate the underlying mechanisms.

Breast cancer (BC) is a genetically heterogeneous disease with high prevalence in Northern Europe. However, there has been no detailed investigation into the Scandinavian somatic landscape. Here, in a homogeneous Swedish cohort, we describe the somatic events underlying BC, leveraging a targeted next-generation sequencing approach. We designed a 20.5 Mb array targeting coding and regulatory regions of genes with a known role in BC (n = 765). The selected genes were either from human BC studies (n = 294) or from within canine mammary tumor associated regions (n = 471). A set of predominantly estrogen receptor positive tumors (ER + 85%) and their normal tissue counterparts (n= 61) were sequenced to ~ 140 × and 85 × mean target coverage, respectively. MuTect2 and VarScan2 were employed to detect single nucleotide variants (SNVs) and copy number aberrations (CNAs), while MutSigCV (SNVs) and GISTIC (CNAs) algorithms estimated the significance of recurrent somatic events. The significantly mutated genes (q ≤ 0.01) were PIK3CA (28% of patients), TP53 (21%) and CDH1 (11%). However, histone modifying genes contained the largest number of variants (KMT2C and ARID1A, together 28%). Mutations in KMT2C were mutually exclusive with PI3KCA mutations (p ≤ 0. 001) and half of these affect the formation of a functional PHD domain. The tumor suppressor CDK10 was deleted in 80% of the cohort while the oncogene MDM4 was amplified. Mutational signature analyses pointed towards APOBEC deaminase activity (COSMIC signature 2) and DNA mismatch repair (COSMIC signature 6). We noticed two significantly distinct patterns related to patient age; TP53 being more mutated in the younger group (29% vs 9% of patients) and CDH23 mutations were absent from the older group. The increased somatic mutation prevalence in the histone modifying genes KMT2C and ARID1A distinguishes the Swedish cohort from previous studies. KMT2C regulates enhancer activation and assists tumor proliferation in a hormone-rich environment, possibly pointing to a role in ER + BC, especially in older cases. Finally, age of onset appears to affect the mutational landscape suggesting that a larger age-diverse population incorporating more molecular subtypes should be studied to elucidate the underlying mechanisms.

New genome assemblies have been arriving at a rapidly increasing pace, thanks to decreases in sequencing costs and improvements in third-generation sequencing technologies1–3. For example, the number of vertebrate genome assemblies currently in the NCBI (National Center for Biotechnology Information) database4 increased by more than 50% to 1,485 assemblies in the year from July 2018 to July 2019. In addition to this influx of assemblies from different species, new human de novo assemblies5 are being produced, which enable the analysis of not only small polymorphisms, but also complex, large-scale structural differences between human individuals and haplotypes. This coming era and its unprecedented amount of data offer the opportunity to uncover many insights into genome evolution but also present challenges in how to adapt current analysis methods to meet the increased scale. Cactus6, a reference-free multiple genome alignment program, has been shown to be highly accurate, but the existing implementation scales poorly with increasing numbers of genomes, and struggles in regions of highly duplicated sequences. Here we describe progressive extensions to Cactus to create Progressive Cactus, which enables the reference-free alignment of tens to thousands of large vertebrate genomes while maintaining high alignment quality. We describe results from an alignment of more than 600 amniote genomes, which is to our knowledge the largest multiple vertebrate genome alignment created so far., The Progressive Cactus program can create reference-free alignments of hundreds of large vertebrate genomes efficiently, and is used for the alignment of more than 600 amniote genomes.

Additional file 1: Figure S1. Somatic Copy Number Alteration (SCNA) in SweGBM-1 matches observations in the TCGA-GBM dataset. Figure S2. Distribution of Coding and Non-Coding variants per sample for the SweGBM-1 cohort. Figure S3.TERTp mutational profiles for the SweGBM-1 cohort. Missense mutations at two positions in the TERT promoter observed previously in GBM datasets are seen in most samples. Figure S4. Boxplot of the rates of constraint bases in the internal and flanking regions of key GBM and all OPCG. Figure S5. Mutalisk algorithm identifies two major mutational signatures, Cosmic 1 and Cosmic 5 across samples. Figure S6. Workflow for variant and copy number detection in matched tumor-normal samples. Figure S7. Workflow for annotation of non-coding constraint mutations.

The Zoonomia Project is investigating the genomics of shared and specialized traits in eutherian mammals. Here we provide genome assemblies for 131 species, of which all but 9 are previously uncharacterized, and describe a whole-genome alignment of 240 species of considerable phylogenetic diversity, comprising representatives from more than 80% of mammalian families. We find that regions of reduced genetic diversity are more abundant in species at a high risk of extinction, discern signals of evolutionary selection at high resolution and provide insights from individual reference genomes. By prioritizing phylogenetic diversity and making data available quickly and without restriction, the Zoonomia Project aims to support biological discovery, medical research and the conservation of biodiversity., This project was funded by NIH NHGRI R01HG008742 (E.K.K., B.B., D.P.G., R.S., J.T.-M., J.J., H.J.N., B.P. and J. Armstrong), Swedish Research Council Distinguished Professor Award (K.L.-T., V.D.M., E.M. and J.R.S.M.), Swedish Research Council grant 2018-05973 (K.L.-T.), Knut and Alice Wallenberg Foundation (K.L.-T., V.D.M., E.M. and J.R.S.M.), Uppsala University (K.L.-T., V.D.M., E.M., J.R.S.M., J.J., J. Alfoldi and L.G.), Broad Institute Next10 (L.G.), Gladstone Institutes (K.S.P.), NIH NHGRI 5R01HG002939 (A.F.A.S. and R.H.), NIH NHGRI 5U24HG010136 (A.F.A.S. and R.H.), NIH NHGRI 5R01HG010485 (B.P. and M.D.), NIH NHGRI 2U41HG007234 (B.P., M.D. and J. Armstrong), NIH NIA 5PO1AG047200 (V.N.G.), NIH NIA 1UH2AG064706 (V.N.G.), BFU2017-86471-P MINECO/FEDER, UE (T.M.-B.), Secretaria d’Universitats i Recerca and CERCA Programme del Departament d’Economia i Coneixement de la Generalitat de Catalunya GRC 2017 SGR 880 (T.M.-B.), Howard Hughes International Early Career (T.M.-B.), European Research Council Horizon 2020 no. 864203 (T.M.-B.), Obra Social ‘La Caixa’ (T.M.-B.), BBSRC BBS/E/T/000PR9818, BBS/E/T/ 000PR9783 (W.H. and W.N.), BBSRC Core Strategic Programme Grant BB/P016774/1 (W.H., W.N. and F.D.), Sir Henry Dale Fellowship 200517/Z/16/Z jointly funded by the Wellcome Trust and the Royal Society (N.R.C.), FJCI-2016-29558 MICINN (D.J.), Prince Albert II Foundation of Monaco and Canada, Global Genome Initiative, Smithsonian Institution (M.N.), European Research Council Research Grant ERC-2012-StG311000 (E.C.T.), Irish Research Council Laureate Award (E.C.T.), UK Medical Research Council MR/P026028/1 (W.H. and W.N.), National Science Foundation DEB-1457735 (M.S.S.), National Science Foundation DEB-1753760 (W.J.M.), National Science Foundation IOS-2029774 (E.K.K. and D.P.G.), Robert and Rosabel Osborne Endowment (H.A.L. and J.D.), Swedish Research Council, FORMAS 221-2012-1531 (J.R.S.M.), NSF RoL: FELS: EAGER: DEB 1838283 (D.A.R.) and Academy of Finland grant to Center of Excellence in Tumor Genetics Research no. 312042 (T.K. and J.T.).

The identity of the glioblastoma (GBM) cell of origin and its contributions to disease progression and treatment response remain largely unknown. We have analyzed how the phenotypic state of the initially transformed cell affects mouse GBM development and essential GBM cell (GC) properties. We find that GBM induced in neural stem-cell-like glial fibrillary acidic protein (GFAP)-expressing cells in the subventricular zone of adult mice shows accelerated tumor development and produces more malignant GCs (mGC1GFAP) that are less resistant to cancer drugs, compared with those originating from more differentiated nestin- (mGC2NES) or 2,′3′-cyclic nucleotide 3′-phosphodiesterase (mGC3CNP)-expressing cells. Transcriptome analysis of mouse GCs identified a 196 mouse cell origin (MCO) gene signature that was used to partition 61 patient-derived GC lines. Human GC lines that clustered with the mGC1GFAP cells were also significantly more self-renewing, tumorigenic, and sensitive to cancer drugs compared with those that clustered with mouse GCs of more differentiated origin.

Malignant gliomas are among the most severe types of cancer, and the most common primary brain tumors. Treatment options are limited and the prognosis is poor. WNT-5A, a member of the WNT family of lipoglycoproteins, plays a role in oncogenesis and tumor progression in various cancers, whereas the role of WNT-5A in glioma remains obscure. Based on the role of WNT-5A as an oncogene, its potential to regulate microglia cells and the glioma-promoting capacities of microglia cells, we hypothesize that WNT-5A has a role in regulation of immune functions in glioma. We investigated WNT-5A expression by in silico analysis of the cancer genome atlas (TCGA) transcript profiling of human glioblastoma samples and immunohistochemistry experiments of human glioma tissue microarrays (TMA). Our results reveal higher WNT-5A protein levels and mRNA expression in a subgroup of gliomas (WNT-5A(high)) compared to non-malignant control brain tissue. Furthermore, we show a significant correlation between WNT-5A in the tumor and presence of major histocompatibility complex Class II-positive microglia/monocytes. Our data pinpoint a positive correlation between WNT-5A and a proinflammatory signature in glioma. We identify increased presence of microglia/monocytes as an important aspect in the inflammatory transformation suggesting a novel role for WNT-5A in human glioma.

Glioblastoma (GBM) is the most common and lethal primary malignant brain tumor which lacks efficient treatment and predictive biomarkers. Expression of the epithelial stem cell marker Leucine-rich repeat-containing G-protein coupled receptor 5 (LGR5) has been described in GBM, but its functional role has not been conclusively elucidated. Here, we have investigated the role of LGR5 in a large repository of patient-derived GBM stem cell (GSC) cultures. The consequences of LGR5 overexpression or depletion have been analyzed using in vitro and in vivo methods, which showed that, among those with highest LGR5 expression (LGR5

Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools for network construction and interpretation are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as the Cancer Genome Atlas (TCGA). Here, we describe a novel strategy to construct and analyze integrative network models heterogeneous data from multiple cancers. First, we introduce a generalization of sparse inverse covariance selection (SICS) designed to integrate genetic, epigenetic and transcriptional data from multiple cancers into a comparative network. The algorithm is shown to be statistically robust, effective at detecting direct pathway links in data from The Cancer Genome Atlas (TCGA), and uses a new strategy involving non-informative priors to balance different cancers and data types. Second, we propose to rationalize the interpretation of the derived networks by a new and publicly accessible tool (cancerlandscapes.org), in which derived models are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model of genetic, epigenetic and transcriptional data for eight TCGA cancers, using data from 3900 patients. The derived model rediscovered known mechanisms and contained interesting predictions. Possible applications include the prediction of regulatory relationships between genes in particular cancers, comparison of network modules in across multiple forms of cancer, and identification of drug targets in relation to network structure. Citation Format: Teresia Kling, Patrik Johansson, Jose Sanchez, Voichita D. Marinescu, Rebecka Jornsten, Sven Nelander. Efficient exploration of multi-cancer networks by generalized covariance selection and interactive web content. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-35.

Glioblastoma (GBM) is the most frequent and malignant form of primary brain tumor. GBM is essentially incurable and its resistance to therapy is attributed to a subpopulation of cells called glioma stem cells (GSCs). To meet the present shortage of relevant GBM cell (GC) lines we developed a library of annotated and validated cell lines derived from surgical samples of GBM patients, maintained under conditions to preserve GSC characteristics. This collection, which we call the Human Glioblastoma Cell Culture (HGCC) resource, consists of a biobank of 48 GC lines and an associated database containing high-resolution molecular data. We demonstrate that the HGCC lines are tumorigenic, harbor genomic lesions characteristic of GBMs, and represent all four transcriptional subtypes. The HGCC panel provides an open resource for in vitro and in vivo modeling of a large part of GBM diversity useful to both basic and translational GBM research., Highlights • The HGCC resource contains 48 annotated human GBM cell lines and an interactive database • The GBM cell lines are propagated in stem cell conditions and display GSC characteristics • The HGCC resource provides cell lines of all molecular (TCGA) subtypes • All data connected with the HGCC cell lines can be accessed at hgcc.se

We have recently identified LGR5 as a marker of an immature, neural stem cell-like cell of origin for glioblastoma (GBM). Here we have further investigated the role of LGR5 in GBM. We have found that in a panel of 61 patient-derived GBM stem cell lines, LGR5 was differentially expressed and significantly enriched in those of the proneural subtype. High LGR5 expression in proneural GBM lines (LGR5high-PN) was associated with increased self-renewal and tumorigenicity compared to low expressing (LGR5low) and high expressing mesenchymal (LGR5high-MS) GBM cell lines. Knock-down of LGR5 gene expression in LGR5high-PN lines resulted in decreased self-renewal, while over-expression in both LGR5high-PN and LGR5low lines produced a higher self-renewal capacity of the cells. Furthermore, by comparing LGR5high-PN cells with and without si/shLGR5 we found that LGR5 also could regulate tumor cell invasion in vitro and in vivo. When analyzing transcriptome changes upon LGR5 depletion we detected a transition from the proneural to the mesenchymal subtype in LGR5high-PN GBM cells treated with shLGR5 that was coupled to an upregulation of genes of the TNFA/NFKB pathway. Taken together our data suggests that LGR5 could be an essential regulator of a subset of GBMs. To stratify patients carrying LGR5-dependent GBMs we have identified a number of additional candidate biomarkers, which together with LGR5 could help defining LGR5-driven GBM cells.

Glioma-initiating cells (GIC) are considered the underlying cause of recurrences of aggressive glioblastomas, replenishing the tumor population and undermining the efficacy of conventional chemotherapy. Here we report the discovery that inhibiting T-type voltage-gated Ca2+ and KCa channels can effectively induce selective cell death of GIC and increase host survival in an orthotopic mouse model of human glioma. At present, the precise cellular pathways affected by the drugs affecting these channels are unknown. However, using cell-based assays and integrated proteomics, phosphoproteomics, and transcriptomics analyses, we identified the downstream signaling events these drugs affect. Changes in plasma membrane depolarization and elevated intracellular Na+, which compromised Na+-dependent nutrient transport, were documented. Deficits in nutrient deficit acted in turn to trigger the unfolded protein response and the amino acid response, leading ultimately to nutrient starvation and GIC cell death. Our results suggest new therapeutic targets to attack aggressive gliomas. Cancer Res; 77(7); 1741–52. ©2017 AACR.

Malignant glioma continues to be fatal, despite improved insight into its underlying molecular mechanisms. The most malignant form, glioblastoma (GBM), is characterized by aberrant activation of receptor tyrosine kinases (RTK) and infiltrative growth. Heparan sulfate proteoglycans (HSPG), integral components of the extracellular matrix of brain tumors, can regulate activation of many RTK pathways. This prompted us to investigate heparanase (HPSE), which cleaves HSPGs, for its role in glioma. This hypothesis was evaluated using tissue microarrays, GBM cells derived from patients, murine in vitro and in vivo models of glioma, and public databases. Downregulation of HPSE attenuated glioma cell proliferation, whereas addition of HPSE stimulated growth and activated ERK and AKT signaling. Using HPSE transgenic and knockout mice, it was demonstrated that tumor development in vivo was positively correlated to HPSE levels in the brain. HPSE also modified the tumor microenvironment, influencing reactive astrocytes, microglia/monocytes, and tumor angiogenesis. Furthermore, inhibition of HPSE reduces tumor cell numbers, both in vitro and in vivo. HPSE was highly expressed in human glioma and GBM cell lines, compared with normal brain tissue. Indeed, a correlation was observed between high levels of HPSE and shorter survival of patients with high-grade glioma. In conclusion, these data provide proof-of-concept for anti-HPSE treatment of malignant glioma, as well as novel insights for the development of HPSE as a therapeutic target. Implications: This study aims to target both the malignant brain tumor cells per se and their microenvironment by changing the level of an enzyme, HPSE, that breaks down modified sugar chains on cell surfaces and in the extracellular space. Mol Cancer Res; 14(12); 1243–53. ©2016 AACR.

Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool ( ext-link-type="uri" xlink:href="http://cancerlandscapes.org/">cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets.

// Ananya Roy 1,* , Antoine Coum 2,* , Voichita D. Marinescu 3 , Jelena Polajeva 4 , Anja Smits 5,6 , Sven Nelander 3 , Lene Uhrbom 1 , Bengt Westermark 1 , Karin Forsberg-Nilsson 3 , Fredrik Ponten 3 and Elena Tchougounova 1 1 Uppsala University, Department of Immunology, Genetics and Pathology, Rudbeck Laboratory, Uppsala, Sweden 2 Nanoscience Centre, Department of Engineering, Cambridge University, Cambridge, UK 3 Uppsala University, Department of Immunology, Genetics and Pathology, and Science for Life Laboratory, Rudbeck Laboratory, Uppsala, Sweden 4 Cancer Research Technology, LBIC, London, UK 5 Uppsala University, Department of Neuroscience, Neurology, Uppsala, Sweden 6 Present address: Danish Epilepsy Center, Dianalund, Denmark * These authors have contributed equally to this work Correspondence to: Elena Tchougounova, email: // Keywords : mast cell, glioma, PAI-1, SERPINE1, LRP1 Received : April 28, 2015 Accepted : June 12, 2015 Published : June 25, 2015 Abstract Glioblastoma (GBM) is a high-grade glioma with a complex microenvironment, including various inflammatory cells and mast cells (MCs) as one of them. Previously we had identified glioma grade-dependent MC recruitment. In the present study we investigated the role of plasminogen activator inhibitor 1 (PAI-1) in MC recruitment. PAI-1, a primary regulator in the fibrinolytic cascade is capable of forming a complex with fibrinolytic system proteins together with low-density lipoprotein receptor-related protein 1 (LRP1). We found that neutralizing PAI-1 attenuated infiltration of MCs. To address the potential implication of LRP1 in this process, we used a LRP1 antagonist, receptor-associated protein (RAP), and demonstrated the attenuation of MC migration. Moreover, a positive correlation between the number of MCs and the level of PAI-1 in a large cohort of human glioma samples was observed. Our study demonstrated the expression of LRP1 in human MC line LAD2 and in MCs in human high-grade glioma. The activation of potential PAI-1/LRP1 axis with purified PAI-1 promoted increased phosphorylation of STAT3 and subsequently exocytosis in MCs. These findings indicate the influence of the PAI-1/LRP1 axis on the recruitment of MCs in glioma. The connection between high-grade glioma and MC infiltration could contribute to patient tailored therapy and improve patient stratification in future therapeutic trials.

We describe a comprehensive map of putative transcription factor binding sites (TFBSs) across multiple genomes created using a search method that relies on hidden Markov models built from experimentally determined TFBSs. Using the information in the TRANSFAC and JASPAR databases, we built 1134 models for TFBSs and used them to scan regions 10 kb upstream of the start of the transcript for all known genes in the human, mouse and Drosophila melanogaster genomes. The results, together with homology information on clusters of ortholog genes across the three genomes, were used to create a multi-organism catalog of annotated TFBSs. The catalog can be queried through a web interface accessible at http://bio.chip.org/mapper that allows the identification, visualization and selection of TFBSs occurring in the promoter of a gene of interest and also the common factors predicted to bind across the cluster of orthologs that includes that gene. Alternatively, the interface allows the user to retrieve binding sites for a single transcription factor of interest in a single gene or in all genes of the human, mouse or fruit fly genomes.

The success rate of association studies can be improved by selecting better genetic markers for genotyping or by providing better leads for identifying pathogenic single nucleotide polymorphisms (SNPs) in the regions of linkage disequilibrium with positive disease associations. We have developed a novel algorithm to predict pathogenic single amino acid changes, either nonsynonymous SNPs (nsSNPs) or missense mutations, in conserved protein domains. Using a Bayesian framework, we found that the probability of a microbial missense mutation causing a significant change in phenotype depended on how much difference it made in several phylogenetic, biochemical, and structural features related to the single amino acid substitution. We tested our model on pathogenic allelic variants (missense mutations or nsSNPs) included in OMIM, and on the other nsSNPs in the same genes (from dbSNP) as the nonpathogenic variants. As a result, our model predicted pathogenic variants with a 10% false-positive rate. The high specificity of our prediction algorithm should make it valuable in genetic association studies aimed at identifying pathogenic SNPs.

Glioblastoma Multiforme (GBM) is the most frequent adult primary malignant brain tumor that remains incurable despite aggressive treatment. The cell of origin (COO) for GBM is unknown but assumed to be a glial stem or progenitor cell. GBM harbours hierarchical tumor cells called glioma stem cells (GSCs) that maintain tumor growth, drive tumor progression and cause tumor relapse due to their increased resistance to therapy. We have analyzed the significance of cellular origin for GBM development and GSC properties by comparing mouse GBMs and GSCs derived thereof induced in neural stem cells (NSCs), glial-restricted precursor cells (GPCs) or oligodendrocyte precursor cells (OPCs) by identical mutations. There were striking differences in GBM development and the phenotypes of GSCs and their response to drugs owing to the COO. Global gene expression analysis of mouse GSC lines displayed a clear separation due to COO and differential gene expression analysis identified a COO gene signature of 175 genes. Cross-species bioinformatics analyses were performed. First we analyzed the human cancer genome atlas (TCGA) GBM tissue samples and the mouse GSC expression data for a collection of TCGA GBM subtype signature genes. This showed that we could model both Proneural and Mesenchymal GBMs in mice by merely switching the COO. Next, we used the mouse COO gene signature to stratify a large number of newly established human glioma stem cell lines. This produced two groups of human GSCs; the NSC origin group and the progenitor cell (PC) origin group in which the mouse GPC- and OPC-derived genes were combined. Importantly, patient survival was significantly different between the NSC and PC COO groups with a better prognosis for the PC group patients. Thus, the cell of origin is essential for GBM biology and needs to be considered for more accurate patient stratification, target identification and drug discovery.

Tumor-initiating cells are a subpopulation in aggressive cancers that exhibit traits shared with stem cells, including the ability to self-renew and differentiate, commonly referred to as stemness. In addition, such cells are resistant to chemo- and radiation therapy posing a therapeutic challenge. To uncover stemness-associated functions in glioma-initiating cells (GICs), transcriptome profiles were compared to neural stem cells (NSCs) and gene ontology analysis identified an enrichment of Ca2+ signaling genes in NSCs and the more stem-like (NSC-proximal) GICs. Functional analysis in a set of different GIC lines regarding sensitivity to disturbed homeostasis using A23187 and Thapsigargin, revealed that NSC-proximal GICs were more sensitive, corroborating the transcriptome data. Furthermore, Ca2+ drug sensitivity was reduced in GICs after differentiation, with most potent effect in the NSC-proximal GIC, supporting a stemness-associated Ca2+ sensitivity. NSCs and the NSC-proximal GIC line expressed a larger number of ion channels permeable to potassium, sodium and Ca2+. Conversely, a higher number of and higher expression levels of Ca2+ binding genes that may buffer Ca2+, were expressed in NSC-distal GICs. In particular, expression of the AMPA glutamate receptor subunit GRIA1, was found to associate with Ca2+ sensitive NSC-proximal GICs, and decreased as GICs differentiated along with reduced Ca2+ drug sensitivity. The correlation between high expression of Ca2+ channels (such as GRIA1) and sensitivity to Ca2+ drugs was confirmed in an additional nine novel GIC lines. Calcium drug sensitivity also correlated with expression of the NSC markers nestin (NES) and FABP7 (BLBP, brain lipid-binding protein) in this extended analysis. In summary, NSC-associated NES+/FABP7(+)/GRIA1(+) GICs were selectively sensitive to disturbances in Ca2+ homeostasis, providing a potential target mechanism for eradication of an immature population of malignant cells.

Summary: The serial analysis of chromatin occupancy technique (SACO) promises to become a widely used method for the unbiased genome-wide experimental identification of loci bound by a transcription factor of interest. We describe the first web-based automatic tool, termed sequence tag analysis and reporting tool (START), for processing SACO data generated by experiments performed for the yeast, fruit fly, mouse, rat or human genomes. The program uses as input sequences of inserts from a SACO library from which it extracts all SACO tags, maps them to genomic locations and annotates them. START returns detailed information about these tags including the genes, the genomic elements and the miRNA precursors found in their vicinity, and makes use of the MAPPER database to identify putative transcription factor binding sites located close to the tags. Availability: The program is available at Contact: vdmarinescu@chip.org Supplementary information: Supplementary information is available at

Skeletal muscle differentiation is a complex, highly coordinated process that relies on precise temporal gene expression patterns. To better understand this cascade of transcriptional events, we used expression profiling to analyze gene expression in a 12-day time course of differentiating C2C12 myoblasts. Cluster analysis specific for time-ordered microarray experiments classified 2895 genes and ESTs with variable expression levels between proliferating and differentiating cells into 22 clusters with distinct expression patterns during myogenesis. Expression patterns for several known and novel genes were independently confirmed by real-time quantitative RT-PCR and/or Western blotting and immunofluorescence. MyoD and MEF family members exhibited unique expression kinetics that were highly coordinated with cell-cycle withdrawal regulators. Among genes with peak expression levels during cell cycle withdrawal were Vcam1, Itgb3, Itga5, Vcl, as well as Ptger4, a gene not previously associated with the process of myogenesis. One interesting uncharacterized transcript that is highly induced during myogenesis encodes several immunoglobulin repeats with sequence similarity to titin, a large sarcomeric protein. These data sets identify many additional uncharacterized transcripts that may play important functions in muscle cell proliferation and differentiation and provide a baseline for comparison with C2C12 cells expressing various mutant genes involved in myopathic disorders.

Gene arrays demonstrate a promising ability to characterize expression levels across the entire genome but suffer from significant levels of measurement noise. We present a rigorous new approach to estimate transcript levels and ratios from one or more gene array experiments, given a model of measurement noise and available prior information. The Bayesian estimation of array measurements (BEAM) technique provides a principled method to identify changes in expression level, combine repeated measurements, or deal with negative expression level measurements. BEAM is more flexible than existing techniques, because it does not assume a specific functional form for noise and prior models. Instead, it relies on computational techniques that apply to a broad range of models. We use Affymetrix yeast chip data to illustrate the process of developing accurate noise and prior models from existing experimental data. The resulting noise model includes novel features such as heavy-tailed additive noise and a gene-specific bias term. We also verify that the resulting noise and prior models fit data from an Affymetrix human chip set.

We present a new statistically optimal approach to estimate transcript levels and ratios from one or more gene array experiments. The Bayesian Estimation of Array Measurements (BEAM) technique uses a model of measurement noise and prior information to estimate biological expression levels. It provides a principled method to deal with negative expression level measurements, combine multiple measurements, and identify changes in expression level. BEAM is more flexible than existing techniques, because it does not assume a specific functional form for noise and prior models. Rather, it uses a more accurate noise model developed from experimental data, a process we illustrate here using Affymetrix yeast chips.

Although studies performed in the past decade have contributed to a better classification and characterization of GBM, the overall survival and outcome of GBM patients have not seen major improvement. The mesenchymal signature is described as the subtype of GBM with the worst prognosis, primarily due to inherent resistance of the tumors to contemporary therapies. The identification of molecular determinants of mesenchymal transformation could potentially allow for the discovery of new therapeutic targets. The Zing Finger And BTB Domain Containing 18 (ZBTB18; formerly ZNF238) is a transcriptional repressor with a crucial role in brain development and neuronal differentiation. Consistent with our previous study, which provided preliminary evidence of a role for ZBTB18 in a network of mesenchymal transformation in GBM, our new data have discovered epigenetic silencing, that is promoter methylation, as a mechanism to downregulate ZBTB18 in these tumors. We show that ZBTB18 functions as a master regulator of a mesenchymal gene signature in GBM and that loss of ZBTB18 contributes to the highly invasive mesenchymal phenotype. Conversely, re-expression of ZBTB18 reverses the phenotype and impairs tumor-forming ability. Overall, our results support a tumor suppressor role of ZBTB18 in the brain and identify promoter hypermethylation as a mechanism to silence ZBTB18 in the mesenchymal subtype of GBM, which provides a new mechanistic opportunity to specifically target this tumor subclass.

Cis-regulatory modules are combinations of regulatory elements occurring in close proximity to each other that control the spatial and temporal expression of genes. The ability to identify them in a genome-wide manner depends on the availability of accurate models and of search methods able to detect putative regulatory elements with enhanced sensitivity and specificity. We describe the implementation of a search method for putative transcription factor binding sites (TFBSs) based on hidden Markov models built from alignments of known sites. We built 1,079 models of TFBSs using experimentally determined sequence alignments of sites provided by the TRANSFAC and JASPAR databases and used them to scan sequences of the human, mouse, fly, worm and yeast genomes. In several cases tested the method identified correctly experimentally characterized sites, with better specificity and sensitivity than other similar computational methods. Moreover, a large-scale comparison using synthetic data showed that in the majority of cases our method performed significantly better than a nucleotide weight matrix-based method. The search engine, available at http://mapper.chip.org , allows the identification, visualization and selection of putative TFBSs occurring in the promoter or other regions of a gene from the human, mouse, fly, worm and yeast genomes. In addition it allows the user to upload a sequence to query and to build a model by supplying a multiple sequence alignment of binding sites for a transcription factor of interest. Due to its extensive database of models, powerful search engine and flexible interface, MAPPER represents an effective resource for the large-scale computational analysis of transcriptional regulation.