Clinical association analysis of ependymomas and pilocytic astrocytomas reveals elevated FGFR3 and FGFR1 expression in aggressive ependymomas

BACKGROUND

Fibroblast growth factor receptors (FGFRs) are well-known proto-oncogenes in several human malignancies and are currently therapeutically targeted in clinical trials. Among glioma subtypes, activating FGFR1 alterations have been observed in a subpopulation of pilocytic astrocytomas while FGFR3 fusions occur in IDH wild-type diffuse gliomas, resulting in high FGFR3 protein expression. The purpose of this study was to associate FGFR1 and FGFR3 protein levels with clinical features and genetic alterations in ependymoma and pilocytic astrocytoma.

METHODS

FGFR1 and FGFR3 expression levels were detected in ependymoma and pilocytic astrocytoma tissues using immunohistochemistry. Selected cases were further analyzed using targeted sequencing.

RESULTS

Expression of both FGFR1 and FGFR3 varied within all tumor types. In ependymomas, increased FGFR3 or FGFR1 expression was associated with high tumor grade, cerebral location, young patient age, and poor prognosis. Moderate-to-strong expression of FGFR1 and/or FGFR3 was observed in 76% of cerebral ependymomas. Cases with moderate-to-strong expression of both proteins had poor clinical prognosis. In pilocytic astrocytomas, moderate-to-strong FGFR3 expression was detected predominantly in non-pediatric patients. Targeted sequencing of 12 tumors found no protein-altering mutations or fusions in FGFR1 or FGFR3.

CONCLUSIONS

Elevated FGFR3 and FGFR1 protein expression is common in aggressive ependymomas but likely not driven by genetic alterations. Further studies are warranted to evaluate whether ependymoma patients with high FGFR3 and/or FGFR1 expression could benefit from treatment with FGFR inhibitor based therapeutic approaches currently under evaluation in clinical trials.

Abstract 5011: Strong FGFR3 staining is a marker for FGFR3 fusions and poor prognosis in diffuse gliomas

Inhibitors of fibroblast growth factor receptors (FGFRs) have recently arisen as a promising treatment option for patients with FGFR alterations. Gene fusions involving FGFR3 and transforming acidic coiled coil 3 (TACC3) have been detected in diffuse gliomas and other malignancies, and fusion-positive cases have responded well to FGFR inhibitors. As high FGFR3 expression has been detected in fusion positive tumors, we sought to determine the clinical significance of FGFR3 protein expression level and its potential to indicate FGFR3 fusions. We thus performed FGFR3 IHC on tissue microarrays containing 676 grade II-IV astrocytoma and 116 grade II-III oligodendroglial tumor specimens. Selected cases were further analyzed using targeted sequencing. Moderate-to-strong FGFR3 staining was detected in all tumor grades, was more common in females, and associated with poor survival. Targeted sequencing identified FGFR3-TACC3 fusions and an FGFR3-CAMK2A fusion in 10 of 12 strongly stained cases (staining specificity 86%), whereas no fusions were found in 12 negatively-to-moderately stained cases (staining sensitivity 100%). Fusion-positive cases were predominantly female and negative for IDH, TP53, and EGFR/PDGFRA/MET alterations. Importantly, FGFR3 staining revealed intratumoral heterogeneity, with subclonal negative staining in a subpopulation of fusion-positive cases. Taken together, strong FGFR3 protein expression is indicative of FGFR3 fusions and may serve as a cost-effective predictive marker for FGFR-inhibitor-based treatment regimens.

Citation Format: Kirsi J. Granberg, Matti Annala, Birgitta Lehtinen, Juha Kesseli, Joonas Haapasalo, Olli Yli-Harja, Tapio Visakorpi, Hannu Haapasalo, Matti Nykter, Wei Zhang. Strong FGFR3 staining is a marker for FGFR3 fusions and poor prognosis in diffuse gliomas. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 5011.

Dissection of Signaling Pathways in Fourteen Breast Cancer Cell Lines Using Reverse-Phase Protein Lysate Microarray

Signal transduction pathways play a crucial role in breast cancer development, progression, and response to different therapies. A major problem in breast cancer therapy is the heterogeneity among different tumor types and cell lines commonly used in preclinical studies. To characterize the signaling pathways of some of the commonly used breast cancer cell lines and dissect the relationship among a number of pathways and some key genetic and molecular events in breast cancer development, such as p53 mutation, ErbB2 expression, and estrogen receptor (ER)/progesterone receptor (PR) status, we performed pathway profiling of 14 breast cancer cell lines by measuring the expression and phosphorylation status of 40 different cell signaling proteins with 53 specific antibodies using a protein lysate array. Cluster analysis of the expression data showed that there was close clustering of phosphatidylinositol 3-kinase, Akt, mammalian target of rapamycin (mTOR), Src, and platelet-derived growth factor receptor β (PDGFRβ) in all of the cell lines. The most differentially expressed proteins between ER- and PR-positive and ER- and PR-negative breast cells were mTOR, Akt (pThr308), PDGFRβ, PDGFRβ (pTyr751), panSrc, Akt (pSer473), insulin-like growth factor-binding protein 5 (IGFBP5), Src (pTyr418), mTOR (pSer2448), and IGFBP2. Many apoptotic proteins, such as apoptosis-inducing factor, IGFBP3, bad, bax, and cleaved caspase 9, were overexpressed in mutant p53-carrying breast cancer cells. Hexokinase isoenzyme 1, ND2, and c-kit were the most differentially expressed proteins in high and low ErbB2-expressing breast cancer cells. This study demonstrated that ER/PR status, ErbB2 expression, and p53 status are major molecules that impact downstream signaling pathways.

Phase-dependent dynamics of the lac promoter under nutrient stress

To survive, a bacterial population must sense nutrient availability and adjust its growth phase accordingly. Few studies have quantitatively analyzed the single-cell behavior of stress and growth phase-related transcriptional changes in Escherichia coli. To investigate the dynamic changes in transcription during different growth phases and starvation, we analyzed the single-cell transcriptional dynamics of the E. coli lac promoter. Cells were grown under different starvation conditions, including glucose, magnesium, phosphate and thiamine limitations, and transcription dynamics was quantified using a single RNA detection method at different phases. Differences in gene expression over conditions and phases indicate that stochasticity in transcription dynamics is directly connected to cell phase and availability of nutrients. Except for glucose, the pattern of transcription dynamics under all starvation conditions appears to be similar. Transcriptional bursts were more prominent in lag and stationary phase cells starved for energy sources. Identical behavior was observed in exponential phase cells starved for phosphate and thiamine. Noise measurements under all nutrient exhaustion conditions indicate that intrinsic noise is higher than extrinsic noise. Our results, obtained in a relA1 mutational background, which led to suboptimal production of ppGpp, suggest that the single-cell transcriptional changes we observed were largely ppGpp-independent. Taken together, we propose that, under different starvation conditions, cells are able to decrease the trend in cell-to-cell variability in transcription as a common means of adaptation.

Transcriptome Sequencing Reveals PCAT5 as a Novel ERG-Regulated Long Noncoding RNA in Prostate Cancer

Castration-resistant prostate cancers (CRPC) that arise after the failure of androgen-blocking therapies cause most of the deaths from prostate cancer, intensifying the need to fully understand CRPC pathophysiology. In this study, we characterized the transcriptomic differences between untreated prostate cancer and locally recurrent CRPC. Here, we report the identification of 145 previously unannotated intergenic long noncoding RNA transcripts (lncRNA) or isoforms that are associated with prostate cancer or CRPC. Of the one third of these transcripts that were specific for CRPC, we defined a novel lncRNA termed PCAT5 as a regulatory target for the transcription factor ERG, which is activated in approximately 50% of human prostate cancer. Genome-wide expression analysis of a PCAT5-positive prostate cancer after PCAT5 silencing highlighted alterations in cell proliferation pathways. Strikingly, an in vitro validation of these alterations revealed a complex integrated phenotype affecting cell growth, migration, invasion, colony-forming potential, and apoptosis. Our findings reveal a key molecular determinant of differences between prostate cancer and CRPC at the level of the transcriptome. Furthermore, they establish PCAT5 as a novel oncogenic lncRNA in ERG-positive prostate cancers, with implications for defining CRPC biomarkers and new therapeutic interventions.

Abstract 148: Transcriptome sequencing reveals PCAT5 - new ERG-regulated non-coding transcript in prostate cancer

Background: Prostate cancer (PC) is the second most common cancer among men. Most PC-related deaths are due to invasive tumors that are treated with therapies inhibiting androgen production or androgen receptor (AR) activity. After an initial response, tumors invariably progress to castration resistant prostate cancers (CRPCs) for which no effective cure exists.

Methods and Results: We used transcriptome sequencing to study fresh-frozen tissue specimens from 12 benign prostatic hyperplasias (BPHs), 28 PCs, and 13 CRPCs. Reference-based transcriptome assembly uncovered 145 previously unannotated intergenic PC and CRPC associated long non-coding transcripts (lncRNAs) or isoforms. One third of the transcripts were CRPC-specific. We showed that one of the novel transcripts, Prostate Cancer Associated Transcript 5 (PCAT5), expressed in half of the tumors, was likely regulated by ERG, the key transcription factor in ∼50% of prostate cancers. Genome-wide expression analysis of a PCAT5-positive prostate cancer cell line after PCAT5 knockdown suggested significant alterations in proliferation pathways. In vitro validation of the pathway alterations revealed concordantly dramatic effects in phenotype: stalling of cell growth, migration, invasion, and colony forming potential, and increase in the rate of apoptosis.

Conclusions: We identified the key differences between PC and CRPC in transcriptome level, and validated the oncogenic potential of a novel lncRNA in ERG-positive prostate cancers, PCAT5. Our study presents a number of putative lncRNA biomarkers for CRPC, and opportunities for therapeutic intervention.

Citation Format: Antti Ylipää, Kati Kivinummi, Matti Annala, Annika Kohvakka, Leena Latonen, Mauro Scaravilli, Kimmo Kartasalo, Simo-Pekka Leppänen, Serdar Karakurt, Janne Seppälä, Olli Yli-Harja, Teuvo L.J. Tammela, Wei Zhang, Tapio Visakorpi, Matti Nykter. Transcriptome sequencing reveals PCAT5 - new ERG-regulated non-coding transcript in prostate cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 148. doi:10.1158/1538-7445.AM2015-148

Transcription closed and open complex dynamics studies reveal balance between genetic determinants and co-factors

In E. coli, promoter closed and open complexes are key steps in transcription initiation, where magnesium-dependent RNA polymerase catalyzes RNA synthesis. However, the exact mechanism of initiation remains to be fully elucidated. Here, using single mRNA detection and dual reporter studies, we show that increased intracellular magnesium concentration affects Plac initiation complex formation resulting in a highly dynamic process over the cell growth phases. Mg2+ regulates transcription transition, which modulates bimodality of mRNA distribution in the exponential phase. We reveal that Mg2+ regulates the size and frequency of the mRNA burst by changing the open complex duration. Moreover, increasing magnesium concentration leads to higher intrinsic and extrinsic noise in the exponential phase. RNAP-Mg2+ interaction simulation reveals critical movements creating a shorter contact distance between aspartic acid residues and Nucleotide Triphosphate residues and increasing electrostatic charges in the active site. Our findings provide unique biophysical insights into the balanced mechanism of genetic determinants and magnesium ion in transcription initiation regulation during cell growth.

Single-cell kinetics of a repressilator when implemented in a single-copy plasmid

Synthetic genetic clocks, such as the Elowitz-Leibler repressilator, will be key regulatory components of future synthetic circuits. We constructed a single-copy repressilator (SCR) by implementing the original repressilator circuit on a single-copy F-plasmid. After verifying its functionality, we studied its behaviour as a function of temperature and compared it with that of the original low-copy-number repressilator (LCR). Namely, we compared the period of oscillations, functionality (the fraction of cells exhibiting oscillations) and robustness to internal fluctuations (the fraction of expected oscillations that would occur). We found that, under optimal temperature conditions, the dynamics of the two systems differs significantly, although qualitatively they respond similarly to temperature changes. Exception to this is in the functionality, in which the SCR is higher at lower temperatures but lower at higher temperatures. Next, by adding IPTG to the medium at low and high concentrations during microscopy sessions, we showed that the functionality of the SCR is more robust to external perturbations, which indicates that the oscillatory behaviour of the LCR can be disrupted by affecting only a few of the copies in a cell. We conclude that the SCR, the first functional, synthetic, single-copy, ring-type genetic clock, is more robust to lower temperatures and to external perturbations than the original LCR. The SCR will be of use in future synthetic circuits, since it complements the array of tasks that the LCR can perform.

Cancer research in the era of next-generation sequencing and big data calls for intelligent modeling

We examine the role of big data and machine learning in cancer research. We describe an example in cancer research where gene-level data from The Cancer Genome Atlas (TCGA) consortium is interpreted using a pathway-level model. As the complexity of computational models increases, their sample requirements grow exponentially. This growth stems from the fact that the number of combinations of variables grows exponentially as the number of variables increases. Thus, a large sample size is needed. The number of variables in a computational model can be reduced by incorporating biological knowledge. One particularly successful way of doing this is by using available gene regulatory, signaling, metabolic, or context-specific pathway information. We conclude that the incorporation of existing biological knowledge is essential for the progress in using big data for cancer research.

Quantitative analysis of colony morphology in yeast

Microorganisms often form multicellular structures such as biofilms and structured colonies that can influence the organism's virulence, drug resistance, and adherence to medical devices. Phenotypic classification of these structures has traditionally relied on qualitative scoring systems that limit detailed phenotypic comparisons between strains. Automated imaging and quantitative analysis have the potential to improve the speed and accuracy of experiments designed to study the genetic and molecular networks underlying different morphological traits. For this reason, we have developed a platform that uses automated image analysis and pattern recognition to quantify phenotypic signatures of yeast colonies. Our strategy enables quantitative analysis of individual colonies, measured at a single time point or over a series of time-lapse images, as well as the classification of distinct colony shapes based on image-derived features. Phenotypic changes in colony morphology can be expressed as changes in feature space trajectories over time, thereby enabling the visualization and quantitative analysis of morphological development. To facilitate data exploration, results are plotted dynamically through an interactive Yeast Image Analysis web application (YIMAA; http://yimaa.cs.tut.fi) that integrates the raw and processed images across all time points, allowing exploration of the image-based features and principal components associated with morphological development.

Effects of multimerization on the temporal variability of protein complex abundance

We explore whether the process of multimerization can be used as a means to regulate noise in the abundance of functional protein complexes. Additionally, we analyze how this process affects the mean level of these functional units, response time of a gene, and temporal correlation between the numbers of expressed proteins and of the functional multimers. We show that, although multimerization increases noise by reducing the mean number of functional complexes it can reduce noise in comparison with a monomer, when abundance of the functional proteins are comparable. Alternatively, reduction in noise occurs if both monomeric and multimeric forms of the protein are functional. Moreover, we find that multimerization either increases the response time to external signals or decreases the correlation between number of functional complexes and protein production kinetics. Finally, we show that the results are in agreement with recent genome-wide assessments of cell-to-cell variability in protein numbers and of multimerization in essential and non-essential genes in Escherichia coli, and that the effects of multimerization are tangible at the level of genetic circuits.

Characterization of aberrant pathways across human cancers

BACKGROUND

Cancer is a broad group of genetic diseases which account for millions of deaths worldwide each year. Cancers are classified by various clinical, pathological and molecular methods, but even within a well-characterized disease, there is a significant inter-patient variability in survival, response to treatment, and other parameters. Especially in molecular level, tumours of the same category can appear significantly dissimilar due to complex combinations of genetic aberrations leading to a similar malignancy. We extended the current classification methods by studying tumour heterogeneity at pathway level.

METHODS

We computed the rate of alterations in 1994 pathways and 2210 tumours consisting of eight different cancers. Using gene set enrichment analysis, each sample was computed a pathway aberration profile that reflected its molecular state. The profiles were analysed together to infer the characteristic aberration rates for each pathway within each cancer. Subgroups of tumours defined by similar pathway aberrations were identified using clustering analyses. The pathway aberration and gene expression profiles of the subgroups were consecutively compared across all eight cancer types to search for similar tumours crossing the standard classification.

RESULTS

We identified pathways and processes that were common to all cancers as well as traits that are unique to a cancer type or closely related cancers. Studying the gene expression patterns within the pathway context suggested potential alteration mechanisms. Clustering analysis revealed five clinically relevant subgroups of tumours in four cancers that exhibited significant differences in survival compared to others. The cross-cancer analysis of the subgroups resulted in the identification of tumours that shared potentially significant alterations.

CONCLUSIONS

This study represents the first effort to extend the molecular characterizations towards pathway level descriptions across the family of cancers. In addition to providing a proof-of-concept for single sample pathway aberration analysis in this context, we present a comprehensive pathway aberration dataset that can be used to study pathway aberration patterns within or across cancers. Significant similarities between subgroups of different cancers on pathway and gene expression levels provide interesting hypotheses for understanding variable drug response, or transferring treatments across diseases by identifying common druggable pathways or genes, for example.

Multi-scale Gaussian representation and outline-learning based cell image segmentation

Background

High-throughput genome-wide screening to study gene-specific functions, e.g. for drug discovery, demands fast automated image analysis methods to assist in unraveling the full potential of such studies. Image segmentation is typically at the forefront of such analysis as the performance of the subsequent steps, for example, cell classification, cell tracking etc., often relies on the results of segmentation.

Methods

We present a cell cytoplasm segmentation framework which first separates cell cytoplasm from image background using novel approach of image enhancement and coefficient of variation of multi-scale Gaussian scale-space representation. A novel outline-learning based classification method is developed using regularized logistic regression with embedded feature selection which classifies image pixels as outline/non-outline to give cytoplasm outlines. Refinement of the detected outlines to separate cells from each other is performed in a post-processing step where the nuclei segmentation is used as contextual information.

Results and conclusions

We evaluate the proposed segmentation methodology using two challenging test cases, presenting images with completely different characteristics, with cells of varying size, shape, texture and degrees of overlap. The feature selection and classification framework for outline detection produces very simple sparse models which use only a small subset of the large, generic feature set, that is, only 7 and 5 features for the two cases. Quantitative comparison of the results for the two test cases against state-of-the-art methods show that our methodology outperforms them with an increase of 4-9% in segmentation accuracy with maximum accuracy of 93%. Finally, the results obtained for diverse datasets demonstrate that our framework not only produces accurate segmentation but also generalizes well to different segmentation tasks.

ZebIAT, an image analysis tool for registering zebrafish embryos and quantifying cancer metastasis

Background

Zebrafish embryos have recently been established as a xenotransplantation model of the metastatic behaviour of primary human tumours. Current tools for automated data extraction from the microscope images are restrictive concerning the developmental stage of the embryos, usually require laborious manual image preprocessing, and, in general, cannot characterize the metastasis as a function of the internal organs.

Methods

We present a tool, ZebIAT, that allows both automatic or semi-automatic registration of the outer contour and inner organs of zebrafish embryos. ZebIAT provides a registration at different stages of development and an automatic analysis of cancer metastasis per organ, thus allowing to study cancer progression. The semi-automation relies on a graphical user interface.

Results

We quantified the performance of the registration method, and found it to be accurate, except in some of the smallest organs. Our results show that the accuracy of registering small organs can be improved by introducing few manual corrections. We also demonstrate the applicability of the tool to studies of cancer progression.

Conclusions

ZebIAT offers major improvement relative to previous tools by allowing for an analysis on a per-organ or region basis. It should be of use in high-throughput studies of cancer metastasis in zebrafish embryos.

Effects of rate-limiting steps in transcription initiation on genetic filter motifs

The behavior of genetic motifs is determined not only by the gene-gene interactions, but also by the expression patterns of the constituent genes. Live single-molecule measurements have provided evidence that transcription initiation is a sequential process, whose kinetics plays a key role in the dynamics of mRNA and protein numbers. The extent to which it affects the behavior of cellular motifs is unknown. Here, we examine how the kinetics of transcription initiation affects the behavior of motifs performing filtering in amplitude and frequency domain. We find that the performance of each filter is degraded as transcript levels are lowered. This effect can be reduced by having a transcription process with more steps. In addition, we show that the kinetics of the stepwise transcription initiation process affects features such as filter cutoffs. These results constitute an assessment of the range of behaviors of genetic motifs as a function of the kinetics of transcription initiation, and thus will aid in tuning of synthetic motifs to attain specific characteristics without affecting their protein products.

NanoMiner - integrative human transcriptomics data resource for nanoparticle research

The potential impact of nanoparticles on the environment and on human health has attracted considerable interest worldwide. The amount of transcriptomics data, in which tissues and cell lines are exposed to nanoparticles, increases year by year. In addition to the importance of the original findings, this data can have value in broader context when combined with other previously acquired and published results. In order to facilitate the efficient usage of the data, we have developed the NanoMiner web resource (http://nanominer.cs.tut.fi/), which contains 404 human transcriptome samples exposed to various types of nanoparticles. All the samples in NanoMiner have been annotated, preprocessed and normalized using standard methods that ensure the quality of the data analyses and enable the users to utilize the database systematically across the different experimental setups and platforms. With NanoMiner it is possible to 1) search and plot the expression profiles of one or several genes of interest, 2) cluster the samples within the datasets, 3) find differentially expressed genes in various nanoparticle studies, 4) detect the nanoparticles causing differential expression of selected genes, 5) analyze enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) terms for the detected genes and 6) search the expression values and differential expressions of the genes belonging to a specific KEGG pathway or Gene Ontology. In sum, NanoMiner database is a valuable collection of microarray data which can be also used as a data repository for future analyses.

In vivo single-molecule kinetics of activation and subsequent activity of the arabinose promoter

Using a single-RNA detection technique in live Escherichia coli cells, we measure, for each cell, the waiting time for the production of the first RNA under the control of P_BAD promoter after induction by arabinose, and subsequent intervals between transcription events. We find that the kinetics of the arabinose intake system affect mean and diversity in RNA numbers, long after induction. We observed the same effect on P_lac/ara-1 promoter, which is inducible by arabinose or by IPTG. Importantly, the distribution of waiting times of P_lac/ara-1 is indistinguishable from that of P_BAD, if and only if induced by arabinose alone. Finally, RNA production under the control of P_BAD is found to be a sub-Poissonian process. We conclude that inducer-dependent waiting times affect mean and cell-to-cell diversity in RNA numbers long after induction, suggesting that intake mechanisms have non-negligible effects on the phenotypic diversity of cell populations in natural, fluctuating environments.

Abstract 5217: Integrative sequencing reveals novel alterations in untreated and castration resistant prostate cancer

Prostate cancer is the third most common source of male cancer deaths in developed countries. The standard of care for aggressive prostate cancer is androgen ablation, which prolongs survival until the tumor acquires a castration resistant phenotype. The molecular pathology underlying prostate cancer progression is not yet fully understood. We used integrative high throughput sequencing to study cancer-associated alterations in 53 prostate cancer related neoplasia at the DNA, RNA and epigenetic levels. The cohort included both hormone-naive and castration resistant prostate cancers, along with two neuroendocrine prostate cancers. We identified two new fusion genes, one of which associated with neuronal differentiation and castration resistance. We also identified a number of novel prostate cancer associated transcripts, including transcripts specific to castration resistant tumors. Based on ChIP-seq data from prostate cancer cell lines, many of the novel transcripts were regulated by known oncogenes such as ERG and AR. Methylation sequencing revealed a near-identical pattern of promoter hypermethylation in both hormone-naive and castration resistant tumors. Enrichment of hypermethylation was observed at EZH2 binding sites, supporting the role of EZH2 in the recruitment of DNA methyltransferase in prostate cancer. Promoter hypermethylation suppressed the expression of hundreds of genes, but a subset of genes characterized by promoter H3K27 trimethylation responded to hypermethylation with increased expression.

Citation Format: Matti J. Annala, Kati K. Waltering, Antti Ylipää, Kimmo Kartasalo, Kirsi Tuppurainen, Serdar Karakurt, Leena Latonen, Outi Saramäki, Simo-Pekka Leppänen, Janne Seppälä, Hanna E. Rauhala, Teuvo LJ Tammela, Olli Yli-Harja, Wei Zhang, Tapio Visakorpi, Matti Nykter. Integrative sequencing reveals novel alterations in untreated and castration resistant prostate cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5217. doi:10.1158/1538-7445.AM2013-5217

Transcriptome and small RNA deep sequencing reveals deregulation of miRNA biogenesis in human glioma

Altered expression of oncogenic and tumour-suppressing microRNAs (miRNAs) is widely associated with tumourigenesis. However, the regulatory mechanisms underlying these alterations are poorly understood. We sought to shed light on the deregulation of miRNA biogenesis promoting the aberrant miRNA expression profiles identified in these tumours. Using sequencing technology to perform both whole-transcriptome and small RNA sequencing of glioma patient samples, we examined precursor and mature miRNAs to directly evaluate the miRNA maturation process, and examined expression profiles for genes involved in the major steps of miRNA biogenesis. We found that ratios of mature to precursor forms of a large number of miRNAs increased with the progression from normal brain to low-grade and then to high-grade gliomas. The expression levels of genes involved in each of the three major steps of miRNA biogenesis (nuclear processing, nucleo-cytoplasmic transport, and cytoplasmic processing) were systematically altered in glioma tissues. Survival analysis of an independent data set demonstrated that the alteration of genes involved in miRNA maturation correlates with survival in glioma patients. Direct quantification of miRNA maturation with deep sequencing demonstrated that deregulation of the miRNA biogenesis pathway is a hallmark for glioma genesis and progression.

The tumorigenic FGFR3-TACC3 gene fusion escapes miR-99a regulation in glioblastoma

Fusion genes are chromosomal aberrations that are found in many cancers and can be used as prognostic markers and drug targets in clinical practice. Fusions can lead to production of oncogenic fusion proteins or to enhanced expression of oncogenes. Several recent studies have reported that some fusion genes can escape microRNA regulation via 3'-untranslated region (3'-UTR) deletion. We performed whole transcriptome sequencing to identify fusion genes in glioma and discovered FGFR3-TACC3 fusions in 4 of 48 glioblastoma samples from patients both of mixed European and of Asian descent, but not in any of 43 low-grade glioma samples tested. The fusion, caused by tandem duplication on 4p16.3, led to the loss of the 3'-UTR of FGFR3, blocking gene regulation of miR-99a and enhancing expression of the fusion gene. The fusion gene was mutually exclusive with EGFR, PDGFR, or MET amplification. Using cultured glioblastoma cells and a mouse xenograft model, we found that fusion protein expression promoted cell proliferation and tumor progression, while WT FGFR3 protein was not tumorigenic, even under forced overexpression. These results demonstrated that the FGFR3-TACC3 gene fusion is expressed in human cancer and generates an oncogenic protein that promotes tumorigenesis in glioblastoma.