Protein kinases represent the most effective class of therapeutic targets in cancer; therefore determination of kinase aberrations is a major focus of cancer genomic studies. Here, we analyzed transcriptome sequencing data from a compendium of 482 cancer and benign samples from 25 different tissue types, and defined distinct ‘outlier kinases’ in individual breast and pancreatic cancer samples, based on highest levels of absolute and differential expression. Frequent outlier kinases in breast cancer included therapeutic targets like ERBB2 and FGFR4, distinct from MET, AKT2, and PLK2 in pancreatic cancer. Outlier kinases imparted sample-specific dependencies in various cell lines as tested by siRNA knockdown and/or pharmacologic inhibition. Outlier expression of polo-like kinases was observed in a subset of KRAS-dependent pancreatic cancer cell lines, and conferred increased sensitivity to the pan-PLK inhibitor BI 6727. Our results suggest that outlier kinases represent effective precision therapeutic targets that are readily identifiable through RNA-sequencing of tumors.
Pancreatic Cancer; RNA-Seq; Kinases; Outlier Expression; Personalized Medicine
Through a prospective clinical sequencing program for advanced cancers, four index cases were identified which harbor gene rearrangements of FGFR2 including patients with cholangiocarcinoma, breast cancer, and prostate cancer. After extending our assessment of FGFR rearrangements across multiple tumor cohorts, we identified additional FGFR gene fusions with intact kinase domains in lung squamous cell cancer, bladder cancer, thyroid cancer, oral cancer, glioblastoma, and head and neck squamous cell cancer. All FGFR fusion partners tested exhibit oligomerization capability, suggesting a shared mode of kinase activation. Overexpression of FGFR fusion proteins induced cell proliferation. Two bladder cancer cell lines that harbor FGFR3 fusion proteins exhibited enhanced susceptibility to pharmacologic inhibition in vitro and in vivo. Due to the combinatorial possibilities of FGFR family fusion to a variety of oligomerization partners, clinical sequencing efforts which incorporate transcriptome analysis for gene fusions are poised to identify rare, targetable FGFR fusions across diverse cancer types.
MI-ONCOSEQ; integrative clinical sequencing; FGFR fusions; driver mutations; therapeutic targets
A 44-year old woman with recurrent solitary fibrous tumor (SFT)/hemangiopericytoma was enrolled in a clinical sequencing program including whole exome and transcriptome sequencing. A gene fusion of the transcriptional repressor NAB2 with the transcriptional activator STAT6 was detected. Transcriptome sequencing of 27 additional SFTs all revealed the presence of a NAB2-STAT6 gene fusion. Using RT-PCR and sequencing, we detected this fusion in 51 of 51 SFTs, indicating high levels of recurrence. Expression of NAB2-STAT6 fusion proteins was confirmed in SFT, and the predicted fusion products harbor the early growth response (EGR)-binding domain of NAB2 fused to the activation domain of STAT6. Overexpression of the NAB2-STAT6 gene fusion induced proliferation in cultured cells and activated EGR-responsive genes. These studies establish NAB2-STAT6 as the defining driver mutation of SFT and provide an example of how neoplasia can be initiated by converting a transcriptional repressor of mitogenic pathways into a transcriptional activator.
Using a series of detailed experiments, Zhang et al establish that the prostate cancer RNA chimera SLC45A3-ELK4 is generated by cis-splicing between the two adjacent genes and does not involve DNA rearrangements or trans-splicing. The chimera expression is induced by androgen treatment likely by overcoming the read-through block imposed by the intergenic CCCTC-insulators bound by CTCF repressor protein. The chimeric transcript, but not wild type ELK4, is shown to augment prostate cancer cell proliferation.
Pseudogene transcripts can provide a novel tier of gene regulation through generation of endogenous siRNAs or miRNA-binding sites. Characterization of pseudogene expression, however, has remained confined to anecdotal observations due to analytical challenges posed by the extremely close sequence similarity with their counterpart coding genes. Here, we describe a systematic analysis of pseudogene “transcription” from an RNA-Seq resource of 293 samples, representing 13 cancer and normal tissue types, and observe a surprisingly prevalent, genome-wide expression of pseudogenes that could be categorized as ubiquitously expressed or lineage and/or cancer specific. Further, we explore disease subtype specificity and functions of selected expressed pseudogenes. Taken together, we provide evidence that transcribed pseudogenes are a significant contributor to the transcriptional landscape of cells and are positioned to play significant roles in cellular differentiation and cancer progression, especially in light of the recently described ceRNA networks. Our work provides a transcriptome resource that enables high-throughput analyses of pseudogene expression.
Based on their function in cancer micro(mi)RNAs are often grouped as either tumor suppressors or oncogenes. However, miRNAs regulate multiple tumor relevant signaling pathways raising the question whether two oncogenic miRNAs could be functional antagonists by promoting different steps in tumor progression. We recently developed a method to connect miRNAs to biological function by comparing miRNA and gene array expression data from the NCI60 cell lines without using miRNA target predictions (miRConnect).
We have now extended this analysis to three primary human cancers (ovarian cancer, glioblastoma multiforme, and kidney renal clear cell carcinoma) available at the Cancer Genome Atlas (TCGA), and have correlated the expression of the clustered miRNAs with 158 oncogenic signatures (miRConnect 2.0). We have identified functionally antagonistic groups of miRNAs. One group (the agonists), which contains many of the members of the miR-17 family, correlated with c-Myc induced genes and E2F gene signatures. A group that was directly antagonistic to the agonists in all three primary cancers contains miR-221 and miR-222. Since both miR-17 ~ 92 and miR-221/222 are considered to be oncogenic this points to a functional antagonism of different oncogenic miRNAs. Analysis of patient data revealed that in certain patients agonistic miRNAs predominated, whereas in other patients antagonists predominated. In glioblastoma a high ratio of miR-17 to miR-221/222 was predictive of better overall survival suggesting that high miR-221/222 expression is more adverse for patients than high miR-17 expression.
miRConnect 2.0 is useful for identifying activities of miRNAs that are relevant to primary cancers. The new correlation data on miRNAs and mRNAs deregulated in three primary cancers are available at miRConnect.org
Oncogenes; Tumor suppressors; Gene array; microRNA (miRNA) groups; NCI60 cell lines
Individual cancers harbor a set of genetic aberrations that can be informative for identifying rational therapies currently available or in clinical trials. We implemented a pilot study to explore the practical challenges of applying high-throughput sequencing in clinical oncology. We enrolled patients with advanced or refractory cancer who were eligible for clinical trials. For each patient, we performed whole-genome sequencing of the tumor, targeted whole-exome sequencing of tumor and normal DNA, and transcriptome sequencing (RNA-Seq) of the tumor to identify potentially informative mutations in a clinically relevant time frame of 3 to 4 weeks. With this approach, we detected several classes of cancer mutations including structural rearrangements, copy number alterations, point mutations, and gene expression alterations. A multidisciplinary Sequencing Tumor Board (STB) deliberated on the clinical interpretation of the sequencing results obtained. We tested our sequencing strategy on human prostate cancer xenografts. Next, we enrolled two patients into the clinical protocol and were able to review the results at our STB within 24 days of biopsy. The first patient had metastatic colorectal cancer in which we identified somatic point mutations in NRAS, TP53, AURKA, FAS, and MYH11, plus amplification and overexpression of cyclin-dependent kinase 8 (CDK8). The second patient had malignant melanoma, in which we identified a somatic point mutation in HRAS and a structural rearrangement affecting CDKN2C. The STB identified the CDK8 amplification and Ras mutation as providing a rationale for clinical trials with CDK inhibitors or MEK (mitogenactivated or extracellular signal–regulated protein kinase kinase) and PI3K (phosphatidylinositol 3-kinase) inhibitors, respectively. Integrative high-throughput sequencing of patients with advanced cancer generates a comprehensive, individual mutational landscape to facilitate biomarker-driven clinical trials in oncology.
An avalanche of next generation sequencing (NGS) studies has generated an unprecedented amount of genomic structural variation data. These studies have also identified many novel gene fusion candidates with more detailed resolution than previously achieved. However, in the excitement and necessity of publishing the observations from this recently developed cutting-edge technology, no community standardization approach has arisen to organize and represent the data with the essential attributes in an interchangeable manner. As transcriptome studies have been widely used for gene fusion discoveries, the current non-standard mode of data representation could potentially impede data accessibility, critical analyses, and further discoveries in the near future.
Here we propose a prototype, Gene Fusion Markup Language (GFML) as an initiative to provide a standard format for organizing and representing the significant features of gene fusion data. GFML will offer the advantage of representing the data in a machine-readable format to enable data exchange, automated analysis interpretation, and independent verification. As this database-independent exchange initiative evolves it will further facilitate the formation of related databases, repositories, and analysis tools. The GFML prototype is made available at
The Gene Fusion Markup Language (GFML) presented here could facilitate the development of a standard format for organizing, integrating and representing the significant features of gene fusion data in an inter-operable and query-able fashion that will enable biologically intuitive access to gene fusion findings and expedite functional characterization. A similar model is envisaged for other NGS data analyses.
Application of high-throughput transcriptome sequencing has spurred highly sensitive detection and discovery of gene fusions in cancer, but distinguishing potentially oncogenic fusions from random, “passenger” aberrations has proven challenging. Here we examine a distinctive group of gene fusions that involve genes present in the loci of chromosomal amplifications—a class of oncogenic aberrations that are widely prevalent in breast cancers. Integrative analysis of a panel of 14 breast cancer cell lines comparing gene fusions discovered by high-throughput transcriptome sequencing and genome-wide copy number aberrations assessed by array comparative genomic hybridization, led to the identification of 77 gene fusions, of which more than 60% were localized to amplicons including 17q12, 17q23, 20q13, chr8q, and others. Many of these fusions appeared to be recurrent or involved highly expressed oncogenic drivers, frequently fused with multiple different partners, but sometimes displaying loss of functional domains. As illustrative examples of the “amplicon-associated” gene fusions, we examined here a recurrent gene fusion involving the mediator of mammalian target of rapamycin signaling, RPS6KB1 kinase in BT-474, and the therapeutically important receptor tyrosine kinase EGFR in MDA-MB-468 breast cancer cell line. These gene fusions comprise a minor allelic fraction relative to the highly expressed full-length transcripts and encode chimera lacking the kinase domains, which do not impart dependence on the respective cells. Our study suggests that amplicon-associated gene fusions in breast cancer primarily represent a by-product of chromosomal amplifications, which constitutes a subset of passenger aberrations and should be factored accordingly during prioritization of gene fusion candidates.
Breast cancer is a heterogeneous disease, exhibiting a wide range of molecular aberrations and clinical outcomes. Here we employed paired-end transcriptome sequencing to explore the landscape of gene fusions in a panel of breast cancer cell lines and tissues. We observed that individual breast cancers harbor an array of expressed gene fusions. We identified two classes of recurrent gene rearrangements involving microtubule associated serine-threonine kinase (MAST) and Notch family genes. Both MAST and Notch family gene fusions exerted significant phenotypic effects in breast epithelial cells. Breast cancer lines harboring Notch gene rearrangements are uniquely sensitive to inhibition of Notch signaling, and over-expression of MAST1 or MAST2 gene fusions had a proliferative effect both in vitro and in vivo. These findings illustrate that recurrent gene rearrangements play significant roles in subsets of carcinomas and suggest that transcriptome sequencing may serve to identify patients with rare, actionable gene fusions.
Rap1GAP is a critical tumor suppressor gene that is down-regulated in multiple aggressive cancers such as head and neck squamous cell carcinoma, melanoma and pancreatic cancer. However, the mechanistic basis of rap1GAP down-regulation in cancers is poorly understood. By employing an integrative approach, we demonstrate polycomb mediated repression of rap1GAP that involves EZH2, a histone methyltransferase in head and neck cancers. We further demonstrate that the loss of miR-101 expression correlates with EZH2 up-regulation, and the concomitant down-regulation of rap1GAP in head and neck cancers. EZH2 represses rap1GAP by facilitating the trimethylation of H3K27, a mark of gene repression, and also hypermethylation of rap1GAP promoter. These results provide a conceptual framework involving a microRNA-oncogene-tumor suppressor axis to understand head and neck cancer progression.
mir101; EZH2; rap1GAP; rap1; promoter hypermethylation
While recurrent gene fusions involving ETS family transcription factors are common in prostate cancer, their products are considered “undruggable” by conventional approaches. Recently, rare “targetable” gene fusions (involving the ALK kinase), have been identified in 1–5% of lung cancers1, suggesting that similar rare gene fusions may occur in other common epithelial cancers including prostate cancer. Here we employed paired-end transcriptome sequencing to screen ETS rearrangement negative prostate cancers for targetable gene fusions and identified the SLC45A3-BRAF and ESRP1-RAF1 gene fusions. Expression of SLC45A3-BRAF or ESRP1-RAF1 in prostate cells induced a neoplastic phenotype that was sensitive to RAF and MEK inhibitors. Screening a large cohort of patients, we found that although rare (1–2%), recurrent rearrangements in the RAF pathway tend to occur in advanced prostate cancers, gastric cancers, and melanoma. Taken together, our results emphasize the importance of RAF rearrangements in cancer, suggest that RAF and MEK inhibitors may be useful in a subset of gene fusion harboring solid tumors, and demonstrate that sequencing of tumor transcriptomes and genomes may lead to the identification of rare targetable fusions across cancer types.
Protein-DNA interaction constitutes a basic mechanism for the genetic regulation of target gene expression. Deciphering this mechanism has been a daunting task due to the difficulty in characterizing protein-bound DNA on a large scale. A powerful technique has recently emerged that couples chromatin immunoprecipitation (ChIP) with next-generation sequencing, (ChIP-Seq). This technique provides a direct survey of the cistrom of transcription factors and other chromatin-associated proteins. In order to realize the full potential of this technique, increasingly sophisticated statistical algorithms have been developed to analyze the massive amount of data generated by this method.
Here we introduce HPeak, a Hidden Markov model (HMM)-based Peak-finding algorithm for analyzing ChIP-Seq data to identify protein-interacting genomic regions. In contrast to the majority of available ChIP-Seq analysis software packages, HPeak is a model-based approach allowing for rigorous statistical inference. This approach enables HPeak to accurately infer genomic regions enriched with sequence reads by assuming realistic probability distributions, in conjunction with a novel weighting scheme on the sequencing read coverage.
Using biologically relevant data collections, we found that HPeak showed a higher prevalence of the expected transcription factor binding motifs in ChIP-enriched sequences relative to the control sequences when compared to other currently available ChIP-Seq analysis approaches. Additionally, in comparison to the ChIP-chip assay, ChIP-Seq provides higher resolution along with improved sensitivity and specificity of binding site detection. Additional file and the HPeak program are freely available at http://www.sph.umich.edu/csg/qin/HPeak.
Recurrent gene fusions, typically associated with hematological malignancies and rare bone and soft tissue tumors1, have been recently described in common solid tumors2–9. Here we employ an integrative analysis of high-throughput long and short read transcriptome sequencing of cancer cells to discover novel gene fusions. As a proof of concept we successfully utilized integrative transcriptome sequencing to “re-discover” the BCR-ABL1
10 gene fusion in a chronic myelogenous leukemia cell line and the TMPRSS2-ERG
2,3 gene fusion in a prostate cancer cell line and tissues. Additionally, we nominated, and experimentally validated, novel gene fusions resulting in chimeric transcripts in cancer cell lines and tumors. Taken together, this study establishes a robust pipeline for the discovery of novel gene chimeras using high throughput sequencing, opening up an important class of cancer-related mutations for comprehensive characterization.
Transcriptome sequencing; Prostate cancer; Bioinformatics; Gene fusions
Multiple, complex molecular events characterize cancer development and progression1,2. Deciphering the molecular networks that distinguish organ-confined disease from metastatic disease may lead to the identification of critical biomarkers for cancer invasion and disease aggressiveness. Although gene and protein expression have been extensively profiled in human tumors, little is known about the global metabolomic alterations that characterize neoplastic progression. Using a combination of high throughput liquid and gas chromatography-based mass spectrometry, we profiled more than 1126 metabolites across 262 clinical samples related to prostate cancer (42 tissues and 110 each of urine and plasma). These unbiased metabolomic profiles were able to distinguish benign prostate, clinically localized prostate cancer, and metastatic disease. Sarcosine, an N-methyl derivative of the amino acid glycine, was identified as a differential metabolite that was highly elevated during prostate cancer progression to metastasis and can be detected non-invasively in urine. Sarcosine levels were also elevated in invasive prostate cancer cell lines relative to benign prostate epithelial cells. Knockdown of glycine-N-methyl transferase (GNMT), the enzyme that generates sarcosine from glycine, attenuated prostate cancer invasion. Addition of exogenous sarcosine or knockdown of the enzyme that leads to sarcosine degradation, sarcosine dehydrogenase (SARDH), induced an invasive phenotype in benign prostate epithelial cells. Androgen receptor and the ERG gene fusion product coordinately regulate components of the sarcosine pathway. Taken together, we profiled the metabolomic alterations of prostate cancer progression revealing sarcosine as a potentially important metabolic intermediary of cancer cell invasion and aggressivity.
Global molecular profiling of cancers has shown broad utility in delineating pathways and processes underlying disease, in predicting prognosis and response to therapy, and in suggesting novel treatments. To gain further insights from such data, we have integrated and analyzed a comprehensive collection of “molecular concepts” representing > 2500 cancer-related gene expression signatures from Oncomine and manual curation of the literature, drug treatment signatures from the Connectivity Map, target gene sets from genome-scale regulatory motif analyses, and reference gene sets from several gene and protein annotation databases. We computed pairwise association analysis on all 13,364 molecular concepts and identified > 290,000 significant associations, generating hypotheses that link cancer types and subtypes, pathways, mechanisms, and drugs. To navigate a network of associations, we developed an analysis platform, the Molecular Concepts Map. We demonstrate the utility of the approach by highlighting molecular concepts analyses of Myc pathway activation, breast cancer relapse, and retinoic acid treatment.
Cancer; bioinformatics; gene expression signature; network; oncomine
DNA microarrays have been widely applied to cancer transcriptome analysis; however, the majority of such data are not easily accessible or comparable. Furthermore, several important analytic approaches have been applied to microarray analysis; however, their application is often limited. To overcome these limitations, we have developed Oncomine, a bioinformatics initiative aimed at collecting, standardizing, analyzing, and delivering cancer transcriptome data to the biomedical research community. Our analysis has identified the genes, pathways, and networks deregulated across 18,000 cancer gene expression microarrays, spanning the majority of cancer types and subtypes. Here, we provide an update on the initiative, describe the database and analysis modules, and highlight several notable observations. Results from this comprehensive analysis are available at http://www.oncomine.org.
Oncomine; cancer gene expression; microarrays; bioinformatics; differential expression