Gard O. S. Thomassen

A universal assay for detection of oncogenic fusion transcripts by oligo microarray analysis

BackgroundThe ability to detect neoplasia-specific fusion genes is important not only in cancer research, but also increasingly in clinical settings to ensure that correct diagnosis is made and the optimal treatment is chosen. However, the available methodologies to detect such fusions all have their distinct short-comings.ResultsWe describe a novel oligonucleotide microarray strategy whereby one can screen for all known oncogenic fusion transcripts in a single experiment. To accomplish this, we combine measurements of chimeric transcript junctions with exon-wise measurements of individual fusion partners. To demonstrate the usefulness of the approach, we designed a DNA microarray containing 68,861 oligonucleotide probes that includes oligos covering all combinations of chimeric exon-exon junctions from 275 pairs of fusion genes, as well as sets of oligos internal to all the exons of the fusion partners. Using this array, proof of principle was demonstrated by detection of known fusion genes (such as TCF3:PBX1, ETV6:RUNX1, and TMPRSS2:ERG) from all six positive controls consisting of leukemia cell lines and prostate cancer biopsies.ConclusionThis new method bears promise of an important complement to currently used diagnostic and research tools for the detection of fusion genes in neoplastic diseases.

Unscrambling the genomic chaos of osteosarcoma reveals extensive transcript fusion, recurrent rearrangements and frequent novel TP53 aberrations

In contrast to many other sarcoma subtypes, the chaotic karyotypes of osteosarcoma have precluded the identification of pathognomonic translocations. We here report hundreds of genomic rearrangements in osteosarcoma cell lines, showing clear characteristics of microhomology-mediated break-induced replication (MMBIR) and end-joining repair (MMEJ) mechanisms. However, at RNA level, the majority of the fused transcripts did not correspond to genomic rearrangements, suggesting the involvement of trans-splicing, which was further supported by typical trans-splicing characteristics. By combining genomic and transcriptomic analysis, certain recurrent rearrangements were identified and further validated in patient biopsies, including a PMP22-ELOVL5 gene fusion, genomic structural variations affecting RB1, MTAP/CDKN2A and MDM2, and, most frequently, rearrangements involving TP53. Most cell lines (7/11) and a large fraction of tumor samples (10/25) showed TP53 rearrangements, in addition to somatic point mutations (6 patient samples, 1 cell line) and MDM2 amplifications (2 patient samples, 2 cell lines). The resulting inactivation of p53 was demonstrated by a deficiency of the radiation-induced DNA damage response. Thus, TP53 rearrangements are the major mechanism of p53 inactivation in osteosarcoma. Together with active MMBIR and MMEJ, this inactivation probably contributes to the exceptional chromosomal instability in these tumors. Although rampant rearrangements appear to be a phenotype of osteosarcomas, we demonstrate that among the huge number of probable passenger rearrangements, specific recurrent, possibly oncogenic, events are present. For the first time the genomic chaos of osteosarcoma is characterized so thoroughly and delivered new insights in mechanisms involved in osteosarcoma development and may contribute to new diagnostic and therapeutic strategies.

Custom Design and Analysis of High-Density Oligonucleotide Bacterial Tiling Microarrays

Background High-density tiling microarrays are a powerful tool for the characterization of complete genomes. The two major computational challenges associated with custom-made arrays are design and analysis. Firstly, several genome dependent variables, such as the genomes complexity and sequence composition, need to be considered in the design to ensure a high quality microarray. Secondly, since tiling projects today very often exceed the limits of conventional array-experiments, researchers cannot use established computer tools designed for commercial arrays, and instead have to redesign previous methods or create novel tools. Principal Findings Here we describe the multiple aspects involved in the design of tiling arrays for transcriptome analysis and detail the normalisation and analysis procedures for such microarrays. We introduce a novel design method to make two 280,000 feature microarrays covering the entire genome of the bacterial species Escherichia coli and Neisseria meningitidis, respectively, as well as the use of multiple copies of control probe-sets on tiling microarrays. Furthermore, a novel normalisation and background estimation procedure for tiling arrays is presented along with a method for array analysis focused on detection of short transcripts. The design, normalisation and analysis methods have been applied in various experiments and several of the detected novel short transcripts have been biologically confirmed by Northern blot tests. Conclusions Tiling-arrays are becoming increasingly applicable in genomic research, but researchers still lack both the tools for custom design of arrays, as well as the systems and procedures for analysis of the vast amount of data resulting from such experiments. We believe that the methods described herein will be a useful contribution and resource for researchers designing and analysing custom tiling arrays for both bacteria and higher organisms.

Fusion gene microarray reveals cancer type-specificity among fusion genes

Detection of fusion genes for diagnostic purposes and as a guide to treatment is well‐established in hematological malignancies, and the prevalence of fusion genes in epithelial cancers is also increasingly appreciated. To study whether established fusion genes are present within additional cancer types, we have used an updated version of our fusion gene microarray in a systematic survey of reported fusion genes in multiple cancer types. We assembled a comprehensive database of published fusion genes, including those reported only in individual studies and samples, and fusion genes resulting from deep sequencing of cancer genomes and transcriptomes. From the total set of 548 fusion genes, we designed 599,839 oligonucleotides, targeting both chimeric transcript junctions as well as sequences internal to each of the fusion gene partners. We investigated the presence of fusion genes in a series of 67 cell lines representing 15 different cancer types. Data from ten leukemia cell lines with known fusion gene status were used to develop an automated scoring algorithm, and in five cell lines the correct fusion gene was the top scoring hit, and one came second. Two additional fusion genes, BCAS4‐BCAS3 in the MCF‐7 breast cancer cell line and CCDC6‐RET in the TPC‐1 thyroid cancer cell line were validated as true positive fusion transcripts. However, these fusion genes were not new to these cancer types, and none of 548 fusion genes were identified from a novel cancer type. We therefore find it unlikely that the assayed fusion genes are commonly present across multiple cancer types.

Tiling Array Analysis of UV Treated Escherichia coli Predicts Novel Differentially Expressed Small Peptides

Background Despite comprehensive investigation, the Escherichia coli SOS response system is not yet fully understood. We have applied custom designed whole genome tiling arrays to measure UV invoked transcriptional changes in E. coli. This study provides a more complete insight into the transcriptome and the UV irradiation response of this microorganism. Results We detected a number of novel differentially expressed transcripts in addition to the expected SOS response genes (such as sulA, recN, uvrA, lexA, umuC and umuD) in the UV treated cells. Several of the differentially expressed transcripts might play important roles in regulation of the cellular response to UV damage. We have predicted 23 novel small peptides from our set of detected non-gene transcripts. Further, three of the predicted peptides were cloned into protein expression vectors to test the biological activity. All three constructs expressed the predicted peptides, in which two of them were highly toxic to the cell. Additionally, a remarkably high overlap with previously in-silico predicted non-coding RNAs (ncRNAs) was detected. Generally we detected a far higher transcriptional activity than the annotation suggests, and these findings correspond with previous transcription mappings from E. coli and other organisms. Conclusions Here we demonstrate that the E. coli transcriptome consists of far more transcripts than the present annotation suggests, of which many transcripts seem important to the bacterial stress response. Sequence alignment of promoter regions suggest novel regulatory consensus sequences for some of the upregulated genes. Finally, several of the novel transcripts identified in this study encode putative small peptides, which are biologically active.

Survey of 548 oncogenic fusion transcripts in thyroid tumors supports the importance of the already established thyroid fusions genes

Neoplasms frequently present structural chromosomal aberrations that can alter the level of expression of a protein or to the expression of an aberrant chimeric protein. In the thyroid, the PAX8‐PPARG fusion is present in the neoplastic lesions that have a follicular architecture—follicular thyroid carcinoma (FTC) and follicular variant of papillary thyroid carcinoma (FVPTC), and less frequently in follicular thyroid adenoma (FTA), while the presence of RET/PTC fusions are largely restricted to papillary thyroid carcinoma (PTC). The ability to detect fusion genes is relevant for a correct diagnosis and for therapy. We have developed a new fusion gene microarray‐based approach for simultaneous analysis of all known and predicted fusion gene variants. We did a comprehensive screen for 548 known and putative fusion genes in 27 samples of thyroid tumors and three positive controls—one thyroid cancer cell line (TPC‐1) and two PTCs with known CCDC6‐RET (alias RET/PTC1) fusion gene, using this microarray. Within the thyroid tumors tested, only well known, previously reported fusion genes in thyroid oncology were identified. Our results reinforce the pathogenic role played by RET/PTC1, RET/PTC3, and PAX8‐PPARG fusion genes in thyroid tumorigenesis.

Computational Prediction of MicroRNAs Encoded in Viral and Other Genomes

We present an overview of selected computational methods for microRNA prediction. It is especially aimed at viral miRNA detection. As the number of microRNAs increases and the range of genomes encoding miRNAs expands, it seems that these small regulators have a more important role than has been previously thought. Most microRNAs have been detected by cloning and Northern blotting, but experimental methods are biased towards abundant microRNAs as well as being time-consuming. Computational detection methods must therefore be refined to serve as a faster, better, and more affordable method for microRNA detection. We also present data from a small study investigating the problems of computational miRNA prediction. Our findings suggest that the prediction of microRNA precursor candidates is fairly easy, while excluding false positives as well as exact prediction of the mature microRNA is hard. Finally, we discuss possible improvements to computational microRNA detection.

Assessment of fusion gene status in sarcomas using a custom made fusion gene microarray.

Sarcomas are relatively rare malignancies and include a large number of histological subgroups. Based on morphology alone, the differential diagnoses of sarcoma subtypes can be challenging, but the identification of specific fusion genes aids correct diagnostication. The presence of individual fusion products are routinely investigated in Pathology labs. However, the methods used are time-consuming and based on prior knowledge about the expected fusion gene and often the most likely break-point. In this study, 16 sarcoma samples, representing seven different sarcoma subtypes with known fusion gene status from a diagnostic setting, were investigated using a fusion gene microarray. The microarray was designed to detect all possible exon-exon breakpoints between all known fusion genes in a single analysis. An automated scoring of the microarray data from the 38 known sarcoma-related fusion genes identified the correct fusion gene among the top-three hits in 11 of the samples. The analytical sensitivity may be further optimised, but we conclude that a sarcoma-fusion gene microarray is suitable as a time-saving screening tool to identify the majority of the correct fusion genes.