Fast and sensitive validation of fusion transcripts in whole-genome sequencing data.

BACKGROUND: In cancer, genomic rearrangements can create fusion genes that either combine protein-coding sequences from two different partner genes or place one gene under the control of the promoter of another gene. These fusion genes can act as oncogenic drivers in tumor development and several fusions involving kinases have been successfully exploited as drug targets. Expressed fusions can be identified in RNA sequencing (RNA-Seq) data, but fusion prediction software often has a high fraction of false positive fusion transcript predictions. This is problematic for both research and clinical applications. RESULTS: We describe a method for validation of fusion transcripts detected by RNA-Seq in matched whole-genome sequencing (WGS) data. Our pipeline uses discordant read pairs to identify supported fusion events and analyzes soft-clipped read alignments to determine genomic breakpoints. We have tested it on matched RNA-Seq and WGS data for both tumors and cancer cell lines and show that it can be used to validate both new predicted gene fusions and experimentally validated fusion events. It was considerably faster and more sensitive than using BreakDancer and Manta, software that is instead designed to detect many different types of structural variants on a genome-wide scale. CONCLUSIONS: We have developed a fast and very sensitive pipeline for validation of gene fusions detected by RNA-Seq in matched WGS data. It can be used to identify high-quality gene fusions for further bioinformatic and experimental studies, including validation of genomic breakpoints and studies of the mechanisms that generate fusions. In a clinical setting, it could help find expressed gene fusions for personalized therapy.

Improved detection of clinically relevant fusion transcripts in cancer by machine learning classification.

BACKGROUND: Genomic rearrangements in cancer cells can create fusion genes that encode chimeric proteins or alter the expression of coding and non-coding RNAs. In some cancer types, fusions involving specific kinases are used as targets for therapy. Fusion genes can be detected by whole genome sequencing (WGS) and targeted fusion panels, but RNA sequencing (RNA-Seq) has the advantageous capability of broadly detecting expressed fusion transcripts. RESULTS: We developed a pipeline for validation of fusion transcripts identified in RNA-Seq data using matched WGS data from The Cancer Genome Atlas (TCGA) and applied it to 910 tumors from 11 different cancer types. This resulted in 4237 validated gene fusions, 3049 of them with at least one identified genomic breakpoint. Utilizing validated fusions as true positive events, we trained a machine learning classifier to predict true and false positive fusion transcripts from RNA-Seq data. The final precision and recall metrics of the classifier were 0.74 and 0.71, respectively, in an independent dataset of 249 breast tumors. Application of this classifier to all samples with RNA-Seq data from these cancer types vastly extended the number of likely true positive fusion transcripts and identified many potentially targetable kinase fusions. Further analysis of the validated gene fusions suggested that many are created by intrachromosomal amplification events with microhomology-mediated non-homologous end-joining. CONCLUSIONS: A classifier trained on validated fusion events increased the accuracy of fusion transcript identification in samples without WGS data. This allowed the analysis to be extended to all samples with RNA-Seq data, facilitating studies of tumor biology and increasing the number of detected kinase fusions. Machine learning could thus be used in identification of clinically relevant fusion events for targeted therapy. The large dataset of validated gene fusions generated here presents a useful resource for development and evaluation of fusion transcript detection algorithms.

Regulatory networks and 5' partner usage of miRNA host gene fusions in breast cancer.

Genomic rearrangements in cancer cells can create gene fusions where the juxtaposition of two different genes leads to the production of chimeric proteins or altered gene expression through promoter-swapping. We have previously shown that fusion transcripts involving microRNA (miRNA) host genes contribute to deregulation of miRNA expression regardless of the protein-coding potential of these transcripts. Many different genes can also be used as 5' partners by a miRNA host gene in what we named recurrent miRNA-convergent fusions. Here, we have explored the properties of 5' partners in fusion transcripts that involve miRNA hosts in breast tumours from The Cancer Genome Atlas (TCGA). We hypothesised that firstly, 5' partner genes should belong to pathways and transcriptional programmes that reflect the tumour phenotype and secondly, there should be a selection for fusion events that shape miRNA expression to benefit the tumour cell through the known hallmarks of cancer. We found that the set of 5' partners in miRNA host fusions is non-random, with overrepresentation of highly expressed genes in pathways active in cancer including epithelial-to-mesenchymal transition, translational regulation and oestrogen signalling. Furthermore, many miRNAs were upregulated in samples with host gene fusions, including established oncogenic miRNAs such as mir-21 and the mir-106b~mir-93~mir-25 cluster. To the list of mechanisms for deregulation of miRNA expression, we have added fusion transcripts that change the promoter region. We propose that this adds material for genetic selection and tumour evolution in cancer cells and that miRNA host fusions can act as tumour 'drivers'.

Alternative promoters and splicing create multiple functionally distinct isoforms of oestrogen receptor alpha in breast cancer and healthy tissues.

BACKGROUND: Oestrogen receptor alpha (ER) is involved in cell growth and proliferation and functions as a transcription factor, a transcriptional coregulator, and in cytoplasmic signalling. It affects, for example, bone, endometrium, ovaries and mammary epithelium. It is a key biomarker in clinical management of breast cancer, where it is used as a prognostic and treatment-predictive factor, and a therapeutical target. Several ER isoforms have been described, but transcript annotation in public databases is incomplete and inconsistent, and functional differences are not well understood. METHODS: We have analysed short- and long-read RNA sequencing data from breast tumours, breast cancer cell lines, and normal tissues to create a comprehensive annotation of ER transcripts and combined it with experimental studies of full-length protein and six alternative isoforms. RESULTS: The isoforms have varying transcription factor activity, subcellular localisation, and response to the ER-targeting drugs tamoxifen and fulvestrant. Antibodies differ in ability to detect alternative isoforms, which raises concerns for the interpretation of ER-status in routine pathology. CONCLUSIONS: Future work should investigate the effects of alternative isoforms on patient survival and therapy response. An accurate annotation of ER isoforms will aid in interpretation of clinical data and inform functional studies to improve our understanding of the ER in health and disease.

Fine-tuning the metabolic rewiring and adaptation of translational machinery during an epithelial-mesenchymal transition in breast cancer cells.

BACKGROUND: During breast cancer progression, the epithelial to mesenchymal transition has been associated with metastasis and endocrine therapy resistance; however, the underlying mechanisms remain elusive. To gain insight into this process, we studied the transition undergone by MCF7-derived cells, which is driven by the constitutive nuclear expression of a MKL1 variant devoid of the actin-binding domain (MKL1 ΔN200). We characterized the adaptive changes that occur during the MKL1-induced cellular model and focused on regulation of translation machinery and metabolic adaptation. METHODS: We performed a genome-wide analysis at the transcriptional and translational level using ribosome profiling complemented with RNA-Seq and analyzed the expression of components of the translation machinery and enzymes involved in energy metabolism. NGS data were correlated with metabolomic measurements and quantification of specific mRNAs extracted from polysomes and western blots. RESULTS: Our results reveal the expression profiles of a luminal to basal-like state in accordance with an epithelial to mesenchymal transition. During the transition, the synthesis of ribosomal proteins and that of many translational factors was upregulated. This overexpression of the translational machinery appears to be regulated at the translational level. Our results indicate an increase of ribosome biogenesis and translation activity. We detected an extensive metabolic rewiring occurring in an already "Warburg-like" context, in which enzyme isoform switches and metabolic shunts indicate a crucial role of HIF-1α along with other master regulatory factors. Furthermore, we detected a decrease in the expression of enzymes involved in ribonucleotide synthesis from the pentose phosphate pathway. During this transition, cells increase in size, downregulate genes associated with proliferation, and strongly upregulate expression of cytoskeletal and extracellular matrix genes. CONCLUSIONS: Our study reveals multiple regulatory events associated with metabolic and translational machinery adaptation during an epithelial mesenchymal-like transition process. During this major cellular transition, cells achieve a new homeostatic state ensuring their survival. This work shows that ribosome profiling complemented with RNA-Seq is a powerful approach to unveil in-depth global adaptive cellular responses and the interconnection among regulatory circuits, which will be helpful for identification of new therapeutic targets.

Extensive genetic differentiation between recently evolved sympatric Arctic charr morphs.

The availability of diverse ecological niches can promote adaptation of trophic specializations and related traits, as has been repeatedly observed in evolutionary radiations of freshwater fish. The role of genetics, environment, and history in ecologically driven divergence and adaptation, can be studied on adaptive radiations or populations showing ecological polymorphism. Salmonids, especially the Salvelinus genus, are renowned for both phenotypic diversity and polymorphism. Arctic charr (Salvelinus alpinus) invaded Icelandic streams during the glacial retreat (about 10,000 years ago) and exhibits many instances of sympatric polymorphism. Particularly, well studied are the four morphs in Lake Þingvallavatn in Iceland. The small benthic (SB), large benthic (LB), planktivorous (PL), and piscivorous (PI) charr differ in many regards, including size, form, and life history traits. To investigate relatedness and genomic differentiation between morphs, we identified variable sites from RNA-sequencing data from three of those morphs and verified 22 variants in population samples. The data reveal genetic differences between the morphs, with the two benthic morphs being more similar and the PL-charr more genetically different. The markers with high differentiation map to all linkage groups, suggesting ancient and pervasive genetic separation of these three morphs. Furthermore, GO analyses suggest differences in collagen metabolism, odontogenesis, and sensory systems between PL-charr and the benthic morphs. Genotyping in population samples from all four morphs confirms the genetic separation and indicates that the PI-charr are less genetically distinct than the other three morphs. The genetic separation of the other three morphs indicates certain degree of reproductive isolation. The extent of gene flow between the morphs and the nature of reproductive barriers between them remain to be elucidated.