Project description:Genomic profiling efforts have revealed a rich diversity of oncogenic fusion genes, and many are emerging as important therapeutic targets. While there are many ways to identify fusion genes from RNA-seq data, visualising these transcripts and their supporting reads remains challenging. Clinker is a bioinformatics tool written in Python, R and Bpipe, that leverages the superTranscript method to visualise fusion genes. We demonstrate the use of Clinker to obtain interpretable visualizations of the RNA-seq data that lead to fusion calls. In addition, we use Clinker to explore multiple fusion transcripts with novel breakpoints within the P2RY8-CRLF2 fusion gene in B-cell Acute Lymphoblastic Leukaemia (B-ALL).
Project description:We detected fusion genes in 274 fresh surgical samples of gliomas using whole transcriptome sequencing. Using this approach we screened a panel of glioma samples and identified a number of activating novel fusion transcripts. Fusion detection in 274 glioma patients
Project description:We detected fusion genes in 274 fresh surgical samples of gliomas using whole transcriptome sequencing. Using this approach we screened a panel of glioma samples and identified a number of activating novel fusion transcripts.
Project description:The clinical significance of gene fusions detected by DNA-based next generation sequencing remains unclear as resistance mechanisms to EGFR tyrosine kinase inhibitors (TKIs) in EGFR mutant non-small cell lung cancer (NSCLC). Through comprehensive evaluation of potential drug resistance-imparting fusion oncogenes in EGFR mutant lung cancers, we selected candidate samples to be further validated by confirmatory assay. Here, we performed RNA sequencing as an unbiased genome-wide method to identify novel oncogenic fusions. In 11 samples from 10 patients, 3 different fusion callers consistently detected only the DLG1-BRAF fusion in sample 6. None of other putative fusions detected by DNA-based hybrid capture sequencing were validated.We concluded that only a subset of putative fusion was validated by RNA-seq.
Project description:Fusion genes arising from cancer-associated somatic mutations are a potential rich source for highly immunogenic neo-antigens. However, their exploitation as targets for personalized cancer immunotherapy is currently limited by the lack of computational tools allowing transcriptome-wide identification of unique fusion genes in an accurate and sensitive manner. Here, we present EasyFuse, a computational pipeline, to detect individual and cancer-specific fusion genes in next-generation-sequencing transcriptome data obtained from human cancer samples. Using machine learning, EasyFuse predicts personal fusion genes with high precision and sensitivity and outperforms previously described approaches as qualified by an unprecedented ground-truth dataset of >1500 verification experiments in relevant patient samples. By testing immunogenicity with autologous blood lymphocytes from cancer patients we detected pre-established CD4+ and CD8+ T cell responses for 10 of 21 (48%), and for 1 of 30 (3%) of identified fusion genes, respectively. In conclusion, we demonstrate accurate detection of cancer-specific fusion genes. The high frequency of T cell responses detected in cancer patients support the relevance of private fusion genes as neo-antigens for personalized immunotherapies, especially for tumors with low point mutation burdens.