Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods

Haas, Brian J.; Dobin, Alexander; Li, Bo; Stransky, Nicolas; Pochet, Nathalie; Regev, Aviv

doi:10.1186/s13059-019-1842-9

Cited by 425 publications

(448 citation statements)

References 72 publications

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“…STAR BAMs were passed into Salmon 70 v0.14.1 to generate gene-level transcript per million (TPM) quantifications with parameters: . STAR chimeric junctions were supplied to STAR-Fusion 71 v1.7.0 in kickstart mode to call ETS family fusions.…”

Section: Methodsmentioning

confidence: 99%

Transcriptional mediators of treatment resistance in lethal prostate cancer

Cuoco

Crowdis

et al. 2020

Preprint

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Metastatic castration resistant prostate cancer (mCRPC) is primarily treated with therapies that prevent transcriptional activity of the androgen receptor (AR), cause DNA damage, or prevent cell division. Clinical resistance to these therapies, including second-generation androgen-targeting compounds such as enzalutamide and abiraterone, is nearly universal. Other treatment modalities, including immune checkpoint inhibitors, have provided minimal benefit except in rare subsets of patients1,2. Both tumour intrinsic and extrinsic cellular programs contributing to therapeutic resistance remain areas of active investigation. Here we use full-length single-cell RNA-sequencing (scRNA-seq) to identify the transcriptional states of cancer and immune cells in the mCRPC microenvironment. Within cancer cells, we identified transcriptional patterns that mediate a significant proportion of inherited risk for prostate cancer, extensive heterogeneity in AR splicing within and between tumours, and vastly divergent regulatory programs between adenocarcinoma and small cell carcinoma. Moreover, upregulation of TGF-β signalling and epithelial-mesenchymal transition (EMT) were both associated with resistance to enzalutamide. We found that some lymph node metastases, but no bone metastases, were heavily infiltrated by dysfunctional CD8+ T cells, including cells undergoing dramatic clonal expansion during enzalutamide treatment. Our findings suggest avenues for rational therapeutic approaches targeting both tumour-intrinsic and immunological pathways to combat resistance to current treatment options.

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Section: Methodsmentioning

confidence: 99%

Transcriptional mediators of treatment resistance in lethal prostate cancer

Cuoco

Crowdis

et al. 2020

Preprint

Self Cite

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“…RNA-Seq overcomes the challenges associated with DNA sequencing by finding abnormally joined exons. Algorithms such as STAR-Fusion, 52 nFuse, 53 and EricScript 54 use read pairs aligned to different genes to identify translocations 55 (Table 2). STAR-Fusion has been shown to have high accuracy and lower runtime compared with its competitors.…”

Section: Workflow Step 5: Identifying Structural Variation In Dna Andmentioning

confidence: 99%

Assembling and Validating Bioinformatic Pipelines for Next-Generation Sequencing Clinical Assays

SoRelle

Wachsmann

Cantarel

2020

Archives of Pathology &Amp; Laboratory Medicine

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Context.— Clinical next-generation sequencing (NGS) is being rapidly adopted, but analysis and interpretation of large data sets prompt new challenges for a clinical lab setting. Clinical NGS results rely heavily on the bioinformatics pipeline for identifying genetic variation in complex samples. The choice of bioinformatics algorithms, genome assembly, and genetic annotation databases are important for determining genetic alterations associated with disease. The analysis methods are often tuned to the assay to maximize accuracy. Once a pipeline has been developed, it must be validated to determine accuracy and reproducibility for samples similar to real-world cases. In silico proficiency testing or institutional data exchange will ensure consistency among clinical laboratories. Objective.— To provide molecular pathologists a step-by-step guide to bioinformatics analysis and validation design in order to navigate the regulatory and validation standards of implementing a bioinformatic pipelines as a part of a new clinical NGS assay. Data Sources.— This guide uses published studies on genomic analysis, bioinformatics methods, and methods comparison studies to inform the reader on what resources, including open source software tools and databases, are available for genetic variant detection and interpretation. Conclusions.— This review covers 4 key concepts: (1) bioinformatic analysis design for detecting genetic variation, (2) the resources for assessing genetic effects, (3) analysis validation assessment experiments and data sets, including a diverse set of samples to mimic real-world challenges that assess accuracy and reproducibility, and (4) if concordance between clinical laboratories will be improved by proficiency testing designed to test bioinformatic pipelines.

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“…For comparison of relative gene expression levels, data were normalized using Cufflinks with default settings 42 , and visualized using the Qlucore Omics Explorer version 3.5 (Qlucore AB, Lund, Sweden). FusionCatcher v 1.0 and 15 STAR-Fusion v 1.4.0 were used to identify candidate fusion transcripts from the sequence data 43 .…”

Section: Rna Sequencing For Detection Of Gene Fusions and Expression mentioning

confidence: 99%

Dysregulated gene expression throughTP53promoter swapping in osteosarcoma

Saba

Cornmark

Kováč

et al. 2020

Preprint

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The malignant bone tumor osteosarcoma harbors an extreme number of chromosome rearrangements. How such massive DNA errors confer a competitive advantage to a cancer cell has remained an enigma. Osteosarcoma typically presents mutations disrupting normal 5 TP53 gene function, frequently in the form of structural rearrangements that separate the promoter region from the coding parts of the gene. To unravel the consequences of a TP53 promoter relocated in this manner, we performed in-depth genetic analyses of osteosarcoma biopsies (n=148) and cell models. We show that TP53 structural variations not only facilitate further chromosomal alterations, but also allow the constitutively active TP53 promoter to 10 upregulate putative oncogenes erroneously placed under its control. Paradoxically, many of the induced genes are part of the TP53-associated transcriptome, suggesting a need to counterbalance the initial loss of function. Our findings demonstrate how the promoter region of a tumor suppressor gene can functionally turn into an oncogenic driver. 15

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Accuracy assessment of fusion transcript detection via read-mapping and de novo fusion transcript assembly-based methods

Cited by 425 publications

References 72 publications

Transcriptional mediators of treatment resistance in lethal prostate cancer

Transcriptional mediators of treatment resistance in lethal prostate cancer

Assembling and Validating Bioinformatic Pipelines for Next-Generation Sequencing Clinical Assays

Dysregulated gene expression throughTP53promoter swapping in osteosarcoma

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