FusionCatcher - a tool for finding somatic fusion genes in paired-end RNA-sequencing data

Purpose Patients with cancer who graciously consent for autopsy represent an invaluable resource for the study of cancer biology. To advance the study of tumor evolution, metastases, and resistance to treatment, we developed a next-generation rapid autopsy program integrated within a broader precision medicine clinical trial that interrogates pre- and postmortem tissue samples for patients of all ages and cancer types. Materials and Methods One hundred twenty-three (22%) of 554 patients who consented to the clinical trial also consented for rapid autopsy. This report comprises the first 15 autopsies, including patients with metastatic carcinoma (n = 10), melanoma (n = 1), and glioma (n = 4). Whole-exome sequencing (WES) was performed on frozen autopsy tumor samples from multiple anatomic sites and on non-neoplastic tissue. RNA sequencing (RNA-Seq) was performed on a subset of frozen samples. Tissue was also used for the development of preclinical models, including tumor organoids and patient-derived xenografts. Results Three hundred forty-six frozen samples were procured in total. WES was performed on 113 samples and RNA-Seq on 72 samples. Successful cell strain, tumor organoid, and/or patient-derived xenograft development was achieved in four samples, including an inoperable pediatric glioma. WES data were used to assess clonal evolution and molecular heterogeneity of tumors in individual patients. Mutational profiles of primary tumors and metastases yielded candidate mediators of metastatic spread and organotropism including CUL9 and PIGM in metastatic ependymoma and ANKRD52 in metastatic melanoma to the lung. RNA-Seq data identified novel gene fusion candidates. Conclusion A next-generation sequencing–based autopsy program in conjunction with a pre-mortem precision medicine pipeline for diverse tumors affords a valuable window into clonal evolution, metastasis, and alterations underlying treatment. Moreover, such an autopsy program yields robust preclinical models of disease.

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“…For fusion analysis, we used STAR-fusion (STAR-Fusion_v0.5.1), 24,25 FusionCatcher (v0.99.3e), 26 and FusionSeq. 27–31 …”

Section: Methodsmentioning

confidence: 99%

Next-Generation Rapid Autopsies Enable Tumor Evolution Tracking and Generation of Preclinical Models

Pisapia

Salvatore

Pauli

et al. 2017

JCO Precision Oncology

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“…Currently, many RNA-seq gene fusion discovery algorithms utilize spanning reads (one read partially aligns to both genes corresponding to the fusion junction) or encompassing reads (each read of a pair aligns to a different gene, thereby surrounding the fusion junction) such as TopHat-Fusion (Kim and Salzberg 2011), deFuse (McPherson et al 2011a), ChimeraScan , BreakFusion , FusionCatcher (Nicorici et al 2014), pyPRADA (Torres-Garcia et al 2014), and TRUP (Fernandez-Cuesta et al 2015). However, despite the successful application of these algorithms to discover gene fusions, a recent comparison of eight gene fusion discovery tools revealed a lot of variability between callers.…”

Section: [Supplemental Materials Is Available For This Article]mentioning

confidence: 99%

“…To compare the performance of INTEGRATE to other available algorithms, we reanalyzed the HCC1395 data with three WGS and RNA-seq callers (Comrad [McPherson et al 2011b], nFuse [McPherson et al 2012], and BreakTrans [Chen et al 2013]) and five commonly used and recently published RNA-seq gene fusion tools (TopHat-Fusion [Kim and Salzberg 2011], ChimeraScan , FusionCatcher [Nicorici et al 2014], pyPRADA [Torres-Garcia et al 2014], and TRUP [Fernandez-Cuesta et al 2015]). BreakTrans was provided with fusion and SV candidates called by BreakDancer ).…”

Section: Application To Hcc1395 Breast Cancer Cellsmentioning

confidence: 99%

INTEGRATE: gene fusion discovery using whole genome and transcriptome data

et al. 2015

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While next-generation sequencing (NGS) has become the primary technology for discovering gene fusions, we are still faced with the challenge of ensuring that causative mutations are not missed while minimizing false positives. Currently, there are many computational tools that predict structural variations (SV) and gene fusions using whole genome (WGS) and transcriptome sequencing (RNA-seq) data separately. However, as both WGS and RNA-seq have their limitations when used independently, we hypothesize that the orthogonal validation from integrating both data could generate a sensitive and specific approach for detecting high-confidence gene fusion predictions. Fortunately, decreasing NGS costs have resulted in a growing quantity of patients with both data available. Therefore, we developed a gene fusion discovery tool, INTEGRATE, that leverages both RNA-seq and WGS data to reconstruct gene fusion junctions and genomic breakpoints by split-read mapping.

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“…The alignment of the paired-end FASTQ files to the human reference genome (GRCh37) was performed using TopHat2 software (20) and visualized using Integrative Genomics Viewer software (21). The FASTQ files were further analyzed, to find known and/or novel fusion transcripts, using FusionCatcher software (22). To validate the presence of the TPM3-NTRK1 fusion transcript and a mutation in the kinase domain, the total RNA extracted from the two cell lines was also subjected to reverse transcription-PCR (RT-PCR) and Sanger sequencing analysis.…”

Section: Rna Sequencingmentioning

confidence: 99%

Foretinib Overcomes Entrectinib Resistance Associated with the NTRK1 G667C Mutation in NTRK1 Fusion–Positive Tumor Cells in a Brain Metastasis Model

Nishiyama

Yamada

Kita

et al. 2018

Clinical Cancer Research

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Purpose: Rearrangement of the neurotrophic tropomyosin receptor kinase 1 (NTRK1) gene, which encodes tyrosine receptor kinase A (TRK-A), occurs in various cancers, including colon cancer. Although entrectinib is effective in the treatment of central nervous system (CNS) metastases that express NTRK1 fusion proteins, acquired resistance inevitably results in recurrence. The CNS is a sanctuary for targeted drugs; however, the mechanism by which CNS metastases become entrectinib-resistant remains elusive and must be clarified to develop better therapeutics.Experimental Design: The entrectinib-resistant cell line KM12SM-ER was developed by continuous treatment with entrectinib in the brain metastasis-mimicking model inoculated with the entrectinib-sensitive human colon cancer cell line KM12SM, which harbors the TPM3-NTRK1 gene fusion. The mechanism of entrectinib resistance in KM12SM-ER cells was examined by nextgeneration sequencing. Compounds that overcame entrectinib resistance were screened from a library of 122 kinase inhibitors.Results: KM12SM-ER cells, which showed moderate resistance to entrectinib in vitro, had acquired the G667C mutation in NTRK1. The kinase inhibitor foretinib inhibited TRK-A phosphorylation and the viability of KM12SM-ER cells bearing the NTRK1-G667C mutation in vitro. Moreover, foretinib markedly inhibited the progression of entrectinib-refractory KM12SM-ER-derived liver metastases and brain tumors in animal models, predominantly through inhibition of TRK-A phosphorylation.Conclusions: These results suggest that foretinib may be effective in overcoming entrectinib resistance associated with the NTRK1-G667C mutation in NTRK1 fusion-positive tumors in various organs, including the brain, and provide a rationale for clinical trials of foretinib in cancer patients with entrectinibresistant tumors harboring the NTRK1-G667C mutation, including patients with brain metastases. Clin Cancer Res; 1-13. Ó2018AACR.

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FusionCatcher - a tool for finding somatic fusion genes in paired-end RNA-sequencing data

Cited by 334 publications

References 25 publications

Next-Generation Rapid Autopsies Enable Tumor Evolution Tracking and Generation of Preclinical Models

Next-Generation Rapid Autopsies Enable Tumor Evolution Tracking and Generation of Preclinical Models

INTEGRATE: gene fusion discovery using whole genome and transcriptome data

Foretinib Overcomes Entrectinib Resistance Associated with the NTRK1 G667C Mutation in NTRK1 Fusion–Positive Tumor Cells in a Brain Metastasis Model

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