scholarly journals Discovery of Clinically Relevant Fusions in Pediatric Cancer

2021 ◽  
Author(s):  
Stephanie LaHaye ◽  
James R. Fitch ◽  
Kyle J. Voytovich ◽  
Adam C. Herman ◽  
Benjamin J. Kelly ◽  
...  
Keyword(s):  
Pediatric Cancer ◽  
Diagnostic Yield ◽  
Hematologic Malignancies ◽  
Rna Seq ◽  
Blood Disorders ◽  
Knowledge Based ◽  
Genomic Complexity ◽  
Detection Approach ◽  
Superior Approach ◽  
Fusion Detection

AbstractBackgroundPediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions.ResultsOur ensemble fusion detection approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and AWS serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a “known fusion list” prevents failure to report known pathogenic events. We have employed the ensemble fusion-calling pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patient’s medical record, both known and novel fusions provided medically meaningful information.ConclusionsOur ensemble fusion detection pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.

scholarly journals Discovery of clinically relevant fusions in pediatric cancer

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Stephanie LaHaye ◽  
James R. Fitch ◽  
Kyle J. Voytovich ◽  
Adam C. Herman ◽  
Benjamin J. Kelly ◽  
...  
Keyword(s):  
Pediatric Cancer ◽  
Diagnostic Yield ◽  
Hematologic Malignancies ◽  
Rna Seq ◽  
Blood Disorders ◽  
Knowledge Based ◽  
Genomic Complexity ◽  
Genomic Landscape ◽  
Amazon Web Services ◽  
Superior Approach

Abstract Background Pediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions. Results Our Ensemble Fusion (EnFusion) approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and Amazon Web Services (AWS) serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a “known fusion list” prevents failure to report known pathogenic events. We have employed the EnFusion pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patient’s medical record, both known and novel fusions provided medically meaningful information. Conclusions The EnFusion pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.

scholarly journals Diagnostic Validation of a Clinical Laboratory-Oriented Targeted RNA Sequencing System As a Comprehensive Assay for Hematologic Malignancies

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 38-39
Author(s):  
Ha Jin Lim ◽  
Jun Hyung Lee ◽  
Ju-Hyeon Shin ◽  
Seung Yeob Lee ◽  
Hyun-Woo Choi ◽  
...  
Keyword(s):  
Target Gene ◽  
Clinical Laboratory ◽  
Lymphoblastic Leukemia ◽  
Hematologic Malignancies ◽  
Clinical Samples ◽  
Rna Seq ◽  
Rt Pcr ◽  
Analytical Validation ◽  
Gene Coverage ◽  
Fusion Detection

Introduction Targeted RNA sequencing (RNA-seq) is a highly accurate method for sequencing transcripts of interest and can overcome limitations regarding resolution, throughput, and multistep workflow. However, RNA-seq has not been widely performed in clinical molecular laboratories due to the complexity of data processing and interpretation. We developed a customized targeted RNA-seq panel with a data processing protocol and validated its analytical performance for gene fusion detection using a subset of samples with different hematologic malignancies. Additionally, we investigated its applicability for identifying transcript variants and expression analysis using the targeted panel. Methods The target panel and customized oligonucleotide probes were designed to capture 84 genes associated with hematologic malignancies. Libraries were prepared from 800 to 1,500 ng of total RNA using GeneMediKit NGS-Leukemia-RNA kit (GeneMedica, Gwangju, Korea) and sequenced using Miseq reagent kit v3 (300 cycles) and MiseqDx (Illumina, San Diego, CA, USA). The diagnostic samples included one reference DNA (NA12878), one reference RNA (Cat no. 740000, Agilent Technologies), 14 normal peripheral blood (PB) samples, four validation bone marrow (BM) samples with known gene fusions, and 30 clinical BM or PB samples from seven categories of hematologic malignancies. The clinical samples included 27 BM aspirates and three PB samples composed of six acute myeloid leukemia, nine B-lymphoblastic leukemia/lymphoma, four T-lymphoblastic leukemia/lymphoma, three mature B-cell neoplasms, six MPN, one myelodysplastic/myeloproliferative neoplasm, and one myeloid/lymphoid neoplasm with eosinophilia and gene rearrangement. For the analytical validation of fusion detection, target gene coverage, between-run and within-run repeatability, and dilution tests (1:2 to 1:8 dilution) were performed. For the comparative analysis of fusion detection, the RNA-seq data were analyzed by STAR-Fusion and FusionCatcher and processed with stepwise filtering and prioritization strategy (Figure 1), and the result was compared to those of multiplex RT-PCR (HemaVision kit; DNA Technology, Aarhus, Denmark) or FISH (MetaSystems Gmbh, Althusseim, Germany) using 30 clinical samples. The RNA-seq data from clinical samples were additionally analyzed by FreeBayes for variant detection and by StringTie for expression profiling (Figure 1). Results First, the analytical validation showed reliable results in target gene coverage, between-run and within-run repeatability, and linearity tests. The uniformity of coverage (% of base pairs higher than 0.2 × total average depth) was calculated to be 99.8%, which revealed even coverage for the target genes in the panel using the reference DNA. Both in the within-run and between-run tests, the read counts and FFPM (fusion fragments per million) of all replicates showed reliable repeatability (r2 = 0.9655 and 0.9874, respectively). The FFPM of the diluted analytical samples including BCR-ABL1 and PML-RARA showed linear log2-fold-changes (r2 = 0.9852 and 0.9447, respectively). Second, compared to multiplex RT-PCR and FISH using 30 clinical samples, targeted RNA-seq combined with filtering and prioritization strategies detected all 13 known fusions and newly detected 17 fusions. Finally, 16 disease- and drug resistance-associated variants on the expressed transcripts of ABL1, GATA2, IKZF1, JAK2, RUNX1, and WT1 were simultaneously designated and expression analysis showed distinct four clusters of clinical samples according to the cancer subtypes and lineages. Conclusions Our customized targeted RNA-seq system provided a stable analytical performance and a more sensitive identification of gene fusions than conventional molecular methods in various clinical samples. In addition, clinically significant variants in the transcripts and expression profiling could be simultaneously identified directly from the RNA-seq data without the need for additional parallel testing. Our study identified the advantages of the clinical laboratory-oriented targeted RNA-seq system to enhance the diagnostic yield for gene fusion detection and to simplify the diagnostic steps as providing a comprehensive tool for analyzing hematologic malignancies in the clinical laboratory. Figure 1 Disclosures Lee: National Research Foundation of Korea: Research Funding.

A noise reduction method for semi-supervised community detection based on harmonic function

2018 ◽  
Vol 32 (14) ◽  
pp. 1850166 ◽  
Author(s):  
Lilin Fan ◽  
Kaiyuan Song ◽  
Dong Liu
Keyword(s):  
Harmonic Function ◽  
Prior Knowledge ◽  
Community Detection ◽  
Detection Methods ◽  
Detection Process ◽  
Knowledge Based ◽  
Detection Approach ◽  
Series Of Experiments ◽  
Novel Strategy ◽  
The Impact

Semi-supervised community detection is an important research topic in the field of complex network, which incorporates prior knowledge and topology to guide the community detection process. However, most of the previous work ignores the impact of the noise from prior knowledge during the community detection process. This paper proposes a novel strategy to identify and remove the noise from prior knowledge based on harmonic function, so as to make use of prior knowledge more efficiently. Finally, this strategy is applied to three state-of-the-art semi-supervised community detection methods. A series of experiments on both real and artificial networks demonstrate that the accuracy of semi-supervised community detection approach can be further improved.

scholarly journals Impact of RNA degradation on fusion detection by RNA-seq

BMC Genomics ◽  
2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Jaime I. Davila ◽  
Numrah M. Fadra ◽  
Xiaoke Wang ◽  
Amber M. McDonald ◽  
Asha A. Nair ◽  
...  
Keyword(s):  
Rna Degradation ◽  
Rna Seq ◽  
Fusion Detection

scholarly journals Flow Cytometry Immunophenotyping for Diagnostic Orientation and Classification of Pediatric Cancer Based on the EuroFlow Solid Tumor Orientation Tube (STOT)

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4945
Author(s):  
Cristiane de Sá de Sá Ferreira-Facio ◽  
Vitor Botafogo ◽  
Patrícia Mello Ferrão ◽  
Maria Clara Canellas ◽  
Cristiane B. Milito ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Solid Tumors ◽  
Solid Tumor ◽  
Pediatric Cancer ◽  
Diagnostic Procedures ◽  
Hematologic Malignancies ◽  
Multiparameter Flow Cytometry ◽  
Diagnostic Screening ◽  
Pediatric Solid Tumors

Early diagnosis of pediatric cancer is key for adequate patient management and improved outcome. Although multiparameter flow cytometry (MFC) has proven of great utility in the diagnosis and classification of hematologic malignancies, its application to non-hematopoietic pediatric tumors remains limited. Here we designed and prospectively validated a new single eight-color antibody combination—solid tumor orientation tube, STOT—for diagnostic screening of pediatric cancer by MFC. A total of 476 samples (139 tumor mass, 138 bone marrow, 86 lymph node, 58 peripheral blood, and 55 other body fluid samples) from 296 patients with diagnostic suspicion of pediatric cancer were analyzed by MFC vs. conventional diagnostic procedures. STOT was designed after several design–test–evaluate–redesign cycles based on a large panel of monoclonal antibody combinations tested on 301 samples. In its final version, STOT consists of a single 8-color/12-marker antibody combination (CD99-CD8/numyogenin/CD4-EpCAM/CD56/GD2/smCD3-CD19/cyCD3-CD271/CD45). Prospective validation of STOT in 149 samples showed concordant results with the patient WHO/ICCC-3 diagnosis in 138/149 cases (92.6%). These included: 63/63 (100%) reactive/disease-free samples, 43/44 (98%) malignant and 4/4 (100%) benign non-hematopoietic tumors together with 28/38 (74%) leukemia/lymphoma cases; the only exception was Hodgkin lymphoma that required additional markers to be stained. In addition, STOT allowed accurate discrimination among the four most common subtypes of malignant CD45− CD56++ non-hematopoietic solid tumors: 13/13 (GD2++ numyogenin− CD271−/+ nuMyoD1− CD99− EpCAM−) neuroblastoma samples, 5/5 (GD2− numyogenin++ CD271++ nuMyoD1++ CD99−/+ EpCAM−) rhabdomyosarcomas, 2/2 (GD2−/+ numyogenin− CD271+ nuMyoD1− CD99+ EpCAM−) Ewing sarcoma family of tumors, and 7/7 (GD2− numyogenin− CD271+ nuMyoD1− CD99− EpCAM+) Wilms tumors. In summary, here we designed and validated a new standardized antibody combination and MFC assay for diagnostic screening of pediatric solid tumors that might contribute to fast and accurate diagnostic orientation and classification of pediatric cancer in routine clinical practice.

Multicenter Analysis of Genomically Targeted Single Patient Use Requests for Pediatric Neoplasms

2021 ◽  
Author(s):  
Himalee S. Sabnis ◽  
David S. Shulman ◽  
Benjamin Mizukawa ◽  
Nancy Bouvier ◽  
Ahmet Zehir ◽  
...  
Keyword(s):  
Clinical Outcomes ◽  
Drug Administration ◽  
Pediatric Cancer ◽  
Drug Targets ◽  
De Novo ◽  
Food And Drug Administration ◽  
Hematologic Malignancies ◽  
New Drugs ◽  
Single Patient ◽  
Patient Use

PURPOSE The US Food and Drug Administration–expanded access program (EAP) uses a single patient use (SPU) mechanism to provide patient access to investigational agents in situations where no satisfactory or comparable therapy is available. Genomic profiling of de novo and relapsed or refractory childhood cancer has led to increased identification of new drug targets in the last decade. The aim of this study is to examine the SPU experience for genomically targeted therapies in patients with pediatric cancer. PATIENTS AND METHODS All genomically targeted therapeutic SPUs obtained over a 5-year period were evaluated at four large pediatric cancer programs. Data were collected on the type of neoplasm, agents requested, corresponding molecularly informed targets, and clinical outcomes. RESULTS A total of 45 SPUs in 44 patients were identified. Requests were predominantly made for CNS and solid tumors (84.4%) compared with hematologic malignancies (15.6%). Lack of an available clinical trial was the main reason for SPU initiation (64.4%). The median time from US Food and Drug Administration submission to approval was 3 days (range, 0-12 days) and from Institutional Review Board submission to approval was 5 days (range, 0-50 days). Objective tumor response was seen in 39.5% (15 of 38) of all evaluable SPUs. Disease progression was the primary reason for discontinuation of drug (66.7%) followed by toxicity (13.3%). CONCLUSION SPU requests remain an important mechanism for pediatric access to genomically targeted agents given the limited availability of targeted clinical trials for children with high-risk neoplasms. Furthermore, this subset of SPUs resulted in a substantial number of objective tumor responses. The development of a multi-institutional data registry of SPUs may enable systematic review of toxicity and clinical outcomes and provide evidence-based access to new drugs in rare pediatric cancers.

P2790Novel endomyocardial micro-biopsy device for higher precision and reduced complication risks

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
A Chireh ◽  
R Grankvist ◽  
M Sandell ◽  
A K Mukarram ◽  
N Jaff ◽  
...  
Keyword(s):  
Endomyocardial Biopsy ◽  
Diagnostic Yield ◽  
Molecular Data ◽  
Outer Diameter ◽  
The Novel ◽  
Rna Seq ◽  
Device Design ◽  
Biopsy Device ◽  
Different Parts

Abstract Introduction Endomyocardial biopsy (EMB) is the gold standard for diagnosis of several cardiac diseases, yet its use is limited by low diagnostic yield and significant complication risks. The size of the current devices allows only limited steering to different parts of the ventricle walls. In transplant monitoring, repeated biopsies with the current devices can cause scarring that makes it increasingly difficult to obtain adequate samples. We hypothesised that several of the shortcomings of EMB can be avoided with a smaller and more steerable device. Further, we hypothesised that the novel sampling procedure could be coupled to a low-input molecular analysis method, such as RNA-sequencing (RNA-seq), to provide molecular characterisation of the tissue without the need of large biopsy samples. Purpose To develop an EMB device with significantly smaller dimensions, for future use in diagnostics and research investigations. Specific aims were to test feasibility and safety of the procedure, as well as the quality of the generated molecular data. Methods 65 “micro biopsy” (micro-EMB) device prototypes were designed and evaluated in-house. The prototypes were evaluated either in an ex-vivo simulator or in acute non-survival pig experiments (n=23). Once the final device design was reached, an in vivo trial was set up using six naive Yorkshire farm pigs. Micro-EMB, conventional EMB, skeletal muscle and blood samples were collected for RNA-seq characterisation and comparison. In half of the animals (n=3), micro-EMB was the only intervention in order to prioritise safety evaluations. The animals were monitored for one week. Results The final device design has an outer diameter (OD) of 0.4 mm, compared to a conventional 11 mm device (in the opened position), Fig 1A. The device can be directed to different parts of the myocardium in both ventricles. In the in vivo evaluation in swine, 81% of the biopsy attempts (n=157) were successful. High quality RNA-seq data was generated from 91% of the sequenced heart micro-biopsy samples (n=32). The gene expression signatures of samples taken with the novel device were comparable with samples taken with a conventional device, Fig 1B. No major complications were detected either during periprocedural monitoring or during the follow-up. The tissue mark after micro-biopsy was markedly smaller than after conventional endomyocardial biopsy. A) Bioptome dimensions. B) RNA-seq data. Conclusions Our preliminary data suggest that the novel submillimeter biopsy device, coupled with RNA-seq, provides a feasible method to obtain molecular data from the myocardium. The method is less traumatic and has a higher flexibility compared to conventional methods, enabling safer and more specific sampling from different parts of the myocardium. In the long term, the procedure could open unprecedented diagnostic and research possibilities. Future studies should be directed to establish the capabilities of the novel method in a relevant disease model. Acknowledgement/Funding Family Erling Persson Foundation. The Söderberg foundations. KID (Karolinska Institutet). The 4D project. Stockholm county council. Astra Zeneca.

scholarly journals State-of-the-Art Fusion-Finder Algorithms Sensitivity and Specificity

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Matteo Carrara ◽  
Marco Beccuti ◽  
Fulvio Lazzarato ◽  
Federica Cavallo ◽  
Francesca Cordero ◽  
...  
Keyword(s):  
Synthetic Data ◽  
Rna Seq ◽  
Comparison Analysis ◽  
Generating Functional ◽  
Specificity And Sensitivity ◽  
Sensitive Tool ◽  
Fusion Junction ◽  
Synthetic Datasets ◽  
Very High ◽  
Fusion Detection

Background. Gene fusions arising from chromosomal translocations have been implicated in cancer. RNA-seq has the potential to discover such rearrangements generating functional proteins (chimera/fusion). Recently, many methods for chimeras detection have been published. However, specificity and sensitivity of those tools were not extensively investigated in a comparative way.Results. We tested eight fusion-detection tools (FusionHunter, FusionMap, FusionFinder, MapSplice, deFuse, Bellerophontes, ChimeraScan, and TopHat-fusion) to detect fusion events using synthetic and real datasets encompassing chimeras. The comparison analysis run only on synthetic data could generate misleading results since we found no counterpart on real dataset. Furthermore, most tools report a very high number of false positive chimeras. In particular, the most sensitive tool, ChimeraScan, reports a large number of false positives that we were able to significantly reduce by devising and applying two filters to remove fusions not supported by fusion junction-spanning reads or encompassing large intronic regions.Conclusions. The discordant results obtained using synthetic and real datasets suggest that synthetic datasets encompassing fusion events may not fully catch the complexity of RNA-seq experiment. Moreover, fusion detection tools are still limited in sensitivity or specificity; thus, there is space for further improvement in the fusion-finder algorithms.

Diagnostic yield of bronchoscopy with bronchoalveolar lavage in febrile patients with hematologic malignancies and pulmonary infiltrates

2007 ◽  
Vol 87 (4) ◽  
pp. 291-297 ◽  
Author(s):  
Margit Hummel ◽  
Silke Rudert ◽  
Herbert Hof ◽  
Rüdiger Hehlmann ◽  
Dieter Buchheidt
Keyword(s):  
Bronchoalveolar Lavage ◽  
Diagnostic Yield ◽  
Hematologic Malignancies ◽  
Pulmonary Infiltrates

scholarly journals SimFuse: A Novel Fusion Simulator for RNA Sequencing (RNA-Seq) Data

2015 ◽  
Vol 2015 ◽  
pp. 1-5 ◽  
Author(s):  
Yuxiang Tan ◽  
Yann Tambouret ◽  
Stefano Monti
Keyword(s):  
Sample Size ◽  
Rna Sequencing ◽  
High Throughput Sequencing ◽  
Performance Metrics ◽  
Simulated Data ◽  
Real Data ◽  
Rna Seq ◽  
Sequencing Data ◽  
Detection Algorithms ◽  
Fusion Detection

The performance evaluation of fusion detection algorithms from high-throughput sequencing data crucially relies on the availability of data with known positive and negative cases of gene rearrangements. The use of simulated data circumvents some shortcomings of real data by generation of an unlimited number of true and false positive events, and the consequent robust estimation of accuracy measures, such as precision and recall. Although a few simulated fusion datasets from RNA Sequencing (RNA-Seq) are available, they are of limited sample size. This makes it difficult to systematically evaluate the performance of RNA-Seq based fusion-detection algorithms. Here, we present SimFuse to address this problem. SimFuse utilizes real sequencing data as the fusions’ background to closely approximate the distribution of reads from a real sequencing library and uses a reference genome as the template from which to simulate fusions’ supporting reads. To assess the supporting read-specific performance, SimFuse generates multiple datasets with various numbers of fusion supporting reads. Compared to an extant simulated dataset, SimFuse gives users control over the supporting read features and the sample size of the simulated library, based on which the performance metrics needed for the validation and comparison of alternative fusion-detection algorithms can be rigorously estimated.

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