scholarly journals Next Generation Sequencing for Gene Fusion Analysis in Lung Cancer: A Literature Review

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 521
Author(s):  
Rossella Bruno ◽  
Gabriella Fontanini
Keyword(s):  
Lung Cancer ◽  
Next Generation Sequencing ◽  
Gene Fusion ◽  
Repetitive Sequences ◽  
Clinical Samples ◽  
Gene Fusions ◽  
Next Generation ◽  
Hybrid Capture ◽  
Fusion Analysis ◽  
Generation Sequencing

Gene fusions have a pivotal role in non-small cell lung cancer (NSCLC) precision medicine. Several techniques can be used, from fluorescence in situ hybridization and immunohistochemistry to next generation sequencing (NGS). Although several NGS panels are available, gene fusion testing presents more technical challenges than other variants. This is a PubMed-based narrative review aiming to summarize NGS approaches for gene fusion analysis and their performance on NSCLC clinical samples. The analysis can be performed at DNA or RNA levels, using different target enrichment (hybrid-capture or amplicon-based) and sequencing chemistries, with both custom and commercially available panels. DNA sequencing evaluates different alteration types simultaneously, but large introns and repetitive sequences can impact on the performance and it does not discriminate between expressed and unexpressed gene fusions. RNA-based targeted approach analyses and quantifies directly fusion transcripts and is more accurate than DNA panels on tumor tissue, but it can be limited by RNA quality and quantity. On liquid biopsy, satisfying data have been published on circulating tumor DNA hybrid-capture panels. There is not a perfect method for gene fusion analysis, but NGS approaches, though still needing a complete standardization and optimization, present several advantages for the clinical practice.

Next-generation sequencing for tumor mutation quantification using liquid biopsies

2020 ◽  
Vol 58 (2) ◽  
pp. 306-313 ◽  
Author(s):  
Mariano Provencio ◽  
Clara Pérez-Barrios ◽  
Miguel Barquin ◽  
Virginia Calvo ◽  
Fabio Franco ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Next Generation Sequencing ◽  
Correlation Coefficient ◽  
Resistance Mutation ◽  
Response To Treatment ◽  
Clinical Samples ◽  
Next Generation ◽  
Liquid Biopsies ◽  
Nsclc Patients ◽  
Generation Sequencing

AbstractBackgroundNon-small cell lung cancer (NSCLC) patients benefit from targeted therapies both in first- and second-line treatment. Nevertheless, molecular profiling of lung cancer tumors after first disease progression is seldom performed. The analysis of circulating tumor DNA (ctDNA) enables not only non-invasive biomarker testing but also monitoring tumor response to treatment. Digital PCR (dPCR), although a robust approach, only enables the analysis of a limited number of mutations. Next-generation sequencing (NGS), on the other hand, enables the analysis of significantly greater numbers of mutations.MethodsA total of 54 circulating free DNA (cfDNA) samples from 52 NSCLC patients and two healthy donors were analyzed by NGS using the Oncomine™ Lung cfDNA Assay kit and dPCR.ResultsLin’s concordance correlation coefficient and Pearson’s correlation coefficient between mutant allele frequencies (MAFs) assessed by NGS and dPCR revealed a positive and linear relationship between the two data sets (ρc = 0.986; 95% confidence interval [CI] = 0.975–0.991; r = 0.987; p < 0.0001, respectively), indicating an excellent concordance between both measurements. Similarly, the agreement between NGS and dPCR for the detection of the resistance mutation p.T790M was almost perfect (K = 0.81; 95% CI = 0.62–0.99), with an excellent correlation in terms of MAFs (ρc = 0.991; 95% CI = 0.981–0.992 and Pearson’s r = 0.998; p < 0.0001). Importantly, cfDNA sequencing was successful using as low as 10 ng cfDNA input.ConclusionsMAFs assessed by NGS were highly correlated with MAFs assessed by dPCR, demonstrating that NGS is a robust technique for ctDNA quantification using clinical samples, thereby allowing for dynamic genomic surveillance in the era of precision medicine.

scholarly journals Targeted Next-Generation Sequencing of 117 Routine Clinical Samples Provides Further Insights into the Molecular Landscape of Uveal Melanoma

Cancers ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1039 ◽  
Author(s):  
Sophie Thornton ◽  
Sarah Coupland ◽  
Lisa Olohan ◽  
Julie Sibbring ◽  
John Kenny ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Uveal Melanoma ◽  
Copy Number ◽  
Clinical Samples ◽  
Sample Type ◽  
Next Generation ◽  
Hybrid Capture ◽  
Targeted Next Generation Sequencing ◽  
Molecular Landscape ◽  
Generation Sequencing

Uveal melanoma (UM) has well-characterised somatic copy number alterations (SCNA) in chromosomes 1, 3, 6 and 8, in addition to mutations in GNAQ, GNA11, CYSLTR2, PLCB4, BAP1, SF3B1 and EIF1AX, most being linked to metastatic-risk. To gain further insight into the molecular landscape of UM, we designed a targeted next-generation sequencing (NGS) panel to detect SCNA and mutations in routine clinical UM samples. We compared hybrid-capture and amplicon-based target enrichment methods and tested a larger cohort of primary UM samples on the best performing panel. UM clinical samples processed either as fresh-frozen, formalin-fixed paraffin embedded (FFPE), small intraocular biopsies or following irradiation were successfully profiled using NGS, with hybrid capture outperforming the PCR-based enrichment methodology. We identified monosomy 3 (M3)-UM that were wild-type for BAP1 but harbored SF3B1 mutations, novel frameshift deletions in SF3B1 and EIF1AX, as well as a PLCB4 mutation outside of the hotspot on exon 20 coinciding with a GNAQ mutation in some UM. We observed samples that harboured mutations in both BAP1 and SF3B1, and SF3B1 and EIF1AX, respectively. Novel mutations were also identified in TTC28, KTN1, CSMD1 and TP53BP1. NGS can simultaneously assess SCNA and mutation data in UM, in a reliable and reproducible way, irrespective of sample type or previous processing. BAP1 and SF3B1 mutations, in addition to 8q copy number, are of added importance when determining UM patient outcome.

scholarly journals Clinical Impact of Hybrid Capture–Based Next-Generation Sequencing on Changes in Treatment Decisions in Lung Cancer

2017 ◽  
Vol 12 (2) ◽  
pp. 258-268 ◽  
Author(s):  
Anna Belilovski Rozenblum ◽  
Maya Ilouze ◽  
Elizabeth Dudnik ◽  
Addie Dvir ◽  
Lior Soussan-Gutman ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Next Generation Sequencing ◽  
Treatment Decisions ◽  
Clinical Impact ◽  
Next Generation ◽  
Hybrid Capture ◽  
Generation Sequencing

scholarly journals MA12.01 Next Generation Sequencing Based Clinical Framework for Analyses of Treatment Predictive Mutations and Gene Fusions in Lung Cancer

2017 ◽  
Vol 12 (1) ◽  
pp. S409-S410
Author(s):  
Kajsa Ericson Lindquist ◽  
Anna Karlsson ◽  
Per Levéen ◽  
Hans Brunnström ◽  
Christel Reuterswärd ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Next Generation Sequencing ◽  
Gene Fusions ◽  
Next Generation ◽  
Generation Sequencing

scholarly journals SeekFusion - A Clinically Validated Fusion Transcript Detection Pipeline for PCR-Based Next-Generation Sequencing of RNA

2021 ◽  
Vol 12 ◽  
Author(s):  
Jagadheshwar Balan ◽  
Garrett Jenkinson ◽  
Asha Nair ◽  
Neiladri Saha ◽  
Tejaswi Koganti ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Rna Sequencing ◽  
Gene Fusion ◽  
De Novo ◽  
Detection Accuracy ◽  
Gene Fusions ◽  
Next Generation ◽  
Spliced Alignment ◽  
Generation Sequencing ◽  
Fusion Detection

Detecting gene fusions involving driver oncogenes is pivotal in clinical diagnosis and treatment of cancer patients. Recent developments in next-generation sequencing (NGS) technologies have enabled improved assays for bioinformatics-based gene fusions detection. In clinical applications, where a small number of fusions are clinically actionable, targeted polymerase chain reaction (PCR)-based NGS chemistries, such as the QIAseq RNAscan assay, aim to improve accuracy compared to standard RNA sequencing. Existing informatics methods for gene fusion detection in NGS-based RNA sequencing assays traditionally use a transcriptome-based spliced alignment approach or a de-novo assembly approach. Transcriptome-based spliced alignment methods face challenges with short read mapping yielding low quality alignments. De-novo assembly-based methods yield longer contigs from short reads that can be more sensitive for genomic rearrangements, but face performance and scalability challenges. Consequently, there exists a need for a method to efficiently and accurately detect fusions in targeted PCR-based NGS chemistries. We describe SeekFusion, a highly accurate and computationally efficient pipeline enabling identification of gene fusions from PCR-based NGS chemistries. Utilizing biological samples processed with the QIAseq RNAscan assay and in-silico simulated data we demonstrate that SeekFusion gene fusion detection accuracy outperforms popular existing methods such as STAR-Fusion, TOPHAT-Fusion and JAFFA-hybrid. We also present results from 4,484 patient samples tested for neurological tumors and sarcoma, encompassing details on some novel fusions identified.

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