scholarly journals RNA Analysis as a Tool to Determine Clinically Relevant Gene Fusions and Splice Variants

2018 ◽  
Vol 142 (4) ◽  
pp. 474-479 ◽  
Author(s):  
Cristina Teixidó ◽  
Ana Giménez-Capitán ◽  
Miguel Ángel Molina-Vila ◽  
Vicente Peg ◽  
Niki Karachaliou ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Cancer Patients ◽  
Splice Variants ◽  
Treatment Options ◽  
Diagnostic Process ◽  
Gene Fusions ◽  
Next Generation ◽  
Liquid Biopsies ◽  
Rna Analysis ◽  
Generation Sequencing

Context.— Technologic advances have contributed to the increasing relevance of RNA analysis in clinical oncology practice. The different genetic aberrations that can be screened with RNA include gene fusions and splice variants. Validated methods of identifying these alterations include fluorescence in situ hybridization, immunohistochemistry, reverse transcription–polymerase chain reaction, and next-generation sequencing, which can provide physicians valuable information on disease and treatment of cancer patients. Objective.— To discuss the standard techniques available and new approaches for the identification of gene fusions and splice variants in cancer, focusing on RNA analysis and how analytic methods have evolved in both tissue and liquid biopsies. Data Sources.— This is a narrative review based on PubMed searches and the authors' own experiences. Conclusions.— Reliable RNA-based testing in tissue and liquid biopsies can inform the diagnostic process and guide physicians toward the best treatment options. Next-generation sequencing methodologies permit simultaneous assessment of molecular alterations and increase the number of treatment options available for cancer patients.

Next-generation sequencing reveals alternative L-DOPA decarboxylase (DDC) splice variants bearing novel exons, in human hepatocellular and lung cancer cells

Gene ◽  
2021 ◽  
Vol 768 ◽  
pp. 145262 ◽  
Author(s):  
Maria Papatsirou ◽  
Panagiotis G. Adamopoulos ◽  
Pinelopi I. Artemaki ◽  
Vasiliki P. Georganti ◽  
Andreas Scorilas ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Next Generation Sequencing ◽  
Cancer Cells ◽  
Splice Variants ◽  
Lung Cancer Cells ◽  
Dopa Decarboxylase ◽  
Next Generation ◽  
Generation Sequencing

Spectrum of Pathogenic Germline Mutations in Chinese Lung Cancer Patients through Next-Generation Sequencing

2019 ◽  
Vol 26 (1) ◽  
pp. 109-114 ◽  
Author(s):  
Panwen Tian ◽  
Xiangyang Cheng ◽  
Zhengyi Zhao ◽  
Yuzi Zhang ◽  
Celimuge Bao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Next Generation Sequencing ◽  
Cancer Patients ◽  
Germline Mutations ◽  
Next Generation ◽  
Lung Cancer Patients ◽  
Generation Sequencing

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.

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