scholarly journals Diagnostic Accuracy of Metagenomic Next-Generation Sequencing in Sputum-Scarce or Smear-Negative Cases with Suspected Pulmonary Tuberculosis

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ning Zhu ◽  
Daibing Zhou ◽  
Shengqing Li
Keyword(s):  
Next Generation Sequencing ◽  
Pulmonary Tuberculosis ◽  
Diagnostic Accuracy ◽  
Lung Tissue ◽  
Lung Biopsy ◽  
Next Generation ◽  
Tissue Biopsy ◽  
Conventional Tests ◽  
Smear Negative ◽  
Generation Sequencing

Objective. To investigate the diagnostic accuracy of metagenomic next-generation sequencing (mNGS) in bronchoalveolar lavage fluid (BALF) samples or lung biopsy specimens from which suspected pulmonary tuberculosis (PTB) patients have no sputum or negative smear. Materials and Methods. Sputum-scarce or smear-negative cases with suspected PTB ( n = 107 ) were analyzed from January 2018 to June 2020. We collected BALF or lung tissue biopsy samples with these cases of suspected TB during hospitalization. The diagnostic accuracy of mNGS for these samples was compared with those of conventional tests or the T-SPOT.TB assay. Results. 46 cases of PTB patients and 61 cases of non-PTB patients were finally enrolled and analyzed. mNGS exhibited a sensitivity of 89.13%, which was higher than conventional tests (67.39%) but equivalent to those of the T-SPOT.TB assay alone (76.09%) or T-SPOT.TB assay in combination with conventional tests (91.30%). The specificity of mNGS was 98.36%, similar to conventional tests (95.08%) but significantly higher than those of the T-SPOT.TB assay alone (65.57%) or the T-SPOT.TB assay in combination with conventional tests (63.93%). There was no significant difference in the diagnostic accuracy of mNGS in BALF samples and lung biopsy tissue specimens. Conclusion. Our findings demonstrate that mNGS could offer improved detection of Mycobacterium tuberculosis in BALF or lung tissue biopsy samples in sputum-scarce or smear-negative cases with suspected PTB.

Next generation sequencing targeted gene panel in Greek MODY patients increases diagnostic accuracy

2019 ◽  
Vol 21 (1) ◽  
pp. 28-39 ◽  
Author(s):  
Elizabeth B. Tatsi ◽  
Christina Kanaka‐Gantenbein ◽  
Andreas Scorilas ◽  
George P. Chrousos ◽  
Amalia Sertedaki
Keyword(s):  
Next Generation Sequencing ◽  
Diagnostic Accuracy ◽  
Gene Panel ◽  
Next Generation ◽  
Generation Sequencing

Analysis of tissue and circulating tumor DNA by next-generation sequencing of hepatocellular carcinoma.

2017 ◽  
Vol 35 (4_suppl) ◽  
pp. 298-298 ◽  
Author(s):  
Sadakatsu Ikeda ◽  
Jordan S Lim ◽  
Razelle Kurzrock
Keyword(s):  
Hepatocellular Carcinoma ◽  
Next Generation Sequencing ◽  
Mutant Allele ◽  
Circulating Tumor Dna ◽  
Next Generation ◽  
Advanced Hcc ◽  
Tissue Biopsy ◽  
Tumor Dna ◽  
Generation Sequencing ◽  
Allele Fraction

298 Background: Hepatocellular carcinoma (HCC) has limited treatment options. Molecular analysis of its mutational landscape may enable the discovery of new treatment option.However, biopsy is not routinely performed in HCC and involves risks. The utility of analyzing cell-free circulating tumor DNA (ctDNA) by next-generation sequencing (NGS) is not established. Methods: We performed 32 ctDNA NGS and 10 tissue NGS analyses in 26 patients with advanced HCC (January 2015 – October 2015). ctDNA analysis (54 to 70 genes) was performed using Guardant 360, which detects single nucleotide variants, amplifications, fusions, and specific insertion/deletion mutations. The mutant allele fraction for detected alterations was calculated relative to wild type in ctDNA. Tissue NGS was performed using Foundation One or Molecular Health. The study ws conducted in accordance with UCSD Moores Cancer Center Institutional Review Board requirements. Results: Among 32 ctDNA NGS, at least one genomic alteration (excluding variants of uncertain significance (VUS)) was discerned in 27 samples (84.3%) (average = 2.1 alterations per patient [range, 0-5]) with a median mutant allele fraction of 0.92% (range, 0.06% - 40.57%). Ten patients had both ctDNA NGS and tissue NGS. The median time difference between the date of tissue and ctDNA NGS testing was 450 days (range, 29 – 876 days), possibly reflecting the challenge with doing a second tissue biopsy at the time of relapse. The most commonly mutated gene was TP53 (16 samples, 50.0%), followed by CTNNB1 (8 samples, 25.0%), ARID1A (6 samples, 18.6%), EGFR (4 samples, 12.5%) and MYC (4 samples, 12.5%). Amplification was observed in 11 genes, including CDK4, CDK6, CCNE1, EGFR, ERBB2, FGFR1, KRAS, and MYC. No fusions or indels were observed. At least one potentially actionable alteration was observed in 20 of the 26 patients (76.9%). In two patients treated on the basis of the ctDNA alterations alone, a response was seen. Conclusions: ctDNA profiling is feasible in advanced HCC, and may provide a tissue biopsy-free alternative in these difficult-to-biopsy patients. Further study of clinical validity and utility is ongoing.

scholarly journals Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis

2019 ◽  
Author(s):  
Jiahui Wang ◽  
Yelei Han ◽  
Yang Zhou ◽  
Jing Feng
Keyword(s):  
Next Generation Sequencing ◽  
Predictive Value ◽  
Pulmonary Infection ◽  
Fungal Infections ◽  
Next Generation ◽  
Promising Technique ◽  
Infection Diagnosis ◽  
Conventional Tests ◽  
Conventional Test ◽  
Generation Sequencing

Abstract Background. Metagenomic next-generation sequencing (mNGS) is emerging as a promising technique for pathogens detection. However, reports on the application of mNGS in mixed pulmonary infection remain scarce. Methods. From July 2018 to March 2019, 55 cases were enrolled in this retrospective analysis. Cases were classified into mixed pulmonary infection (36 [65.5%]) and non-mixed pulmonary infection (19 [34.5%]) according to primary diagnoses. The performances of mNGS and conventional test on mixed pulmonary infection diagnosis and pathogen identification were compared. Results. The sensitivity of mNGS in mixed pulmonary infection diagnose was much higher than that of conventional test (97.2％ vs 13.9％; P< 0.01) , but the specificity was the opposite (63.2％ vs 94.7％; P= 0.07) . The positive predictive value of mNGS was 83.3% (95% CI: 68.0-92.5%) , and the negative predictive value was 92.3% (95% CI: 62.1-99.6%) . A total of 5 (9.1%) cases were identified as mixed pulmonary infection by both conventional tests and mNGS, however, the pathogens identification results were consistent between these two methods in only 1(1.8%) case. In summary, the pathogens detected by mNGS in 3 (5.5%) cases were consistent with those by conventional test, and only 1(1.8%) case was mixed pulmonary infection. According to our data, mNGS had a broader spectrum for pathogen detection than conventional tests. In particular, application of mNGS improved the diagnosis of pulmonary fungal infections. Within the 55 cases, mNGS detected and identified fungi in 31 (56.4%) cases, of which only 10 (18.2%) cases were positive for the same fungi by conventional test. The most common pathogen detected was H uman cytomegalovirus in our study, which was identified in 19(34.5%) cases of mixed pulmonary infection . Human cytomegalovirus and Pneumocystis jirovecii, which were detected in 7(12.7%) cases, were the most common co-pathogens in the group of mixed pulmonary infection. Conclusions. mNGS is a promising technique to detect co-pathogens in mixed pulmonary infection, with potential benefits in speed and sensitivity.

scholarly journals Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis

2019 ◽  
Author(s):  
Jiahui Wang ◽  
Yelei Han ◽  
Jing Feng
Keyword(s):  
Next Generation Sequencing ◽  
Predictive Value ◽  
Pulmonary Infection ◽  
Fungal Infections ◽  
Next Generation ◽  
Promising Technique ◽  
Infection Diagnosis ◽  
Conventional Tests ◽  
Conventional Test ◽  
Generation Sequencing

Abstract Background: Metagenomic next-generation sequencing (mNGS) is emerging as a promising technique for pathogens detection. However, reports on the application of mNGS in mixed pulmonary infection remain scarce.Methods: From July 2018 to March 2019, 55 cases were enrolled in this retrospective analysis. Cases were classified into mixed pulmonary infection (36 [65.5%]) and non-mixed pulmonary infection (19 [34.5%]) according to primary diagnoses. The performances of mNGS and conventional test on mixed pulmonary infection diagnosis and pathogen identification were compared.Results: The sensitivity of mNGS in mixed pulmonary infection diagnose was much higher than that of conventional test (97.2％ vs 13.9％; P< 0.01) , but the specificity was the opposite (63.2％ vs 94.7％; P= 0.07) . The positive predictive value of mNGS was 83.3% (95% CI: 68.0-92.5%) , and the negative predictive value was 92.3% (95% CI: 62.1-99.6%) . A total of 5 (9.1%) cases were identified as mixed pulmonary infection by both conventional tests and mNGS, however, the pathogens identification results were consistent between these two methods in only 1(1.8%) case. In summary, the pathogens detected by mNGS in 3 (5.5%) cases were consistent with those by conventional test, and only 1(1.8%) case was mixed pulmonary infection. According to our data, mNGS had a broader spectrum for pathogen detection than conventional tests. In particular, application of mNGS improved the diagnosis of pulmonary fungal infections. Within the 55 cases, mNGS detected and identified fungi in 31 (56.4%) cases, of which only 10 (18.2%) cases were positive for the same fungi by conventional test. The most common pathogen detected was H uman cytomegalovirus in our study, which was identified in 19(34.5%) cases of mixed pulmonary infection . Human cytomegalovirus and Pneumocystis jirovecii, which were detected in 7(12.7%) cases, were the most common co-pathogens in the group of mixed pulmonary infection.Conclusions: mNGS is a promising technique to detect co-pathogens in mixed pulmonary infection, with potential benefits in speed and sensitivity.

scholarly journals Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis

2019 ◽  
Author(s):  
Jiahui Wang ◽  
Yelei Han ◽  
Yang Zhou ◽  
Jing Feng
Keyword(s):  
Next Generation Sequencing ◽  
Human Cytomegalovirus ◽  
Predictive Value ◽  
Pulmonary Infection ◽  
Next Generation ◽  
Promising Technique ◽  
Infection Diagnosis ◽  
Conventional Tests ◽  
Conventional Test ◽  
Generation Sequencing

Abstract Background. Metagenomic next-generation sequencing (mNGS) is emerging as a promising technique for pathogens detection. However, reports on the application of mNGS in mixed pulmonary infection remain scarce. Methods. From July 2018 to March 2019, 55 cases were enrolled in this retrospective analysis. Cases were classified into mixed pulmonary infection (36 [65.5%]) and non-mixed pulmonary infection (19 [34.5%]) according to primary diagnoses. The performances of mNGS and conventional test on mixed pulmonary infection diagnosis and pathogen identification were compared. Results.The sensitivity of mNGS in mixed pulmonary infection diagnose was much higher than that of conventional test (97.2％ vs 13.9％;P<0.01) , but the specificity was the opposite (63.2％ vs 94.7％;P=0.07) . The positive predictive value of mNGS was 83.3% (95% CI: 68.0-92.5%) , and the negative predictive value was 92.3% (95% CI: 62.1-99.6%) . A total of 5 (9.1%) cases were identified as mixed pulmonary infection by both conventional tests and mNGS, however, the pathogens identification results were consistent between these two methods in only 1(1.8%) case. In summary, the pathogens detected by mNGS in 3 (5.5%) cases were consistent with those by conventional test, and only 1(1.8%) case was mixed pulmonary infection. According to our data, mNGS had a broader spectrum for pathogen detection than conventional tests. In particular, application of mNGS improved the diagnosis of pulmonary fungal infections. Within the 55 cases, mNGS detected and identified fungi in 31 (56.4%) cases, of which only 10 (18.2%) cases were positive for the same fungi by conventional test. The most common pathogen detected was Human cytomegalovirus in our study, which was identified in 19(34.5%) cases of mixed pulmonary infection. Human cytomegalovirus and Pneumocystis jirovecii, which were detected in 7(12.7%) cases, were the most common co-pathogens in the group of mixed pulmonary infection. Conclusions. mNGS is a promising technique to detect co-pathogens in mixed pulmonary infection, with potential benefits in speed and sensitivity.

scholarly journals Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Jiahui Wang ◽  
Yelei Han ◽  
Jing Feng
Keyword(s):  
Next Generation Sequencing ◽  
Human Cytomegalovirus ◽  
Predictive Value ◽  
Pulmonary Infection ◽  
Next Generation ◽  
Promising Technique ◽  
Infection Diagnosis ◽  
Conventional Tests ◽  
Conventional Test ◽  
Generation Sequencing

Abstract Background Metagenomic next-generation sequencing (mNGS) is emerging as a promising technique for pathogens detection. However, reports on the application of mNGS in mixed pulmonary infection remain scarce. Methods From July 2018 to March 2019, 55 cases were enrolled in this retrospective analysis. Cases were classified into mixed pulmonary infection (36 [65.5%]) and non-mixed pulmonary infection (19 [34.5%]) according to primary diagnoses. The performances of mNGS and conventional test on mixed pulmonary infection diagnosis and pathogen identification were compared. Results The sensitivity of mNGS in mixed pulmonary infection diagnosis was much higher than that of conventional test (97.2% vs 13.9%; P < 0.01), but the specificity was the opposite (63.2% vs 94.7%; P = 0.07). The positive predictive value of mNGS was 83.3% (95% CI, 68.0–92.5%), and the negative predictive value was 92.3% (95% CI, 62.1–99.6%). A total of 5 (9.1%) cases were identified as mixed pulmonary infection by both conventional tests and mNGS, however, the pathogens identification results were consistent between these two methods in only 1 (1.8%) case. In summary, the pathogens detected by mNGS in 3 (5.5%) cases were consistent with those by conventional test, and only 1 (1.8%) case was mixed pulmonary infection. According to our data, mNGS had a broader spectrum for pathogen detection than conventional tests. In particular, application of mNGS improved the diagnosis of pulmonary fungal infections. Within the 55 cases, mNGS detected and identified fungi in 31 (56.4%) cases, of which only 10 (18.2%) cases were positive for the same fungi by conventional test. The most common pathogen detected by mNGS was Human cytomegalovirus in our study, which was identified in 19 (34.5%) cases of mixed pulmonary infection. Human cytomegalovirus and Pneumocystis jirovecii, which were detected in 7 (12.7%) cases, were the most common co-pathogens in the group of mixed pulmonary infection. Conclusions mNGS is a promising technique to detect co-pathogens in mixed pulmonary infection, with potential benefits in speed and sensitivity. Trial registration (retrospectively registered): ChiCTR1900023727. Registrated 9 JUNE 2019.

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