Leiomyosarcoma and endometrial stromal sarcoma are the most common types of uterine sarcoma, a group of rare and clinically aggressive mesenchymal cancers. These two sarcomas may have overlapping clinical presentation, morphology and protein expression profiles, making their diagnosis occasionally difficult. This article discusses molecular approaches that may be applied to the diagnosis of these two cancers and may generate data expanding our therapeutic options and patient outcome.
by Professor Ben Davidson
Introduction
The majority of cancers affecting the uterine corpus are carcinomas, i.e. tumours of epithelial origin. Uterine sarcomas, tumours that are of mesenchymal origin, are a group of rare and clinically aggressive tumours constituting 7 % of all soft tissue sarcomas and 3 % of malignant uterine tumours [1, 2]. The most common entities within this group are leiomyosarcoma (LMS) and endometrial stromal sarcoma (ESS) [2, 3]. Although LMS and ESS are readily diagnosed based on morphology and a limited immunohistochemistry (IHC) panel in many cases, some tumours may pose diagnostic difficulty, and currently used antibodies are not 100 % sensitive or specific [4]. Improved understanding of the molecular make-up of these tumours may lead to more accurate diagnosis and better understanding of their biology, eventually improving our ability to design targeted therapy approaches with the objective of improving patient outcome.
The genetic make-up of ESS and LMS
Low-grade ESS, the more common type of ESS, is characterized by several gene rearrangements creating fusion genes, of which the first described was fusion of the zinc finger gene 1 JAZF1, located at 7p15, and JJAZ1, also termed SUZ12, at 17q21 through a 7;17-translocation. Other fusions in low-grade ESS include the one between JAZF1 and the PHD finger protein 1 gene (PHF1) in 6p21, as well as between PHF1 and enhancer of polycomb homologue 1 (EPC1) gene at 10p11 and the MYST/Esa1 associated factor 6 gene (MEAF6) at 1p34. X chromosome rearrangements include fusion of the open reading frame CXorf67 and the BCL-6 interacting corepressor (BCOR) gene, both at Xp11, with the MBT domain-containing protein 1 gene (MBTD1) at 17q21 and with the zinc finger CCCH-type containing 7B gene (ZC3H7B) at 22q13, respectively.
High-grade ESS is characterized by a fusion between the tyrosine 3/tryptophan 5 monooxygenase gene (YWHAE) gene at 17p13 and the NUT family member gene (NUTM2; previously known as FAM22) at 10q22, creating YWHAE-NUTM fusion through a 10;17-translocation (reviewed by Davidson and Micci, invited review submitted to Expert Rev Mol Diagn). These alterations were recently confirmed by analysis of the ESS transcriptome and/or whole-exome sequencing, including the application of next generation sequencing [5–7].
The body of data with respect to the molecular characteristics of LMS is more limited. An observation found in several studies is the presence of exon 2 mutations in the mediator complex subunit 12 (MED12) gene on chromosome band Xq13.1 in some LMS. MED12 protein forms complex with MED13, cyclin-dependent kinase 8 (CDK8), and cyclin C, termed the CDK8 submodule of the Mediator, the mediator being a large multiprotein complex regulating transcription [8]. Though less frequent in LMS compared to leiomyomas, the benign counterpart of LMS, this finding appears to be absent in other malignant soft tissue sarcomas, and is rare in carcinomas, and is thus potentially relevant in the diagnostic setting (reviewed by Croce & Chibon [9]).
RNA sequencing of 99 LMS, of which 49 were uterine, identified 3 distinct molecular subtypes. Leiomodin (LMOD1) and ADP-ribosylation factor-like 4C (ARL4C) were found to be markers for type I and II tumours, respectively, and the latter group was associated with poor prognosis when located in the uterus [10].
Comparative molecular analysis of ESS and LMS
Our group performed two studies of uterine LMS and ESS with the aim of identifying novel biomarkers that may expand the arsenal of markers currently used in diagnosing these tumours, as well as improving our understanding of their unique biology.
In the first study, the gene expression profiles of 7 ESS and 13 LMS were compared using the HumanRef-8 BeadChip from Illumina. We identified 549 unique probes that were significantly differentially expressed in the two tumour entities, of which 336 and 213 were overexpressed in ESS and LMS, respectively. Genes found to be overexpressed in ESS included CCND2, ECEL1, ITM2A, NPW, SLC7A10, EFNB3, PLAG1 and GCGR, whereas genes overexpressed in LMS included FABP3, TAGLN, CDKN2A, JPH2, GEM, NAV2 and RAB23. qPCR analysis confirmed these differences for 14 of 16 genes selected for validation. Five protein products were selected for validation by IHC, including the LMS markers fatty acid binding protein (FABP3), transgelin (TAGLN) and neuron navigator 2 (NAV2) and the ESS markers cyclin D2 (CCND2) and integral membrane protein 2A (ITM2A). All were found to be significantly differentially expressed in LMS vs ESS (Fig. 1) [11]. Data for FABP3, TAGLN, NAV2 and CCND2 were recently confirmed in a large (approx. 350 tumours) uterine sarcoma series [Davidson et al., manuscript submitted].
Recently, we compared the microRNA (miRNA) profiles of primary ESS (n=9), primary LMS (n=8) and metastatic LMS (n=8) using Taqman Human miRNA Array Cards. Ninety-four miRNAs were significantly differentially expressed in ESS vs LMS, of which 76 and 18 were overexpressed in ESS and LMS, respectively. Forty-nine miRNAs were differentially expressed in primary and metastatic LMS, among which 45 and 4 were overexpressed in primary and metastatic LMS, respectively. Twenty miRNAs found to be most significantly differentially expressed in primary ESS vs LMS or in primary vs metastatic LMS were further studied in a validation series of 44 tumours using qPCR. Of these, 10 were confirmed to be differentially expressed in these groups, including overexpression of 7 miRNAs (mir-15b, mir-21, mir-23b, mir-25, mir-145, mir-148b and mir-195) in ESS compared to primary LMS. The remaining 3 differentially expressed miRNAs were in comparative analysis of primary and metastatic LMS (lower mir-15a and mir-92a levels and higher mir-31 levels in primary LMS). Differentially expressed miRNA regulated the mitogen-activated protein kinase (MAPK) signaling pathway, Wnt signaling, focal adhesion, the mTOR signaling pathway and the transforming growth factor-β (TGF-β) signaling pathway. As Wnt signaling pathway genes are controlled by miRNAs 15a, 31 and 92a in LMS, we looked at the biological role of Frizzled-6 in LMS cells and found that Frizzled-6 silencing by siRNA significantly inhibited cellular invasion, wound closure and matrix metalloproteinase (MMP-2) activity [12]
Conclusion and future perspectives
Recent years have brought about considerable progress in our understanding of the molecular events occurring in ESS and LMS. Our studies and data from other groups may aid in the diagnosis and classification of these cancers, hopefully providing rationale for targeted therapy. Uterine sarcomas express different cancer-related molecules that may be targeted (reviewed by Cuppens et al. [13]). Anti-hormonal treatment is used in patients with hormone receptor-positive tumours, and expression of progesterone receptor was recently shown to be a prognostic marker in stage I LMS [14]. In two studies, targeting of mTOR, Aurora kinases and other mitotic checkpoint regulators has been suggested as therapeutic modality in LMS [15,16]. Additional studies are likely to identify new relevant targets in the future, hopefully improving the outcome of uterine sarcoma patients.
Acknowledgement
The work of Dr Davidson is supported by the National Sarcoma Foundation at the Norwegian Radium Hospital.
References
1. Toro JR, Travis LB, Wu HJ, Zhu K, Fletcher CD, Devesa SS. Incidence patterns of soft tissue sarcomas, regardless of primary site, in the surveillance, epidemiology and end results program, 1978-2001: an analysis of 26,758 cases. Int J Cancer 2006; 119: 2922–2930.
2. D’Angelo E, Prat J. Uterine sarcomas: a review. Gynecol Oncol. 2010; 116: 131–139.
3. Kurman RJ, Carcangiu ML, Herrington CS, Young RH (Eds.). WHO classification of tumours of female reproductive organs. IARC 2014.
4. Abeler VM, Nenodovic M. Diagnostic immunohistochemistry in uterine sarcomas: a study of 397 cases. Int J Gynecol Pathol. 2011; 30: 236–243.
5. Micci F, Gorunova L, Agostini A, Johannessen LE, Brunetti M, Davidson B, Heim S, Panagopoulos I. Cytogenetic and molecular profile of endometrial stromal sarcoma. Genes Chromosomes Cancer 2016; 55: 834–846.
6. Choi YJ, Jung SH, Kim MS, Baek IP, Rhee JK, Lee SH, Hur SY, Kim TM, Chung YJ, Lee SH. Genomic landscape of endometrial stromal sarcoma of uterus. Oncotarget 2015; 6: 33319–33328.
7. Li X, Anand M, Haimes JD, Manoj N, Berlin AM, Kudlow BA, Nucci MR, Ng TL, Stewart CJ, Lee CH. The application of next-generation sequencing-based molecular diagnostics in endometrial stromal sarcoma. Histopathology 2016; 69: 551–559.
8. Clark AD, Oldenbroek M, Boyer TG. Mediator kinase module and human tumorigenesis. Crit Rev Biochem Mol Biol. 2015; 50: 393–426.
9. Croce S, Chibon F. MED12 and uterine smooth muscle oncogenesis: state of the art and perspectives. Eur J Cancer 2015; 51: 1603–1610.
10. Guo X, Jo VY, Mills AM, Zhu SX, Lee CH, Espinosa I, Nucci MR, Varma S, Forgó E, Hastie T, Anderson S, Ganjoo K, Beck AH, West RB, Fletcher CD, van de Rijn M. Clinically relevant molecular subtypes in leiomyosarcoma. Clin Cancer Res. 2015; 21: 3501–3511.
11. Davidson B, Abeler VM, Hellesylt E, Holth A, Shih IeM, Skeie-Jensen T, Chen L, Yang Y, Wang TL. Gene expression signatures differentiate uterine endometrial stromal sarcoma from leiomyosarcoma. Gynecol Oncol. 2013; 128: 349–355.
12. Ravid Y, Formanski M, Smith Y, Reich R, Davidson B. Uterine leiomyosarcoma and endometrial stromal sarcoma have unique miRNA signatures. Gynecol Oncol. 2016; 140: 512–517.
13. Cuppens T, Tuyaerts S, Amant F. Potential therapeutic targets in uterine sarcomas. Sarcoma 2015; 2015: 243298.
14. Davidson B, Kjæreng ML, Førsund M, Danielsen HE, Kristensen GB, Abeler VM.. Progesterone receptor expression is an independent prognosticator in FIGO stage I uterine leiomyosarcoma. Am J Clin Pathol. 2016; 145: 449–458.
15. Brewer Savannah KJ, Demicco EG, Lusby K, Ghadimi MP, Belousov R, Young E, Zhang Y, Huang KL, Lazar AJ, Hunt KK, Pollock RE, Creighton CJ, Anderson ML, Lev D.. Dual targeting of mTOR and aurora-A kinase for the treatment of uterine leiomyosarcoma. Clin Cancer Res. 2012; 18: 4633–4645.
16. Shan W, Akinfenwa PY, Savannah KB, Kolomeyevskaya N, Laucirica R, Thomas DG, Odunsi K, Creighton CJ, Lev DC, Anderson ML. A small-molecule inhibitor targeting the mitotic spindle checkpoint impairs the growth of uterine leiomyosarcoma. Clin Cancer Res. 2012; 18: 3352–3365.
The author
Ben Davidson1,2 MD, PhD
1Department of Pathology, Norwegian Radium Hospital, Oslo University Hospital, N-0310 Oslo, Norway
2University of Oslo, Faculty of Medicine, Institute of Clinical Medicine, N-0316 Oslo, Norway
*Corresponding author
E-mail: bend@medisin.uio.no
Molecular differentiators of uterine leiomyosarcoma and endometrial stromal sarcoma
, /in Featured Articles /by 3wmediaLeiomyosarcoma and endometrial stromal sarcoma are the most common types of uterine sarcoma, a group of rare and clinically aggressive mesenchymal cancers. These two sarcomas may have overlapping clinical presentation, morphology and protein expression profiles, making their diagnosis occasionally difficult. This article discusses molecular approaches that may be applied to the diagnosis of these two cancers and may generate data expanding our therapeutic options and patient outcome.
by Professor Ben Davidson
Introduction
The majority of cancers affecting the uterine corpus are carcinomas, i.e. tumours of epithelial origin. Uterine sarcomas, tumours that are of mesenchymal origin, are a group of rare and clinically aggressive tumours constituting 7 % of all soft tissue sarcomas and 3 % of malignant uterine tumours [1, 2]. The most common entities within this group are leiomyosarcoma (LMS) and endometrial stromal sarcoma (ESS) [2, 3]. Although LMS and ESS are readily diagnosed based on morphology and a limited immunohistochemistry (IHC) panel in many cases, some tumours may pose diagnostic difficulty, and currently used antibodies are not 100 % sensitive or specific [4]. Improved understanding of the molecular make-up of these tumours may lead to more accurate diagnosis and better understanding of their biology, eventually improving our ability to design targeted therapy approaches with the objective of improving patient outcome.
The genetic make-up of ESS and LMS
Low-grade ESS, the more common type of ESS, is characterized by several gene rearrangements creating fusion genes, of which the first described was fusion of the zinc finger gene 1 JAZF1, located at 7p15, and JJAZ1, also termed SUZ12, at 17q21 through a 7;17-translocation. Other fusions in low-grade ESS include the one between JAZF1 and the PHD finger protein 1 gene (PHF1) in 6p21, as well as between PHF1 and enhancer of polycomb homologue 1 (EPC1) gene at 10p11 and the MYST/Esa1 associated factor 6 gene (MEAF6) at 1p34. X chromosome rearrangements include fusion of the open reading frame CXorf67 and the BCL-6 interacting corepressor (BCOR) gene, both at Xp11, with the MBT domain-containing protein 1 gene (MBTD1) at 17q21 and with the zinc finger CCCH-type containing 7B gene (ZC3H7B) at 22q13, respectively.
High-grade ESS is characterized by a fusion between the tyrosine 3/tryptophan 5 monooxygenase gene (YWHAE) gene at 17p13 and the NUT family member gene (NUTM2; previously known as FAM22) at 10q22, creating YWHAE-NUTM fusion through a 10;17-translocation (reviewed by Davidson and Micci, invited review submitted to Expert Rev Mol Diagn). These alterations were recently confirmed by analysis of the ESS transcriptome and/or whole-exome sequencing, including the application of next generation sequencing [5–7].
The body of data with respect to the molecular characteristics of LMS is more limited. An observation found in several studies is the presence of exon 2 mutations in the mediator complex subunit 12 (MED12) gene on chromosome band Xq13.1 in some LMS. MED12 protein forms complex with MED13, cyclin-dependent kinase 8 (CDK8), and cyclin C, termed the CDK8 submodule of the Mediator, the mediator being a large multiprotein complex regulating transcription [8]. Though less frequent in LMS compared to leiomyomas, the benign counterpart of LMS, this finding appears to be absent in other malignant soft tissue sarcomas, and is rare in carcinomas, and is thus potentially relevant in the diagnostic setting (reviewed by Croce & Chibon [9]).
RNA sequencing of 99 LMS, of which 49 were uterine, identified 3 distinct molecular subtypes. Leiomodin (LMOD1) and ADP-ribosylation factor-like 4C (ARL4C) were found to be markers for type I and II tumours, respectively, and the latter group was associated with poor prognosis when located in the uterus [10].
Comparative molecular analysis of ESS and LMS
Our group performed two studies of uterine LMS and ESS with the aim of identifying novel biomarkers that may expand the arsenal of markers currently used in diagnosing these tumours, as well as improving our understanding of their unique biology.
In the first study, the gene expression profiles of 7 ESS and 13 LMS were compared using the HumanRef-8 BeadChip from Illumina. We identified 549 unique probes that were significantly differentially expressed in the two tumour entities, of which 336 and 213 were overexpressed in ESS and LMS, respectively. Genes found to be overexpressed in ESS included CCND2, ECEL1, ITM2A, NPW, SLC7A10, EFNB3, PLAG1 and GCGR, whereas genes overexpressed in LMS included FABP3, TAGLN, CDKN2A, JPH2, GEM, NAV2 and RAB23. qPCR analysis confirmed these differences for 14 of 16 genes selected for validation. Five protein products were selected for validation by IHC, including the LMS markers fatty acid binding protein (FABP3), transgelin (TAGLN) and neuron navigator 2 (NAV2) and the ESS markers cyclin D2 (CCND2) and integral membrane protein 2A (ITM2A). All were found to be significantly differentially expressed in LMS vs ESS (Fig. 1) [11]. Data for FABP3, TAGLN, NAV2 and CCND2 were recently confirmed in a large (approx. 350 tumours) uterine sarcoma series [Davidson et al., manuscript submitted].
Recently, we compared the microRNA (miRNA) profiles of primary ESS (n=9), primary LMS (n=8) and metastatic LMS (n=8) using Taqman Human miRNA Array Cards. Ninety-four miRNAs were significantly differentially expressed in ESS vs LMS, of which 76 and 18 were overexpressed in ESS and LMS, respectively. Forty-nine miRNAs were differentially expressed in primary and metastatic LMS, among which 45 and 4 were overexpressed in primary and metastatic LMS, respectively. Twenty miRNAs found to be most significantly differentially expressed in primary ESS vs LMS or in primary vs metastatic LMS were further studied in a validation series of 44 tumours using qPCR. Of these, 10 were confirmed to be differentially expressed in these groups, including overexpression of 7 miRNAs (mir-15b, mir-21, mir-23b, mir-25, mir-145, mir-148b and mir-195) in ESS compared to primary LMS. The remaining 3 differentially expressed miRNAs were in comparative analysis of primary and metastatic LMS (lower mir-15a and mir-92a levels and higher mir-31 levels in primary LMS). Differentially expressed miRNA regulated the mitogen-activated protein kinase (MAPK) signaling pathway, Wnt signaling, focal adhesion, the mTOR signaling pathway and the transforming growth factor-β (TGF-β) signaling pathway. As Wnt signaling pathway genes are controlled by miRNAs 15a, 31 and 92a in LMS, we looked at the biological role of Frizzled-6 in LMS cells and found that Frizzled-6 silencing by siRNA significantly inhibited cellular invasion, wound closure and matrix metalloproteinase (MMP-2) activity [12]
Conclusion and future perspectives
Recent years have brought about considerable progress in our understanding of the molecular events occurring in ESS and LMS. Our studies and data from other groups may aid in the diagnosis and classification of these cancers, hopefully providing rationale for targeted therapy. Uterine sarcomas express different cancer-related molecules that may be targeted (reviewed by Cuppens et al. [13]). Anti-hormonal treatment is used in patients with hormone receptor-positive tumours, and expression of progesterone receptor was recently shown to be a prognostic marker in stage I LMS [14]. In two studies, targeting of mTOR, Aurora kinases and other mitotic checkpoint regulators has been suggested as therapeutic modality in LMS [15,16]. Additional studies are likely to identify new relevant targets in the future, hopefully improving the outcome of uterine sarcoma patients.
Acknowledgement
The work of Dr Davidson is supported by the National Sarcoma Foundation at the Norwegian Radium Hospital.
References
1. Toro JR, Travis LB, Wu HJ, Zhu K, Fletcher CD, Devesa SS. Incidence patterns of soft tissue sarcomas, regardless of primary site, in the surveillance, epidemiology and end results program, 1978-2001: an analysis of 26,758 cases. Int J Cancer 2006; 119: 2922–2930.
2. D’Angelo E, Prat J. Uterine sarcomas: a review. Gynecol Oncol. 2010; 116: 131–139.
3. Kurman RJ, Carcangiu ML, Herrington CS, Young RH (Eds.). WHO classification of tumours of female reproductive organs. IARC 2014.
4. Abeler VM, Nenodovic M. Diagnostic immunohistochemistry in uterine sarcomas: a study of 397 cases. Int J Gynecol Pathol. 2011; 30: 236–243.
5. Micci F, Gorunova L, Agostini A, Johannessen LE, Brunetti M, Davidson B, Heim S, Panagopoulos I. Cytogenetic and molecular profile of endometrial stromal sarcoma. Genes Chromosomes Cancer 2016; 55: 834–846.
6. Choi YJ, Jung SH, Kim MS, Baek IP, Rhee JK, Lee SH, Hur SY, Kim TM, Chung YJ, Lee SH. Genomic landscape of endometrial stromal sarcoma of uterus. Oncotarget 2015; 6: 33319–33328.
7. Li X, Anand M, Haimes JD, Manoj N, Berlin AM, Kudlow BA, Nucci MR, Ng TL, Stewart CJ, Lee CH. The application of next-generation sequencing-based molecular diagnostics in endometrial stromal sarcoma. Histopathology 2016; 69: 551–559.
8. Clark AD, Oldenbroek M, Boyer TG. Mediator kinase module and human tumorigenesis. Crit Rev Biochem Mol Biol. 2015; 50: 393–426.
9. Croce S, Chibon F. MED12 and uterine smooth muscle oncogenesis: state of the art and perspectives. Eur J Cancer 2015; 51: 1603–1610.
10. Guo X, Jo VY, Mills AM, Zhu SX, Lee CH, Espinosa I, Nucci MR, Varma S, Forgó E, Hastie T, Anderson S, Ganjoo K, Beck AH, West RB, Fletcher CD, van de Rijn M. Clinically relevant molecular subtypes in leiomyosarcoma. Clin Cancer Res. 2015; 21: 3501–3511.
11. Davidson B, Abeler VM, Hellesylt E, Holth A, Shih IeM, Skeie-Jensen T, Chen L, Yang Y, Wang TL. Gene expression signatures differentiate uterine endometrial stromal sarcoma from leiomyosarcoma. Gynecol Oncol. 2013; 128: 349–355.
12. Ravid Y, Formanski M, Smith Y, Reich R, Davidson B. Uterine leiomyosarcoma and endometrial stromal sarcoma have unique miRNA signatures. Gynecol Oncol. 2016; 140: 512–517.
13. Cuppens T, Tuyaerts S, Amant F. Potential therapeutic targets in uterine sarcomas. Sarcoma 2015; 2015: 243298.
14. Davidson B, Kjæreng ML, Førsund M, Danielsen HE, Kristensen GB, Abeler VM.. Progesterone receptor expression is an independent prognosticator in FIGO stage I uterine leiomyosarcoma. Am J Clin Pathol. 2016; 145: 449–458.
15. Brewer Savannah KJ, Demicco EG, Lusby K, Ghadimi MP, Belousov R, Young E, Zhang Y, Huang KL, Lazar AJ, Hunt KK, Pollock RE, Creighton CJ, Anderson ML, Lev D.. Dual targeting of mTOR and aurora-A kinase for the treatment of uterine leiomyosarcoma. Clin Cancer Res. 2012; 18: 4633–4645.
16. Shan W, Akinfenwa PY, Savannah KB, Kolomeyevskaya N, Laucirica R, Thomas DG, Odunsi K, Creighton CJ, Lev DC, Anderson ML. A small-molecule inhibitor targeting the mitotic spindle checkpoint impairs the growth of uterine leiomyosarcoma. Clin Cancer Res. 2012; 18: 3352–3365.
The author
Ben Davidson1,2 MD, PhD
1Department of Pathology, Norwegian Radium Hospital, Oslo University Hospital, N-0310 Oslo, Norway
2University of Oslo, Faculty of Medicine, Institute of Clinical Medicine, N-0316 Oslo, Norway
*Corresponding author
E-mail: bend@medisin.uio.no
STart Max
, /in Featured Articles /by 3wmediaAntibio-Resistance is rising up!
, /in Featured Articles /by 3wmediaClinical Chemistry Anti-CCP
, /in Featured Articles /by 3wmediaThe first Quantitative POC–test for the detection of Bladder Cancer!
, /in Featured Articles /by 3wmediaMassTrak Vitamin D Solution
, /in Featured Articles /by 3wmediaMILFORD, Mass.–(BUSINESS WIRE)–Waters Corporation (NYSE:WAT) announced today that its fully-validated Waters® MassTrak™ Vitamin D Solution is now CE-marked in accordance with IVD Directive 98/79/EC for the quantitative measurement of 25(OH) D2 and D3 (25-OH-VitD) from human plasma and serum. The IVD LC-MS/MS system solution is a complete solution consisting of reagents and consumables, instrumentation, and software and support services for clinical laboratories routinely measuring vitamin D in patient samples*. The Vitamin D solution is currently only available for sale in Europe.
“Our MassTrak Vitamin D Solution is the first, CE-marked, single-vendor system to measure both vitamins 25(OH) D2 and D3 independently and accurately from human plasma and serum in a single analysis,” said Jose Castro-Perez, Ph.D., Director of Health Sciences, Global Marketing Operations, Waters Corporation. “It’s the ideal solution for clinical laboratories performing (25-OH-VitD) analysis on a routine basis and which may want to standardize (25-OH-VitD) analysis on a single-vendor platform.”
Vitamin D, the main source of which is from dietary intake, is part of a family of fat-soluble secosteroids and has two major forms – ergocalciferol (plant derived Vitamin D2) and cholecalciferol (animal derived Vitamin D3). Vitamin D is also derived from the conversion of 7-dihydrocholesterol to Vitamin D3 following exposure of skin to sunlight. Increasingly, research is revealing the importance of vitamin D in protecting against a host of health problems. In the absence of vitamin D, bones can become brittle and thin. The research also suggests that vitamin D plays a role in modulating cell growth, the function of neuromuscular and immune systems, and reducing inflammation. It also indicates that those most at risk of a vitamin D deficiency are those who shun the sun, have milk allergies, adhere to a strict vegan diet, or suffer from certain diseases.
Due, in part, to mounting evidence of the importance of vitamin D to human health and to news media coverage, Medicare reimbursement volumes of vitamin D laboratory tests rose 83-fold from 2000 – 20101.
The MassTrak Vitamin D Solution is configured with the Waters ACQUITY UPLC® I-Class/ Xevo® TQD IVD System, the MassTrak Vitamin D Kit and MassLynx® (IVD) Mass Spectrometry Software. The MassTrak Vitamin D Kit provides metrological traceability to the National Institute of Standards and Technology (NIST) Standard Reference Material (SRM) 2972, enabling standardization and lot- to-lot consistency for an easy comparison of results from multiple laboratories. For high-volume vitamin D testing, the system is designed for use with an automated liquid handler and custom pipetting script provided with the kit.
LC-MS/MS in the Clinical Laboratory
In pursuit of better accuracy and precision for clinical laboratory analyses, liquid chromatography tandem mass spectrometry (LC-MS/MS) is rapidly becoming the analytical technique of choice. Waters IVD LC-MS/MS medical devices provide clinical laboratories access to technologies delivering selectivity, sensitivity and versatility – performance characteristics that traditional methods, such as immunoassays, often struggle to meet.
Designed to optimize workflow from pre-analytics to post-analytics, our system portfolio offers a wide variety of options for the routine use of LC-MS/MS in the clinical laboratory.
Towards shared responsibility for family planning
, /in Featured Articles /by 3wmediaNext-generation sequencing in clinical virology diagnostics
, /in Featured Articles /by 3wmediaIntroduction
Methodologies to detect pathogenic viruses in clinical specimens have transitioned from classic cell culture and antibody–antigen techniques to more sensitive molecular methods such as polymerase chain reaction (PCR). The targeted nature of these methodologies inhibits their ability to accommodate the true diversity of human pathogens in a clinical specimen, especially viruses [1]. Next-generation sequencing (NGS) technologies are quickly demonstrating their ability to provide broad detection of infectious agents in a target-independent manner [2–7]. NGS has many advantages beyond the improved detection of all suspected, unsuspected, or even novel pathogens in a clinical specimen [8]. Familiarization with pathogen genomic sequences within clinical specimens enhances our understanding of infectious disease through further discovery of pathogen variability and genotyping [9–11], drug resistance or response to therapy [12], vaccine development and efficacy monitoring [13], and further characterization of the metagenome [14]. The use of NGS for routine use in clinical diagnostics is emerging with its own set of limitations and challenges [13, 15]. Focusing on viruses of public health importance, we compared the performance of NGS alongside other more common viral detection methodologies.
Conventional methods versus NGS
We investigated applications of NGS in a clinical laboratory to detect pathogenic viruses in common specimen types and compared NGS data to that which could be obtained by more conventional methods for detecting and characterizing the following viruses of public health importance: adenovirus, herpesvirus, hepatitis C virus, and influenza [16]. We compared results obtained by NGS to viral culture, immunofluorescence staining, serum neutralization, and PCR in terms of turnaround time as well as the clinical relevance of the information obtained.
Table 1 describes the turnaround time of conventional methods to NGS for detecting adenoviruses and herpesviruses, both DNA viruses. The amount of time it takes to grow a virus in culture is variable, ranging from 1 day for herpes simplex viruses to 18 days for adenoviruses. All NGS data could be obtained in 4 days, which includes nucleic acid extraction, sequencing library preparation, sequencing and data analysis. Although most laboratories are not currently equipped with in-house bioinformaticians, much of the analysis can be done simply using common sequencing analysing software and the quickly growing number of applications online. For data analysis, we used PathSeq™Virome which enabled us to feed large read files into the application which would generate a report describing the viruses present, including a ‘detection score’ to distinguish strong and weak presence. NGS data provided much more information regarding the exact isolate which may aid health professionals in tracking and relating individual cases with others. Group C adenoviruses are treatable with cidofovir and NGS data was able to identify the amino acid motif that most affects antiviral resistance.
Hepatitis C virus (HCV) is a growing concern for public health and tends to be difficult to design targeted methodologies around owing to the high variability of viral genomes known, even within the same patient. NGS is a powerful tool for characterizing HCV infections and, in our experience, more informational than targeted genotyping assays (Table 2). As we were able to sequence nearly the entire HCV genome (coverage ranged from 92.4–95.6%), data could be generated describing the mutations at key locations across the genomes that are known to cause drug resistance. Antiviral resistance is also critical when characterizing current circulating influenza virus strains and NGS was able to identify viruses that would be considered susceptible to neuraminidase inhibitors (Table 3). In two cases, the viral load of the specimen was too low to achieve good genome coverage across the neuraminidase gene, but this issue could be resolved by screening specimens for high titre (i.e. qPCR) or using enrichment techniques such as ultracentrifugation or filtration of other background nucleic acid.
In most cases, with the exception of one specimen where no cytomegalovirus was definitively identified (HSV5, Table 1), information retrieved by NGS met or exceeded that of conventional methodologies. NGS proves to be a laboratory tool capable of not only detecting pathogenic viruses in clinical specimens, but also predicting the effects of drug treatment as well.
Summary
Through increased use of NGS technologies, reference databases of whole genome sequences can grow and enhance our ability to more definitively identify sequencing reads. Although this review describes conventional methods versus NGS for detecting specific viruses, there was also evidence of the presence of co-infecting viruses such as hepatitis G and Torque Teno virus that weren’t originally targeted. The standard 4-day turnaround time needed to complete NGS could be improved with extraction and library preparation automation, as well as advances in sequencing technology (each run ~40 hours). Based on our laboratory’s experience and the growing body of literature, NGS will change our approach as clinical laboratorians and improve our ability to detect and more fully characterize agents of infectious disease in clinical specimens in a non-targeted manner.
References
1. Köser CU, Ellington MJ, Cartwright EJ, Gillespie SH, Brown NM, Farrington M, Holden MT, Dougan G, Bentley SD, Parkhill J, Peacock SJ. Routine use of microbial whole genome sequencing in diagnostic and public health microbiology. PLoS Pathogens 2012; 8(8): e1002824.
2. Bzhalava D, Johansson H, Ekström J, Faust H, Möller B, Eklund C, Nordin P, Stenquist B, Paoli J, Persson B, Forslund O, Dillner J. Unbiased approach for virus detection in skin lesions. PLoS One 2013; 8(6): e65953.
3. Cheval J, Sauvage V, Frangeul L, Dacheux L, Guigon G, Dumey N, Pariente K, Rousseaux C, Dorange F, Berthet N, Brisse S, Moszer I, Bourhy H, Manuguerra CJ, Lecuit M, Burguiere A, Caro V, Eloit M. Evaluation of high-throughput sequencing for identifying known and unknown viruses in biological samples. J Clin Microbiol 2011; 49(9): 3268–3275.
4. Chan BK, Wilson T, Fischer KF, Kriesel JD. Deep sequencing to identify the causes of viral encephalitis. PLoS One 2014; 9(4): e93993.
5. Kriesel JD, Hobbs MR, Jones BB, Milash B, Nagra RM, Fischer KF. Deep sequencing for the detection of virus-like sequences in the brains of patients with multiple sclerosis: detection of GBV-C in human brain. PLoS One 2012; 7(3): e31886.
6. Moore RA, Warren RL, Freeman JD, Gustavsen JA, Chénard C, Friedman JM, Suttle CA, Zhao Y, Holt RA. The sensitivity of massively parallel sequencing for detecting candidate infectious agents associated with human tissue. PLoS One 2011; 6(5): e19838.
7. Yozwiak NL, Skewes-Cox P, Stenglein MD, Balmaseda A, Harris E, DeRisi JL. Virus identification in unknown tropical febrile illness cases using deep sequencing. PLoS Negl Trop Dis 2012; 6(2): e1485.
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The authors
Jayme Parker1,2 PhD and Jack Chen*1,2 PhD
1Department of Biology and Wildlife, Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
2Alaska State Public Health Virology Laboratory, Fairbanks, AK 99775, USA
*Corresponding author
E-mail: j.chen@alaska.edu
Molecular diagnostics of pathogens using next-generation sequencing
, /in Featured Articles /by 3wmediaIntroduction
The correct identification and characterization of pathogens is essential for the successful treatment of infections and safety of patients. However, not every pathogen can be successfully cultured and the available molecular tests, mainly focusing on specific pathogens, are inadequate to detect novel genetic features in emerging pathogens. Undetected pathogens can spread easily through a hospital, resulting in a possible outbreak and putting patients admitted to hospitals at a higher risk for infections.
In recent decades, molecular diagnostic tests have improved rapidly and their role in clinical microbiology laboratories became progressively more important [1]. The turnaround time from receiving a sample to the final diagnostic result has been drastically reduced. Molecular methods, such as real-time polymerase chain reaction (PCR), Sanger sequencing and next-generation sequencing (NGS), make it possible to detect non-culturable micro-organisms. Nevertheless, some of these technologies, such as real-time PCR, require knowledge of the genomes of the micro-organisms. In addition, bioinformatics expertise is often needed to interpret the results.
This paper addresses the use of Sanger sequencing and whole genome sequencing (WGS) in the clinical microbiology laboratory for the characterization of pathogens and outbreak management, as it is used in the University Medical Center Groningen (UMCG), one of the largest university hospitals in The Netherlands. The clinical microbiology laboratory at the UMCG receives around 5750 samples per year for detailed molecular analysis, of which approximately 1500 samples are analysed by NGS [2].
Sanger sequencing
Sanger sequencing is used to answer different molecular questions, such as the identification of bacteria and fungi in patient material or pure cultures, and the identification of mutations in specific genomic regions of interest in bacteria or viruses. In general, Sanger sequencing is used to investigate a short DNA sequence (± 500 bp) after amplification of the region of interest by PCR. After amplification, two different sequence reactions (forward and reverse) are performed and can be used to identify bacterial or fungal species based on the analyses of the sequenced 16S ribosomal DNA (rDNA) and 18S rDNA of the internal transcribed spacer (ITS) region, respectively [2].
One of the disadvantages of Sanger sequencing is that species identification in clinical materials containing more than one species is difficult, if not impossible. Furthermore, the costs and the labour needed for investigating multiple genomic regions of interest makes this method of limited use in modern clinical microbiology laboratories.
Next-generation sequencing (NGS)
NGS determines the whole genome sequence of different pathogens in one single sequencing run. This technology allows sample multiplexing and, thus, simultaneously provides genomic sequence information on diverse pathogens isolated from different patients. NGS also allows determination of microbial genomes in complex multi-species patient samples by shotgun metagenomics (third generation sequencing) [3]. In comparison to Sanger sequencing, NGS is a considerable improvement owing to the usage of one protocol for all pathogens [4]. A schematic overview of the general workflow used for the sequence analysis in the UMCG is shown in Figure 1.
Using NGS, the whole genome of a pathogen is sequenced in a random way. As benchtop next-generation sequencers can sequence DNA fragments between 100 and 1000 bases, the genome is fragmented before sequencing [5, 6]. Third generation sequencers are an exception to this, as they can handle larger fragments of over 200 kb [2]. NGS requires the preparation of libraries, in which fragments of DNA or RNA are linked to adapters and barcodes. At a later stage, this enables the identification of the sequenced fragments (reads) to the pathogens. After fragmentation, clonal amplification, normalization and a sequencing run is performed. For this, a robust preparation of libraries and standardized protocols are key [3].
Software for data analysis
A huge challenge for the introduction of NGS in a clinical setting is the data analysis. This requires specific software as well as scientific knowledge to interpret the results. There are, so far, only a few user-friendly software packages available to perform data analyses with little bioinformatics knowledge. However, the costs of these software packages is relatively high. However, there a numerous freely available software packages to answer different scientific questions, but knowledge of bioinformatics is often required [2].
After high-throughput sequencing, the reads can be assembled, either by mapping or de novo assembly [2]. Software packages, such as CLC Genomics Workbench (Qiagen), SPAdes and Velvet, can be used for assembly. The genetic relatedness between isolates can be investigated using a gene-by-gene approach using multi-locus sequence typing (MLST), core genome MLST (cgMLST) or whole genome MLST (wgMLST) using SeqSphere+ (Ridom), Bionummerics (Biomérieux), or online tools, such as Enterobase (https://enterobase.warwick.ac.uk) and BIGSdb (http://bigsdb.readthedocs.io). Currently, it is still a matter of debate how many alleles two genomes may differ by to call them genetically related. The same problem applies for comparing two genomes by single nucleotide polymorphism (SNP) typing.
There are a number of web-based tools to perform additional NGS analysis [2]. One of them is the website of the Centre for Genomic Epidemiology (www.genomicepidemiology.org) that can be used for the detection of resistance and virulence genes. Another web-based tool is the Rapid Annotation using Subsystem Technology (RAST) website (http://rast.nmpdr.org) for annotating bacterial genomes.
One of the advantages of web-based tools is that, in general, no knowledge of bioinformatics is necessary. However, a disadvantage may be the lack of tweaking the software settings while performing the analysis. In addition, it may be necessary to confirm the results obtained through web-based tools using other methods [2].
NGS in clinical microbiology
NGS is already applied in several medical microbiology laboratories where it is used for outbreak management, molecular case findings, characterization and surveillance of pathogens, for example [2].
Indeed NGS can be extremely useful in outbreak detection, by monitoring the evolution and dynamics of multi-drug resistant pathogens [7]. A number of studies have highlighted the effectiveness of WGS-based typing for assessing of (newly) emerging pathogens. In our hospital, NGS was used for the characterization of a newly emerging CTX-M-15 producing Klebsiella pneumoniae clone [8]. Transmission of this K. pneumoniae strain between patients has been traced using genomic phylogenetic analysis (Fig. 2). In addition, the study showed the usefulness of a unique marker PCR, in which a clone-specific PCR was developed to investigate the transmission between patients [4].
In addition to tracing and characterizing outbreaks, NGS can be used for the implementation of control measures to avoid the spread of resistance bacteria [9]. An outbreak of a colistin-resistant carbapenemase-producing K. pneumoniae (KPC) with inter-institutional spread in The Netherlands was identified and characterized using NGS and, partially based on these findings, controlled by transferring all positive patients to a separate location [9].
Furthermore, NGS data stored in databases can be used to search retrospectively for molecular case studies. A study from Bathoorn et al. showed that a New Delhi Metallo-?-lactamas-5 (NDM-5)-producing K. pneumoniae was isolated from a Dutch patient. Molecular case findings showed that the Dutch strain is clonally related to strains isolated from four Danish patients in 2014. There was no obvious epidemiological link between the cases in the Dutch and Danish hospitals [10].
These studies and many others highlight the importance of NGS in clinical microbiology. NGS can be used either as a highly discriminatory tool to discriminate between bacterial clones with specific features and to use the information for patient management, infection prevention and evolutionary studies [2] or to characterize bacterial isolates in more detail [8]. Furthermore, web-based databases can be in silico screened retrospectively for the presence of novel (antibiotic-resistance) genes.
Conclusion and outlook
Using NGS, one laboratory protocol can be used to generate sequencing data from samples obtained from different sources. After data analysis, information on the presence of virulence factors and antibiotic resistance genes, as well as other relevant genes are obtained. In addition, NGS makes it possible to standardize typing methods, although cut-off values regarding cgMLST, wgMLST and SNP analysis have to be established internationally in order to distinguish related or unrelated isolates and being able to compare results between laboratories. In the next few years, the role of NGS will surely increase in medical microbiology laboratories, both for research as well as for molecular diagnostic purposes, infection prevention and molecular-epidemiological investigations.
Nonetheless, improvement of the NGS workflow is still needed, focusing on easier and faster ways of library preparation, shorter run-times and further reduction in costs. Furthermore, automatic pipelines for data analyses and easy to use software have to be developed. In addition, the development of proficiency testing panels are important for external quality controls. Only with implementation of the above items at local, (inter)regional and international level will broad use of NGS be allowed in clinical microbiological laboratories for patient and infection control management, including defining a tailor-made antibiotic therapy for each patient, leading to personalized microbiology.
Acknowledgement
A full version of this work is published in the review ‘Application of next generation sequencing in clinical microbiology and infection prevention’, Journal of biotechnology 2017; 243: 16–24.
References
1. Buchan BW, Ledeboer NA. Emerging technologies for the clinical microbiology laboratory. Clin Microbiol Rev 2014; 27(4): 783–822.
2. Deurenberg RH, Bathoorn E, Chlebowicz MA, Couto N, Ferdous M, Garcia-Cobos S, Kooistra-Smid AM, Raangs EC, Rosema S, Veloo AC, Zhou K, Friedrich AW, Rossen JW. Application of next generation sequencing in clinical microbiology and infection prevention. J Biotechnol 2017; 243: 16–24.
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The authors
Sigrid Rosema BSc; Ruud H. Deurenberg PhD; Monica A. Chlebowicz PhD; Silvia García-Cobos PhD; Alida C. M. Veloo PhD; Alexander W. Friedrich MD, PhD; John W. A. Rossen PhD, MMM
Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, The Netherlands
*Corresponding author
E-mail: j.w.a.rossen@rug.nl
Next-generation sequencing as a diagnostic tool in respiratory infections
, /in Featured Articles /by 3wmediaBackground
The term ‘acute respiratory tract infection’ (ARTI) encompasses a spectrum of conditions ranging from the common cold to pneumonia. Multiple organisms may infect the human respiratory tract, such as bacteria and fungi, but the majority of episodes are thought to be viral in origin [1]. The diagnosis of ARTI is made clinically but the lack of pathognomonic features means etiology cannot be determined clinically. Identifying causative organisms is challenging owing to the number of possibilities but is important for many reasons. In severe cases identification of an organism will guide therapeutics, although specific treatments for viral ARTI are generally limited to influenza. At the other end of the clinical spectrum identifying a viral cause in mild cases allows the physician to defer treatment with antibiotics and reassure the patient. This is an essential component of antimicrobial stewardship as high rates of antibiotic use are associated with circulating antimicrobial resistance [2]. The majority of antibiotic prescriptions issued in the UK are for respiratory tract infections [3] yet antibiotic use puts patients at risk of adverse drug reactions and in many cases will not lessen the duration of symptoms [4].
Respiratory infection diagnostics
Until recently viral diagnostics relied on cell culture or animal/egg inoculation. These time-consuming and laborious methods provided only a retrospective diagnosis and were, therefore, of little use in the management of acute infections. Nucleic acid amplification tests (NAAT), directly detecting the RNA or DNA of pathogens, have largely superseded these.
Many methods of RNA/DNA detection are available (Table 1). The most widely used is polymerase chain reaction (PCR). This has the benefit of rapid turnaround times and high levels of sensitivity and specificity in comparison to cell culture [5]. A pair of primers is required for each target but it is possible to use multiple primer sets within a single reaction (up to four) without compromising test sensitivity over the monoplex assay. This chemistry is now available as closed systems providing rapid results as a near-patient test (GeneXpert by Cepheid, Cobas by Roche).
Other examples of molecular NAATs which are available but not commonplace would be loop-mediated isothermal amplification (LAMP) and microarrays. LAMP detects nucleic acids but does not rely on thermocycling. It does, however, require multiple primers for each target (usually six) and as a consequence the sensitivity is more likely to be affected by genome mutations than standard PCR. This also makes multiplexing multiple targets within a single reaction more complicated.
Microarrays (also known as DNA chips or biochips) use a collection of oligonucleotide probes, about 70 bases in length, immobilized on a solid surface. The probes are complementary portions of DNA or RNA designed to match conserved regions of a genome; thus, if present, the target will bind to the corresponding probe which can then be quantified. Multiple probes may be attached to a single surface, screening for a large number of pathogens in a single reaction. As probes are targeted against conserved regions of the pathogen genome they may also detect related but novel pathogens.
To be used as a comprehensive diagnostic test numerous targets must be included to cover the likely pathogens. In the case of respiratory infections commercial assays are available with around 33 targets over 8 reactions [6]. Despite this approach a viral pathogen is detected in only a minority of specimens [7] and it remains the case that a pathogen will only be detected if actively sought.
Several significant respiratory viruses have been identified in recent years; those which may have circulated for many years, such as the human metapneumovirus, or emerging pathogens, such as SARS and, more recently, MERS. Whereas these are related to other known pathogens they are genetically distinct and, therefore, would evade detection with molecular methods.
What is next-generation sequencing?
The term ‘next-generation sequencing’ (NGS) refers to the practice of sequencing millions of DNA fragments in parallel. Numerous platforms are available to carry this out and the exact chemistry varies greatly between each. In practice, either all genetic material within a sample can be sequenced – metagenomics; or, hybrid capture allows a more focused approach to an area or genome of interest, this is termed ‘target enrichment’.
Advantages of NGS
Applying metagenomic NGS to clinical samples would allow an untargeted approach to identify all the genetic material contained within. This method has demonstrated potential for use in a diagnostic setting [8, 9].
The lack of pathogen targeting means that multiple pathogens can be detected without selection (Fig. 1), including novel or emerging or divergent pathogens. In the case of many viral pathogens evolution and mutations over time can reduce detection with specific PCR reactions. Mutations affecting primer binding sites may reduce binding affinity during the reaction and, for this reason, the performance of diagnostic assays must be monitored closely and at times altered. NGS could, therefore, be used as an adjunct in the quality control of PCR assays.
It is possible to detect full genome sequences from diagnostic samples and even with partial genome sequence it is feasible to subtype viral pathogens. Real-time knowledge of the circulating viral subtype is of particular importance in the management of influenza where this informs anti-viral choice, potential resistance and vaccine efficacy. This is currently carried out using additional PCR assays and Sanger sequencing, although this is not always possible in real-time.
Laboratory workflow
Currently the identification of rare or unusual pathogens using molecular methods necessitates samples to be batched to make the process cost effective; alternatively the test is centralized to a single laboratory to which samples must be sent. Either results in an increase in turn-around time. The use of NGS without any enrichment or targeting would permit samples to be treated in the same manner irrespective of type or likely pathogen.
Challenges
A major barrier in introducing NGS to the diagnostic setting is cost. Although the cost of NGS is decreasing rapidly it remains considerably more expensive than multiplex PCR. It is, therefore, unlikely to be cost effective to use this method for pathogen detection in non-severe infections for the time being. However, any cost–benefit analysis on introducing NGS to a diagnostic setting should also consider, on the positive side of the balance sheet, the likely savings NGS would offer in reductions to epidemiological and public health testing.
Complexity and turnaround time
Current methods of library preparation are complex requiring multiple user interventions and additional equipment to that found in a diagnostic laboratory with attendant implications for the time and cost of the process. To be carried out as a routine diagnostic assay these processes would need to be simplified and, ideally, automated to reduce hands-on time and the potential for contamination and human error.
The commonly used sequencing platforms take several hours or even days to generate sequence information. It should be noted that the third generation platforms that use single-molecule real-time (SMRT) technology are rapid and, as the name suggests, can be analysed in almost real-time.
Data analysis
Data analysis and storage is a major bottleneck in the NGS process. The computational power required for analyses would be beyond the current capabilities of diagnostic services. The methods used in data analysis pose a further challenge. Currently there is no agreed method as to the best approach for data analysis; indeed this is an entire specialty in itself, bioinformatics. Development of software programmes will both make the analysis more feasible in a diagnostic service to non-bioinformaticians and will lead to standardization of data processing.
Discussion
NGS undoubtedly has potential to dramatically change the landscape of infection diagnostics. Whether it will replace current molecular methods remains to be seen. The cost and complex sample processing remains prohibitive but these novel technologies are still in an exponential phase of development. Even current methodologies are yielding promising results in this field. The lack of pathogen targeting means that there is potential for a single work flow to be applied to all specimens, no matter what the syndrome which could even be extended to non-viral pathogens, resulting in a pan-microbial diagnostic test.
The generation of virus sequence as part of a diagnostic assay has substantial management and epidemiological benefits. In terms of respiratory infections this is currently limited to resistance testing and strain analysis of influenza. However, in the management of blood-borne viruses, particularly HIV and hepatitis C virus (HCV), point mutations and minor populations may impact greatly on the management and prognosis of patients. With the introduction of novel therapies or vaccines against viral respiratory infections NGS will have an even greater clinical benefit.
Acknowledgements
I would like to thank Dr Rory Gunson and Dr Emma Thomson for reviewing the manuscript.
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The author
Fiona Thorburn PhD
NHS Greater Glasgow and Clyde, Glasgow G12 0XH, UK
E-mail: Fionathorburn@nhs.net