Background: Infections by several DNA viruses can severely impact outcomes in paediatric immunocompromised patients. Current testing, which is generally limited to singleplex qPCR assays, can miss both common and rarer viruses if they are not targeted. Objectives: To evaluate the performance of the Galileo Viral Panel (Galileo), a sample-to-result shotgun metagenomics platform for the detection and quantification of 12 DNA viruses, compared to standard of care qPCR assays. Study design: A clinical performance evaluation was carried out using 43 prospectively collected EDTA plasma samples positive for one or more DNA viruses. Agreement between assays was assessed by overall, positive, and negative percent agreement, as well as quantitative agreement by linear regression and Bland-Altman analysis. Results: Overall positive percent agreement was 84% (95% CI: 76%-90%), and negative percent agreement was 95% (95% CI: 92%-97%). There was a high correlation between Galileo and qPCR for ADV, CMV, EBV, and VZV (R2 = 0.91) and a mean difference by Bland Altman of -0.43 log10 IU or cp/ml (95% limits of agreement, -1.37 to 0.51). In addition, there was a high correlation between Galileo Signal Score and qPCR for TTV (R2 = 0.85). Conclusion: We observed high qualitative and quantitative agreement between qPCR and Galileo. Galileo identified additional viruses that were not tested with routine qPCR and could impact clinical outcomes

Breuer, J

Brown, JR

Carpenter, ML

Lee, JCD

Shah, D

Wall, G

English

UCL Discovery

Journal of Clinical Virology Plus 2 (2022) 100073 Contents lists available at ScienceDirect Journal of Clinical Virology Plus journal homepage: www.elsevier.com/locate/jcvp Use of a sample-to-result shotgun metagenomics platform for the detection and quantification of viral pathogens in paediatric immunocompromised patients Divya Shah a , Julianne R. Brown a , Jack C.D. Lee a , Meredith L. Carpenter b , ∗ , Gavin Wall c , Judith Breuer a , d a Microbiology, Virology and Infection Prevention and Control, Great Ormond Street Hospital for Children NHS Foundation Trust, UK b Arc Bio, LLC, Scotts Valley, CA and Cambridge, MA, USA c QIAGEN, Hilden, Germany d Division of Infection and Immunity, University College London, UK a r t i c l e i n f o Keywords: Metagenomics Transplant viruses Next-generation sequencing Paediatric viral infection qPCR a b s t r a c t Background: Infections by several DNA viruses can severely impact outcomes in paediatric immunocompromised patients. Current testing, which is generally limited to singleplex qPCR assays, can miss both common and rarer viruses if they are not targeted. Objectives: To evaluate the performance of the Galileo Viral Panel (Galileo), a sample-to-result shotgun metage- nomics platform for the detection and quantification of 12 DNA viruses, compared to standard of care qPCR assays. Study design: A clinical performance evaluation was carried out using 43 prospectively collected EDTA plasma samples positive for one or more DNA viruses. Agreement between assays was assessed by overall, positive, and negative percent agreement, as well as quantitative agreement by linear regression and Bland-Altman analysis. Results: Overall positive percent agreement was 84% (95% CI: 76%-90%), and negative percent agreement was 95% (95% CI: 92%-97%). There was a high correlation between Galileo and qPCR for ADV, CMV, EBV, and VZV ( R 2 = 0.91) and a mean difference by Bland Altman of -0.43 log 10 IU or cp/ml (95% limits of agreement, -1.37 to 0.51). In addition, there was a high correlation between Galileo Signal Score and qPCR for TTV ( R 2 = 0.85). Conclusion: We observed high qualitative and quantitative agreement between qPCR and Galileo. Galileo identi- fied additional viruses that were not tested with routine qPCR and could impact clinical outcomes. 1 m  r  p  i  E  a  d  i  s  c  t p  m  t  s  f  t  (  m  v  a  a  G  m  hR2(. Introduction Infections by DNA viruses are a common cause of morbidity andortality in both adult and paediatric immunocompromised transplantecipients [ 1 , 2 ]. However, children are at an increased risk of some com-lications of these viruses due to their immune status and history ofnfection [2] . For example, children are more likely than adults to bepstein-Barr virus (EBV) seronegative before transplant, putting themt an increased risk of developing post-transplant lymphoproliferativeisorders [2] . Primary CMV infections are also seen more frequently inmmunocompromised paediatric patient, and CMV viral load has beenhown to predict CMV disease and death [3–6] . BK virus (BKV), vari-ella zoster virus (VZV), and adenovirus (ADV) are also of clinical impor-ance, particularly in paediatric bone marrow transplant patients [2] . ∗ Corresponding author. E-mail address: meredith@arcbio.com (M.L. Carpenter). t  v  ttps://doi.org/10.1016/j.jcvp.2022.100073 eceived 8 December 2021; Received in revised form 11 February 2022; Accepted 17667-0380/© 2022 The Authors. Published by Elsevier Ltd. This is an open access ar http://creativecommons.org/licenses/by-nc-nd/4.0/ ) Standard of care testing for these viruses relies on an array of single-lex quantitative PCR (qPCR) assays. Use of these targeted assays caniss less-common viruses, as well as putative biomarkers of immune sta-us such as torque teno virus (TTV) [7] . In contrast to targeted PCR as-ays, metagenomic next-generation sequencing (mNGS) is a hypothesis-ree testing approach that can be used to identify and quantify a po-entially unlimited array of microbes. The Galileo Viral Panel (Galileo)Arc Bio, LLC, Scotts Valley, CA) is a Research Use Only sample-to-resultNGS platform designed to simultaneously detect and quantify 10 DNAiruses [herpes simplex virus 1 and 2 (HSV1/2), human herpesvirus 6And B (HHV-6A/B), VZV, CMV, EBV, JC virus (JCV), BKV, and ADV]nd to semi-quantitatively detect Parvovirus B19 (B19) and TTV [ 8 , 9 ].alileo includes all reagents, controls, and software required to performNGS on plasma samples in under 48 h. The objective of this study waso evaluate the clinical performance characteristics of a pre-commercialersion of the Galileo platform compared with standard-of-care qPCR March 2022 ticle under the CC BY-NC-ND license D. Shah, J.R. Brown, J.C.D. Lee et al. Journal of Clinical Virology Plus 2 (2022) 100073 Table 1 Performance characteristics of PCR assays used in this study. PCR Target Limit of Detection Linear Range (for quantitative assays) VZV 2000 cp/ml 2000–20 M cp/ml CMV 50 IU/ml or 200 cp/ml 200–20 M cp/ml EBV 18 IU/ml or 200 cp/ml 200–20 M cp/ml HHV-6 1000 cp/ml N/A JCV 100 cp/ml N/A BKV 100 cp/ml N/A hADV 200 cp/ml 200–20 M cp/ml a  d22 u  E  12 m  o  t  o  s  r  i  p  c  s  G  y  y2 E  n  p  e  c  p  (  c  a  B  p2 i  m  s  kTable 2 Demographic characteristics of the 26 patients included in the study. Detail Number (%/range) Median Age 6 years (8 months–62 years) Sample Date 07/11/18 – 01/04/19 Male 13 (50%) Female 13 (50%) Underlying Condition Post-HSCT 12 (46.2%) Malignancy 5/12 Congenital immune disorders 4/12 Congenital metabolic disorders 3/12 Post-SOT 2 (7.7%) No transplant 10 (38.5%) Congenital immune disorders 6/10 Congenital Metabolic disorder 1/10 Malignancy 1/10 General medical admission 2/10 Unknown 2 (7.7%) 2 t  w  i  l  t  r2 w  g3 s  2  b  l  p r  i  b  m  9  a  o  2  p  b  t  -2  2  c  H  f t  q  (ssays using prospectively collected residual plasma from viremic pae-iatric immunocompromised patients. . Materials and methods .1. Patient consent statement This study utilized existing ethics approval for use of resid-al, blinded samples for method evaluation (National Researchthics Service (NRES) Committee London – Fulham (REC reference:7/LO/1530)). .2. Clinical samples Samples chosen for evaluation included those from immunocompro-ised patients that tested positive by qPCR in whole blood for at leastne or more of ten transplant-related viruses used in routine clinicalesting (CMV, BKV, EBV, ADV, VZV, HHV-6, HSV1/2, and JCV). A totalf 50 whole blood samples were refrigerated for up to 3 weeks beforeeparation of EDTA plasma for this study. The whole blood samples wereefrigerated to ensure availability of samples for additional routine clin-cal testing. After exclusion of samples without detectable viral DNA inlasma (due to differences in viral detection in whole blood vs. plasmaompartments) or sufficient residual volume for testing, a total of 43amples from 26 patients were selected for further processing with thealileo workflow. All patients were between the ages of 8 months – 14ears with the exception of one (62 years), and the median age was 6ears. .3. DNA extraction, library preparation, and sequencing DNA was extracted from 400 𝜇L samples/Galileo controls using theZ1 Virus Mini Kit v2 (Qiagen, Ltd.) and eluted into 90 𝜇L. Galileo inter-al controls were added to all samples before extraction. Negative andositive full-process controls (Arc Bio, LLC) were processed alongsideach run of 10 clinical samples. Sequencing libraries were prepared ac-ording to the manufacturer’s protocols (Arc Bio, LLC) and as describedreviously [9] . Libraries were sequenced on the NextSeq 500 sequencerIllumina) using the paired-end, high-output 150-cycle v2.5 kit. Beforelinical sample testing, an initial calibration run was performed using multianalyte panel of whole-virus controls spiked into plasma (Arcio, LLC) to generate a standard curve and estimate the viral load asreviously described [ 8 , 9 ]. .4. PCR assays Initial qPCR testing of plasma samples was performed using val-dated laboratory-developed tests [10–13] on the ABI 7500 (Ther-oFisher). The limits of detection and linear ranges of these assays arehown in Table 1 . TTV was tested using a commercially available qPCRit (RUO, TTV-R-GENE, Biomerieux). 2 .5. Bioinformatics analysis System-level NextSeq quality metrics were evaluated according tohe manufacturer’s recommendations (Illumina, Inc.). The sample sheetas downloaded from Galileo Analytics (Arc Bio, LLC), and demultiplex-ng was performed using bcl2fastq 2.20 with default parameters and noane splitting. The resulting FASTQ files were uploaded and analyzed tohe Galileo Analytics cloud-based software. QC criteria were assessed asecommended by the manufacturer and as previously described [ 8 , 9 ]. .6. Statistical analysis Statistical analysis (including Bland-Altman analysis) and graphingas performed in R version 4.0.4 [14] , using the dplyr (v1.0.6) [15] andgplot2 (v3.3.3) [16] packages. . Results Demographic characteristics of the patient cohort included in thistudy are shown in Table 2 . An average of 50 M total reads (range5,599,402–98,222,454) were sequenced per library. Of the 43 li-raries, one was negative for one internal control, and two runs hadow-level viral DNA detected in the negative controls (pooled humanlasma). From the 43 plasma samples, 56 viruses were initially detected byoutine qPCR. Galileo identified an additional 51 viruses for 107 virusesn total ( Table 3 , Supplemental Table 1). Any additional viruses detectedy Galileo were then assessed by plasma qPCR. Positive percent agree-ent was 84% (95% CI: 76–90%), and negative percent agreement was5% (95% CI: 92–97%). There were 11 viruses detected by Galileo onlynd 16 viruses detected by qPCR only. The viruses detected by Galileonly were 1 CMV (154 IU/ml), 1 EBV (477 IU/ml), 2 HHV-6 (333 and153 cp/ml), and 7 JCV (20–13,372 IU/ml); all JCV samples were BKVositive, so these were likely false positives due to genome homologyetween BKV and JCV, as previously reported [ 8 , 9 ]. The viruses de-ected by qPCR only were 5 EBV (1689–12,545 cp/ml), 3 CMV (1263 2054 cp/ml), 1 JCV (Ct 37), 1 HHV-6 (Ct 38), and 6 TTV (2000–48,000 cp/ml); all had Ct values 35–39, except TTV with Ct values4–41. Galileo differentiates between HHV-6A and -6B; however, be-ause the qPCR assay does not, these results were grouped together asHV-6. See Supplemental Table 1 for the full qPCR and Galileo resultsor each patient. For those viruses with quantatitive PCR data, 40 samples with quan-itative results by both methods were compared ( Fig. 1 ). There was highuantitative correlation between the assays for all viruses ( R 2 = 0.91) Fig. 1 A). Quantitative agreement by Bland-Altman was –0.43 log 10 D. Shah, J.R. Brown, J.C.D. Lee et al. Journal of Clinical Virology Plus 2 (2022) 100073 Fig. 1. Quantitative agreement between Galileo mNGS and qPCR. (A) Linear regression of qPCR titer in log 10 IU or cp/ml on the X -axis and Galileo mNGS log 10 IU or cp/ml on the Y -axis for the four viruses where qPCR results were available, and where viruses were detected by both assays. The regression line (solid line), line of identity (dotted line), and 95% confidence intervals (gray shaded areas) are shown. (B) Quantitative agreement by Bland-Altman plot with mean (blue line), 95% limits of agreement (dashed lines), and zero line (gray line) shown. (C) Linear regression of qPCR vs Galileo Signal Score for TTV, where whole-virus calibration material is not available to convert GSS to cp/ml. Three outliers from the same patient are indicated (red circle). 95% confidence intervals are shown in gray. (D) Linear regression of qPCR titer and Galileo Signal Score with three outliers removed. 95% confidence intervals are shown in gray (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.). Table 3 Summary of viral detection by Galileo mNGS and qPCR across the sample set. Positive percent agreement (PPA) and negative percent agreement (NPA) are also shown. qPCR PPA NPA + –Galileo All viruses ( + ) 87 11 84% 95% All viruses (-) 16 230 HHV-6 ( + ) 8 2 89% 94% HHV-6 (–) 1 32 BKV ( + ) 9 0 100% 100% BKV (–) 0 34 JCV ( + ) 1 7 50% 83% JCV (–) 1 34 VZV ( + ) 1 0 100% 100% VZV (–) 0 42 ADV ( + ) 21 0 100% 100% ADV (–) 0 22 CMV ( + ) 7 1 70% 97% CMV (–) 3 32 EBV ( + ) 11 1 69% 96% EBV (–) 5 26 TTV ( + ) 29 0 83% 100% TTV (-) 6 8 I  (  e  w  f  t  t  w4 s  p  G  t  i  t  c  a  b  u  a q  m  t  t v  q  3 U or cp/ml mean difference (95% limits of agreement, -1.37 to 0.51) Fig. 1 B). Focusing on TTV (which was quantified by Galileo’s propri-tary Signal Score metric due to the lack of a commercially availablehole-virus calibration material for TTV), results from the 29 samplesrom 15 patients positive by both assays showed high correlation be-ween Galileo and qPCR, except for three outliers, all from the same pa-ient, that showed lower quantification by qPCR ( Fig. 1 C). When theseere removed, the linear regression R 2 was 0.85 ( Fig. 1 D). . Discussion This study demonstrates overall high levels of agreement betweeningle-target qPCR assays and the Galileo Viral Panel shotgun mNGSlatform. While both assays missed several low titer/late Ct samples,alileo detected an additional 51 viruses that had not initially beenested for by standard of care PCR, all of which would be importantf encountered during patient care at clinically significant levels. Whilehe low-level signals in both assays may represent noise (as is likely thease for the detection of JCV in samples positive for BKV), they maylso indicate early viral replication or latent viral genomes, which areoth of uncertain clinical significance. Quantitatively, Galileo slightlynderquantified compared with qPCR assays, suggesting that only onessay should be used to monitor a patient. Interestingly, three samples from the same patient showed loweruantification of TTV by qPCR compared with Galileo. This discrepancyay be due to sequence differences in the primer binding region relatedo the high genetic variation in TTV [17] . In contrast, Galileo can utilizehe entire TTV genome to detect and quantify. Although Galileo can detect many viruses simultaneously, includingiruses that might be missed in standard care singleplex qPCR testing,PCR remains less costly, less laborious, and faster than mNGS. TheD. Shah, J.R. Brown, J.C.D. Lee et al. Journal of Clinical Virology Plus 2 (2022) 100073 G  i  P  a  i  m (  t  r  a  i  cD aA g  V  s  sS tR             [  [   [  [   [  [  [  [  alileo workflow takes approximately 48 h (of which 20 h is sequenc-ng), while qPCR requires 4 to 6 h, including extraction, reaction setup,CR, and analysis. However, the expansion of the Galileo assay to reportdditional pathogens (e.g., bacteria and fungi), coupled with reductionsn sequencing time, could increase the utility and diagnostic yield ofNGS, making it more amenable to routine clinical use. Limitations of this study include the low numbers of certain viruses1 VZV sample, no HSV1/2 or B19 included); extended sample storageime prior to plasma analysis; low total number of samples; and its ret-ospective nature for the purposes of method comparison rather thannalysis of clinical outcomes. In conclusion, the Galileo mNGS platforms a promising method for comprehensive viral monitoring in immuno-ompromised paediatric patients. eclaration of Competing Interest MLC and GW are current or former employees of Arc Bio, LLC. JB is clinical advisor for Arc Bio. cknowledgments The authors would like to thank Jannine Forst for assistance withraph generation. Arc Bio provided a precommercial version of Galileoiral Panel and access to the Galileo Analytics software used in thistudy. JB receives funding from the NIHR UCL/UCLH Biomedical Re-earch Centre. JL is funded by an NIHR award NIHR200652 to JB. upplementary materials Supplementary material associated with this article can be found, inhe online version, at doi: 10.1016/j.jcvp.2022.100073 . eferences [1] J.A. Fishman , Infection in organ transplantation, Am. J. Transplant. 17 (2017)856–879 . 4 [2] M.G. Michaels , M. Green , Infections in pediatric transplant recipients: not just smalladults, Hematol. Oncol. Clin. N. Am. 25 (2011) 139–150 . [3] A. Stern , et al. , CMV viral load kinetics predict CMV end-organ disease and mortalityafter hematopoietic cell transplant (HCT), J. Infect. Dis. 224 (2021) 620–631 . [4] W. Lu , H.P. Chen , Y.J. Chan , F.D. Wang , Clinical significance of post-treatment viralload of cytomegalovirus in patients with hematologic malignancies, J. Microbiol.Immunol. Infect. 54 (2021) 245–252 . [5] E.R. Duke , et al. , CMV viral load kinetics as surrogate endpoints after allogeneictransplantation, J. Clin. Invest. 131 (1) (2021) e133960–e133974 . [6] S.B. Vora , J.A. Englund , Cytomegalovirus in immunocompromised children, Curr.Opin. Infect. Dis. 28 (2015) 323–329 . [7] W. Mouton , et al. , Torque teno virus viral load as a marker of immune functionin allogeneic haematopoietic stem cell transplantation recipients, Viruses 12 (11)(2020) 1292–1302 . [8] M.L. Carpenter , et al. , Metagenomic next-generation sequencing for identificationand quantitation of transplant-related DNA viruses, J. Clin. Microbiol. 57 (12) (2019)e01113–e01119 . [9] S.S. Sam , et al. , Evaluation of a next-generation sequencing metagenomics assay todetect and quantify DNA viruses in plasma from transplant recipients, J. Mol. Diagn.23 (2021) 719–731 . 10] M.L. Quinlivan , et al. , Effect of viral load on the outcome of herpes zoster, J. Clin.Microbiol. 45 (2007) 3909–3914 . 11] W.J. Jabs , et al. , Normalized quantification by real-time PCR of epstein-barr virusload in patients at risk for posttransplant lymphoproliferative disorders, J. Clin. Mi-crobiol. 39 (2001) 564–569 . 12] A. Heim , C. Ebnet , G. Harste , P. Pring-Akerblom , Rapid and quantitative detectionof human adenovirus DNA by real-time PCR, J. Med. Virol. 70 (2003) 228–239 . 13] T.J. Dumonceaux , C. Mesa , A. Severini , Internally controlled triplex quantitativePCR assay for human polyomaviruses JC and BK, J. Clin. Microbiol. 46 (2008)2829–2836 . 14] R. Team, R: a language and environment for statistical computing.http://www.r-project.org (2020). 15] R.F. Hadley Wickham, L. Henry, K. Müller, dplyr: a grammar of data manipulation.https://dplyr.tidyverse.org/ (2021). 16] H. Wickham , ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag, NewYork, 2016 . 17] S. Hino , H. Miyata , Torque teno virus (TTV): current status, Rev. Med. Virol. 17(2007) 45–57 . 

Use of a sample-to-result shotgun metagenomics platform for the detection and quantification of viral pathogens in paediatric immunocompromised patients

https://discovery.ucl.ac.uk/10146983/1/Breuer_Use%20of%20a%20sample-to-result%20shotgun%20metagenomics%20platform%20for%20the%20detection%20and%20quantification%20of%20viral%20pathogens%20in%20paediatric%20immunocompromised%20patients_VoR.pdf

Use of a sample-to-result shotgun metagenomics platform for the detection and quantification of viral pathogens in paediatric immunocompromised patients

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