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Dr Jethro Herberg @ MRF's Meningitis & Septicaemia in Children and Adults 2017

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Distinguishing bacterial and viral infections by host gene expression
https://www.meningitis.org/mrf-conference-2017

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Dr Jethro Herberg @ MRF's Meningitis & Septicaemia in Children and Adults 2017

  1. 1. Distinguishing  bacterial  and  viral   infections  by  host  gene  expression MRF  2017  Conference Jethro  Herberg Clinical  Senior  Lecturer,  paediatric  infectious  disease,  Imperial  College  London
  2. 2. The  problem  to  be  addressed Febrile  children: • Unreliable  clinical  diagnosis  of   bacterial,  viral,  inflammatory  disease • Lab  tests  slow,  inaccurate • Many  admitted  for  antibiotics • Most  leave  without  a  diagnosis • Most  didn’t  need  antibiotics  or   admission We  need  better  tests  that  can  discriminate  febrile   children  with  bacterial  infection,  viral  infection  or   inflammation
  3. 3. Fatal  SBI  -­‐ missed  opportunity  for  early  antibiotic  treatment Minority  with  severe  illness  requiring  paediatric  intensive   care Unknown  number  of  children  with  SBI  missed  by  current   clinical  diagnostic  tools 25%  ED  attendances  for  fever 3%  caused  by  SBI  – 20%  receive  antibiotics Blood  tests,  on  discretion  of  clinician.   20%  have  SBI  -­‐ 75%  receive  antibiotics 4-­‐6  episodes/year.  90%  have  medical   consultation.  0.1%  have  SBI  – 10%  get   antibiotics SBI   fatal Need  PICU Confirmed  SBI Febrile  children  presenting  to hospital Febrile  children  having   blood  tests Febrile  children  in  the  community/ primary  care Scale  of   the   problem St  Mary’s  Hospital:   26,000  children/year 9,000  with  fever 2,300  admitted   1,500  on  antibiotics How  many  really  needed   admission  on  antibiotics??
  4. 4. We  could  give  antibiotics  to   everyone,  just  to  be  safe… …in  fact  we  do… (almost)
  5. 5. Multidrug  resistant  Klebsiella   pneumoniae,   ECDC  EARS-­Net http://ecdc.europa.eu/en/healthtopics/antimicrobial_resista nce/database
  6. 6. Is  better  pathogen  detection  the  answer?   • Viral  diagnostics   – Molecular  diagnostics  very  sensitive  (80%  of  patients  positive) – Problems  of  clinical  interpretation • Culture-­‐based  bacterial  diagnostics – Low  sensitivity – Specificity  problems  with  commensal  organisms,  especially  for   non-­‐sterile  site  samples • Molecular  bacterial  diagnostics – PCR  – not  greatly  increased  sensitivity  compared  to  blood  culture – Still  issues  to  resolve  with  sensitivity  and  specificity – Antimicrobial  resistance  detection  lags  behind  pathogen   detection
  7. 7. Differential  stimulation   of  host inflammation How  about  using  host  biomarkers? Pathogens (bacteria,     viruses) Multiple  levels of omics data Viral Response Host Infection Bacterial Response
  8. 8. Does  CRP  discriminate  bacterial   vs  viral  infection?  CRP values in bacterial, viral or unknown infectionC onfirm ed bacterial Probable bacterialC onfirm ed viral D on'tknow 1 10 100 1000 Infection group logCRP • CRP  separates  confirmed  bacterial   and  viral  well • CRP  not  much  help  for  “don’t   know”  group  – where  you  need  it   most! Philipp  E  Geyer  et  al.  Mol  SystBiol2017;;13:942 • Massive  (11  log)  range  in  serum  protein  concentrations • Wide  range  of  chemistries  – can  only  interrogate  a  tiny   proportion  at  one  time • Most  discovery  based  on  candidate  molecules • New  tests  for  bacterial  infection,  eg MeMed – not  yet   for  inflammation Why  don’t  we    have  better  biomarkers? Only  5  logs
  9. 9. Gliddon,  Herberg,  Levin,  Kaforou  Immunology 2017 Transcriptomics:   • Measure  the  expression   level  of  all  genes  in  cells   from  blood • For  each  gene,  compare   levels  in  patients  to  controls   or  other  groups • Look  for  gene  expression   biomarkers  of  infection  or   inflammation
  10. 10. Immunopathology  of  Respiratory,  Inflammatory  and  Infectious  Disease  Study Child with  suspected  infection Apply  best  available  diagnostics Simple  rules  to  classify patients  with  bacterial   infection,  and  those  without   Run  whole  blood  samples  on  Illumina   microarrays
  11. 11. Patients  with  a  similar  clinical  presentation   have  distinct  gene  expression  patterns Comparison   of  gene   expression   in  RSV  and   influenza -­-­-­-­-­-­-­Flu  patients-­-­-­-­-­-­-­ -­-­-­-­-­-­-­RSV  patients-­-­-­-­-­-­ Each  infection  has  a  unique  gene  expression  signature Red  squares:  increased  expression Blue  squares:  decreased  expression
  12. 12. Assign  a  predictive  value  to  each   individual  by  combining  up  and   down  regulated  genes Simplify  the  transcript  signature  into  a  single   value  – Disease  Risk  Score   Pooled  up-­ regulated  probes Pooled  down-­ regulated  probes+ Disease  Risk   Score   or =
  13. 13. H1N1 RSV Disease  Risk   Score  separates   H1N1  and  RSV Pooled  up-­ regulated  probes Pooled  down-­ regulated  probes+ Disease  Risk   Score   or =
  14. 14. Apply  this  approach  to  a  useful  question:   Do  patients  with  bacterial  infection  have  a   common  gene  expression  signature? (…and  can  we  use  this  approach  to  diagnose  other   tricky  conditions?)
  15. 15. Recruitment  of  febrile  patients Categorisation  of  patients  based  on  clinical  data INFLAMMATORY   SYNDROMES No  accurate  test VIRAL  syndrome Don’t  need   antibiotics BACTERIAL   syndrome Need  antibiotics Probable   Bacterial Unknown   Bacterial   or  Viral Definite   Bacterial Probable   Viral Definite   Viral Inflamm.   diagnosis Review  clinical  investigation  results Whole  blood  transcriptomic  profile Study  design
  16. 16. Recruitment  of  febrile  patients Categorisation  of  patients  based  on  clinical  data INFLAMMATORY   SYNDROMES No  accurate  test VIRAL  syndrome Don’t  need   antibiotics BACTERIAL   syndrome Need  antibiotics Probable   Bacterial Unknown   Bacterial   or  Viral Definite   Bacterial Probable   Viral Definite   Viral Inflamm.   diagnosis Review  clinical  investigation  results Whole  blood  transcriptomic  profile Study  design 2010 2013 2014 2015 1096  febrile   children 672  samples   viral/bact 438  arrays Discover   signatures • 292-­case   discovery  set • 130-­case   validation  set • Multiple   published   datasets  for   external   validation
  17. 17. 8565  SDE   transcripts Compare  ‘Definite  Bacterial’  patients  to  ‘Definite  Viral’ 38  SDE   transcripts elastic  net Many  transcripts  are   highly  correlated Variable  selection   algorithm  identifies   optimal  minimum   discriminatory  signature Log2 fold  change -­log10(corrected  P  value) 80%  training  set
  18. 18. Heat  map:  clustering  based  on  the  bacterial  vs.  viral  38-­transcript  signature. • red  -­ patients  with  Bacterial   infection • blue  -­ patients  with  Viral   infection • red  squares  – upregulated  genes • green  squares  – downregulated  genes
  19. 19. Using  a  novel  variable  selection  algorithm:  forward   selection  – partial  least  squares  (FS-­PLS) Can  we  shrink  signature  further?   2  transcripts  can  identify  bacterial  vs  viral  cases   Pool  up-­regulated   probes Pool  down-­ regulated  probes + Disease  Risk   Score   or =
  20. 20. Bacterial  vs   JIA,  HSP Bacterial  vs   SLE Bacterial  vs   viral Bacterial  vs   viral Published  cohortsTest  set  (20%) Berry  et  alRamiloet  alHu  et  alWright  et  al AUC  0.963   (0.874-­1.0) Validation  set AUC  0.974   (0.912-­1.0) Diagnostic Test Accuracy of a 2-Transcript Host RNA Signature for Discriminating Bacterial vs Viral Infection in Febrile Children Jethro A. Herberg, PhD; Myrsini Kaforou, PhD; Victoria J. Wright, PhD; Hannah Shailes, BSc; Hariklia Eleftherohorinou, PhD; Clive J. Hoggart, PhD; Miriam Cebey-López, MSc; Michael J. Carter, MRCPCH; Victoria A. Janes, MD; Stuart Gormley, MRes; Chisato Shimizu, MD; Adriana H. Tremoulet, MD; Anouk M. Barendregt, BSc; Antonio Salas, PhD; John Kanegaye, MD; Andrew J. Pollard, PhD; Saul N. Faust, PhD; Sanjay Patel, FRCPCH; Taco Kuijpers, PhD; Federico Martinón-Torres, PhD; Jane C. Burns, MD; Lachlan J. M. Coin, PhD; Michael Levin, FRCPCH; for the IRIS Consortium Research JAMA | Preliminary Communication | INNOVATIONS IN HEALTH CARE DELIVERY
  21. 21. Give   antibiotics Uncertain   role  for   antibiotics No  need  for   antibiotics Could  2-­transcript  test  be  basis  of  “traffic  light”  test  for  antibiotic  treatment?  
  22. 22. Percentage  of  patients Definite Viral Probable Viral Unknown Bacterial  or  Viral Probable Bacterial Definite Bacterial Comparison  of  Disease  Risk  Score  values  and   antibiotic  treatment Grey  bars  -­ proportion  of  patients  receiving  antibiotics Coloured  bars  -­ proportion  predicted  to  be  bacterial  by  DRS
  23. 23. How  can  we  validate  findings?   • Pilot  data  suggest:  need  for  antibiotics  may  be   identified  by  test  based  on  very  few  transcripts • BUT  current  data: – Case  control  design  favours obvious  cases – Samples  collected  late,  not  at  presentation – Incomplete  range  of  febrile  phenotypes – Not  enough  cases  to  compare  severe/mild   phenotypes What  do  we  do  next?  
  24. 24. Association of RNA Biosignatures With Bacterial Infections in Febrile Infants Aged 60 Days or Younger Prashant Mahajan, MD, MPH, MBA; Nathan Kuppermann, MD, MPH; Asuncion Mejias, MD, PhD; Nicolas Suarez, PhD; Damien Chaussabel, PhD; T. Charles Casper, PhD; Bennett Smith, BS; Elizabeth R. Alpern, MD, MSCE; Jennifer Anders, MD; Shireen M. Atabaki, MD, MPH; Jonathan E. Bennett, MD; Stephen Blumberg, MD; Bema Bonsu, MD; Dominic Borgialli, DO, MPH; Anne Brayer, MD; Lorin Browne, DO; Daniel M. Cohen, MD; Ellen F. Crain, MD, PhD; Andrea T. Cruz, MD, MPH; Peter S. Dayan, MD, MSc; Rajender Gattu, MD; Richard Greenberg, MD; John D. Hoyle Jr, MD; David M. Jaffe, MD; Deborah A. Levine, MD; Kathleen Lillis, MD; James G. Linakis, MD, PhD; Jared Muenzer, MD; Lise E. Nigrovic, MD, MPH; Elizabeth C. Powell, MD, MPH; Alexander J. Rogers, MD; Genie Roosevelt, MD; Richard M. Ruddy, MD; Mary Saunders, MD; Michael G. Tunik, MD; Leah Tzimenatos, MD; Melissa Vitale, MD; J. Michael Dean, MD, MBA; Octavio Ramilo, MD; for the Pediatric Emergency Care Applied Research Network (PECARN) IMPORTANCE Young febrile infants are at substantial risk of serious bacterial infections; however, the current culture-based diagnosis has limitations. Analysis of host expression patterns (“RNA biosignatures”) in response to infections may provide an alternative diagnostic approach. OBJECTIVE To assess whether RNA biosignatures can distinguish febrile infants aged 60 days or younger with and without serious bacterial infections. DESIGN, SETTING, AND PARTICIPANTS Prospective observational study involving a convenience sample of febrile infants 60 days or younger evaluated for fever (temperature >38° C) in 22 emergency departments from December 2008 to December 2010 who underwent laboratory evaluations including blood cultures. A random sample of infants with and without bacterial infections was selected for RNA biosignature analysis. Afebrile healthy infants served as controls. Blood samples were collected for cultures and RNA biosignatures. Bioinformatics tools were applied to define RNA biosignatures to classify febrile infants by infection type. EXPOSURE RNA biosignatures compared with cultures for discriminating febrile infants with and without bacterial infections and infants with bacteremia from those without bacterial infections. MAIN OUTCOMES AND MEASURES Bacterial infection confirmed by culture. Performance of RNA biosignatures was compared with routine laboratory screening tests and Yale Observation Scale (YOS) scores. RESULTS Of 1883 febrile infants (median age, 37 days; 55.7% boys), RNA biosignatures were measured in 279 randomly selected infants (89 with bacterial infections—including 32 with bacteremia and 190 without bacterial infections), and 19 afebrile healthy infants. Sixty-six classifier genes were identified that distinguished infants with and without bacterial infections in the test set with 87% (95% CI, 73%-95%) sensitivity and 89% (95% CI, 81%-93%) specificity. Ten classifier genes distinguished infants with bacteremia from those without bacterial infections in the test set with 94% (95% CI, 70%-100%) sensitivity and 95% (95% CI, 88%-98%) specificity. The incremental C statistic for the RNA biosignatures over the YOS score was 0.37 (95% CI, 0.30-0.43). CONCLUSIONS AND RELEVANCE In this preliminary study, RNA biosignatures were defined to distinguish febrile infants aged 60 days or younger with and without bacterial infections. Editorial page 824 Related article page 835 Supplemental content Author Affiliations: Author affiliations are listed at the end of this article. Group Information: Members of the Pediatric Emergency Care Applied Research Network (PECARN) are Research JAMA | Preliminary Communication | INNOVATIONS IN HEALTH CARE DELIVERY • Mahajan  et  al:  ED-­‐based  study,   22  centres,  diverse  population • Focusing  on  children  <  60  days • Active  selection  of  most   difficult  cases  (eg exclude   sepsis,  focal  infection) • Identification  of  66-­‐gene  and  a   10-­‐gene  signatures  using   microarrays How  does  the  2-­gene   signature  perform  on   these  data?  
  25. 25. Diagnosis of Bacterial Infection Using a 2-Transcript Host RNA Signature in Febrile Infants 60 Days or Younger Kaforou, Herberg et al JAMA. 2017;317(15):1577-1578. doi:10.1001/jama.2017.1365 Disease Risk Score and ROC Curves Based on the 2-Transcript Signature (combined IFI44L and FAM89A Expression Values)
  26. 26. ? ? Can  we  make  a  simple  diagnostic  test   using  gene  expression  signatures?  
  27. 27. Protein   dipsticks Rapid   sequencing Quantum  dot   nanoparticles Nanostring Gold   nanoparticles Host  responsePathogen Biofire PCR   CRISPR-­based   diagnostic Culture Modular  PCR ….not   forgetting   clinical   features   An   accurate quick   cheap test
  28. 28. • Horizon  2020  PERFORM  grant:   – 5,000  patient  proteomic  &  transcriptomic  validation  study  – ED,   PICU,  ward – Full  range  of  febrile  illness,  recruited  at  point  of  presentation   • Platform  development  for  a  diagnostic  test PERFORMPersonalised managementof febrile illness Horizon  2020
  29. 29. Acknowledgements   Mike  Levin  group,   Imperial  College  (UK)   Myrsini  Kaforou,  Victoria  Wright,   Hannah  Shailes,  Hariklia   Eleftherohorinou,  Clive  Hoggart,   Sobia  Mustafa,  Stuart  Gormley And  the  clinical  teams Micropathology Ltd (UK) Colin  Fink,  Elli  Pinnock,  Ed   Sumner Southampton  (UK) Saul  Faust,  Jenni  McCorkill,   Sanjay  Patel Oxford  (UK) Andrew  J  Pollard Universitario de  Santiago   (Spain) Miriam  Cebey Lopez,  Antonio   Salas,  Federico  Martinón  Torres   and  GENDRES  consortium University  of  California  San   Diego   John  T.  Kanegaye,  Chisato Shimizu,  Adriana  Tremoulet,  Jane   Burns Academic  Medical  Centre,   Amsterdam Anouk  M  Barendregt,  Taco   Kuijpers University  of  Queensland:   Lachlan  JM  Coin,   Funding  at  Imperial:  

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