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

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. 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. 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. We could give antibiotics to everyone, just to be safe… …in fact we do… (almost)

5. Multidrug resistant Klebsiella pneumoniae, ECDC EARS-Net http://ecdc.europa.eu/en/healthtopics/antimicrobial_resista nce/database

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. 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. 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. 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. 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. 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. 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 upregulated probes Pooled downregulated probes+ Disease Risk Score or =

13. H1N1 RSV Disease Risk Score separates H1N1 and RSV Pooled upregulated probes Pooled downregulated probes+ Disease Risk Score or =

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. 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. 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. 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. 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. 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 downregulated probes + Disease Risk Score or =

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. Give antibiotics Uncertain role for antibiotics No need for antibiotics Could 2-transcript test be basis of “traffic light” test for antibiotic treatment?

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. 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. 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. 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. ? ? Can we make a simple diagnostic test using gene expression signatures?

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. • 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. 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:

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

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

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