Gene transcript profiles to diagnose infectious diseases. ESCMID Conference on Diagnosing Infectious Diseases: Future and Innovation

Transcription

1 Gene transcript profiles to diagnose infectious diseases ESCMID Conference on Diagnosing Infectious Diseases: Future and Innovation October 2011, Venice, Italy Matthew Berry Consultant Respiratory Physician, Department of Respiratory Medicine, Imperial College Healthcare NHS Trust Associate Clinician Scientist, Division of Immunoregulation, MRC National Institute for Medical Research

2 Using the host response to diagnose disease Chaussabel et al. BMC Biology :84

3 Whole Blood mrna transcript Profiling 1. Isolate RNA 2. Amplify & Label RNA 3. Microarray Reverse transcribe cdna Precipitate Purify Whole Blood in Stabilising Tube Extract total RNA Remove Globin mrna DNA polymerase Double stranded DNA Transcription template RNA polymerase In Vitro Transcription Amplified, Labelled crna Hybridise crna to Probes on array Scan arrays Process & Analyse

4 Heatmaps: A microarray data primer 1 Gene 1 Gene 2 Gene 3 Gene 4 Gene 5 Gene 6 Gene 7 Gene 8 Patient 1 Patient 2 Patient 3 Patient 4 Patient 5 Patient 6 Patient 7 Patient 8 Each row = transcript Each column = patient (or control) Transcript abundance for a given gene shown relative to baseline: median of samples across that gene compared to control levels High Median Low

5 Microarray reveals distinct host responses to pathogens Distinct dendritic cell responses to bacteria, fungi and viruses Signatures of Tuberculoid or Lepromatous leprosy skin lesions Huang, et al. Science 294, 870 (2001) Bleharski, et al. Science 301, 1527 (2003)

6 Infections: whole blood transcript profiles distinguish Malaria from other febrile illnesses 2005 Infectious Diseases Society of America Griffiths M J et al. J Infect Dis. 2005;191:

7 Training & Test sets Define diagnostic / predictive transcript signature on set of patients with known disease classification: Training Set Validate diagnostic / predictive transcript signature on set of patients with blinded disease classification: Test Set

8 Training & Test sets Define diagnostic / predictive transcript signature on set of patients with known disease classification: Training Set Validate diagnostic / predictive transcript signature on independent set of patients with blinded disease classification: Test Set Unblind to assess accuracy

9 Infections: PBMC 10-gene transcript signature of HIV-1 Pre-treatment Some on antiretrovirals 2005 Infectious Diseases Society of America Ockenhouse C F et al. J Infect Dis. 2005;191:

10 . Transcript profiles distinguish bacterial & viral illnesses Training set classifier Test set 1: 95% accuracy Test set 2: Staph: 87% accuracy 2007 by American Society of Hematology Ramilo O et al. Blood 2007;109:

11 . Transcript profiles distinguish bacterial infections Test set: 85% accuracy Is this due to differences in site of infection? 2007 by American Society of Hematology Ramilo O et al. Blood 2007;109:

12 . Transcript profiles distinguish viral & bacterial lower respiratory tract infection Test set on different array platform 2007 by American Society of Hematology Ramilo O et al. Blood 2007;109:

13 TB is a complex spectrum of disease with many unanswered questions & needs: can transcriptomics help? 9 million cases/yr 2 million deaths/yr 2 billion infected

14 Recruitment of a training set to define a transcriptional signature of TB ALL PRE-TREATMENT 59 participants recruited 43 participants meeting final study criteria Controls 12 IGRA- / TST - Training Set (London, UK) 42 samples Latent TB 17 IGRA+ / TST + 1 sample insufficient RNA after processing Active TB 13 Culture + 15 excluded: did not meet final study criteria: 1 patient over original age exclusion, 5 Active TB culture negative; 7 Latent TB negative IGRA; 2 controls TST positive, 1 control IGRA indeterminant

15 A 393-transcript whole blood signature of active TB MPR Berry et al. Nature 466, (2010) Training Set London

16 Recruitment of a test set to validate the transcriptional signature of TB ALL PRE-TREATMENT IGRA- / TST - IGRA+ / TST + Culture +

17 Does the signature repeat in an independent cohort? MPR Berry et al. Nature 466, (2010) Test Set London

18 The 393-transcript signature of active TB is robust MPR Berry et al. Nature 466, (2010) Test Set London

19 The signature is significantly associated with clinical classification but independent of other characteristics p = MPR Berry et al. Nature 466, (2010)

20 The signature is significantly associated with clinical classification but independent of other characteristics p = MPR Berry et al. Nature 466, (2010)

21 MPR Berry et al. Nature 466, (2010) What explains the active TB outliers?

22 Transcriptional signature reflects the radiographic extent of disease MPR Berry et al. Nature 466, (2010)

23 The signature is diminished during successful therapy MPR Berry et al. Nature 466, (2010)

24 Transcript profiles: insight into pathogenesis of infectious disease TB immune response involves more than T cells and IFN-γ! MPR Berry et al. Nature 466, (2010)

25 TB is a Global Disease: is the signature seen in endemic countries?

26 Recruitment of a validation set in South Africa to confirm the transcriptional signature of TB persists in endemic countries ALL PRE-TREATMENT IGRA+ Culture + With Tolu Oni, Katalin Wilkinson and Robert Wilkinson

27 The signature is significantly associated with clinical classification but independent of other characteristics, even in an endemic country p = MPR Berry et al. Nature 466, (2010)

28 What explains the latent TB outliers? 5/21 Latent test set, 3/31 latent validation set cluster with active TB patients: i.e. have Active TB signature Could these be the 10% who will progress? MPR Berry et al. Nature 466, (2010)

29 Significance analysis derives disease specific signatures

30 Significance analysis derives disease specific signatures

31 Significance analysis derives disease specific signatures

32 A TB specific 86-transcript signature? Derived from test set Training set: 92% Validation set: 90% Pooled Specificity: 83% MPR Berry et al. Nature 466, (2010)

33 But.. 86-transcript signature does not distinguish from Sarcoidosis Koth et al, online; doi: /rccm oc

34 Koth et al. derive separate classifier distinguishing Sarcoid and TB Needs Test set Koth et al, online; doi: /rccm oc

35 New technologies more applicable to clinical use? Chaussabel et al. BMC Biology :84 Digital / Quantitative / Increased sensitivity No need for amplification True transcriptome wide profile No need for probe design

36 Examples of other infectious diseases with whole blood transcript signatures S.aureus BACTERIA Ardura, PLoS One, 2009 B.pseudomallei Pankla, Genome Biol, 2009 Other TB studies Jacobsen, J Mol Med, 2007 Mistry, JID, 2007 Maertzdorf, Genes Immun 2010 VIRAL Dengue Long, JID, 2009 Adenovirus vs. Kawasaki Popper, JID, 2009 H1N1 influenza Berdal, J.Infect, 2011

37 200µl 1ml whole blood Summary Insight into complex disease IFNs / neutrophils TB New vaccine/ therapeutic targets Diagnosis Whole blood whole genome transcriptional signature Independent of age, ethnicity, gender, geography Needs cross-sectional study with Sarcoid / Lung cancer / Pneumonias to derive specific signature Monitor response to therapy Signature is diminished by successful treatment Predict prognosis? Some latent patients have this signature (10%) Needs longitudinal studies

38 Anne O Garra Chris Graham Fin McNab Healthy Volunteers Funding: Acknowledgements The Organisers and Faculty, ESCMID Onn Min Kon TB clinical team Susannah Bloch Damien Chaussabel Jacques Banchereau Virginia Pascual Ranju Dhawan Zhaohui Xu Susannah Bloch Qunyh-Anh Nguyen The Patients Jason Skinner Univ Cape Town, IIDMM Robert Wilkinson Katalin Wilkinson Tollulah Oni