developing an rna signature predictive of tb progression daniel … - zak... · 2017-03-22 ·...
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Developing an RNA signature predictive of TB progression
Daniel Zak
Center for Infectious Disease Research
Seattle, WA
21ST ANNUAL CONFERENCE OF THE UNION North America Region
Vancouver BC, February 25th, 2017
Who is going to get sick?
(Modified from: Kaufmann 2011)
Prophylactic treatment (standard or shortened?) Therapeutic vaccination
Breakdown of immune control
TUBERCULOSIS DISEASE Kaufmann et al., 2010
LATENT INFECTION
Spectrum of TB progression
Barry CE 3rd, et al. Nat Rev Microbiol. 2009;7(12):845-55. MTB
infection
Spectrum of TB progression
Barry CE 3rd, et al. Nat Rev Microbiol. 2009;7(12):845-55. MTB
infection
“LTBI”
Gene Xpert
Diagnostic tests
Spectrum of TB progression
Barry CE 3rd, et al. Nat Rev Microbiol. 2009;7(12):845-55. MTB
infection
“LTBI”
Gene Xpert
Diagnostic tests
Correlate of risk
(CoR) ?
CONFIDENTIAL
Developing a correlate of risk (CoR) biomarker that is predictive of TB progression
Need to collect samples before people become sick
…this means a large prospective cohort is needed
…which means we need to rely on accessible tissue compartments
…but the measurements must be robust and information rich
…and preferably unbiased
One option: unstimulated whole blood transcriptomes
Benefits
• Accessible: Paxgene and Tempus tubes make collection easy
• Robust: Microarrays, RNA-Seq, and PCR are mature technologies
• Information rich: integrated inflammatory milieu
• Unbiased: RNA-Seq
• Portable: PCR and quantitative real-time PCR (qRT-PCR)
Caveats
• Indirect to disease process (periphery, not lung!)
• Indirect to biological function (mRNAs, not proteins!)
• A complex measurement (cell state + composition)
• A potentially over-simplified measurement
• no intra-personal heterogeneity
• Specificity is ultimately limited
Fortunately, the blood is a great readout for active disease…
latent infection active disease
VS.
latent infection active disease
VS.
393 gene disease signature
Fortunately, the blood is a great readout for active disease…
latent infection active disease
VS.
Fortunately, the blood is a great readout for active disease…
active disease
latent infection
latent infection
latent infection
What about healthy people that have not yet developed TB disease?
vs.
2 yrs 2 yrs
?
Hassan Mahomed, Willem Hanekom, Thomas Scriba, & many others
Hassan Mahomed, Willem Hanekom, Thomas Scriba, & many others
Enrollment (6,000!)
• Latently MTB infected (QFT+ &/or TST+)
• No TB for first 6 months after enrollment
• HIV-negative
• Sample blood every 6 mos
M. tb infection
The SATVI Adolescent Cohort Study (ACS)
Hassan Mahomed, Willem Hanekom, Thomas Scriba, & many others
Enrollment (6,000!)
1.Training (n=36)
2.Validation (n = 8)
Progressors (n = 44)
M. tb infection
Follow up
2 years
The SATVI Adolescent Cohort Study (ACS)
• Latently MTB infected (QFT+ &/or TST+)
• No TB for first 6 months after enrollment
• HIV-negative
• Sample blood every 6 mos
Hassan Mahomed, Willem Hanekom, Thomas Scriba, & many others
Enrollment (6,000!)
Controls (n = 90)
1. Training (n=74)
2. Validation (n = 16)
1.Training (n=36)
2.Validation (n = 8)
Progressors (n = 44)
M. tb infection
Follow up
2 years
The SATVI Adolescent Cohort Study (ACS)
• Latently MTB infected (QFT+ &/or TST+)
• No TB for first 6 months after enrollment
• HIV-negative
• Sample blood every 6 mos
Healthy
Enrollment 6 24 months 12 18
TB p
rog
ress
ors
C
on
tro
ls
TB
24 18 0 months 12 6
Time
before
TB
Analyzing a TB progression cohort
TSS
Detected sequences(“reads”)
Overallcoverage
Splice junctions
Blood transcriptomics!
(RNA-Seq)
While CD14 expression is about equal in progressors and controls…
PTID: 06_0231 (Control) PTID: 06_0127 (Progressor)
TSS
PTID: 06_0231 (Control) PTID: 06_0127 (Progressor)
…SERPING1 expression is much higher in progressors than controls
Non-progressor ProgressorSE
RP
ING
1
APOL1
Considering gene pairs increases specificity
The ACS transcriptional CoR for TB progression
257 pairs
62 primers
16 genes
Genes
ETV7
FCGR1A
FCGR1B
GBP1
GBP2
GBP5
SCARF1
SERPING1
STAT1
TAP1
APOL1
ANKRD22
BATF2
GBP4
SEPT4
TRAFD1
Zak, Penn-Nicholson, Scriba, et al., The Lancet, 2016
6-12mos before TB 60% sensitivity, 80% specificity
How well do we predict TB progression in the ACS?
Zak, Penn-Nicholson, Scriba, et al., The Lancet, 2016
CONFIDENTIAL
Will the ACS CoR work elsewhere?
CONFIDENTIAL
Will the ACS CoR work elsewhere?
CONFIDENTIAL
Will the ACS CoR work elsewhere?
GC6-2013 HHC study
• Design: adult household contacts of TB index cases, followed for 18mos
• Sites: Stellenbosch, South Africa (SUN; PI: Walzl) &
Gambia (MRC; PI: Sutherland)
• Material:
• RNA samples collected at sites (Paxgene)
• Processing , QC, and qRT-PCR performed at SATVI
• Collaborators:
• Gerhard Walzl, Jayne Sutherland
• Stefan Kaufmann, Tom Scriba, Willem Hanekom
• January Weiner, Jeroen Maertzdorf
• Sara Suliman, Katrina Downing
• Tom Ottenhoff, Rawleigh Howe,
• Harriet Manyanya-Kizza
• Bonnie Thiel, Gian Van der Spuy
• Hazel Dockrell, Henry Boom, and many others!
GC6-2013
MPIIB Stefan H. E. Kaufmann (PI) Shreemanta Parida Robert Golinski Jeroen Maertzdorf January Weiner Marc Jacobson Gayle McEwen
Stanford Univ. Gary Schoolnik Gregory Dolganov Tran Van
LUMC Tom Ottenhoff Michel Klein Marielle Haks Kees Franken Annemieke Geluk Krista Meijgaarden Simone Joosten
MRC Gambia Martin Ota Jayne Sutherland Simon Donkor Ifedayo Adetifa Martin Antonio Toyin Togun Philip Hill Richard Adegbola Tumani Corrah
AHRI Rawleigh Howe Adane Mihret Abraham Aseffa Yonas Bekele Rachel Iwnetu Mesfin Tafesse Lawrence Yamuah
EHNRI Desta Kassa Almaz Abebe Tsehayenesh Mesele Belete Tegbaru
UMCU Debbie van Baarle Frank Miedema
Makerere Harriet Mayanja-Kizza Moses Joloba Sarah Zalwango Mary Nsereko Brenda Okware
CWRU W. Henry Boom Bonnie Thiel
KPS Mia Crampin Neil French Bagrey Ngwira Anne Ben Smith Kate Watkins Lyn Ambrose Felanji Simukonda
LSHTM Hazel Dockrell Maeve Lalor Steve Smith Patricia Gorak-Stolinska Yun-Gyoung Hur Ji-Sook Lee
SUN Gerhard Walzl Gillian Black Gian van der Spuy Kim Stanley Daleen Kriel Nelita Du Plessis Nonhlanhla Nene Andre Loxton Novel Chegou
UCT Willem Hanekom Tom Scriba Hassan Mahomed Jane Hughes
AERAS Jerry Sadoff Donata Sizemore S Ramachandran Lew Barker Mike Brennan Frank Weichold Stefanie Muller Larry Geiter
SSI Peter Anderson Ida Rosenkrands Mark Doherty Karin Weldingh
Biomarkers of Protective Immunity against TB in the
context of HIV/AIDS in Africa (GC6-74)
Stefan Kaufmann, Gerhard Walzl, Willem Hanekom, Jayne
Sutherland, Sara Suliman, many others
Controls (n = 198) TB progressors (n = 66)
Enrollment (~3000!)
Household exposure
Follow up
2 years
• Household contact of smear+ TB case
• No TB for first 3 months post-enrollment
• HIV-negative
• Adults
GC6-2013 HHC cohort
AUC
0-360: 0.718
361-720: 0.648
P-value
1.76 x 10-7
0.0048
The ACS CoR validates Blind prediction on GC6-74 adults from SA and Gambia
(407 samples)
Zak, Penn-Nicholson, Scriba, et al., The Lancet, 2016
CONFIDENTIAL
IGRA- IGRA+
Based on SA estimates in HIV-negatives (80% IGRA+, 1% incidence)
Based on QFT sensitivity and specificity from Pai et al., Annals of Int. Med 2008
Can the CoR have a clinical impact?
Tom Scriba
CONFIDENTIAL
TB
cases IGRA- IGRA+
Number treated: 800
Number treated per case averted: 135
Based on SA estimates in HIV-negatives (80% IGRA+, 1% incidence)
Based on QFT sensitivity and specificity from Pai et al., Annals of Int. Med 2008
Can the CoR have a clinical impact?
Tom Scriba
CONFIDENTIAL
CoR-
CoR+
Based on CoR sensitivity and specificity at 60% vote threshold for 0-12
months, 1% incidence
Tom Scriba
Can the CoR have a clinical impact?
CONFIDENTIAL
CoR-
CoR+
TB
cases
Based on CoR sensitivity and specificity at 60% vote threshold for 0-12
months, 1% incidence
Number treated: 293
Number treated per case averted: 43
Tom Scriba
Can the CoR have a clinical impact?
Genes
ETV7
FCGR1A
FCGR1B
GBP1
GBP2
GBP5
SCARF1
SERPING1
STAT1
TAP1
APOL1
ANKRD22
BATF2
GBP4
SEPT4
TRAFD1
From a biomarker to hypothesis generation What is special about the genes in the ACS CoR?
Zak, Penn-Nicholson, Scriba, et al., The Lancet, 2016
Genes
ETV7
FCGR1A
FCGR1B
GBP1
GBP2
GBP5
SCARF1
SERPING1
STAT1
TAP1
APOL1
ANKRD22
BATF2
GBP4
SEPT4
TRAFD1
Zak, Penn-Nicholson, Scriba, et al., The Lancet, 2016
From a biomarker to hypothesis generation What is special about the genes in the ACS CoR?
STAT1
Kinetics of gene expression during TB progression
Orchestrated waves of gene expression during TB progression
ACS CoR
genes
Inflammation
module genes
Interferon
module genes
The CoR genes are expressed in broad leukocyte populations in active disease
Microarray data from:
Zak, Penn-Nicholson, Scriba, et al., The Lancet, 2016
Monocytes Neutrophils CD4+ T cells CD8+ T cells PBMC WB
CONFIDENTIAL
Questions and implications
Is this subclinical disease?
Would TB prophylaxis prevent progression?
Was there a viral “trigger” to this process?
Can we improve the prediction accuracy?
Can we make it cost-effective?
Will it still predict in the presence of HIV co-infection?
Does it tell us anything about the response to treatment?
PET/CT-based measurement of disease resolution/non-resolution after bacteriological cure as shown in:
The RNA CoR predicts stratifies different treatment responses before, during, and after treatment
CONFIDENTIAL
Collaborators
Thomas Scriba Adam Penn-Nicholson
Sara Suliman Katrina Downing
Gerhard Walzl
Jayne Sutherland
MPIIB Stefan Kaufmann
Willem Hanekom
Zak Lab
Ethan Thompson Fergal Duffy
Smitha Shankar Ying Du
Joe Valvo Jackie Braun
Aderem Lab Alan Aderem Lynn Amon