Developing an RNA signature predictive of TB progression Daniel … - Zak... · 2017-03-22 · Developing an RNA signature predictive of TB progression Daniel Zak Center for Infectious

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



infection

“LTBI”

Gene Xpert

Diagnostic tests



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.


VS.

393 gene disease signature



VS.


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


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)


Enrollment (6,000!)

1.Training (n=36)

2.Validation (n = 8)

Progressors (n = 44)

M. tb infection

Follow up

2 years




• HIV-negative



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




• HIV-negative


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?


CONFIDENTIAL

Will the ACS CoR work elsewhere?

CONFIDENTIAL


CONFIDENTIAL


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)


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


Tom Scriba

CONFIDENTIAL

CoR-

CoR+

Based on CoR sensitivity and specificity at 60% vote threshold for 0-12

months, 1% incidence

Tom Scriba


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


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?


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?

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:


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

Documents

Developing an RNA signature predictive of TB progression Daniel … - Zak... · 2017-03-22 · Developing an RNA signature predictive of TB progression Daniel Zak Center for Infectious