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Non-Cultural Methods
for Aspergillosis
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Kieren A. Marr MD
Professor of Medicine, Johns Hopkins School of Medicine
Professor of Oncology, Sidney Kimmel Comprehensive Cancer Center
Professor of Business, JH Carey School of Business
Director, Transplant and Oncology Infectious Diseases
Disclosures
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• Consultant / advisory board
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– Astellas, Merck, Pfizer
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• Research grantne
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– Astellas, Merck
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• Licensed
technology / patent
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– MycoMed
Technologies
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Non-Culture based Methods
• Background
• Available methods
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• Methods in development
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• Remaining
problems
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– Lessons learned
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Background: Non-cultural
methods
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• Detection of a biomarker
• A biomarker is anything that can be used as
an indicator of a particular disease state or
some other physiological state of an
organism
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– Diagnose
disease
risk
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Detect theupresence of a disease
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endpoint to measure therapy
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Background: Diagnosis of IFI
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• Culture of an organism is actually a biomarker
• Antigen-based diagnostics have been used for
many infections
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– PJP, Histoplasma, Coccidioides, Cryptococcus
• IA: Culture sensitivity for Aspergillus in lavage and tissues
is poor (<50%)
– Yet.. Considered “gold standard”
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• Technology
is appropriately
altering the way that
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a
aspergillosis
Ewe diagnose
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©
Commercially available tests
• Country specific
• Antigen – based assays
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– Galactomannan Enzyme Immunoassay (GM EIA)
• Serum, BAL
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• Blood
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Molecular assays
(PCR)
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– Blood, y
compartments (BAL, tissue)
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– b-glucan
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Galactomannan
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•
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Linear core of mannan with a1,2 and a1,6
elinkages
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Platelia Aspergillus: EbA2 detects antigenic
side chain of b1,5
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galactofuronosyl (multiple epitopes)
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– Double sandwich ELISA; monoclonal
IgM
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Serum
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BAL
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Performance of GM EIA
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• A lot of controversy, still
• Historical literature is irrelevant
– Utilized inappropriate (high) cut-offs
– Difficulty interpreting clinical studies
• Better studies demonstrate utility, especially when integrated
into clinical context, and used with other markers
– Screening in high-risk patients
– Adjunct to diagnosis in people with high probability of
infection– BAL and serum
– To measure clinical response
• History with this assay has taught a lot of lessons
8
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Lesson #1. Do it right from the
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beginning
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• Early failure to define appropriate endpoints for this test
hampered progress
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– Cut-off shifted from 1.5  1.0  0.5
• Sensitivity / Specificity using this cut-off: 80% / 80%
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ROC CURVE
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100
Sensitivity (N=24 Pts.)
90
0.2
0.4
0.1
0.0
0.3
80
Timing (proven / probable)
Cut-off
1.5
Cut-off
1.0
Cut-off
0.5
IA diagnosis
-1 days
-1 days
-10 days
Clinical
onset
0 days
0 days
-6 days
0.5
70
0.6
0.8
60
1.0
50
1.5
40
30
20
10
0
0
10
20
30
40
50
60
70
80
1- Specificity ( N=472 sera)
90 100
Marr et al. J Infect Dis (2004) 190(3): 641-9
Leeflang et al. Cochrane Database Syst Rev 2008 9
Lesson #2. Analyses need to
account for disease
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Difficulty in assessing DISEASE vs. NO DISEASE
• Presence of organism does not necessitate disease  Risk
Natural history of infection not well understood
• When do people become infected?
If culture is the gold standard, does this person really have disease?
• What is a false-positive vs. false negative test?
Marr and Leisenring. Clin Infect Dis 2005:41: S381
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Statistics
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• If timing of the test matters, antifungals
effect
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performance, and multiple
tests are taken
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per patient, then per-patient
analyses are
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difficult to interpret
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– Per patient: one
person has disease
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• Case vs. control
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• Timing of test,o
and
other time-dependent variables not
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accounted
for
appropriately
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analyses
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• Need
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Per-test analyses
•
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Allow depiction of changes in sensitivity according
to timing
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relative to clinical diasnotis and antifungals
(fungal burden)
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No antifungals
With antifungals
Marr et al. Clin Infect Dis 2005; 40:1762 - 9
What do you do when you have
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a sub-optimal “gold standard”? bra
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• Our tests now work better than old gold-standards
(culture, histopathology)
• One solution: Have a consensus committee define
disease using new (accepted) tests in a syndromic
definition: EORTC/MSG: possible, probable, proven
– Arbitrary (opinion)
– Definitions derived for treatment trials
(conservative)
– Definitions not validated using large databases
– What happens if method is better than those
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included?
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Imperfect gold standards
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– Proven / probable (informed by
radiography, clinical
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signs / symptoms, culture of
respiratory tract,
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histopathology and galactomannan
(serum and
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BAL) defines IA lin
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• If sensitivity is
< 100%, specificity of a new diagnostic will
O
be falselyD
low
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• If specificity
is < 100%,
specificity will be falsely low
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NewSscience
(statistical) solution
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– Bayesian
latent class models (calculates results
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without
© assuming a gold-standard)
• Convention
•
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“New” Developments: GM EIA
•
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Continued understandings and uses
of the
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galactomannan EIA
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– Galactomannan (or polysaccharides
that crossL
react with the EbA2 antibody)
is present in many
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sources
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• Non-Aspergillus
O fungi (ex. Histoplasma, Fusarium)
D Food
• Blood products.
products (popsicles)
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• Antibiotics
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– Biological
vs.
technical (test) false-positive
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Nucci et al. PLoS One 2014
Guigue et al. NEJM 2013; 369(1)
Martin-Rabadan et al. Clin Infect Dis 2012 55(4) 15
“New” Developments: GM EIA
•
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BAL
– Early studies (HSCT): Increased sensitivity compared to culture
– Cut-off 0.5 – 1.0 (depending on study / population evaluated)
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Pooled SEN
(95% CI)
Pooled SPE
(95% CI)
Overall analyses 13
0.90 (0.79-0.96)
0.94 (0.90-0.96)
Cutoff of 0.5 for
positivity
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0.86 (0.70-0.94)
Cutoff of 1 for
positivity
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Cutoff of 1.5 for
positivity
Cutoff of 2 for
positivity
Studies
No.
Studies
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Pooled PLR
(95% CI)
Pooled NLR (95% CI)
14.87 (8.8924.90)
0.10 (0.04-0.24)
0.89 (0.85-0.92)
7.69 (5.7510.28)
0.15 (0.07-0.35)
0.85 (0.72-0.93)
0.94 (0.89-0.97)
14.29 (8.3324.50)
0.16 (0.08-0.31)
9
0.70 (0.49-0.85)
0.96 (0.93-0.98)
18.97 (10.9332.93)
0.31 (0.17-0.57)
5
0.61 (0.38-0.80)
0.96 (0.92-0.98)
16.13 (8.0732.25)
0.40 (0.23-0.70)
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Becker MJ, Br J Haematol 2003
Musher et al, J Clin Microbiol 2004
Maertens et al, Clin Infect Dis 2009
Guo et al, Chest 2010
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BAL and Serum
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• Single center cohort of
210 allogeneic HSCT
• All methods have
independent contributions
to diagnosis
• BAL GMI sensitivity not
impacted by antifungals
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Fisher et al. Transplant Infect Dis 2014
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GM EIA: “Funny” Issues
• Interpretation of result based on
OD sample / OD threshold
control (TC is said to be serum
sample with predicted
concentration of 1ng/mL)
• Significant lack of reproducibility
associated with different TC
results from lot to lot
• Sample to sample variability
observed in several studies
(high positive / low negative
from run to run)
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Upton et al. J Clin Microbiol 2005
Oren et al. Transplant Infect Dis 2011
Furfaro et al.; Bizzini et al Transplant Infect Dis 2012
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“New” Developments: b-D glucan
• Nature of marker: Less specific
than galactomannan
• Assays differ worldwide (in
reagents and cut-offs)
• Cut-offs established for Candida
infections in neutropenic
patients
• Comparative GM vs. BDG study
in 105 patients
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– GM more specific; BDG more
sensitive (high FP rate with
bacteremia)
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Sulahian et al. J Clin Microbiol 2014
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PCR - Blood
• Many assays, different
performance, commercial
availability & clinical use varies
across countries
• Blood: Meta-analysis
– 10,000 blood, serum or
plasma from 1618 patients
from 16 studies
– Sens 75%; Spec 87%
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Mengoli et al. Lancet Infect Dis 2009
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PCR - BAL
• Meta-analysis of17 studies with
1191 patients
– Sens 91%, Spec 92%
• Comparative studies vary in
conclusions about performance
relative to GM EIA
• Commercial assays available
with good data
• Combination of markers may
provide best results
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Sun et al. PloS One 2011
Guinea et al. PloS One 2013
Torelli et al. J Clin Microbiol 2011
Heng et al. Diag Microbiol Infect Dis 21
2014
New Tests
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• Aspergillus-LFD: JF5 – extracellular glycoprotein
• Small prospective studies on serum assay – comparable to GM
EIA for screening; lower sensitivity if need confirmatory result
• BAL - comparison of 4 methods in BAL (culture, GM, PCR,
LFD). Sens 70 – 88%; performance best with combination
• Caution: all these results are biased towards GM if that is
accepted in definitions of gold-standard “proven / probable”
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Wiederhold et al. J Clin Microbiol 2013
Held et al. Infection 2013
White et al. J Clin Microbiol 2013
Hoenigl et al. J Clin Microbiol 22
2014
In Development
• Urinary GM – like
antigens
– MAb476
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• Electronic nose
technology
• HPLC-MS/MS
• PCR – MS
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Dufresne et al. PloS One 2012
De Heer et al. J Clin Microbiol 2013 23
Cerqueira et al. PLoS One 2014
Many Molecular Platforms
• At this meeting:
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– Several qPCRs
– PCR that also detects resistance determinants
– Detection of gliotoxin
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Conclusions
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D isonotr going to help
I
– A bigger telescope
you findM
the next tplanet
when you don’t
h
C
u
know
how to use
it
S
a
–E
Better understanding
of disease, risks
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b of analysis
and methods
©
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• Culture based diagnostics -poor
• Markers are a mandatory adjunct to
culture
• Disease is not well understood
• Better technology is not the only
answer
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Thank you n
O
Dhave aogold
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Do we really
standard that can support
M
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analyses
of newttechnologies
without bias?
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©
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