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Discovery of proteic biomarkers for Crohn Disease
and Ulcerative Colitis by SELDI-TOF-MS
1Laboratory of Medical Chemistry and Human
Genetics, CTCM, CHU Sart-Tilman, 4000 Liege 2
Stochastic Methods, Sart Tilman 4000 LG. 3
Hepato-Gastroenterology. CHU Sart Tilman Lg. 4
Clinical Sciences- Rhumatology, CHU, Sart Tilman,
Lg. CBIG Université de Liège.
INTRODUCTION
Technic SELDI -TOF-MS
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Crohn Disease ( CD) and Ulcerative Colitis (UC)
both generally known as Inflammatory Bowel
Disease (IBD) are chronic autoimmune inflammatory
pathologies affecting the gastro intestinal
tract. Their ethiopathogenesis has not been fully
elucidated and involve a complex interplay among
genetic, environmental, pathogenic and immune
factors. The still growing knowledge in the
ethiology of these disorders gave rise to new
promising treatments. Nevertheless, the success
of those drugs are cases dependent CD or UC.
Therefore, accurate and early diagnosis is a real
important step in circumventing these
pathologies. Today, clinical diagnosis are made
on many biological data such as CRP, ASCA, ANCA
determinations, according to severity of symptoms
( recorded in Harvey-Bradshow test) or with
invasive techniques as gastro-endoscopies. No
simple, rapid, unique and efficient technique is
able to discriminate IBD from other inflammatory
diseases (as infectious colitis) or among IBD
itself CD versus UC. Here, we present a strategy
of sera protein profiling on SELDI-TOF ( Surface
Enhanced Laser Desorption-Ionization, Time of
Flight Mass Spectrometry ) and the downstream
statistical analysis.
Methods Protein profiling by SELDI-TOF-MS
(Surface Enhanced Lazer Desorption Ionisation
-Time Of Flight - Mass Spectrometry)
Different types of surfaces interactions Ion
exchange, hydrophobe, normal phase, IMAC,
(affinity antibody capture)
Sera sample loadded on chip in specific
conditions ( pH, salinity, detergent)
Washing steps discarding unbonded material
Addition of energy absorbing molecule, EAM
To discover powerful biomarkers able to
discriminate sera of patients suffering from
CROHN disease CD, from patients UC (Ulcero
Hemoragic Recto Colitis) or sera from IBD
patients (CD and UC) from other inflammatory
diseases (IC) and healthy controls (HC).
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AIMS
Trace View
 reading  of chip
IBD
PROFILES
Laser
HC
Detector
TOF-MS
Gel View
Comparaisons of profiles on  ion exchange 
surfaces CM10 Q10 CROHN (C) - 30
samples UHRC (UC) - 30 samples healthy controls
(HC) - 30 samples non IBD inflammatory controls
(IC) - 30 samples 120 samples, in
quadruplicate, on 2 different surfaces 960
spectra !!!!!!!!!!!!!!! Many comparisons CD
vs UC CD vs (HC, IC, UC) UC vs
(HC, IC, UC) IBD (UC, CD) vs ( IC,
HC)
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STRATEGY
IBD
  pValue  calculation by  Biomarker
Wizards Ciphergen Models of Classification
based on calculations of Decision Trees (unique
or multiple) by different statistical
approches (Geurts et al., 2004) Note
Calculations are made on 2 sets of data
integrated peaks and  raw data  every m/z
increments of the spectra.
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STATISTICS
Probability of missclassification - good if
lt10 -3
statistical analysis
Sensitivity (true positives) and Specificity (
true negatives) of the models Multiple decision
trees by boosting and extra tree are the best
methods and are selfs validated
RESULTS
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Specificity and sensitivity of the proposed
models by  boosting 
Classification of m/z values used to build this
model with  boosting  and associated
 pValue  illustrating their power of
discrimination
comparison Q10 CM10
specificity sensitivity specificity
sensitivity CD vs UC 80.83 81.67
85.00 90.83 CD vs (HC, IC, UC) 95.83
70.83 92.76 77.50 UC vs (HC, IC,
UC) 92.22 37.50 96.38 43.33 IBD
(UC, CD) vs ( IC, HC) 88.75 82.92 90.42
86.25
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• Q10
• CD vs UC 1.26 10 8
• CD vs IC lt10 12 et UC vs IC lt10 12
• 4827 CD vs UC lt10 12 et IC 6.10 10
• 12608 CD vs UC 8.10 6 et IBD vs IC 1.76
  10 5

CM10 2663 CD vs UC lt10 12 2681 CD vs UC lt10
12 5822 CD vs UC lt10 12 8604 IBD vs IC lt10
12 et CD vs UC1.33 10 6
Q 10 Intensity mean of peaks per categories
CM10 Intensity mean of peaks per categories
Importance of each value of m/z on each surface
and for every comparisons
Validation of our  models on a new batch of
samples (10 of each categories) Correlation
with existing tests (ANCA - ASCA CRP)
Purification of the  more powerful  proteins in
terms of discrimination and their identification
by MS-MS.
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PERSPECTIVES
m/z Values peaks proteins are not so
powerful in discriminating taken independently.
All together combined in  models , they are a
lot more efficient and discrimination of samples
is obtained with good scores of specificity and
sensitivity. Combination of the 2 models
corresponding to the 2 surfaces Q10 and CM10
would may be increase specificity and sensitivity
!?!