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Copyright © 2015
SCK•CEN
Gene and exon signatures as
radiation biomarkers
Roel Quintens
DoReMi Biomarker workshop
April 20, 2015
HMGU, Munich, Germany
Copyright © 2015
SCK•CEN
Presentation outline
Radiation biomarkers
What?
Why?
Which?
Gene expression signatures
Gene versus exon signatures
Suitability for real biodosimetry
Different radiation qualities
Further perspectives
Conclusions
Considerations for mass casualty screening
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What are radiation biomarkers?
Biomarker: Any measurement reflecting an interaction between a
biological system and an environmental agent, which may be chemical,
physical or biological.
Biological system Environmental agent Result of interaction
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Why do we need radiation biomarkers?
Biodosimetry
Dose optimisation
Exposure/Health effects/Susceptibility
Crew selection
Pernot et al., Mut Res 2012
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Which radiation biomarkers exist?
Pernot et al., Mut Res 2012
Cytogenetics Epigenomics
Inherited
mutations
Induced
mutations
Others DNA damage
Transcription/translation
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Why do we need new biomarkers?
Validated
Specific to radiation
Sensitive to doses of 0,1-5,0 Gy
Dicentric chromosome assay – the “gold standard”
But…
Laborious
Experienced personnel needed
Not sensitive to doses below 0,1 Gy
Lloyd and Purrot, Radiat Prot Dosim 1981
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Gene expression signatures as radiation biomarkers
Dose-dependent induction of p53-regulated genes up to 72h after
exposure to 0.2-2.0 Gy Amundson et al., Rad Res 2000
4 hours 24 hours
48 hours 72 hours
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Gene expression signatures as radiation biomarkers
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Consistent results between different studies
Radiation-responsive gene expression signatures are very
comparable between different studies, independent of
• platform used (qRT-PCR, qNPA, NanoString, microarrays)
• time after irradiation (up to ~48-72 h)
• radiation dose
• radiation quality (X-rays, g-rays, a-particles)
• cell/tissue/species type (whole blood, PBMCs, fibroblasts, human,
mice)
Gene expression signatures are very suitable biomarkers
for the (early) response to radiation exposure
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Gene- versus exon-level analysis of radiation exposure
Exp
ressio
n s
ign
al
GeneChip
0.0 Gy
0.1 Gy
1.0 Gy
Affymetrix Human Gene 1.0ST
~28,000 genes
~253,000 exons
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Gene- versus exon-level analysis of radiation exposure
Exp
ressio
n s
ign
al
GeneChip
0.0 Gy
0.1 Gy
1.0 Gy
Affymetrix Human Gene 1.0ST
~28,000 genes
~253,000 exons
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Radiation-induced alternative splicing (FDXR)
Macaeva et al., In preparation
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Prediction analysis
Assessing model performance using k-fold cross-validation (10-,
5- and 2-fold) • Divide the dataset in k partitions
• Leave out one of the k partitions as test set, and use the other k-1 partitions for training
• Samples from the same donor must be in the same partition 2-fold CV (252 models)
Classification models:
• Generalized linear models (GLM)
• Nearest shrunken centroids (PAM)
• Random forests
Variable selection: 2, 5, 10, 20, 50, 100 or all variables
Predictive performance was calculated using the Area Under the
ROC Curve (AUC), in which 0.5 corresponds to random prediction
behavior and 1 represents optimal model performance.
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Gene and exon signatures as classifiers
Equal performance of gene and exon signatures in predicting radiation exposure to
doses of 0.1 and 1.0 Gy
Model 2 5 10 20 50 100 ALL
G E G E G E G E G E G E G E
GLM Net 0.985 0.942 0.965 0.926 0.932 0.922 0.949 0.946 0.953 0.952 0.952 0.951 0.959 0.953
Random Forests 0.985 0.968 0.998 0.993 1.000 0.998 1.000 1.000 1.000 1.000 1.000 1.000 0.918 0.955
PAM 0.999 0.997 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.997 1.000 0.999 1.000
G – gene level analysis
E – exon level analysis
Prediction analysis performed in collaboration
with Prof. Yvan Saeys (UGent)
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Suitability of gene/exon signatures for real biodosimetry
RENEB – Interlaboratory comparison experiment qRT-PCR (2 labs) versus microarrays (two labs)
Peripheral whole blood, ex vivo irradiation, RNA extraction 24 h post-IR
Calibration samples: 2 donors, 7 doses (0, 0.25, 0.5, 1, 2, 3, 4 Gy)
Blind samples: 5 donors, 10 unknown doses
Examine interindividual variation by using blood samples from different donors
Endpoints: - Exposure status (yes/no)
- Dose estimates
- Response time
Preliminary results: - Exposure status: All labs 100% correct
- Dose estimates: Very good below 2 Gy
- Response time: - qRT-PCR: ~7-9 h
- Arrays: ~35-45 h
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Prediction of high-dose exposure
RENEB interlab comparison: difficulties for predicting doses >2 Gy due to
plateau in transcriptional response at high doses.
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Prediction of high-dose exposure
RENEB interlab comparison: difficulties for predicting doses >2 Gy due to
plateau in transcriptional response at high doses.
Specific exon signatures may be more suitable
for predicting high-dose exposure
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Exon signatures as biomarkers for radiation quality
Expression levels of different variants are not completely comparable between different radiation
qualities Radiation-type specificity?
0
5
10
15
20
25
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Fo
ld c
ha
ng
e
Probe set
X-rays
C-ions
3
4
5
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8
9
Ex
pre
ssio
n (
log
2)
0 Gy
1 Gy
X-rays
C-ions
3
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5
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Ex
pre
ss
ion
(lo
g2)
0 Gy
1 Gy
Rank-rank analysis
1.0 Gy
0.0 Gy
X-rays
0.0 Gy 1.0 Gy C-ions
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Customized biodosimetry platform
Microarrays are an invaluable tool for whole-genome radiation
response studies but…
• …too expensive
• …highly dependent on the sample quality (RNA)
• …results are easily influenced by too many (technical) factors
• …rather long response time
• …processing equipment is not available in the majority of the labs
• …data analysis is complex
• …the vast majority of the genes are not responsive to radiation
not suitable for use in large-scale accidents.
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Customized biodosimetry platform: Qiagen RT2 Profiler PCR arrays
Customized panel of 24 genes
chosen based on the prediction
analysis results (responsive to
X-rays, carbon and iron ions)
For the genes exhibiting
alternative splicing, the exons
with the highest ratio of up-
regulation were chosen
Test lower doses (e.g. 25, 50
mGy) and more time points (up
to 48 h) on a larger cohort of
donors (300 samples)
DoReMi ad hoc funding
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Conclusions
Genome-wide expression analyses have identified the most appropriate
gene expression biomarkers for (early) response to radiation exposure
Exon-level data indicate improved sensitivity of certain exons (especially
for low-dose exposure; specific for radiation quality?)
Genome-wide analysis is not suitable for large-scale accidents
Cost
Response time
High amount of non-informative datapoints
For any primer-based method of mRNA quantification, knowledge
about the exon-level expression is pivotal!
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Important considerations for mass casualty biodosimetry
The transcriptional response to radiation is transient (72 h max)
Samples should be taken rather quickly after exposure
It would be rather easy to obtain control samples from the same individual
The transcriptional response to radiation mainly depends on p53
activation
Not very radiation-specific (can be activated by other stresses)
Gene/exon signatures seem to be very sensitive
Important for the large public who received (very) low doses
Gene/exon expression can be measured
- from small biological samples
- relatively easily
- fast
Gene/exon signatures are useful as biomarkers for radiation exposure,
possibly in combination with other markers.
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Acknowledgements
SCK•CEN
Ellina Macaeva
Kevin Tabury
Ann Janssen
Arlette Michaux
Mercy Njima
Sarah Baatout
VIB – Plant Systems Biology
Yvan Saeys
GSI
Nicole Averbeck
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Study objectives
Gene/exon expression
Cytokine expression
Biomarkers of radiation exposure
DNA damage (gH2AX) DNA repair kinetics
Biomarkers of individual
radiation sensitivity
Identify new biomarkers (genes, exons, cytokines)
Biodosimetry for exposure to low doses of high- and low-LET radiation
Predict individual radiosensitivity
Obtain more insight into the early biological effects of radiation
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Gene and exon signatures as classifiers
Gene level 2 5 10 20 50 100 All
GLM 0.985 0.965 0.932 0.949 0.953 0.952 0.959
Random Forest 0.985 0.998 1.000 1.000 1.000 1.000 0.918
PAM 0.999 1.000 1.000 1.000 1.000 0.997 0.999
PAM + Random
Forest
1.000 1.000 1.000 1.000 1.000 1.000 0.917
Exon level 2 5 10 20 50 100 All
GLM 0.942 0.926 0.922 0.946 0.953 0.951 0.953
Random Forest 0.968 0.993 0.998 1.000 1.000 1.000 0.955
PAM 0.997 1.000 1.000 1.000 1.000 1.000 0.999
PAM + Random
Forest
0.997 0.999 1.000 1.000 0.999 1.000 0.953
Equal performance of gene and exon signatures in predicting radiation exposure to
doses of 0.1 and 1.0 Gy
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Prediction of high-dose exposure from RENEB samples
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Radiobiology Unit @ SCK•CEN
Head of unit: Prof. Dr. Sarah Baatout
7 scientists/post-docs
5 lab technicians; 2 animal caretakers
8 PhD students
~10 master/bachelor students per year
Main research lines:
Biodosimetry
Radiation effects on brain development and neural tube closure
Cardiovascular effects of radiation exposure
Radiation-induced cancer (thyroid) and cancer treatment
(radiopharmaceuticals, hadron therapy)
Effects of space stressors on the immune system
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Biodosimetry @SCK-CEN – Study objectives
To determine and to compare the effects of different radiation
qualities using gene expression as biomarker
To identify more robust biomarkers of exposure to low-doses of
IR (transcript variants)
To design a customized biodosimetry platform suitable for a fast
and simple analysis of a large cohort of individuals
To establish the functional basis and to understand the biological
mechanisms of activation of gene expression biomarkers
PhD topic Ellina Macaeva
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A B
Up at 0.1 Gy
Genes Exons0
1
2
3
4
5
p = 0.08
FC
mR
NA
exp
ressio
n
Up at 1.0 Gy
Genes Exons0
1
2
3
4
55
101520 ***
FC
mR
NA
exp
ressio
n
A B
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