Download Antidepressant Response Biomarkers: Lessons from

Antidepressant Response Biomarkers:
Lessons from Lymphoblastoid Cell Lines
Ayelet Morag1, Varda Oron-Karni2,
Metsada Pasmanik-Chor2, Moshe Rehavi1,
Julia Stingl3 and David Gurwitz1,4
1Sackler
Faculty of Medicine & 2Faculty of Life-Sciences,
Tel Aviv University, Israel
3Institute of Clinical Pharmacology, University Ulm,
Germany
4Funding: National Institute for Psychobiology in Israel
The need of biomarkers for
treating depression
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Major depression: lifetime prevalence of 10%-20%
(for men & women, respectively)
•
WHO estimate: by 2020 depression will become the leading cause
of healthcare morbidity in developed countries
•
Most common first-line antidepressants:
Selective serotonin reuptake inhibitors (SSRIs):
e.g. fluoxetine (Prozac), paroxetine (Seroxat); citalopram (Cipralex)
•
Other classes of antidepressant drugs:
- SNRIs: serotonin-norepinephrine reuptake inhibitors
e.g. venlafaxine (Effexor)
- Tricyclic antidepressants (“1st generation antidepressants”)
- Serotonin ligands, e.g. mirtazapine (Remeron)
The need of biomarkers for
treating depression
3
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SSRIs are the most-common first-line treatment for depression
•
>30% of patients do not respond to SSRIs
•
It takes >4 weeks to know if a patient responds to treatment
•
Drugs and dosages are replaced by ‘trial-and-error’
•
Biomarkers are needed for individualizing treatment!
Candidate Gene Studies for SSRI Response:
The serotonin transporter promoter length polymorphism
First report: 1998
12 years later: 742 PubMed papers on “SERT promoter polymorphism”
Meta-analysis verdict: no clear association with SSRI response
Genome-Wide Association Studies for SSRI Response
Genome-Wide Association Studies for SSRI Response:
No reliable DNA biomarkers found (2)
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BIOL PSYCHIATRY 2010;67:133–138
Genome-Wide Association Studies for SSRI Response:
No reliable DNA biomarkers found (3)
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None of these 3 studies reported results with genome-wide significance
Rare genotypes and epigenetics may have a role in antidepressant response
AOP November 2010
Genome-Wide Expression Profiling
for SSRI Response Biomarkers
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The approach:
•
Comparing the sensitivity of human lymphoblastoid cell lines from
individual healthy donors to the growth-inhibition effects of SSRIs
•
Comparing the expression profiles of ‘edge’ cell lines:
LCLs with high vs. low sensitivity to growth-inhibition by SSRI drugs
•
Studying the genes (including deep sequencing, promoter methylation,
relevant miRNAs) exhibiting differential expression
•
Later: clinical samples (patients’ lymphocytes)
Lymphoblastoid cell lines (LCLs)
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• HapMap and HGDP use LCLs for studying human genome diversity
• LCLs retain most of the phenotypic properties of B lymphocytes
- and can be grown for few months (vs. few days for PBL)
• LCLs faithfully represent the donor’s DNA sequence
(EBV does not incorporate into the host genome)
• The serotonin transporter is functionally expressed by PBL and LCLs
(serotonin is an immune modulator)
PBL and LCLs
express a functional serotonin transporter
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SERT pharmacology is similar for brain and PBL
Gene expression in LCLs correlated with PBL
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- A fine correlation is observed for gene expression in LCLs and PBL
from the same individuals (with very few exceptions)
- Over 4,100 brain transcripts are also expressed in PBL
Am J Med Genet Part B 153B:919–936 (2010)
LCLs faithfully reflect the donor’s methylome
~92% of genes show similar methylation profiles in PBL and LCLs
from the same donors
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Source for LCLs:
The National Laboratory for the
Genetics of Israeli Populations (NLGIP)
http://nlgip.tau.ac.il
SSRI biomarkers:
Phenotyping for in vitro paroxetine response
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- Cells are plated in 96-well plates (20,000 cells per well) for 3 days,
with different drugs added immediately after plating
- On the third day, XTT reagent is added (last 6 h) and the absorption
is measured with an ELISA spectrophotometer
- The XTT assay reflects the activity of mitochondrial enzymes
and thereby the number of live cells
- The XTT color generation is not affected by tested drugs (last 6 h)
The XTT assay can be easily scaled-up
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Large spectrum of LCLs
growth-inhibition by paroxetine
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LCLs were tested for growth-inhibition (72 h) by 10 µM paroxetine
Each bar represents 3-5 experiments (each in triplicates)
Error bars represent SD for repeated experiments
Similar growth inhibition observed
by direct counting vs. XTT assay
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Growth inhibition by paroxetine determined by direct counting vs. XTT (ELISA)
LCLs were exposed to 10 µM paroxetine for 72 h (in triplicate)
Paroxetine sensitivity phenotypes are stable
(new batches were thawed and tested)
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Paroxetine sensitivities are independent
of cell growth rates (A) or densities (B)
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Growth-inhibition of human LCLs by 10 µM paroxetine as function of:
(A) Basal cell replication rates
(B) Cell concentration immediately prior to experiments
Linear regression analysis of pooled data; R2 = goodness-of-fit values
Correlation of cell growth inhibition
by different SSRIs
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Growth-inhibition phenotypes are related to the SSRI cellular pathway!
Paroxetine vs. Citalopram
Paroxetine vs. Fluvoxamine
Growth inhibition of human LCLs by paired drugs; each dot is a different cell line
R2 = goodness-of-fit values
Poor correlations for cell growth
inhibition by different drug classes
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A novel drug discovery tool – see Morag et al 2010
Paroxetine vs. Beclomethasone
Dexamethasone vs. Beclomethasone
Growth inhibition of human LCLs by paired drugs; each dot is a different cell line
R2 = goodness-of-fit values
Depression treatment biomarkers:
Next phase – searching biomarkers
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80 LCLs were screened for paroxetine sensitivity (healthy female donors)
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Most LCLs had average sensitivities (IC50 of 10 uM paroxetine)
•
14 lines selected for whole-genome profiling (7 from each phenotypic ‘edge’)
•
Expression profiles of the ‘edge’ LCLs were compared with the
Affymetrix GeneChip Human Gene 1.0 ST array kit (28K genes)
Affymetrix Human Genome 1.0 ST arrays:
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• 61 genes show >1.5-fold basal expression differences
between “edge paroxetine sensitivity lines” with p<0.001
• Our top candidate biomarker gene: CHL1
Cell adhesion molecule with homology to L1CAM
6.3-fold lower expression in paroxetine-sensitive
cells; p=2.56E-05
- Verified by real-time PCR: 36-fold difference
(compression by expression microarrays is common)
- Expressed in brain
- Implicated in cortical neurons differentiation
- Reported among schizophrenia ‘risk genes’
- Implicated in novelty-seeking behavior
CHL1
Cell adhesion molecule with homology to L1CAM
Microarray expression levels correlated with
paroxetine-mediated growth inhibition phenotypes
GUSB: reference gene for LCLs
CHL1
Cell adhesion molecule with homology to L1CAM
Wild type mice: L1 and CHL1 are co-expressed in the dorsal thalamus and
along the thalamo-cortical projections in the cortex
Tissue culture: L1 and CHL1 are co-localized on growth cones and neurites
of cortical and thalamic neurons
L1 & CHL1(-/-)/ double mutant mice:
Posterior shift of axons from motor thalamic nuclei to the visual cortex
L1 and CHL1 guide thalamic axons to distinct neocortical areas
Neuronal progenitor cells from CHL1−/− mice
show increased proliferation and self-renewal
- Neuronal tissue cultures from CHL1 −/− mice:
Enhanced proliferation of neuronal progenitor cells
- Neuronal progenitor cells may participate in recovery from depression!
Affymetrix Human Genome 1.0 ST arrays:
2nd ‘top candidate biomarker gene’: GAP43
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GAP43: Growth-cone associated protein 43
p= 1.78E-04
Fold-difference = -1.79 (high vs. low sensitivity)
Real-time PCR = 30-fold difference
Dev Neurobiol. 2010 Nov;70(13):897-911
Affymetrix Human Genome 1.0 ST arrays
3rd ‘top candidate biomarker gene’: ENPP2
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ENPP2: Autotaxin; Ectonucleotide pyrophosphatase/phosphodiesterase 2
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Implicated in myelin formation
p= 1.55E-04
Fold-difference = -2.45 (high vs. low sensitivity)
Real-time PCR = 6-fold difference
Some genes are expressed at higher levels in
the paroxetine-sensitive LCLs
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SEPT10 shows +3.51 higher expression in the paroxetine-sensitive LCLs
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Septins are cytoskeletal proteins with GTPase activity
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Chromosomal localization at 2q13 is associated
with depression!
Depression treatment biomarkers :
Next phases – TAU
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- Western blotting for proteins coded by ‘top candidate’ genes
- Effects of SSRI treatment on gene expression
- DNA: common SNPs, sequencing, promoter methylation
- MicroRNAs with conserved 3’UTR binding sites on ‘top genes’
Depression treatment biomarkers :
Next phases – Clinical samples
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PADRE ERA-NET project (2011-2014; 0.5M Euro):
- Clinical samples from Germany, Italy, Poland
- Examine gene expression and protein levels in lymphocytes from
depressed patients with known SSRI clinical response phenotype
- Real-Time PCR for candidate genes (treatment onset)
- Search genes with best correlations for SSRI clinical response
- Build a “predictor tool” for SSRI response (with several genes)
Blood cells expression profiling biomarkers:
Eight-gene predictor for Infliximab response in RA
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LCLs from healthy donors can facilitate
early-phase research
(prior to studies with patients’ samples)
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- Stable phenotypes and close similarity to lymphocytes
- Unlimited resource for DNA, mRNA, microRNAs and proteins
- Cell growth may serve as surrogate drug response phenotype
(other potential phenotypes: intracellular ATP or Ca2+ )
- Essential for study design:
The drug target must be expressed in LCLs
(human lymphocytes express ~half the human genome)
This work was possible thanks to…
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Microarray experiments and data analysis performed by:
Dr. Varda Oron-Karni and Dr. Metsada Pasmanik-Chor
TAU Bioinformatics Unit
Everything else:
Ayelet Morag
M.D. / M.Sc. Honors Student
Tel Aviv University, Faculty of Medicine
http://NLGIP.TAU.AC.IL