Can we exploit the power of NGS to move towards personalized medicine?, Center for Translational Genomics and Bioinformatics, San Raffaele Scientific Institute, Elia Stupka Copenhagenomics 2012
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Behind the Scenes From the Manager's Chair: Decoding the Secrets of Successfu...
Can we exploit the power of NGS to move towards personalized medicine?, Center for Translational Genomics and Bioinformatics, San Raffaele Scientific Institute, Elia Stupka Copenhagenomics 2012
1. Can
we
exploit
the
power
of
NGS
to
move
towards
personalized
medicine?
Elia Stupka - stupka.elia@hsr.it
giovedì 14 giugno 12
2. BACKGROUND
• Biology background
• Part of the core human genome analysis team
within the Ensembl group -> pure
bioinformatics
• In Singapore, headed the Fugu informatics
team, again bioinformatics, involved in
Fantom3 project
• Since then, tried to get closer to the biology
(added a wet lab 8 years ago)... and then to
the clinic (added medical staff to the group
last year)
giovedì 14 giugno 12
3. DRIVING QUESTION: GENOME FUNCTION!
• Bioinformatics Pipelines can only reflect the current
(biased) understanding of what the data should be
telling…
• Classic examples:
–“Surely a gene can’t produce so many transcripts, put a
higher cut-off on your predictions”
–“Delete all genes which do not encode for a protein”
–“Delete all genes which are less than 200nt long”
• Thus I grew an interest in… rubbish (US:garbage), or to
be more precise, in elements of the genome and its
grammar which did not fit accepted rules…
giovedì 14 giugno 12
5. A VIEW OF THE GENOME...
Coding gene…
giovedì 14 giugno 12
6. A VIEW OF THE GENOME...
RNASeq: Transcripts, splicing, expression profiling
Non-coding RNA
RNASeq
Coding gene…
giovedì 14 giugno 12
7. A VIEW OF THE GENOME...
RNASeq: Transcripts, splicing, expression profiling
Promoter
CAGE Seq
Non-coding RNA
RNASeq
Coding gene…
giovedì 14 giugno 12
8. A VIEW OF THE GENOME...
RNASeq: Transcripts, splicing, expression profiling
Methylation
Promoter
CAGE Seq
Non-coding RNA
RNASeq
Coding gene…
giovedì 14 giugno 12
9. A VIEW OF THE GENOME...
RNASeq: Transcripts, splicing, expression profiling
Methylation
Promoter
CAGE Seq
Non-coding RNA
Enhancer
RNASeq
P300 ChiPSeq
Coding gene…
giovedì 14 giugno 12
10. A VIEW OF THE GENOME...
RNASeq: Transcripts, splicing, expression profiling
Methylation
Promoter
Insulator CTCF
CAGE Seq
ChIP Seq
Non-coding RNA
Enhancer
RNASeq
P300 ChiPSeq
Coding gene…
giovedì 14 giugno 12
11. A VIEW OF THE GENOME...
RNASeq: Transcripts, splicing, expression profiling
Methylation
Histone Promoter
Insulator CTCF
Modifications CAGE Seq
ChIP Seq
Non-coding RNA
Enhancer
RNASeq
P300 ChiPSeq
Coding gene…
giovedì 14 giugno 12
12. A VIEW OF THE GENOME...
RNASeq: Transcripts, splicing, expression profiling
Methylation
SNP
Histone Promoter
Insulator CTCF
Modifications CAGE Seq
ChIP Seq
Non-coding RNA
Enhancer
RNASeq
P300 ChiPSeq
Coding gene…
giovedì 14 giugno 12
13. GENETICS...
• We are at a crossroads between:
• Existing and ongoing GWAS data, based on genotyping
microarrays, still cheapest and most mature for studies of
10,000s of cases
• Heavy emphasis on exome sequencing, with many projects
in completion examining hundreds or low1,000s of samples
• BUT, no major breakthroughs in complex disease....
• Whole genome sequencing also ongoing, numbers
increasing rapidly, but still quite costly for high coverage
giovedì 14 giugno 12
14. EXOME SEQUENCING IN RARE DISEASES:
FUNCTION!
Collaboration with Phil
A Waters, F Lescai et al, submitted
Beales, UCL
giovedì 14 giugno 12
15. EXOME SEQUENCING IN RARE DISEASES:
FUNCTION!
Collaboration with Phil
A Waters, F Lescai et al, submitted
Beales, UCL
giovedì 14 giugno 12
16. EXOME SEQUENCING IN RARE DISEASES:
FUNCTION!
Collaboration with Phil
A Waters, F Lescai et al, submitted
Beales, UCL
giovedì 14 giugno 12
17. EXOME SEQUENCING IN RARE DISEASES:
FUNCTION!
Collaboration with Phil
A Waters, F Lescai et al, submitted
Beales, UCL
giovedì 14 giugno 12
19. RNA: STILL A LOT OF
UNKNOWNS
• ncRNAs? We need a real catalogue, so far incomplete!
giovedì 14 giugno 12
20. Why it matters: an example in a Parkinson
locus
Collaboration with
Stefano Gustincich Lab, SISSA
giovedì 14 giugno 12
21. A NEW CLASS OF NCRNAS!
Nature, 3rd revision
giovedì 14 giugno 12
22. A NEW CLASS OF NCRNAS!
Nature, 3rd revision
giovedì 14 giugno 12
23. A NEW CLASS OF NCRNAS!
Nature, 3rd revision
giovedì 14 giugno 12
24. A NEW CLASS OF NCRNAS!
Nature, 3rd revision
giovedì 14 giugno 12
25. DNA METHYLATION
• Pooled 10 individuals through neurofibroma
progression: Collaboration
with
•Schwann Cells (healthy) Stephan Beck Lab
•Benign tumour
A Feber et al,
•Malignant tumour Genome
Research, 2011
• Checked DNA Methylation by Medip-Seq
giovedì 14 giugno 12
26. REPEAT ELEMENTS...
Collaboration
with
Stephan Beck Lab
A Feber et al,
Genome
Research, 2011
giovedì 14 giugno 12
27. EPIGENETICS IN FETAL
PROGRAMMING OF DISEASE
Collaboration with:
Prof. Adrian Clark,
Barts and The
London School of
Medicine
Prof. Simon Langley
Evans, University of
Nottingham
giovedì 14 giugno 12
30. HOW TO MOVE ALL THIS TO
THE CLINIC
• Moved to San Raffaele Hospital in Milan, a highly integrated
campus with1,600 scientists, 5,000 hospital staff and a University
with a Medical Faculty
• Primary challenges:
• PEOPLE: Cultural barriers, burocratic barriers, etc.
• IT: Sharing data, knowledge, databases, etc.
• ETHICS and BIOBANKING and CLINICAL CULTURE
• EXPERIMENTAL DESIGN: plan early, impossible to fix later!
giovedì 14 giugno 12
31. MULTIPLE SCLEROSIS FAMILY
Collaboration
with
INSPE
Dr Martinelli-
Boneschi
Prof. Giancarlo
Comi
giovedì 14 giugno 12
32. HETEROGENEITY OF PATIENTS:
OK IF KNOWN?
Whole
genome
SNP arrays
Gene
Expression
RNA-Seq
Medip-Seq
Chip-Seq
giovedì 14 giugno 12
33. WEIGHTED GENETIC
RISK SCORE
1.5
Family cases
Family controls
HSR cases
HSR controls
1.0
Density
0.5
0.0
7 8 9 10 11 12 13
GRS
giovedì 14 giugno 12
35. INTEGRATION NOT AS SCARY AS IT SEEMS
a, Whole-genome view of the gene ranks based on integrating ChIP-on-chip,
methylation and gene expression results. The y axis shows −log(P), where P is the P
value of the Qstatistic corrected for multiple testing. Significantly (false discovery rate,
≤10%) downregulated (green) or upregulated (red) genes are shown.
SNPs, CNVs, DNA Methylation, Histone Methylation, Gene Expression, etc.
J Zhang et al, Nature, 2012
...and... Wilks, S. Order Statistics. Bull Amer Math 54,
6-50 (1948) !!!! :)
giovedì 14 giugno 12