Whole Exome and RNA Sequencing in a Pre

Whole Exome and RNA
Sequencing in a Pre-treatment
and Post-treatment Case
Study
Andrew S. Peek, Ph.D.
Senior Director of Bioinformatics
GE Healthcare
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SeqWright Background
Genomics Platform Portfolio; Keeping up with Technology Trends
Capillary
Electrophoresis
qPCR
Microarray
Analysis
Next Generation & Third Generation
Sequencing Platforms
ABI 3730xl Sanger Sequencer
ABI Real-Time Quantitative PCR System
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Supporting Bioinformatics
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Solid 5500xl
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Updates:
2500 Rapid Run
Life Tech. Ion Torrent
Illumina MiSeq™
Roche 454 GS FLX is a registered trademark of Roche.
Illumina HiSeq 2000 and Illumina MiSeq is a trademark of Illumina Inc.
Life Tech. SOLiD 5500xl, Life Tech. Ion Torrent PGM and Life Tech. Ion Proton is a
trademark of Life Technology Corporation.
Updates:
400bp reads
Updates:
250bp reads
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The Value Spectrum of NGS to Drug Development & Dx
Novel
Biomarkers
Demographics & Disease
Incidence
Pathology
Disease Risk
Evaluation
Sample
Heterogeneity
Pharmacogenomics
Molecular Mechanisms of Diseases
Avak Kahvejian et al., Nature Biotechnology. Volume 26 (10) 2008
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Why Leverage Sequencing for
Discovery and Development
1. Growing Number of Therapeutic Targets
• Need for single assay system for cost, speed, sample limitations
• Targeted drugs approved in one indication may be effective in another
• Retroactive assessment of previous data sets analyzed with new
biological knowledge
2. Multi-target Therapeutic Models Hold Promise
• Enable combination therapy models
High
Throughput
Sequencing
• Need to understand “molecular landscape”
3. High Failure Rates at Clinical Trial Stage
• Need to increase data-driven participant screening
• Failures due to secondary factors influencing molecular pathway
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Target/Drug Discovery Lifecycle >10 Years
Thousands of Potential Markers
Trials
FDA Review
MFG
~ 5 Years
Phase 3
10,000+ Compounds &
Potential Biomarkers
Phase 1
Phase 2
Discovery Phase – Many Targets
Discovery + Preclinical
~ 7 Years
1 FDA
approved
~ 1-2 Years
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Accelerated Discovery Through Genomics
Technology
Preclinical Next-Generation Sequencing
Genomic Assay
Selection &
Workflow
Effective
Bioinformatics
Interpretation
Tools
• 1000’s of
Potential
Genomic
Variables
• SNPs
• Indels
• CNV’s
• Gene Expression
• QA/QC
• NGS Seq, Orthogonal Chemistries
• Controlled Infrastructure
• Somatic Status (matched normal)
• Filtering
• False pos/neg
• Annotation
• Annotation
• Comprehensive variant reporting
Guided Discovery
through Genomics
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Gene Expression
Somatic Variation
Workflow and experimental setup overview
Post-Treatment
Pre-Treatment
(drug & radiation)
(drug & radiation)
NGS Whole Exome (Illumina HiSeq™)
Matched Normal
Matched Normal
Tumor Tissue
Tumor Tissue
Confirmed Somatic
Confirmed Somatic
NGS Whole
Transcriptome Seq
(Illumina HiSeq™)
NGS Whole
Transcriptome Seq
(Illumina HiSeq™)
Matched Normal
Matched Normal
Illumina HiSeq 2000 and Illumina MiSeq is a trademark of Illumina Inc.
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From Genomics To Meaningful Biology
Whole Exome Sequencing:
• SNPs, Indels, Rearrangements & CNVs
• Detect coding mutations: target 1% of genome that contains 85% of characterized
disease-causing mutations1
• High sensitivity of variant detection with deep, targeted coverage
Whole Exome Workflow
Analysis and Visualization Tools
Pathway Analysis
Choi et al. Genomic diagnosis by whole exome capture and massively parallel DNA
sequencing. (2009) PNAS 106(45) : 19096-19101
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From Genomics To Meaningful Biology
RNA Sequencing:
• Gene expressions levels, SNPs, alternative splice variants, gene fusion
products, miRNA and ncRNA
• Large dynamic range and data persistence
• No probe bias, digital information
RNA-Seq Workflow
Analysis and Visualization Tools
Gene Ontology and Functional
Relationships
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Leveraging the Power of WES and RNA Seq
Whole Exome Seq
•
•
•
•
SNPs,
insertions/deletions
Copy number variants
Rearrangements
Highly sensitive for rare
allele detection
RNA Seq
•
•
•
•
Gene expression
Alternative splice
variants
Novel transcripts/splice
variants
Capture Non coding
transcripts
Capture a broad spectrum of genomic
output within any disease state
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RNA-Seq Tumor Pre-treatment versus Posttreatment expression changes
Fold Change (Log Scale)
16
14
12
10
8
6
4
2
0
Transcriptome RNA-Seq
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Genes both up and down regulated on
chromosome 2
gene
locus
sample_1
sample_2
log2(fold_change)
p_value
Gene Function
POU3F3
ODC1
2:105471968-105476929
2:10580093-10588630
Pre-treatment
Pre-treatment
Post-treatment
Post-treatment
5.58132
1.78755
0.000278405
0.0400902
CNS development
Metabolism, amino acid metabolism
EDAR
2:109510926-109605828
Pre-treatment
Post-treatment
2.11154
0.0403649
TNF signaling, Integrated breast cancer pathway
LRP1B
2:140988991-142895399
Pre-treatment
Post-treatment
5.13462
0.0179457
Protein transport, endocytosis
KIF5C
MYCN
2:149632818-149883273
2:16060520-16087129
Pre-treatment
Pre-treatment
Post-treatment
Post-treatment
3.72928
2.83592
0.00181658
0.00363109
Cytoskeleton remodeling, immunology
V-Myc, oncogenesis, apoptosis
GRB14
2:165349321-165478358
Pre-treatment
Post-treatment
2.11136
0.0116339
STAT, NFkB EGFR signaling, insulin sequencing, vascularization
SLC38A11
2:165752695-165812035
Pre-treatment
Post-treatment
4.08178
0.0057227
Sodium transport
SCN2A
2:166095911-166248818
Pre-treatment
Post-treatment
4.42017
0.000525448
Sodium chanels and transport
SCN7A
2:167260082-167350757
Pre-treatment
Post-treatment
-4.70227
0.000174562
Axon guidance
FRZB
MATN3
2:183698001-183731890
2:20189977-20212455
Pre-treatment
Pre-treatment
Post-treatment
Post-treatment
3.00019
-4.23894
1.73E-05
1.85E-05
WNT signaling
Skeleton development
UNC80
2:210636716-210864024
Pre-treatment
Post-treatment
4.22512
0.0392969
Membrane transport
MYL1
2:211154873-211179914
Pre-treatment
Post-treatment
1.79769e+308
0.0485732
Cell adhesion, cAMP signaling
CXCR2
2:218990011-219001976
Pre-treatment
Post-treatment
3.5251
0.00816006
Chemokine, GPCR signaling
DNER
2:230222344-230579274
Pre-treatment
Post-treatment
6.45392
5.96E-12
Notch signaling, cancer
KIF1A
OTOF
PLEKHH2
BCYRN1
NRXN1
CD207
FABP1
IGKC,IGKJ1,IGKJ2,IGKJ4,IGKJ5
IGKV4-1
IGKV1-5
IGKV1-9
IGKV3-20
CNGA3
2:241653180-241759725
2:26680070-26781566
2:43864411-43995126
2:47419543-47572213
2:50145642-51259674
2:71056477-71062953
2:88422509-88427635
2:89109983-89165653
2:89184912-89185669
2:89246818-89247475
2:89309478-89310012
2:89442056-89442643
2:98947851-99015803
Pre-treatment
Pre-treatment
Pre-treatment
Pre-treatment
Pre-treatment
Pre-treatment
Pre-treatment
Pre-treatment
Pre-treatment
Pre-treatment
Pre-treatment
Pre-treatment
Pre-treatment
Post-treatment
Post-treatment
Post-treatment
Post-treatment
Post-treatment
Post-treatment
Post-treatment
Post-treatment
Post-treatment
Post-treatment
Post-treatment
Post-treatment
Post-treatment
5.94937
-1.79769e+308
-4.63385
2.71963
5.74534
1.47501
1.79769e+308
-9.39859
-10.1527
-8.63059
-8.44419
-8.8478
-4.98719
0.015777
0.0426211
0.0497663
6.48E-06
6.25E-05
0.0397105
0.0419851
4.44E-16
7.07E-11
0
1.49E-13
0
4.96E-05
Cell death, microtubule-based movement
Endocytosis
Actin binding regulation
Non coding RNA
Cell adhesion, GABA receptor
Immune system
Energy metabolism
Immune system, actin dynamics
Immune system, actin dynamics
Immune system
Immune system
Phagosome formation, phagocytosis
cAMP activation
Transcriptome RNASeq
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Gene expression changes based on Copy
Number Variation (CNV)?
Chromosome 2, duplication?
Array Based Hybridization
Whole Genome Sequencing
Whole Exome Sequencing?
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DNA-Seq coverage depth
Whole Exome DNASeq
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Can we use frequency rather than count?
Diploid Cell
A
Sequence Reads
T
Align Reads to Reference
~50% of reads A
~50% of reads T
Whole Exome DNASeq4
Triploid Cell
A
T
T
Sequence Reads
Align Reads to Reference
~33% of reads A
~67% of reads T
Whole Exome DNASeq
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Tumor
Pre
Tumor
Post
Chr1
Whole Exome DNASeq
Chr2
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Allele frequency by chromosomal position
Normal
Tumor
Post
Chr1
Whole Exome DNASeq
Chr2
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Wilcoxon rank sum test with continuity correction
Normal
P < 2.2e-16
P = 0.5939
Tumor
Post
Chr1
Whole Exome DNASeq
Chr2
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Two predictions for gene dosage
1) Genes with increased copy number should
have higher levels of expression
2) The allele that has multiple copies should be
expressed at higher levels than the single copy
allele
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Expectations of 2 versus 3 copies for DNA and RNA
Diploid Cell
A
DNASeq
T
Align Reads to Reference
~50% of reads A
~50% of reads T
Whole Exome DNASeq
RNASeq
~50% of reads A
~50% of reads U
Transcriptome RNASeq
Triploid Cell
DNASeq
A
T
T
Align Reads to Reference
~33% of reads A
~67% of reads T
Whole Exome DNASeq
RNASeq
~33% of reads A
~67% of reads U
Transcriptome RNASeq
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Transcriptome RNASeq
Gene expression versus copy number
Chr1
Whole Exome DNASeq
Chr2
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Gaining Mechanistic Insights Through WTS & WES
WTS
Gene Expression
Read Out
Mechanisms?
• Promoter region
mutations?
• Epigenetics?
• Rearrangements?
• Copy number
variations?
• Activating mutations
in transcription
factors?
Read Out
Mechanisms?
• Copy number
variations??
WES
• Treatment survival mechanism may involve the duplication of genomic
regions (for example; chromosome 2)
• This could induce changes in gene expression of genes within that
genomic region
• As a potential survival mechanism, the genes up and down regulated on
chromosome 2 may be the functional out put for that survival mechanism
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Conclusions
1) Tumor treatment escape mutation involves multiple
genes, primarily up regulated expression on chromosome 2
2) Chromosome 2 appears to be duplicated
a) frequency distribution of polymorphism not
consistent with diploid, possible triploid or more
b) expression levels of the more frequent allele are
higher
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Technology Conclusions
• Combining multiple types of technology methods
for discovery purposes can result in stronger
conclusions
• DNA-Seq and RNA-Seq are individually powerful,
but together can combine to be even more
informative
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Thanks to the SeqWright Team
Xin-Xing Tan, Ph.D.
Lee T. Szkotnicki, Ph.D.
Victor Venegas, Ph.D.
Enning Zhou, Ph.D.
Yanglong Mou, Ph.D.
Brad Thomas, Ph.D.
Adam Pond, Ph.D.
Fei Lu, M.D.
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