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John A. Thorson, M.D., Ph.D. Associate Professor of Pathology
Director, Clinical Genomics Laboratory UC San Diego
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Presenter: John A. Thorson, M.D., Ph.D.
I have financial interest/arrangement or affiliation with:
Name of Organization Relationship Unlabeled Product Usage UC San Diego Health System Employee N/A Illumina, Inc. Consultant N/A
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Genetic/genomic/other “omic” conditions ultimately control phenotype, drive disease states
Precise characterization of state of individual’s genome (other “omes”) informs: ◦ “Personalized” understanding of disease ◦ Ability to effectively treat disease
Knowledge of an individual’s genome (clinical genomics) key to realizing personalized medicine
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Human genome contains: ◦ ~21,000 genes ◦ 3 billion base pairs (nucleotides) of DNA sequence 1% represents protein coding sequence (“exome”)
“Sequencing” the genome ◦ Determine identity/relative position
(sequence) of nucleotide building blocks ◦ First practical method developed by Fred
Sanger at Cambridge in mid-1970’s Electrophoretic separation of randomly
terminated extension products Sanger method used for Human
Genome Project ◦ Limitations: throughput and cost ◦ First genome required 13 yrs, $2.7B
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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
$100M
$10M
$1M
$100K
$10K
$1K
Moore’s Law equivalent (50% decrease/2 years)
Cost
per
gen
ome
Introduction of Next Generation Sequencing (NGS) technology
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A B C
Separate, individual targets, 1 target/reaction
Entire genome, fragmented,
pooled
Up to 384 reactions in individual wells
3 x 109 independent reactions on one flow cell
6 Mb 72 hrs
(ABI 3730)
1.8 Tb 72 hrs
(HiSeq X) 300,000X
A B C
Amplify pieces using PCR
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Library preparation ◦ Creating/capturing fragmented pieces of genome to
sequence
Clonal amplification of library ◦ Necessary to generate detectable signal
Parallel sequencing of amplified library ◦ Millions of individual pieces sequenced simultaneously
Data analysis ◦ Nucleotide identification, sequence alignment, variant calls
Interpretation/Annotation ◦ Linking variants to biological information
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In-solution capture
Removal of unbound fragments
Elution from support
Fragmentation End-repair Adapter ligation
Array-based capture Whole Genome Library
Exome = protein coding only (~1% of genome)
Hybridize PCR primers for amplicon-based library
Multiplex PCR
PCR product clean-up
Targeted Panel Library
No fragmentation
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Apply library in limited concentration to surface containing immobilized complimentary adapters
PCR using immobilized adapters as primers, tethering products to surface
Multiple rounds of PCR create clonal clusters of each library element on surface
Flow cell surface
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Imaging step: sequential cycles allow “reading” the sequence
Cycle Number: 1 2 3 4 5 6
Top: CATCGT Bottom: CCCCCC
For each cycle: 1. Based-pair directed incorporation of
reversibly labelled, 3’ blocked nucleotide 2. Wash unincorporated nucleotides, detect
fluorochrome (“imaging”) 3. Unblock 3’ site, remove fluorochrome
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Millions of genomic fragments from population of cells Align short sequences to reference using “best fit” algorithms
ACCGTCAGCC GTGGTAAAAT TAGTAACTTTGTGGA CTAG
ACCGTCAGC AGTGGTA TCGTAGTA TTTGTGGAT TAG CGTC CCTAGTGG AATCGTTG ACTTTGT ATCCTAG
ACCGTCAGCCTAGTGGTAAAATCGTAGTAACTTTGTGGATCCTAG
DNA fragmented via sonication
ACCGTCA CTAGTGGTA GTTGTAACTT GGATCCTA
Low Coverage
A>T
TATA
Library creation
Parallel sequencing
TTTGTGGAT
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SNVs, short insertions/deletions ◦ Very good reliability ◦ Indels > 10-12 bp may be inaccurate
Large insertions/deletions ◦ Short read lengths unable to span ◦ Alignment difficulties
Copy number variants (CNVs) ◦ True amplification vs. polysomy? (biology NOT equivalent) ◦ ISH, microarray far superior
Structural variants (translocations/inversions) ◦ Not detectable with exome or “panel” sequencing ◦ Requires introns/whole genome/transcriptome ◦ Bioinformatic challenges (align one read/multiple genes)
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The “rate limiting” step in genomic analyses Focus is on variants causing deleterious changes in protein
Pathogenic Benign
KRAS c.34G>T ⇒ p.G12C vs KRAS c.36T>C ⇒ p.G12G TP53 c.742C>T ⇒ p.R248W vs TP53 c.215C>G ⇒ p.P72R
Curation via literature/databases time consuming, incomplete Frequent use of “predictive” software algorithms ◦ Conservation throughout evolution ◦ Amino acid characteristics
Many variants of uncertain significance (VUS) Some “benign” variants likely not benign ◦ No change in amino acid, but may alter RNA binding site, etc.
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Multidisciplinary sequencing review board
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Inherited disease >2850 disease genes recognized vs <100 in year 2000 Assign patients with unclear diagnosis to known disease Recognition of undescribed Mendelian disorders
Oncology Expanded the use of targeted therapeutics Understand development of drug resistance Potential to provide data to accelerate biomedical progress
Infectious disease Undiagnosed disease Host-microbiome interactions
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250 clinical cases, WES ordered by physicians ◦ Genetics, pediatrics, neurology, cardiology, endocrinology ◦ Range of phenotypes suggestive of genetic cause 80% children with neurologic condition
◦ Extensive prior workups non-diagnostic Metabolic screens, chromosomal studies, targeted sequencing
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Overall, 25% (62/250) patients assigned definitive diagnosis ◦ Harbored mutations meeting diagnostic criteria, e.g., genes previously
associated with phenotype/predicted deleterious 4/62 had mutations accounting for more than one genetic
condition (“blended” phenotypes) 200,000 to 400,000 SNVs/indels identified/exome ◦ “Filtering” reduced to 400-700/sample with potential clinical
significance ◦ 30/250 had actionable/reportable incidental findings as per ACMG Carrier status for cystic fibrosis Cardiomyopathy-related variants Heritable cancer syndrome-related variants
Undiagnosed? ◦ Incomplete understanding of variant effects? ◦ Non-coding regions?
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Clinical utility vs. personal utility ◦ Definitive diagnosis can change management in the absence of
specific therapeutics Bone marrow transplant Anticipatory guidance “Peace of mind”/end of diagnostic odysseys
Cost of WES less than battery of non-diagnostic tests Problems: ◦ Only 25% benefit (cases which eluded previous diagnosis) ◦ Incomplete knowledge of variants ◦ Incidental findings/ethical considerations
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Three-year pilot begun in 2006
Goals: ◦ Identify genomic changes in
20 cancer types
◦ Understand how changes interact to drive disease
Data from ~9,700 samples
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3299 tumor samples Alterations in driver
genes from four major oncogenic pathways
Many alterations present across tumor types ◦ Alterations to > 1 pathway
observed in almost all samples in all tumor types
◦ Majority directly or indirectly actionable with current anti-cancer drugs
Possible treatment based upon genotype rather than histology p53-DNA Repair
Cell Cycle
PI3K-AKT-mTOR
RTK-RAS-RAF
Ciriella et al, Nature Genetics 45:1127 (2013)
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Analyze each tumor for profile of driver gene mutations ◦ Irrespective of tissue type
Categorize into molecular subsets for which novel therapeutics are available or evaluable ◦ Cross-cancer similarity
Utilize information to optimize therapy ◦ Targeting multiple variants may be necessary due to: Intra-tumor heterogeneity Resistance - classical vs. dependence upon alternate pathways
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Question Does genomic analysis of advanced stage/metastatic cancer and use of matched targeted therapy lead to improved clinical outcome?
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Tsimberidou et al, Clin Cancer Res 18:6373 (2012)
Molecular analysis (12 genes)
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Gene
s an
alyz
ed
Only thyroid
Proportion of patients with variants by gene
Tsimberidou et al, Clin Cancer Res 18:6373 (2012)
Tumor Types/Percentage with Detected Variants (n=1144)
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291 patients with one variant, enrolled in 51 trials: ◦ 175 treated with matched therapy ◦ 116 with non-matched
Matched Non-Matched
Response rates (imaging RECIST complete or partial):
Matched: 27% overall Non-matched: 5% overall
Median Time To Failure (off therapy/death): Matched: 5.2 months Non-matched: 2.2 months
Median survival: Matched: 13.4 months Non-matched: 9.0 months
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Observational study suggests there is benefit Additional, controlled randomized trials needed Benefits may be less than possible due to: ◦ Evaluation done in cases of advanced stage cancer Pts heavily pre-treated (median 5 prior pretreatments)
◦ Other driver mutations not detected/treated? The exome vs. the genome
The case for data collection ◦ Clinical purposes don’t require exome analyses ◦ Cost of exome/large panel analyses approaching that of 3-4
targeted mutation analyses Single assay vs. multiple assays = more efficient use of tissue “Collect all/Use some”
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Interpretation ◦ Knowledge base limited/potential for over-interpretation ◦ Need for significant amounts of basic research
Results reporting ◦ Presentation of data? Data dump vs. curated/condensed Challenging the abilities of most LIS and HIS types
Education of healthcare providers ◦ Human resources to handle patient counseling ◦ Ethical issues surrounding incidental findings/informed consent
Technical challenges – sensitivity and accuracy Turn-around times ◦ Results available in clinically useful time frame?
Reimbursement ◦ Limited at present; future remains unclear
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“Here’s my sequence…”
Comprehensive genotyping (NGS)
Optimization of outcomes
New Yorker, 2002
Knowledge Base
Clinical Trials
Therapy
Discovery