John A. Thorson, M.D., Ph.D. Associate Professor of Pathology

Director, Clinical Genomics Laboratory UC San Diego

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

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

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

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

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

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

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

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

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

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

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)

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.

Multidisciplinary sequencing review board

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

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

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?

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

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

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)

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

Question Does genomic analysis of advanced stage/metastatic cancer and use of matched targeted therapy lead to improved clinical outcome?

Tsimberidou et al, Clin Cancer Res 18:6373 (2012)

Molecular analysis (12 genes)

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)

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

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”

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

“Here’s my sequence…”

Comprehensive genotyping (NGS)

Optimization of outcomes

New Yorker, 2002

Knowledge Base

Clinical Trials

Therapy

Discovery