Leveraging in silico genomic panels to

translate next generation sequencing into

clinically actionable information

Corey Nislow, Ph.D. Associate Professor | UBC Pharmaceutical Sciences

Director | UBC Sequencing Centre at Pharmaceutical Sciences

Webinar July 17, 2014

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Overview

• 3 Bottlenecks to incorporating genomics into

personalized medicine

– Generating the data

– Assembling the infrastructure

– Managing and analyzing the Data

Genetic variation 101

Single Nucleotide Polymorphisms

“SNPs” 3million/individual

What can we hope to learn from

studying variations (SNPs) ?

• Identify SNPs associated with disease development –diabetes, heart disease, addiction,

Alzheimer's etc.

• Identify patients who will benefit from drugs

• Predict differential response to drugs –adjust dose of drugs

1st Bottleneck: Generating the Data SOLVED. (mostly)

2003 10 years $3 Billion

2014 1 day $1000

2023 2015

?

UBC Sequencing Centre at Pharmaceutical Sciences

• First (and only) in North

America dedicated to

pharmacogenomics

• Illumina hiSeq 2500 600GB

• Illumina hiSeq 2500 1TB

• Illumina miSeq

• Extensive automation

How big is the human genome? 3 billion bases

• Convert all 4 bases to 0 or 1

• One “byte” (8 bits) represents all 4 DNA bases

00, 01, 10, and 11

6×109 base pairs

diploid genome×

1 byte

4 base pairs=

1.5×109 bytes

genome

• 1 Human Genome = 1.5 Gigabytes

How big is the human genome? 3 billion bases

• Raw Sequence (30X) 700 GB • Aligned Data 300 GB

• Variant Data 1.5 GB

• SNP Data (~3 million) 3 MB

• Clinically Testable? 103

• Clinically Actionable? 101

2nd Bottleneck: Interpreting the Data WORK IN PROGRESS

Kidney function

Liver function

Gene function

One solution…

knoSYSTM The Human Genome Interpretation Platform

Gene panels are in silico tests

A panel has multiple queries

Each query has its own filters and…

…its own targets: genome, gene list, region,

known sites…

…and comparisons

Generating Reports: Variant Data, Classification and Action Layers

2nd Bottleneck: Interpreting the Data WORK IN PROGRESS

Kidney function

Liver function

Gene function

3rd Bottleneck: Populating the Database An Urban Planning Project

Bad Data Perfect Model

Bad Results

Perfect Data

Bad Model

Bad Results

Bottleneck 3: Populating the Database An Urban Planning Project

Metabolome

Transcriptome Clinical test

data

Phenotypic

data

Challenges identified: Moving to

understanding adverse events

• Overarching goals

• Some mechanics

• Ongoing efforts

• Our pilot

• The future

Goal: Identify potential for toxicity or

reduced response BEFORE treatment

Marsh S , and McLeod H L Hum. Mol. Genet. 2006;15:R89-R93

Paradigm Shift: Pro-active not Reactive

Related Efforts

• Kerr Drug: Single Pharmacy: Interventional:

Completed

• eMERGE PGx: 20+ sites: Observational and

Interventional: Ongoing

• PRIMe: 2014

Kerr Drug Single Pharmacy Study: Implementation of a pharmacogenomics

service in a community pharmacy J Am Pharm Assoc (2003)

• Main outcome measures –Patient participation

–Prescriber acceptance

– Time for test

–Claims outcome

Kerr Drug Single Pharmacy Study: Implementation of a pharmacogenomics

service in a community pharmacy J Am Pharm Assoc (2003)

• Results:

– 41 patients met criteria, 18 (43.9%) completed

–9 patients WT, nine with variants

–Pharmacist recommendations for

modifications were accepted by prescribers

– 17 patients filed reimbursement claims

• 5 not billed

• 12 billed, not paid

Kerr Drug Single Pharmacy Study: Implementation of a pharmacogenomics

service in a community pharmacy J Am Pharm Assoc (2003)

• Conclusion:

–A pharmacogenomics service can be an

extension of medication therapy

management services in a community

pharmacy.

• Prescribers receptive, but reimbursement

is a challenge.

More details:

eMERGE-PGx : 9,000 client project 20+ centers, 84 pharmacogenes

eMERGE pharmacogenes

UBC Case Study: Pharmacogenomics Registry for

Individualized Medicine (PRIMe)

• Our two-pronged approach:

1. Collaborate to collect well-phenotyped

patients

2. Utilize the trust and accessibility of

community pharmacists to collect

patients

UBC Case Study: Pharmacogenomics Registry for

Individualized Medicine (PRIMe)

• Our two-pronged approach:

1. Collaborate to collect well-phenotyped

patients

2. Utilize the trust and accessibility of

community pharmacists to collect

patients

HOW???

Crowdsourcing Pharmacogenomics: Building a population of individuals

Sequencing makes sense: One-time cost, lifetime amortization

Genome allows for truly personalized

medicine

Acquire once query indefinitely

Sequence today for the questions of

tomorrow

Collecting Data via Collaboration: Genotype well-phenotyped samples

• 26,000 well-phenotyped samples

• Sequence to populate database

• Develop protocols to link data

Collecting Data via Pharmacists: Genotype AND Phenotype

• Standardized consent process using Microsoft Surface Tablets

• Non-invasive sampling using saliva collection kits

• SOPs to govern all activities

SOPs and ethics

SOPs and ethics

Warfarin

Gold standard for pharmacogenomics

Using Warfarin as a benchmark

• Efficacy crucial to health

• Potential for severe adverse drug reactions

• Narrow therapeutic index

• Requires close monitoring to titrate dose

Warfarin history

• 1933 -1939 –dicoumarol discovered as an anticoagulant

• 1948 Wisconsin Alumni Research Foundation (WARF) developed Coumadin: better solubility and bioavailability

• 1954 – Coumadin approved by FDA

• 1978 – Mechanism of action defined:

Warfarin inhibits coagulation that is dependent on Vitamin K

Warfarin and CYP2C9, VKORC1, GGCX,

CYP4F2

Weight 9% Age

7%

VKORC1 23%

CYP2C9 17%

Unknown 44%

Warfarin SNPs (18) in PCR validation

panel

SNP panel validates exome seq

• Immune against incidental findings

• Walk before we run

• Populate the database

• Then….

FDA drug-gene interactions - 161 Drugs involved - 141 Genes involved - 48

Managing these? – Priceless (or with an exome sequence)

Using knoSYS to make the case

• 1st Report – 18 Warfarin SNPs

• 2nd Report – 161 FDA drug-gene

interactions

• nth Report – Every new Rx

Thanks to our Partners

• UBC Pharmaceutical Sciences

• UBC IT

• BC Generations Project

• BC College of Pharmacists

• BC Pharmacy Association

• Canadian Foundation for Innovation

• Illumina

• Microsoft

Thanks

We appreciate your feedback!

corey.nislow@ubc.ca