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Novel Genomic De-Identification Methodto Enable Research

Session 220, February 14, 2019

Kenneth Park MD, VP, Offering Development, IQVIARonald Miller PhD, Assoc Dir, Offering Development, IQVIA

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Conflicts of Interest

Kenneth Park, MD

Has no real or apparent conflicts of interest to report.

Ronald Miller, PhD

Has no real or apparent conflicts of interest to report.

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• Genomic Data – What Is It and Why Do We Care?

• Genomic Privacy Issues and Policies

• Genomic Privacy Protection and De-Identification

Agenda

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Learning Objectives

• Describe the growing awareness and concern for genomic data privacy

• Explain why traditional de-identification approaches do not work with genomic data

• Evaluate current genomic data privacy approaches and their relative risks and benefits

• Identify the key features of a comprehensive genomic de-identification approach

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What Is Genomic Data?

Genomic Data

• DNA sequence information

- Impacts and can predict a

person’s physical characteristics

and disease

- Can directly cause traits /

diseases or effect likelihood of

outcome

• Genomic research often require

linkage with phenotypic data for

(e.g., observation or clinical

outcome data)

Phenotypic Data

• Observable traits

- Eye Color

- Disease onset

• Influenced by both genetics and

environment

• Measured through observation or

clinical testing

- EMR

- Claims

- Rx

SOURCE: https://www.news-medical.net/health/Genetics-of-Eye-

Color.aspx

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The Cost of Doing a Genomic Sequence Has Approximately Halved Each Year

SOURCE: https://www.genome.gov/27565109/the-cost-of-sequencing-a-human-genome/

$1,000

$10,000

$100,000

$1,000,000

$10,000,000

$100,000,000

Watson’s Genome

Completed ~$1M

Sequencing now

available for ~$1,000

per Genome

First consumer

genome sequencing

available ~$50,000

Cost per Genome Sequence

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Countries

reporting WGS

Completed

(2018)

UK 90,000

Iceland 40,000

China 32,000

Saudi Arabia 2,400

Singapore 400

Portugal 100

US 0

Global Growth of Genomic Data

Despite the growth of genomic data, ongoing challenges remain

• Limited applicability to clinical care

• Datasets not linkable with other patient data

• No focus on patient consent / privacy

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Growth of Genomic Data Is Driving New Research Applications

Discovery /basic science

Pre-clinical Clinical Commercial

Disease pathobiology studies

Molecular target ID

Predictive biomarker ID & validation

Clinical study planning and design

Disease natural history (genomic factors)

Comparative effectiveness

Target Product Profile (TPP) definition

Contributors & decision makers vary across product lifecycle

Drug MOA elucidation

Drug safety

New indication identification / selection

Post marketing

/ lifecycle mgmt.

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Industry Is Investing in Genomic Data and Genomic Research…

2015 2017

Subje

cts

pro

vid

ing g

enom

ic info

rmation

200k

400k

600k

800k

0

20182016

1 1 1

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1 Pfizer / 23andMe, Calico / Ancestry, and Roche / FMI / Flatiron subject totals not published.2 GSK 23andMe has an opt in / opt out model whereby some 80% of the 5 million 23andMe subjects have, by default opted in, but can opt out any time.

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…Identifying Genetic Associations with Disease…

Disease Gene Discovered

Aortic aneurysm APC / MYH11

Breast-ovarian cancer BRCA1 / BRCA2

Familial hypercholesterolemia APOB / LDLR

Loeys-Dietz syndrome TGFBR1 / TGFBR2 / SMAD3

Long QT Syndrome KCNQ1 / KCNH2 / SCN5A

Lynch Syndrome MLH1 / MSH6 / PMS2

Marfan’s Syndrome TGFBR1 / MEN1

Retinoblastoma RB1

SOURCE: https://www.ncbi.nlm.nih.gov/clinvar/docs/acmg/

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…and Leading to New Drugs and Therapies

Disease Gene Discovered Known or Candidate Drug

Type 2 DiabetesSLC30A8

KCNJ11

ZnT-8 antagonists

Glyburide

Rheumatoid ArthritisPADI4

IL6R

BB-Cl-amidine

Tocilizumab

Psoriasis IL23A Risankizumab

OsteoporosisRANKL

ESR1

Denosumab

Raloxifene and HRT

Schizophrenia DRD2 Anti-psychotics

LDL Cholesterol HMGCR Pravastatin

SOURCE: Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J.

10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017 Jul 6;101(1):5-22. doi: Review.

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Hallmarks of an Ideal Genomic Dataset

• Whole genome sequence

• Rich clinical history

• Sample size >1M

• Diverse ethnic, environmental,

social and geographic

representation

• Broad disease representation

Key Characteristics Current Challenges

Balancing patient privacy

with research use

Getting comprehensive

environmental, social, and

clinical data

Aggregating data at scale

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• Genomic Data – What Is It and Why Do We Care?

• Genomic Privacy Issues and Policies

• Genomic Privacy Protection and De-Identification

Agenda

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Genomic Data Is Personally Unique and Identifying

Fingerprints Genomic Data

• Calculations estimate there are

~64B possible combinations

• 4^3.2B all possible combinations

4^335M based on observed

mutations

SOURCE: http://www.biometricbits.com/Galton-Fingerprints-1892.pdf

https://www.ncbi.nlm.nih.gov/projects/SNP/snp_summary.cgi

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Public Genealogy Databases Can Be Used to Identify Individuals or Relatives

• In 2013, researchers compared genetic profiles with public

genealogy databases to identify surnames of individuals

• Information such as age and geographic area enabled

researchers to link an individual back to the genomic data

• Early effort underscored re-identification risk (~12%)

• In 2018, additional researchers increased identification to ~60%

by expanding criteria to 3rd cousins

SOURCE: Gymrek M, McGuire AL, Golan D, Halperin E, Erlich Y. Science. 2013 Jan 18;339(6117):321-4.

Erlich Y, Shor T, Pe'er I, Carmi S. Identity inference of genomic data using long-range familial searches. Science. 2018 Nov 9;362(6415):690-694.

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Genomic Data Can Identify Individuals Based on Physical Traits

• Researchers used genomic data to predict skin color,

eye color, age, height, and biometric features

• Applying this information to 3D facial models, they

could correctly identify the subject from a pool of

1000 diverse individuals more than 80% of the time

Limitations / Criticisms

SOURCE: Lippert et al. Identification of individuals by trait prediction using whole-

genome sequencing data. Proc Natl Acad Sci U S A. 2017 Sep 19;114(38):10166-10171.

• Model success dropped to

50% when using a pool of

ethnically similar subjects

• Critics of the study claimed

that the diversity and limited

size of the dataset made it

relatively easy to identify

individuals

• Despite criticisms, this study

highlights the dynamic

nature of genomic re-

identification risk

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Policy Approaches to Genomic Privacy

Government Approaches Private Organization Recommendations

• HIPAA

• Genetic Information

Nondiscrimination Act

• Over 60 state statutes

• The responsible and secure sharing of

genomic and health data is key to

accelerating research and improving

human health.

• Individuals' rights include privacy,

autonomy, and the ability to choose for

themselves how they want to manage risk,

consistent with their own personal values

and life situations

SOURCE: https://www.hhs.gov/ohrp/regulations-and-policy/guidance/guidance-on-genetic-information-nondiscrimination-act/index.html;

https://www.genome.gov/policyethics/legdatabase/pubsearch.cfm; https://www.federalregister.gov/documents/2013/01/25/2013-01073/modifications-

to-the-hipaa-privacy-security-enforcement-and-breach-notification-rules-under-the https://www.ncbi.nlm.nih.gov/pubmed/29187736

• GDPR

• Country-level legislation

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• Genomic Data – What Is It and Why Do We Care?

• Genomic Privacy Issues and Policies

• Genomic Privacy Protection and De-Identification

Agenda

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Two Main Approaches to Protect Genomic Data

Process Controls Masking / Encryption

Methodology

Limitations • Assumes that good behavior of

users

• Data still identifiable and unable

to linked to de-identified data

• Vulnerable in the event of a

breach

• Research utility of the dataset

limited to provide data privacy

• Often increases analytic

complexity making large scale

analyses difficult

• Non-genomic identifiers (name /

address / etc.) are removed and

process controls or established

user agreements protect privacy

• Data is concealed or encrypted

to protect the privacy of

individuals

• Approaches include

homomorphic encryption, Yao’s

protocol, and other cryptographic

techniques

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An Alternative Approach –Deterministic Variant Tokenization

Patient-Level Tokenized

Genomic Data

ABC123

…

XYZ789

Tokenized Aggregated

Analytic Results

Variant Outcome p value

ABC123 T2DM .0023

DEF456 FBS .0014

Detokenized Aggregated Results

Chr Pos Mut Outcome p value

20 7241 C T2DM .0023

4 8902 T FBS .0014

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3 4

Tokenization

Analysis De-tokenization of

Aggregated Results

CTGCTCATCGCTCCTGTCATCGAGGCCCCTGG

CCCAATGGCAGGCGTCTCCCCCTCCTCTGGC

CTGGTCCCGCCTCTCCTGCCCCTTGTGCTCAG

CGCTACCTGCTGCCCGGACAAATCCAGAGCT

G

1 Genomic Sequencing

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De-Identified Genomic Data Can Still Be Annotated for Research

Gene Structure Information

• Identification of location within tokenized genes

• Exon / intron / promoter / enhancer

Variant Classification Information

• Insertion / Deletion

• Non-synonymous / synonymous / frameshift / stop-gain

Pathway Information

• Biological Pathway Data

• Gene Ontology Data

Clinical Information

• Clinical Significance

• Disease Pathway

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Model for De-ID and Linkage of Genomic and Clinical Data from Multiple Sites

Analytic Results

De-ID

Genomic

Data

Analytic Results

Research Query Research Query

Genomics

Consortium

Analytics

Platform Biopharma

De-ID

Engine

Data Sharing Model Goals

Linked Data

Tokenized

Variants

De-ID Clinical Data

One-Way Hash

• Whole genome variant data

• Linked to rich clinical history

• Sample size >1M

• Diverse ethnic,

environmental, social, and

geographic representation

• Broad disease representation

De-ID

Clinical

Data

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De-Identification and Data Linking Enable Ideal Data Model

Getting comprehensive

environmental, social, and

clinical data

Aggregate data at scale

De-identification of genomic data

enables linkage with other de-

identified data assets

De-identification of data reduces

privacy and competitive concerns

from contributing institutions

Balancing patient privacy

with research use

Genomic data is never readable at

the patient level throughout the

research process

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• Deterministic de-identification of genomic sequences offers a novel, pragmatic approach to patient privacy while maintaining research

• Challenges remain in managing genomic data storage and computational capacity given both the number and volume of genomic data

• Future policy discussions should look to enable research while still maintaining genomic data privacy

The Future of Genomic Data Privacy

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Questions

Kenneth Park, MD

kenneth.park@IQVIA.com

Ronald Miller, PhD

ronald.miller@IQVIA.com

Please complete online session evaluation