Clinical Working Group

(1)

(2)

Clinical Working Group

Global Alliance for Genomics and Health

March 4

th

, 2014

(3)

Clinical Working Group Members

Name

Institution

Kathryn North (Co-Chair)

Murdoch Childrens Research Institute, Melbourne, Australia

Charles Sawyers (Co-Chair)

Memorial Sloan Kettering Cancer Center, New York, United States

Gil Alterovitz

Harvard Medical School, Boston, United States

Kym Boycott

Children’s Hospital of Eastern Ontario Research Institute, Canada

Paul Burton

University of Bristol, Bristol, United Kingdom

Mark Lawler

Queen’s University Belfast, Belfast, United Kingdom

John Mattison

Kaiser Permanante, Pasadena , United States

Andrew Morris

University of Dundee, Dundee, United Kingdom

Anthony Philippakis

Genome Bridge LLC, Cambridge, United States

Heidi Rehm

Harvard Medical School, Boston, United States

Dan Roden

Vanderbilt University School of Medicine, Nashville, United States

Lillian Siu

University Health Network, Toronto, Canada

Khee Chee Soo

National Cancer Centre of Singapore, Singapore

Volker Straub

Newcastle University, Newcastle upon Tyne, United Kingdom

(4)

Mandate and Scope

Mandate:



How do we represent phenotypic data and link it to genotypic

information?

Scope:



Initial focus on rare genetic disorders and cancer



Widen scope to complex traits and ID with time

Approach:



Avoid re-inventing the wheel



Add value to existing endeavors



Address both research and clinical use scenarios

(5)

C

LINICAL

W

ORKING

G

ROUP

Approach

1. Define the need and potential solutions

2. Document existing efforts

3. Assess compatibility of approaches

4. Identify gaps and propose solutions to fill them

5. Select demonstration projects

6. Implement solutions in an R&D environment - and when ready,

integrate into clinical use

7. Ensure sustainability of the solution and adapt as need

evolves

8. Communicate globally - white papers, networks, international

forums

(6)

C

LINICAL

W

ORKING

G

ROUP

Activities To Date

(7)

Learning Tour

Phenotype Ontology



PhenoTips and PhenomeCentral, Recording and Sharing of Detailed Patient Data

Michael Brudno (University of Toronto)



Human Phenotype Ontology

Peter Robinson (Charité Universitätsmedizin Berlin)



GeneMatcher and PhenoDB Tools

Ada Hamosh (Johns Hopkins University School of Medicine)

Data Harmonization



Paul Burton (University of Bristol) and Isabel Fortier (McGill University)

Biomedical Informatics, EHRs and Data Extraction



CancerLinQ: The Future of Cancer Care

Lillian Siu (University Health Network)



Phenotyping in the Electronic Medical Record – Challenges and Opportunities

Dan Roden (Vanderbilt University School of Medicine)



Advances in Health Database Technologies: Use of EHR Data for Clinical Discovery

George Hripcsak (Columbia University)

(8)

Rare Disease

Mapping of Existing Tools

Initiative

Ontology

Tool

Database

Matchmaker

Phenotype

Genotype

PhenoTips

_X

DECIPHER

_X

PhenomeCentral

_X

ClinGen

_X

ClinVar

_X

PhenoDB

_X

OMIM

_X

HPO

_X

LDDB

_X

Gene Matcher

_X

GEM.app

_X

LOVD

_X

Cafe Variome

_X

(9)

Rare Disease:

Mapping of Existing Initiatives

Group

North America

Europe

International

IRDiRC

CARE for RARE

_X

NORD

X

Human Variome Project

X

Genetic Alliance

Orphanet

NIH Office of Rare Disease

Research (ORDR)

RD-Connect

Neuromics

X

ICCG

(10)

Example of Approach

Phenotype Ontology

1. Define Need Physicians, researchers and clinical laboratories are in need of systems to enable standardized and structured phenotypic data to be

collected from patients. 2. Document Existing

Efforts

A number of phenotype ontologies exist today including:

● Human Phenotype Ontology (HPO) created by Peter Robinson is the most comprehensive ontology

● PhenoDB created by Ada Hamosh for the Centers for Mendelian Genomics (intended for quick and simple use by a physician)

● London Dysmorphology

● SNOMED (Coding system for electronic health records and other medical data systems)

● UMLS

● MedGen (NCBI ontology)

In addition, several databases exist that catalog diseases including OMIM and Orphanet.

Several tools exist to support data collection using these ontologies including PhenoDB and PhenoTips 3. Assess Compatibility

if Multiple Approaches Exist

Given different uses, it is unlikely that a single ontology could support all needs. Instead, recent efforts have focused on identifying the most commonly used terms and mapping these across all ontologies to support interoperability. This effort, called ICHPT, is being led by Ada Hamosh and Peter Robinson and resulted in the identification of 2300 such terms.

4. Identify Gaps and Propose Solutions

● Clinical areas within these ontologies for which expansion of terms are needed?

o

Work with clinical domain experts to identify gaps and propose new terms to fill them

● Are there barriers to implementation within the medical and research environments?

o

Slowness of EHR system development

o

Need to support IT integration of tools into clinical laboratory and research systems

5. Implementation into Clinical and Research Use

Provide phenotyping tools that can operate as standalone systems as well as integrated into other systems such as electronic health records.

6. Communicate Communicate the availability of tools and solutions to the broader community through publications, talks and websites.

7. Sustain ● Need to maintain and evolve ontologies to meet community needs.

● Need to update tools as ontologies evolve.

1. Define Need

Physicians, researchers and clinical laboratories are in need of systems to enable

standardized and structured phenotypic data to be collected from patients.

(11)

Example of Approach

Phenotype Ontology

Efforts

● UMLS

o

2. Document Existing Efforts

A number of phenotype ontologies exist today including:

Human Phenotype Ontology (HPO) created by Peter Robinson is the most comprehensive

ontology

PhenoDB created by Ada Hamosh for the Centers for Mendelian Genomics (intended for

quick and simple use by a physician)

London Dysmorphology

SNOMED (Coding system for electronic health records and other medical data systems)

UMLS

MedGen (NCBI ontology)

Several databases exist that catalog diseases including OMIM and Orphanet.

(12)

Example of Approach

Phenotype Ontology

Efforts

● UMLS

o

3. Assess Compatibility if Multiple Approaches Exist

Given different uses, it is unlikely that a single ontology could support all needs.

Instead, recent efforts have focused on identifying the most commonly used terms

and mapping these across all ontologies to support interoperability. This effort,

called ICHPT, is being led by Ada Hamosh and Peter Robinson and resulted in the

identification of 2300 such terms.

(13)

Example of Approach

Phenotype Ontology

Efforts

● UMLS

o

4. Identify Gaps and Propose Solutions

Clinical areas within these ontologies for which expansion of terms are needed?

Work with clinical domain experts to identify gaps and propose new terms to fill them

Barriers to implementation within the medical and research environments.

Slowness of EHR system development

(14)

Example of Approach

Phenotype Ontology

Efforts

● UMLS

o

5. Implementation into Clinical and Research Use

Provide phenotyping tools that can operate as standalone systems as well as integrated

into other systems such as electronic health records.

6. Communicate

Communicate the availability of tools and solutions to the broader community through

publications, talks and websites.

7. Sustain

Need to maintain and evolve ontologies to meet community needs.

Need to update tools as ontologies evolve.

(15)

Phenotype Ontology

PhenoTips

Source:

http://phenotips.org/FeaturePreview

(16)

PhenoTips

Source: http://phenotips.org/FeaturePreview

Configurable Phenotype Checklist

(17)

PhenoTips

Source: http://phenotips.org/FeaturePreview

Measurement Recording and Interpretation

(18)

PhenoTips

Source: http://phenotips.org/FeaturePreview

Pedigree Drawing

(19)

Matchmaker

1. Define Need The existence of extremely rare phenotypes and genetic variation results in the need for systems to support the algorithmic

matching of unsolved genomic cases based upon overlapping phenotypic and genotypic data. Matching can be simplistic using high level phenotypes and candidate genes or comprehensive (deep phenotypes and full vcfs)

2. Document Existing Efforts ● Gene Matcher within PhenoDB created by Ada Hamosh

● PhenomeCentral created by Michael Brudno

● DECIPHER created by Matt Hurles and Helen Firth

● GEM.app creatde by Stephen Zuchner

● LOVD created by Johan den Dunnen

● Café Variome creatde by the Gen2Phen project

3. Assess Compatibility if Multiple Approaches Exist

● Systems are currently not interconnected.

● Near term proposal is to interface local systems with each system acting as portal to the others.

● Long term solution may involve a centralized site.

● Need to assess data models for each system and agree upon a common format for data exchange

● Need to develop algorithms to enable phenotypic and genotypic matching with scores for degree of match

Work with other sequencing sites to encourage use of one of the interfaced systems

6. Communicate Communicate the availability of matching and storage systems to the broader community of clinical labs, physicians and

researchers through publications, talks and websites.

7. Sustain ● Need to determine which resources are needed to sustain the endeavor and how will the resources be provided.

● Need buy-in from data suppliers and data users.

1. Define Need

The existence of extremely rare phenotypes and genetic variation results in the need for

systems to support the algorithmic matching of unsolved genomic cases based upon

overlapping phenotypic and genotypic data.

Matching can be simplistic using high level phenotypes and candidate genes or

comprehensive (deep phenotypes and full vcfs)

(20)

Data Harmonization

Need:



Analyses regularly demand large numbers of individuals each measured carefully for a variety of

phenotypes. Questions cannot be answered using any one single study and it is often essential that

data can be brought together across several sources

Key Issues:



Consideration of ethic-legal frameworks that apply to different studies



There are numerous approaches to harmonization – each with pros and cons. However, whichever

approach is decided, it must be consistently adopted for each variable across all studies



Once-data have been epidemiologically harmonized, one of the key challenges is how to co-analyze

the data



Harmonization is labor intensive and context specific and therefore entails substantial additional work

each time a new project is proposed. Therefore rules and algorithms need to be reported



Need for support from funders and journals to require data harmonization

Existing Efforts

COGENE

GenomEUtwin

P

3

_G

_PHOEBE

_DataSHIELD

BioShaRE-eu

BBMRI

OBiBa

Maelstrom

Research

DataSHaPER

PHENX Toolkit

CLOSER

(21)

Data Extraction from EHRs

Need:



Validated methods to extract phenotypic information from EHRs with varying

architectures

Key Issues:



A larger catalog of validated phenotypes



Validation of automated methods for phenotype development and data extraction

from EHRs

Existing Efforts

eMERGE

HL7

Biobank UK

Kadoorie

biobank

Kaiser Northern

California

(22)

Rare Disease

IRDiRC Road Map

Courtesy of Kym Boycott

Avoid reinventing the wheel

Add value to existing endeavours

Convene-Collaborate-Catalyse

Globally

(23)

Cancer

1. Clinical Implementation of Next Generation Sequencing:



Defining actionability

(druggable, predictive, prognostic)



Functional significance of variants detected

(e.g. known in same tumor type, known in another tumor type, uncertain)



Actionable Gene Panels for Cancer: consensus

2. Linking phenotyping data to genomic data:



Shared goals with rare disease initiatives

3. Global “Matchmaking” for rare tumors/ mutations:



Sharing information for potential clinical trials

(24)

Cancer

Mapping of Current Initiatives

Institution, Consortium or Region Trial or Program Name Profiling Platform/s or Technique/s Genes and or Mutations Cancer(s) Archival or Fresh Tumour Ref And/or Trial Identifier

Cancer Research UK Stratified Medicine

Programme

PCR 9 genes _{Melanoma, NSCLC, CRC,} Breast, Prostate, Ovarian cancer

Archival 121 FISH 3 genes

Dana Farber Cancer

Institute PROFILE Sequenom

OncoMap 41 genes, 471

mutations All solid tumours Archival

122

Institut Curie, Institut

National du Cancer SHIVA

Ion Torrent PGM Ampliseq 46 genes

All solid tumours Fresh biopsy NCT01771458123

Cytoscan HD 29 genes

Institut Gustav Roussy (non-pediatric trials)

MOSCATO aCGH PCR

Not Applicable 96 mutations

Solid tumour phase I

patients Fresh Biopsy NCT01566019

75

SAFIR01 aCGH PCR

Not Applicable

2 genes Breast cancer Fresh Biopsy NCT01414933

124 MSN PCR FISH Seqcan 30 genes 5 genes Melanoma, SCLC, NSCLC Fresh Biopsy 125 Massachusetts General

Hospital NS SNaP Shot 14 genes, >50 mutations

NSCLC, CRC, Melanoma,

Breast cancer Archival

126,127

MD Anderson Cancer Center

T9 Program Sequenom 40+ genes All solid tumours Archival 128 IMPACT

PCR 10 genes

All solid tumours Archival 73

NCT00851032 FISH 1 gene

Clearing House

Protocol PCR ~100 genes All solid tumours

Archival and or Fresh Biopsy

129

(25)

Cancer

Mapping of Current Initiatives

Institution, Consortium or Region Trial or Program Name Profiling Platform/s or Technique/s Genes and or Mutations Cancer(s) Archival or Fresh Tumour Ref And/or Trial Identifier Memorial Sloan-Kettering Cancer Center IMPACT

Illumina HiSeq 275 genes (Research

assays) _{All solid tumours} _Archival _NCT01775072 Sequenom or MiSeq NS (Clinical assays)

Netherlands

Centre for Personalized Cancer Treatment

Ion Torrent PGM ~150 genes

Solid tumours Fresh Biopsy 130 SOLiD 5500xl >2000 genes

Norwegian Cancer Genomics

Consortium

Nationwide

program NS Whole Exome

9 tumour types both solid and haematopoietic Archival and or Fresh Biopsy 131 Princess Margaret

Cancer Centre IMPACT

MiSeq TSACP 48 genes, >700

mutations _{Selected solid}

tumours Archival NCT01505400

60

Sequenom Customized panel 23 genes, 279 mutations Vall D’Hebron

Institute of Oncology NS

Sequenom OncoCarta 19 genes, 238 mutations

Breast cancer, Solid tumour phase I patients

Archival 72,132 Illumina GAIIx NS

Vanderbilt-Ingram

Cancer Center PCMI SNaPshot 6-8 genes, >40 mutations

Melanoma, NSCLC,

CRC, Breast cancer Archival

133

WIN Consortium WINTHER study NGS NS Solid tumours

Fresh Biopsy (Tumour and Matched Normal)

NCT0185629683

(26)

CWG Next Steps

(27)

Clinical Working Group

Tomorrow’s Goals

Meeting Objectives:



To identify the best driver/demonstration projects for the CWG



To expand on the work currently being done in Rare Disease and Cancer



To determine CWG next steps (1 and 3 year plan) and timeline

Cancer Topics:



Clinical Implementation:



Next generation sequencing: overcoming barriers to implementation in the clinic



Actionable gene panels for cancer: achieving a consensus

