The road to personalized medicine is paved with data and information
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The Road to Personalized Medicine is Paved with Data and Information. John Quackenbush NCI Second Generation Sequencing May 3, 2012. Disease Progression and Personalized Care. Birth. Treatment. Death. Quality Of Life. Natural History of Disease. Clinical Care. Environment

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The road to personalized medicine is paved with data and information

The Road toPersonalized Medicine is Paved with Data and Information

John Quackenbush

NCI Second Generation Sequencing

May 3, 2012


Disease progression and personalized care

Disease Progression and Personalized Care

Birth

Treatment

Death

Quality

Of Life

Natural History of Disease

Clinical Care

Environment

+ Lifestyle

Outcomes

Treatment

Options

Disease

Staging

Patient

Stratification

Early

Detection

Genetic

Risk

Biomarkers


Turning the vision into a reality

Assure access to samples and rational consent

Develop a technology platform

Make information integration as a central mission

Conduct research as a vital component

Present data and information to the local community

Enable research beyond your own

Engage corporate partners

Communicating the mission to the community.

Turning the vision into a reality


Assure access to samples

Assure Access to Samples


Access research security

Patients want to be part of the process of curing disease

Informed consent needs to be structured to allow patients to be partners in the research process

HIPPA requires both informed consent and that we assure patient confidentiality

But “identifiability” is a moving target in a genomic age

With the <$1000 genome, in the age of Facebook, what this means remains unclear

The new Genomics is a disruptive technology.

Access, Research, Security


Develop a technology platform

Develop aTechnology Platform


The cost decreases exponentially with time

The cost decreases exponentially with time

Illumina GAII

ABI SOLiD

Continuing the Regression:

Genomes for $100 in February 2014

The $1000 Genome:

October 2012


2010 enabling a new era in genome analysis

2010: Enabling a New Era in Genome Analysis

IlluminaHiSeq

100Gb (~30X genome coverage)

150bp reads

Two samples/week

<$10,000 per genome


Just announced the life technologies ion torrent proton

Just Announced: The Life TechnologiesIon Torrent Proton

The Promise from LTI

A Genome in ~24 hours for $1000

Promised in Q3 2012


Let the games begin the oxford nanopore minion

Let the games begin!

The Oxford NanoporeMiniON

The USB sequencer


The challenge

The Challenge

New technologies inspired by the Human Genome Project are transforming biomedical research from a laboratory science to an information science

We need new approaches to making sense of the data we generate

The winners in the race to understand disease are going to be those best able to collect, manage, analyze, and interpret the data.


Make information integration as a central mission

Make information integration as a central mission


Beating information overload

Beating Information Overload

Improved Diagnostics

Individualized Therapies

More Effective Agents

Cytogenomics

Genomics

Clinical

Data

Metabolomics

Transcriptomics

Proteomics

Epigenomics

CentralWarehouse

Chemical

Biology

Published

Datasets

PubMed

The

Genome

Clinical

Trials

The

HapMap

DrugBank

Etc.

Disease

Databases

(OMIM)


Conduct research as a vital component

Conduct research as a vital component


Data generation

Data Generation

Illumina partnered with us to generate comprehensive mRNA, microRNA, and methylation, and copy number variation (CNV) profiles on these FFPE ovarian cancer samples

Renee Rubio and Kristina Holton developed protocols for efficient extraction of mRNA/microRNA and genomic DNA from FFPE cores

Quality was validated using BioAnalyzer and hybridizations to Illumina DASL arrays

mRNA/microRNA and DNA were extracted from 132 samples and profiled in collaboration with Illumina on a prototype 12k DASL array

Data were normalized and analyzed using the ISIS class discovery algorithm.


Identifying modules using isis

Identifying modules using ISIS*

Module:

Set of genes supporting a bi-partition

ISIS searches for stratifications of samples into two groups that maximize a DLD score.

*ISIS: Identifying splits of clear separation (von

Heydebreck et al., Bioinformatics 2001)


Module 2 gene expression

Module 2 (gene expression)


Module 2 gene set enrichment analysis

Module 2Gene set enrichment analysis


Survival and validation

Survival and Validation

1090 high grade, late stage serous tumors

1606 published ovarian tumors


Present data and information to the local community

Present data and information to the local community


Lgrc research portal

LGRC Research Portal


Lgrc data download

LGRC Data Download

  • Data download

  • Browse by basic metadata

  • Browse by clinical / phenotype attributes

  • Download ‘raw’ data

  • Secure transfer via single use ‘tickets’ . Enables authorized users access to the specified result basket for a single session.


Lgrc research portal1

LGRC Research Portal


The road to personalized medicine is paved with data and information

PAGE DETAILS

Search

-Facets

-Search within results

-Keyword prompts

-Search history

Table:

-Paged results

-Sortable columns

Actions:

-Go to Gene detail page

-Add genes to ‘gene set’


The road to personalized medicine is paved with data and information

PAGE DETAILS

Annotation summary & summary view for each assay/data type:

Accordion style sections

GEXP – expression profile across major Dx categories

RNASeq – Exon structure of the gene

SNPs – Table of SNPs in region of gene, highlighting association with major Dx group

- Methylation – Methylation profile in region around gene

Genomic alterations – table of CNVs & alterations observed w/ freq in region around gene

Actions:

- Click through to assay detail page

Add gene to set

Annotation

Summary

Gene Expression Summary

RNASeq


Lgrc research portal2

LGRC Research Portal


Lgrc research portal3

LGRC Research Portal


The road to personalized medicine is paved with data and information

PAGE DETAILS

- View aggregate statistics

-View cohort details

-Build cohort sets

-Build composite phenotypes

Actions:

-Go to data download for selected cohort

-Go to assay detail for selected cohort

-Go to cohort manager


Lgrc research portal4

LGRC Research Portal


Engage corporate partners

Engage corporate partners


We need to find the best tools

We need to find the best tools

We received an $1M Oracle Commitment grant to create our integrated clinical/research data warehouse

We’ve partnered with IDBS to create data portals

We are working with Illumina on a variety of projects

We are forging relationships with Thomson-Reuters to link genomic profiling data to drug, trial, and patent information

We are building partnerships with Roche, Genomatix, NEB, and others interested in entering the personal genomics space.


Enable research beyond your own

Enable research beyondyour own


John quackenbush director mick correll associate director

John Quackenbush, DirectorMick Correll, Associate Director


The mission

The Mission

The mission of the CCCB is to provide broad-based support for the analysis and interpretation of ‘omic data and in doing so to further basic, clinical and translational research. CCCB also will conduct research that opens new ways of understanding cancer.


Cccb collaborative consulting model

CCCB

Collaborative Consulting Model

Initial meeting to understand project scope and objectives

Development of an analysis plan and time/cost estimate

During project execution, data and results are exchanged through a secure, password-protected collaboration portal

Available as ad-hoc service, or larger scale support agreements

Sequencing

IT Infrastructure

Consulting


Communicate the mission to the community

Communicate the mission to the community.


The lgrc

The LGRC


Why patient involvement is essential

Why Patient Involvement is Essential

Patients want to be our partners in curing disease

The incentive structure in medical research is skewed away from success

We all say, “We want to cure disease.”

We mean, “We want to cure disease, but only if I am the one to cure disease.”

The only way to break the logjam is to have patients involved in the process.


Genomics is here to stay

Genomics is here to stay


Acknowledgments

Acknowledgments

The Gene Index Team

CorinaAntonescu

ValentinAntonescu

Fenglong Liu

Geo Pertea

Razvan Sultana

John Quackenbush

Array Software Hit Team

Katie Franklin

Eleanor Howe

John Quackenbush

Dan Schlauch

RaktimSinha

Joseph White

Eskitis Institute

Christine Wells

Alan Mackay-Sim

Center for Cancer

Computational Biology

Mick Correll

Victor Chistyakov

HowieGoodell

LanHui

Lev Kuznetsov

Niall O'Connor

Jerry Papenhausen

Yaoyu Wang

John Quackenbush

http://cccb.dfci.harvard.edu

Gene Expression Team

FiedaAbderazzaq

Stefan Bentink

AedinCulhane

Kathleen Fleming

Benjamin Haibe-Kains

Jessica Mar

Melissa Merritt

MeghaPadi

Renee Rubio

(Former) Stellar Students

Martin Aryee

KavehMaghsoudi

Jess Mar

Systems Support

Stas Alekseev, Sys Admin

PriyaKaranam, DBA

Administrative Support

Joan Coraccio

JuliannaCoraccio

http://compbio.dfci.harvard.edu

<[email protected]>

http://compbio.dfci.harvard.edu


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