TRANSLATING GENETIC BIOMARKERS TO THE CLINIC

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PLEASE STAND BY… the webinar

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Webinar Series Science

Sponsored by:

Participating Experts:

Timothy J. Yeatman, M.D. Moffitt Cancer Center, Tampa, FL and Gibbs Cancer Center, Spartanburg, SC

Henk Viëtor, M.D., Ph.D. Skyline Diagnostics Rotterdam, The Netherlands

Brought to you by the Science/AAAS Custom Publishing Office

Webinar Series Science

3 October, 2012

THE PROMISE AND PITFALLS OF DEVELOPING ROBUST, RELIABLE SIGNATURES

TRANSLATING GENETIC BIOMARKERS TO THE CLINIC:

Translating Genetic Biomarkers to the Clinic: The Promise and Pitfalls of Developing

Robust, Reliable Signatures

Timothy J. Yeatman, M.D. Moffitt Cancer Center, Tampa, FL

Gibbs Cancer Center, Spartanburg, SC

webinar.sciencemag.org October 3, 2012

PROLIFERATION OF GENE SIGNATURES

The Promise

• 500-800 new targeted drugs emerging in pipelines

• Drugs developed with companion diagnostics may dramatically reduce time to market : – BRC-ABL: 41 years to Gleevek – ERBB2: 13 years to Herceptin – BRAF: 8 years to PLX-4032 – ALK: 3 years to crizotinib

• Right target, right drug, best response

Challenges • FF vs FFPE • CLIA LDT vs FDA Approved 510K • Technical validation vs clinical validation • Single gene, multiple gene, multi-analyte • Meaningful use • Who pays for the test development? For the test? • Achieving CMS approval vs insurance coverage • ASCO and NCCN recognition • Emerging technologies • Tumor heterogeneity • Tumor biopsy

DISCOVERY DEVELOPMENT DELIVERY

• Gene Expression Profiling • NextGen Sequencing • Proteomics

• Translation from FF to FFPE • New Algorithms • Analytical & Clinical Validation

• Commercial production • CLIA vs FDA • Community adoption

The Biomarker Development Pathway

“But For” Test

• Test must address an “actionable” clinical need

• Test must make a difference in a critical clinical decision; e.g. to treat or not to treat

• Test must have substantial power to predict outcome; i.e. RAS mutation = “0” Response to EGFRi

• Test should save time, money and lives

Identify the End User Who will order & use the test?

• Surgeon? • Medical Oncologist? • Radiation oncologist? • Pathologist?

Examples: • OncoDx: Medical Oncologist • Mammoprint: Surgeon/Medical Oncologist • PathworksDx: Pathologist/Medical Oncologist

The Biopsy Challenge

• Primary tumor often used for testing – Large surgical sample – Easy access – Unknowns: how different is the metastasis from

the primary?

• Metastatic tumors generally biopsied by very small needles and samples are preserved as FFPE

Challenge of Tumor Heterogeneity

• Signatures often developed using primary tumors

• Confounding problem of tumor stroma, necrosis

• Metastatic tumors may be the samples tested in the future

Platform

• FFPE vs FF • Gene expression • Sequencing • Proteomics

Gene Expression vs Sequencing

• Gene expression – Relative scale – May “summarize” effect of multiple mutations on

pathways – Difficult to compare samples

• Sequencing

– Digital – Universal – Easy to compare samples, even when evaluated using

different platforms (PCR, Sequenom, SnapShot, etc)

The Challenge of Translating a Signature

• If signature produced on FF tissues, new algorithm will be required for FFPE tissues (even if precisely same platform used) – FFPE still requires different RNA extraction

protocols – Macrodissection – FFPE methods (sample preservation not a priority)

Global Gene Expression Analysis—Frozen Tissue

Identification of Gene Signature Correlating to Outcome

Validating Gene Signature on Independent Gene Expression Test Sets

Translate Signature to FFPE Platform • New samples • New algorithms • Normalization Considerations

Re-test signature using Independent Set of FFPE Samples

Multiple Publications

Physician Adoption

Commercialized Test

Development Pathway

Normalization Challenge: n+1

• MAS5 • RMA • Ref RMA/Incremental RMA

Sequencing: Is more better?

• Capillary PCR & SNV analysis (Single point mutations)

• SNV Analysis: Sequenom & SNaPshot (20 genes)

• Targeted Exome Sequencing: Foundation Medicine (200 genes, fully sequenced)

• Whole Exome Sequencing • Whole Genome Sequencing (Complete

Genomics)

Integrating Gene Expression + Sequencing

• Gene expression signature + mutation X = bad prognosis

• Gene expression signature – mutation X = good prognosis

• Simple, linear algorithms best

Research vs SOC

• Can samples stored for clinical purposes also be used for research?

• How are clinical samples used for research? – Informed consent? – Re-contact? – Waiver of informed consent?

• Limited data sets

Case Example

• 65 year old male with metastatic colon cancer • Failed FOLFOX/Avastin • RAS testing: WT • Responded then failed Irinotecan/Cetuximab • Sequenom testing: P53 mutant; FBXW7 mutant • Research data suggest HDAC inhibitors might be

effective • Who will pay for “off-label” use? • Who will order off-label drug therapy?

Challenges of “n of one”

• Payers generally wont reimburse for “off-label” use of drugs that are not SOC

• No indication for HDAC inhibitors in colon cancer

• Only those who can afford out of pocket costs could try this last ditch effort

• Few medical oncologists would prescribe outside of a trial scenario

FDA 510K vs CLIA LDT

• FDA 510K – Instrument approval – Kits developed and approved – Clinical and analytical test validations

• CLIA LDT – Requires laboratory approval – Clinical and analytical test validations

More Questions than Answers

• Can we afford an ever increasing number of single tests?

• Can we afford more expensive, comprehensive tests?

• Can we afford to treat patients with the wrong regimen?

• Should we invest more into the “proper diagnosis” of cancer before treating?

THANK YOU FOR YOUR ATTENTION!

Personalized medicine is already here; now how to proceed (?) and make a profitable business. Henk Viëtor

Mission Statement

Skyline Diagnostics develops and commercializes diagnostic microarrays with a high clinical additive value for patients

with (hematological) cancers

Our Company

Skyline (2005) is a spin-off from Hematology and Bioinformatics at Erasmus University MC, NL

Expertise in hemato-oncology, bioinformatics and genomics

Flagship product for acute myeloid leukemia – the AMLprofiler™.

CE-registered IVD product, clinical trial for FDA PMA ongoing

7 assays and 3 different technologies on 1 microarray

Significant growth potential incl. companion diagnostic profilers ™

28

Translational development

Translating Science into Product

Marketing and sales

Contract research

Collaboration with EMC since 1995

Landmark discovery in Valk et al. (NEJM, 2004)

Algorithm development

Microarray design

Assay development

Software development

IT-infrastructure building

Independent validation

Skyline’s complete IVD Solution

Customized Microarray and Instructions-for-Use Affymetrix Reagents

Affymetrix DX2 Instrumentation IT-infrastructure

Acute Myeloid Leukemia (AML) background

Most common leukemia in adults

– in the US (2010): 12.330 new AML cases

– in the developed world: incidence is 4:100,000

AML is a collection of diseases (subtypes)

Treatment choice is often based on risk profile , indicated by subtype/prognostic marker

For patients with Acute Myeloid Leukemia, assessing the prognosis and thereby directly facilitating the treatment decision making process

First product; the AMLprofiler

The AMLprofiler™ incorporates 3 different Technologies

I. Gene Expression Signatures

inv(16)

t(8;21)

t(15;17)

CEBPAdm (CEBPA double mutants)

II. Direct mutation detection

NPM1 A mutation

NPM1 B mutation

NPM1 D mutation

EVI1

BAALC III. Gene expression of prognostic genes

0

25

50

75

100

0 12 24 26 48 60

Overall Survival

Cum

ulat

ive

perc

enta

ge

Favorable Risk Group

Intermediate Risk Group

Unfavorable Risk Group

Months

10%

75%

15%

75%

Cytogenetic risk classification of AML

How to classify cytogenitically normal patients?

Validated and Secure Remote data analysis

DX2 auto starts Skyline’s proprietary remote data analysis Results are automatically reported back to the originating DX2

Skyline provides diagnostic analysis using its proprietary software in a secure FDA compliant hosted server environment

Accuracy Study for FDA Approval (PMA)

Item Characteristic

Duration 12-18 months

Principal Investigator A Ulm, Germany

Principal Investigator B ErasmusMC, Rotterdam, Netherlands

Principal Investigator C London / Cardiff, UK

Principal Investigator D Columbus (Ohio), USA

Principal Investigator E Memorial Sloane-Kettering, USA

Molecular Diagnostics and AMLprofiler ref lab Sanquin, Amsterdam, Netherlands

Infrastructure Training Repro- duceability Clinical Trials

US labs EU labs Current Level of advancement

Q4 2012

AMLprofiler Regulatory Pathway

2011 2012 2013 2014

VALID

FDA approval

Apr 2011

Full IVD

Mar 2011

US commercial launch

MMprofiler

2010

ALLprofiler

Companion

2011 2012 2013 2014 2015

AMLprofiler

Product Pipeline Overview

MMprofiler

Risks profile based on 92-gene panel allowing physicians to distinguish between high and low recurrence risk patients which will drive treatment decisions

Identification of cytogenetic subgroups t(4;14), t(11;14), t(14;16) and del(17p)

Extensive gene expression profiling studies on tumor samples in over 800 patients may lead to additional clinically relevant markers currently evaluated for their feasibility and clinical relevance

Second most frequent hematologic cancer with over 55,000 new cases per year

Diagnostics market estimated at €100-€150mm in developed countries

Tumors induce massive bone destruction, renal failure and immunodeficiency

Limited diagnostic options, which are insufficient in addressing the heterogeneity of the disease

Signatures published in BLOOD: Broyl and Sonneveld et al

Profiles are based on the U133 plus 2.0 GeneChip® and does not require a specific dedicated, customized array

Entering development phase – Commercially available as an RUO Q1 2012

Product Development Multiple Myeloma

Features

Most effective anti-myeloma drug

– Millennium/Takeda (US) Johnson & Johnson (EU) and Janssen Cilag

Typical treatment includes 6-9 cycles (4 doses)

Induced polyneuropathy in 30-50% patients

Most frequent dose limiting adverse effect, 35% has to reduce or discontinue Velcade

Translates in reduced response and survival

Large scale clinical trial double randomized, two arms 800 patients;

Arm 1 standard therapy and Thalidomide (400)

Arm 2 standard therapy and Velcade (400)

Initiated in 2006, now 5 year follow-up clinical data

Velcade

Velcade side effect predictor

Companion Diagnostic Example - Velcade

Samples of 600 cases collected

Analysed with custom build SNP (single nucleotide polymorphism) array, 3540 SNPS, 900 genes based on biology. First results very promising.

Preliminary Results

40

Thank you for your attention

Henk Viëtor

henk.vietor@drugdiscoveryfactory.nl

Sponsored by:

Participating Experts:

Brought to you by the Science/AAAS Custom Publishing Office

To submit your questions, type them into the text box

and click .

Webinar Series Science

3 October, 2012

THE PROMISE AND PITFALLS OF DEVELOPING ROBUST, RELIABLE SIGNATURES

TRANSLATING GENETIC BIOMARKERS TO THE CLINIC:

Henk Viëtor, M.D., Ph.D. Skyline Diagnostics Rotterdam, The Netherlands

Timothy J. Yeatman, M.D. Moffitt Cancer Center, Tampa, FL and Gibbs Cancer Center, Spartanburg, SC

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For related information on this webinar topic, go to: www.affymetrix.com/pba_partners

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Sponsored by:

Brought to you by the Science/AAAS Custom Publishing Office Brought to you by the Science/AAAS Custom Publishing Office

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Webinar Series Science

3 October, 2012

THE PROMISE AND PITFALLS OF DEVELOPING ROBUST, RELIABLE SIGNATURES

TRANSLATING GENETIC BIOMARKERS TO THE CLINIC: