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TRANSLATING GENETIC BIOMARKERS TO THE CLINIC
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PLEASE STAND BY… the webinar
will begin shortly…
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
Look out for more webinars in the series at: webinar.sciencemag.org
For related information on this webinar topic, go to: www.affymetrix.com/pba_partners
To provide feedback on this webinar, please e-mail your comments to webinar@aaas.org
Sponsored by:
Brought to you by the Science/AAAS Custom Publishing Office Brought to you by the Science/AAAS Custom Publishing Office
Continue the conversation on the Affymetrix blog:
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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:
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