Copyright ©2015 Q2 Solutions. All rights reserved.
COMPANY CONFIDENTIAL
Systematic Design and Development of The Q2 Solutions
Comprehensive Cancer Panel – A Case Study in Iterative
Design and Validation Thomas Halsey, Ph.D.
Associate Director, Genomic Research and Development
Victor Weigman, Ph.D.
Associate Director, Translational Genomics
Precision Medicine Congress | 15 September 2015
2 COMPANY CONFIDENTIAL
• Describe QCCP content, how it was selected and design
considerations
• Review QCCP performance characteristics and impact of
bioinformatic enhancement
• Bioinformatics Pipeline Development and Validation
• Considerations in using the panel for genomic profiling of
cancer patients
Key Objectives
3 COMPANY CONFIDENTIAL
Focusing on pairing patient to treatment
– Established relationship between mutation and drug response / resistance
– Signaling pathways related to cancer that are predicted to affect drug
response and have shown high frequency of variation in cancer samples
– Associated with novel drug classes and chemotherapies
– High frequency of genomic variation in cancer generally
– High frequency of genomic variation in specific cancers even if effects on
drug response / resistance are unknown
Q2 Solutions Comprehensive Cancer Panel: QCCP Initial gene selection criteria
4 COMPANY CONFIDENTIAL
CARD11
BCL2
CASP8
TERT
CCND2
CCND3
CDC73
SMARCA4
EP300 KDM6A
SETD2
H3F3A
ATR CHEK1 CHEK2 ERCC2 FANCA MRE11A NBN P63
TOP1
TOP2A
ERRFI1 SOCS1
STAT3
STAT1
SRC
GRIN2A AURKA
AURKB
PPP2R1A
AKT3
PIK3R2
PIK3R5
RICTOR
RAPTOR
PIK3CG
mTOR
ARAF
CRAF
MAP2K1
MAP2K2
MAP2K4
MAP3K1
CSF1R
INSR
FLT1
FLT4
ESR1
PGR
IL-7R
NTRK3
PTCH2
GLI1
SUFU
U2AF1
ACVR1B
BCOR
MITF
PHF6
PRDM1
NFE2L2
GATA3
KLF4
SRSF2
FAM123B
AXIN1
Apoptosis
PI3K-
AKT
DNA
Repair
Cell
Cycle Chromatin
Modification
RAS-
MAPK
Receptor/
Growth
Factor
TGFβ
Signaling
Hedgehog
Signaling
Transcriptional
Regulation
JAK-
STAT
EGFR
Signaling
Mitotic
Pathway DNA
Replication
WNT-
APC
Phosphatase Metabolism Splicing
Oncogenic Signaling pathways targeted in cancer
P73 PARP1 RAD50 RAD51 ERCC3 XPC WRN BLM
Important markers for
chemotherapy and
novel inhibitors e.g.
PARPi
High frequency variants in genes involved in important cancer pathways
Pathway Targets for QCCP
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QCCP Final Release
• Genes selected by Key Opinion Leaders in Oncology: Quintiles TRDO, JHMI, Key
Customer Accounts
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Standard SureSelect Capture Process
Targeted capture process
• Manual low-throughput process
• 2 µg fragmented genomic DNA
converted to library
• Multiple purifications using Ampure
beads
• Sample loss through attrition
• Hybridization against biotinylated
bait library
• Bait capture via streptavidin
conjugated magnetic beads.
• Remove baits and amplify library
• Sequence
Image taken from www.genomics.agilent.com
Standard SureSelect capture procedure is
very good, but input amount requires
improvement to enable Oncology profiling
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QCCP: Modified SureSelect Capture Process
• Automated on the Beckman FX
• 150 ng fragmented genomic DNA converted
to library
• Library molecules undergo multiple
cycles of binding and elution to the
same aliquot of beads
• Less sample loss through attrition.
• Hybridization against biotinylated bit library
• Bait capture via streptavidin conjugated
magnetic beads
• Remove baits and amplify library
• Sequence Fisher et al. 2011, Genome Biology
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Bioinformatics enhances assay performance
Company Confidential
Alpha performance vs. production performance
• Optimized
– Library preparation
• 75% reduction in sequencing artifacts
– Efficiency of baits
• 30% increase in reads mapping
• 24% increase of bases in target
• 25% increase in uniformity
Example depth improvement for 4 random exons
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• 102 individual specimens used in the validation
– HorizonDx Multiplex DNA Reference Standard
– Tru-Q NGS Reference Standards 1-7
– 69 Patient samples derived from FFPE
• 25 breast cancer
• 22 lung cancer
• 22 colorectal cancer
– 3 patient samples from each cancer type were included as FF case-matched material
– NA12878
– NA18855 and NA10855
– Admixtures of cell line DNA
Summary of Validation
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Sample
Type Coverage
Mean
Depth
%
Bases
on
Target
% ROI
Found
% Target
with 250x
F/R
%Bases
with at
least 500X
coverage
Cell Lines 99.54 2694.3 71.91 99.68 94.31 99.05
FF 99.54 2688.2 71.5 99.68 93.81 98.92
FFPE 99.52 2998.4 77.34 99.68 93.99 97.52
HDx-
FFPE 99.54 3081.4 66.67 99.68 95.09 99.26
HDx-FISH 99.45 2720.1 72.33 99.66 93.10 98.47
TruQ 99.54 2645.2 70.56 99.68 93.70 99.05
All
Samples 99.52 2796.2 73.45 99.68 93.90 98.44
Variant Linearity
Coverage Info Variant Sensitivity
Other key assay performance characteristics Assay now passes key clinical characteristics
Variant Calling Power
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• Assay produces high-quality sequence data with a mean % bases on target of
66.8% with 93.9% of ROI callable at 5% allele frequency.
• Accuracy is on average 99.75% using a known, reference control DNA.
• Sensitivity of SNV detection is 100% for mutations present at ≥ 5%. Indel
detection is also highly sensitive (sensitivity of 90% for mutations present at
≥4%).
• Repeatability and reproducibility are high for SNVs (98.0%, 97.8%), Indels
(86.4%, 84.1%) and gene fusions (98.78%, 97.1%).
• Variant calling between case-matched FF and FFPE samples also shows high
overall concordance in all variant classes (96.3% for SNVs, 74.7% for Indels
and 90.2% for gene fusions).
Validation Summary Performance
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Bioinformatics Considerations
for Pipeline Development
Victor Weigman, Ph.D., Associate Director, Translational Genomics
15 Sep 2015
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• There are many good systems for measuring risk
• Identify the major determinates of risk in your system
– Infrastructure: Sample tracking, networking and logical securities
– Software: Genotype caller, variant caller, quantifier, automated processes
• If the artifacts you are producing don’t make you feel better about this risk..
– Change priority!
• Systems compliance integrated with CLIA assays provide excellent risk controls
– Method validation requires: positive/negative controls, real world samples, replicates
Risk Management The reason for the season
1. https://onsdagsfonden.wordpress.com/2015/04/05/idiotsakert-att-satta-pengarna-pa-borsen-eller/
1
The Patient Yourself / Institution Scientific Community
> >
How does your system affect:
2. Siconolfi & Bishop: RAMP
2
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• LIMS typically holds the role as project management interface
Components affected – The System Think of all facets as a part of the package
SOPs
Project
Instructions
Version Control
System
Part 11 Compliant
Clinical Data Arena
Report
Sig
n O
ut
Instrument
Control
Software
Pipeline
Computers
Backup/
Archive
Reports
IQs
(Instrument
s / Servers /
Pipeline )
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• You run these panels to detect genomic alterations
• Shifting from single-analyte to multiple increases risk – Each variant has it own error and is compounded when
you interrogate 1.34M positions
• First goal is to detect variants < 10% – High depth is needed (higher depth increases noise)
• In same assay we also want to look at breakpoints and
other complex indels
• Variant calling should account for risks with sequencer error
Accuracy is critical
Company Confidential
Search for low frequency variants & complex changes has risk
Ris
k
# loci assayed
Co
lin P
ritc
ha
rd (
Wa
sh
U)
Indels
Rare
Alleles
Breakpoints
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New considerations A variant caller should be aware of and able to handle the following
Context Specific Error Strandedness
Complex Mutation
Polymerase Error
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• Open source under GNU public license written in R and perl
• Validated and characterized via QCCP assay
– Tested analytical specificity using NA12878
– Tested analytical sensitivity using verified variants in HDx and TruQ reference samples
– Tested for matrix effect using matched FF and FFPE samples
– Tested across range of input material (50-250ng)
• Fast for targeted panels
• Allows for parameter tuning to match sequencing chemistry (Illumina, Ion
Torrent), sample type (FF or FFPE), enrichment type (PCR or hybridization)
– Error model is built for every sample and then used for variant calling on that sample
Variant PROfiling with Logistic Regression (VarPROWL) Software details
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Using Genomic Panels for
Precision Medicine
Victor Weigman, Ph.D., Associate Director, Translational Genomics
15 Sep 2015
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Image Source: Wikimedia Commons
Classic “One Size Fits All”
Treated
Population
Lack of Efficacy
Lack of Safety
Population of Interest
Responders
Non-responders
Adverse Event
Patients
Use Case - Changing how drugs are delivered
Identify non-responders and safety issues before prescribing or treating
Personalized/ Precision Approach
Prescreened
Population
Predictive Bio-
marker Testing
Responders
Adverse Event
Patients
Non-responders
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• Typically Labs and Academic centers form tumor boards to drive consensus for actionability of mutations
• ACMG / AMP have set a recent guidelines (05Mar2015)
– “Adherence to these standards and guidelines are voluntary…”
– “…the clinical laboratory geneticist should apply his or her own professional judgment to the specific circumstances presented by individual patient”
– “It is not intended for the interpretation of somatic variation, PGx variants, or variants in genes associated with multigenic non-Mendelian complex disorders”
• EA communicated to sponsor this ask from N-of-1 and kept rules that we adopted transparent
– Sponsor commented on rules and we modified based on their comments to have formal rule set
• EA created documentation and dual processing procedure to ensure that variant filtering and prioritization was repeatable
– Documentation exists for personnel performing each of these steps
Going from 1000 non-reference to a usable number
20
Variant Prioritization
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The Problem Patients needed to test targeted oncology therapeutics are difficult to find due to low
prevalence of specific genomic biomarkers
• Phase 1 study of drug X indicates exquisite sensitivity in patients with a genomic alteration in gene coding protein Y
• Effect seen in CRC and NSCLC patients only
• Literature for CRC and NCSLC estimates genomic alteration in gene Y to be:
– 2% of CRC specimens
– 10% of NSCLC specimens
• Commercial assessment data suggest strong clinical benefit
• You design your phase 2 program accordingly:
– 2 strata – one of NSCLC and one for CRC
– All subjects must have genomic alteration in gene Y
• Phase 2 timeline expected to be 2 years with high screen failure rate and high zero enrollers
• The portfolio committee will approve if timelines can be 1.5 years, with the same N
• What options exist to more effectively find the right patients?
How do I cost-
effectively
develop a drug
with an
anticipated high
screen failure
rate in a timely
fashion?
The scene at your company
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Pre-profiling
Patient Presents- ICF, Patient Enrollment Sample Collection
Genomic Testing
Bioinformatic Analysis
Clinical Annotation and Reporting
Physician-Patient discuss options
Site request just-in-time start-up, ICF signed,
patient enrolled in treatment study
Providing individualized care… …compilation for long term benefit Genomic testing registry
Genomic testing at
Q2 Solutions
Clinical Report
Site staff activities of treatment
decisions and study initiation
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Optimized Assay Drives Better Outcome
• QCCP has benefited from optimization
• Revised genetic content with fewer coverage gaps to existing content
• Reduced input requirement > 10-fold from 2 mg to 150 ng
• Limiting and challenging samples including FFPE are enabled
• Automated handling of data and generation of reports with intrinsic QC
• VarProwl handles different error modes so that sensitivity is neither
underestimated nor overestimated
• Assay can be used relatively quickly to help power individualized
care
• Genomic panels can help drive site recruitment and trial enrollment
for a broad spectrum of indications
• Wealth of genomic data creates wellspring of new information for
similar
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• Research and Development
– Pat Hurban
• Sequencing
– Steven Abbott
– Ben Smith
• Statistics and Bioinformatics
– Zhancheng Zhang
– Gunjan Hariani
– Wendell Jones
– Chad Brown
– Matt Schu
Acknowledgments
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Questions?