systematic design and development of the q …/media/q2labs/library...• manual low-throughput...

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


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


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


QCCP Final Release

• Genes selected by Key Opinion Leaders in Oncology: Quintiles TRDO, JHMI, Key

Customer Accounts


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


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


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


• 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

10 COMPANY CONFIDENTIAL 10

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


• 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


Bioinformatics Considerations

for Pipeline Development

Victor Weigman, Ph.D., Associate Director, Translational Genomics

15 Sep 2015


• 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


• 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 )


• 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


New considerations A variant caller should be aware of and able to handle the following

Context Specific Error Strandedness

Complex Mutation

Polymerase Error


• 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


Using Genomic Panels for

Precision Medicine

Victor Weigman, Ph.D., Associate Director, Translational Genomics

15 Sep 2015


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

//upload.wikimedia.org/wikipedia/commons/4/44/Sanger_sequencing_read_display.gif


• 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


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


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


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


• Research and Development

– Pat Hurban

• Sequencing

– Steven Abbott

– Ben Smith

• Statistics and Bioinformatics

– Zhancheng Zhang

– Gunjan Hariani

– Wendell Jones

– Chad Brown

– Matt Schu

Acknowledgments


Questions?

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