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ERASE-Seq: Accurate Detection of Low Frequency Somatic Variants in cfDNA

Cristian Ionescu-Zanetti

CTO and Founder

Fluxion Biosciences, South San Francisco, CA

Liquid Biopsies Provide non-Invasive Complement to Tissue Biopsy

Source: AACR

ISOFLUX™ SYSTEM Circulating tumor cell extraction

Liquid Biopsy Customers

-Databases like COSMIC and TCGC allowing matching thousands of somatic mutations to diagnosis and treatment options -Drop in sequencing cost allows NGS to evaluate large numbers of mutations in parallel -It is important to detect inactivating mutations across entire oncogenes like TP53 -Significant sensitivity / specificity improvements are needed to address a majority of patients

NGS Analysis of cfDNA and CTCs Provides the Opportunity for Comprehensive Analysis of Somatic Alterations

The problem: NGS data contains many false positives in the low frequency allele range

Position in Abl Coding Sequence

AF

True positive E279K imatinib resistance mutation obscured

Molecular Barcoding: Ideal vs Practical Performance

ERASE-Seq

Elimination of Recurrent Artifacts and Stochastic Errors Concept: False positives in NGS data arise from either error prone loci that produce recurrent artifacts or from stochastic polymerase errors. Recurrent artifacts can be eliminated by comparing sample read counts to a control background and stochastic errors can be eliminated through technical replication.

Recurrent Artifacts

template

copy

- random - recurrent

Stochastic Errors

template

- random

True Variants Recognized by Consistent Presence in Sample Replicates and Absence in Control Replicate

ERASE-Seq quantitative Works by Comparing Sample Technical Replicates to Control Technical Replicates in Order to Identify

Real Variants

True positives show consistent signal in sample replicates and low signal in control

Stochastic errors show inconsistent signal in sample replicates

Recurrent artifacts show consistent signal in control replicates

Simple ERASE-Seq Bench Protocol

Performed by Fluxion

Replicate 1…

Replicate 1…

Replicate n

Replicate n

Sample

Control

Input Fastqs Pileup Generation Matrix Building Statistics

-BWA-Mem alignment of fastqs to create initial BAM files -GATK indel realignment and base quality recalibration of BAM files -Lofreq pileup feature used to create base quality filtered pileup of all base calls across panel for each replicate

Somatic Calls

-ERASE-Seq software converts pileups to data matrix -Every variant observed in quality filtered base calls becomes a row in data matrix -Data from each sequenced replicate becomes a column containing a quantized allele frequency value for each variant row

-Data matrix is processed using the negative binomial test to test for significant enrichment of variant signal in sample versus control replicates

-ERASE-Seq software combines statistical score, strand-bias and allele frequency measurements to produce final VCF of somatic variant calls.

Bioinformatics Integrates Sample and Control Measurements into Statistical Test

Low Frequency Variant Analytical Detection Test: DNA from multiple tumor cell lines spiked at 0.25-1% allele frequency into normal DNA

0.25% A549

0.25% H1975

99% normal DNA

0.25% 12878

0.25% MDA

Cell Line DNA

Analytical Test Mixture: 0.07%-1% AF variants

Ultralow frequency variants are buried in background noise using traditional calling parameters, most variants completely uncallable

ERASE-Seq Calling Separates True Variants from False Positives Down to 0.07% Allele Frequency with 94% sensitivity and >99.99% Specificity

ERASE-Seq Outperforms Molecular Barcoding Methods

Background Model Provides Large Improvements in Specificity by Eliminating the Majority of Errors

449

116

39

8

2

4

1

1235

136

52

19

810

87

2

28

1

10

100

1000

10000

0.05-0.1 0.1-0.15 0.15-0.2 0.2-0.25 0.25-0.3 0.3-0.35 0.35-0.4 0.4-0.45 0.45-0.5 >0.5

NumberofFalsePosi

veVariantCalls

AlleleFrequencyInterval(%)

56GFalsePosi vesComposi on

Stochas cErrors

RecurrentAr facts

False Positives Decrease with Replicate Number, Restoring Specificity in the Ultralow AF Range

449

116

39

8

2

4

1

56

16

2

1 1

14

2 2

7

2

1

10

100

1000

0.05-0.1 0.1-0.15 0.15-0.2 0.2-0.25 0.25-0.3 0.3-0.35 0.35-0.4 0.4-0.45 0.45-0.5 >0.5

NumberofFalsePosi

veVariantCalls

AlleleFrequencyInterval(%)

56GFalsePosi vesUsingERASE-Seq

1Replicate

2Replicates

3Replicates

4Replicates

Can ERASE-Seq go deeper to <0.1% while retaining specificiy?

• Example collaboration with HCL, Molecular and Biochemistry Laboratory of CHLS, France

• Samples selected were positive for mutations by BEAMING and/or ddPCR

• ERASE-Seq 56G calls (2x replicates) is presented along side calls obtained using two replicates and standard pipelines

• Both NGS callers used the same starting fastq data

*Data provided by Jessica Garcia and Lea Payen-Gay

ddPCR Concordance

ERASE-Seq 100%(7/7)

Standard 43%(3/7)

ConcordancewithddPCR

*Data generated by Jessica Garcia and Lea Payen-Gay

*Collaboration with HCL, Molecular and Biochemistry Laboratory of CHLS, France

BEAMING

ERASE-SeqNGS56G PROT L858R delEx19 T790M L858R delEx19 T790M L858R delEx19 T790M T790M

604 DELEX19 WT 0.15% WT WT WT WT WT 0.02% WT x

603 DELEX19 WT 0.20% WT WT WT WT WT 0.06% WT x

753 DELEX19 x 1.333% 0.286% WT 0.46% WT WT 0.334% 0.254% 0.075%

626 L858R 0.08% WT WT WT WT WT 0.05% WT WT x

742 L858R 0.269% WT x 0.25% WT WT 0.114% WT WT WT

655 L858R 4.28% WT WT 2.82% WT WT 2.49% WT WT x

ERASE-SeqNGS56GSample

delEX1

9/

L858R/

T790M

ddPCR STANDARDNGS56G

BEAMING T790M Concordance

*Data generated by Jessica Garcia and Lea Payen-Gay

*Collaboration with HCL, Molecular and Biochemistry Laboratory of CHLS, France

BEAMING STANDARDNGS56G ERASE-SeqNGS56G

%MF %MF %MF

649 0.44% 0.33% 0.33%

728 0.08% WT 0.17%

637 0.66% 1.10% 1.10%

608 1.89% 2.47% 2.47%

724 0.12% WT WT

Sample

T790M

ConcordancewithBeaming

ERASE-Seq 80%(4/5)

Standard 60%(3/5)

ERASE-Seq: Meeting the Liquid Biopsy Technical NGS Challenge

COMPANY OVERVIEW

• ERASE-Seq: Only widely available NGS variant detection software that uses a background error model and duplicates

• Spotlight 59 pan cancer targeted panel provides 2-4x replicates from 20-40ng DNA input at 20,000x total depth

• Enables highly sensitive and specific detection of SNV and Indel variants down to <0.1% AF (across the full panel)

• Validation work shows that very high sensitivity is maintained while calling across thousands of mutations.

Contributors

Fluxion Biosciences Cristian Ionescu- Zanetti Nick Kamps-Hughes

Swift Biosciences Drew McUsic Julie LaLiberte

Illumina Prithwish Pal Claire Ray

HCL, Molecular and Biochemistry Laboratory of CHLS Jessica Garcia Lea Payen-Gay