© 2014 Personalis, Inc. All rights reserved.

Pioneering Genome-Guided Medicine

Perspectives on analytical validityDeanna M. Church, PhD Senior Director of Genomics and Content Personalis, Inc

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Disclosure

I work for Personalis, Inc. A company that provides whole genome and augmented whole exome sequencing, analysis services and clinical interpretation services.

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•  What RMs would be useful for analytic validation of somatic variants? •  How does targeted sequencing differ from WGS in terms of analytic

validation needs? •  What’s the role of benchmarking data sets in validating bioinformatics? •  Is there a role for “benchmark” or “reference” pipelines? •  What GIAB products other than RMs should we produce?

–  Would a product like a whitepaper outlining the common pitfalls in analytic validation for NGS be a good product?

–  Is there a need for other process controls (e.g., FFPE-embedded, mixtures, etc.)?

–  What role can spike-ins play in validation? What would they look like? For somatic mutations? For germline mutations?

•  What are the most specific knowledge gaps in how to do analytic  validation for NGS?

© 2014 Personalis, Inc. All rights reserved.

ACE Clinical Exome™ with Enhanced Diagnostic Yield

Assay development and evaluation

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Deficits in Coverage in a Key GeneVariants in RPGR cause ~80% of X-linked retinitis pigmentosa

Previously described

variants

Depth Coverage Plot of RPGR Dark blue represents coverage at 1 standard deviation from mean

>20x Coverage (required to call heterozygous SNVs and indels accurately)

* Coverage plots are representative sequence coverage based upon N=16

6 >20x Coverage (required to call heterozygous SNVs and indels accurately)

Previously described

variants

Depth Coverage Plot of RPGR Dark blue represents coverage at 1 standard deviation from mean

* Coverage plots are representative sequence coverage based upon N=16

Assay improvementVariants in RPGR cause ~80% of X-linked retinitis pigmentosa

Enhanced Exome

Standard Exome p.Glu809Glyfs*25

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Coverage in medically interpretable genes

Percent bases with >20X local high quality coverage depth: finishing metric

Perc

ent f

inis

hed

exon

s

coding non-coding

Augmented exome

Exome 1

Exome 2 Exome 3

Exome 4

31X PCR-free WGS

Patwardhan et al., Genome Medicine 2015

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Identifying low frequency allelesACE Exome 12G

ACE Cancer Panel 12G

WGS 100G (30x)

TP53

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Breakdown

• How does targeted sequencing differ from WGS in terms of analytic validation needs?

Targeted sequencing is an important part of improved performance. The analytical needs are similar, but the reference data must contain variants in all parts of the genome, even the hard ones.

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Breakdown

• Is there a role for “benchmark” or “reference” pipelines? This is really of limited utility as custom assay development often has custom informatics.

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Breakdown

• What are the most specific knowledge gaps in how to do analytic validation for NGS? Increased transparency on exact intervals being tested and metrics based on variant type and allelic fraction.

© 2014 Personalis, Inc. All rights reserved.

ACE Clinical Exome™ with Enhanced Diagnostic Yield

Cancer clinical validation study

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ACE Cancer Panel CLIA Validation Results

ACE Cancer Panel Performance Specifications

Sensitivity Base Substitutions >99% MAF ≥ 5%

Indels >99% MAF ≥ 10%

CNAs 97% tumor content ≥ 20%

Gene Fusions >99%

Specificity >99%*

Typical Median Depth >500X

Sample Types Fresh Frozen or FFPE Tumor Samples ≥ 20% Tumor

* Based on Base substitutions and Indels, others pending

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Breakdown

• What are the most specific knowledge gaps in how to do analytic validation for NGS? Increased transparency on exact intervals being tested and metrics based on variant type and allelic fraction.

© 2014 Personalis, Inc. All rights reserved.

ACE Clinical Exome™ with Enhanced Diagnostic Yield

Real life samples

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FFPE sample challenge

Large range of performance for FFPE samples

40 50 60 70 80 90

100

MC

C43

7_D

NA

MC

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DN

AS

EQ

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A_7

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2037

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A

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NA

_8

% mapped

% mapped

20 30 40 50 60

MC

C43

7_M

CC

438_

DN

AS

EQ

2M

TSC

C_D

SA

M20

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harm

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14

0020

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1106

-091

-A

Z_D

NA

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Z_D

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2037

AZ_

DN

A_

Qmap

Qmap

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RT 37C 45C

3 day 1 day 3 day 1 day 3 day 1 day

1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6

1.  0% PBS 2.  20% PBS 3.  40% PBS 4.  60% PBS 5.  80% PBS 6.  100% PBS

Genomic DNA Extracted from FFPE samples

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RT 37C 45C

3 day 1 day 3 day 1 day 3 day 1 day

1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6 1 2 3 4 5 6

1.  0% PBS 2.  20% PBS 3.  40% PBS 4.  60% PBS 5.  80% PBS 6.  100% PBS

DNA Library Generated from FFPE samples

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Breakdown

• What RMs would be useful for analytic validation of somatic variants? More RMs that represent the variable sample quality seen in real clinical specimens.