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© 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
C43
8_D
NA
DN
AS
EQ
2_tu
mM
TSC
C_D
NA
SA
M20
3709
21 5
S06
_417
75_A
1 4 A
02-2
1A
S09
_353
7_A
2 C
13-2
710A
1 O
NY
X88
78
JK-3
S
AM
2037
0921
P
harm
B
S04
_429
81_A
1 S
14_1
7554
_B2
Pha
rmA
03-2
2957
A1
PS
13_9
876_
A8
PR
13_2
69_5
2A
GLU
T1_N
orm
oxS
06_2
1760
_A2
SA
M20
3709
37
B1
PR
13_2
69_1
3A
JK-9
P
R13
_269
_31A
JK
-11
05-2
9776
34
67B
_DN
A 14
-021
82A
16
SA
M20
3709
20
13
JK-8
14
0020
3252
13
2600
6032
S
AM
2037
0924
A
Z_D
NA
_1
AZ_
DN
A_3
11
06-0
91-2
01B
-A
Z_D
NA
_10
AZ_
DN
A_7
S
AM
2037
0932
A
Z_D
NA
_8
% mapped
% mapped
20 30 40 50 60
MC
C43
7_M
CC
438_
DN
AS
EQ
2M
TSC
C_D
SA
M20
37 5 S
06_4
177 4
A02
-21A
S
09_3
537
C13
-271
0O
NY
X88
7JK
-3
SA
M20
37P
harm
B
S04
_429
8S
14_1
755
Pha
rmA
03-2
2957
PS
13_9
87P
R13
_269
GLU
T1_N
S06
_217
6S
AM
2037 B
1 P
R13
_269
JK-9
P
R13
_269
JK-1
1 05
-297
76
3467
B_D
14-0
2182
SA
M20
37 13
JK-8
14
0020
3213
2600
60S
AM
2037
AZ_
DN
A_
AZ_
DN
A_
1106
-091
-A
Z_D
NA
_A
Z_D
NA
_S
AM
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
19
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.