The document discusses Washington University's cancer mutation profiling test, which uses next-generation sequencing (NGS) to detect somatic mutations including single nucleotide variants (SNVs), insertions/deletions (indels), and structural variation. The test provides 1000x average coverage across 25 clinically reported genes and can detect variants present at 10% allele frequency. Considerations for clinical validation of the assay include DNA input quality and quantity from small biopsies or FFPE samples, as well as detection of low frequency variants due to tumor heterogeneity or presence of non-tumor DNA. Validation also requires accounting for detection of indels, copy number variation, and translocations across different NGS platforms and references.

1. Detec%on
of
Soma%c
Muta%ons
by
Targeted
NGS
Andy
Bredemeyer
Washington
University
in
St.
Louis
Pathology
Consult
Service
Genomics
and
Pathology
Services
Washington
University’s
Cancer
Muta%on
Profiling
Test
• 1000X
avg
coverage
• Detec%on
of
SNVs,
indels,
• High
sensi%vity
of
coding
• 25
genes
reported
clinically
structural
varia%on
region
variants
at
10%
AF
Considera%ons
for
clinical
valida%on
of
assay
and
informa%cs:
1. DNA
input
quan%ty
and
quality
• Small
tumor
biopsies
leading
to
low
library
complexity
• FFPE
specimens
and
variability
of
fresh
%ssue
quality
2. Low
frequency
variant/low
allele
burden
detec%on
• Soma%c
variants
present
in
a
frac%on
of
the
genomes
sampled
(10-­‐25%
or
lower)
‒ Presence
of
non-­‐tumor
derived
DNA
‒ Clonal
complexity
‒ Future
considera%on:
minimal
residual
disease
detec%on
3. Indel,
copy
number
varia%on,
and
transloca%on
detec%on
• NGS
references
should
incorporate
the
spectrum
of
varia%ons
and
variant
complexity

2. One slide introduction : John West, CEO, Personalis Inc.
• New VC-backed startup in Menlo Park, CA (www.personalis.com)
• Clinical-quality genome sequencing & medically-relevant interpretation
• Founders from Stanford (majority MD’s) & Solexa / Illumina
• Collaborations : Stanford, Harvard, ISB
• Extensive publication record in genetic analysis accuracy issues (esp. NGS)
• Work guided by extensive manually-curated databases linking genetic
variation with disease & drug metabolism
• 2010 : Quake-genome clinical interpretation
• 2009-2011 : Family quartet genome analysis
• Current work :
– Larger families including CEPH1463 / NA12878; Multiplatform
– Lab & informatics for accuracy (vs absolutely lowest cost)
• Using “gold-standard” genomes to characterize error mechanisms
• Design lab & informatic methods to address them
• Interested in joining NIST consortium

3. Jim
Mullikin,
Director
NIH
Intramural
Sequencing
Center
(NISC)
• Computa%onal
genomics
researcher
from
the
days
of
the
ramp-­‐up
of
the
human
genome
project
in
1997.
• Involved
in
The
SNP
Consor%um
project,
and
later
the
HapMap
project.
• I
worked
on
the
early
structural
varia%on
analysis
with
Evan
Eichler
and
selected
NA12878
as
one
of
the
eight
samples
for
that
project.
• The
1000
genomes
project
selected
this
individual
as
well
– She
is
of
European
ancestry
– Together
with
her
parents
cons%tuted
one
of
the
trios
for
the
1000G
pilot
project
• NISC
is
keenly
interested
in
working
with
new
sequencing
technologies.
• The
Genome-­‐in-­‐a-­‐Bocle
goal
of
providing
a
standard
human
reference
sample
for
this
field
will
be
invaluable.

4. Church
et
al.,
2011
PLoS
Biology
http://genomereference.org

5. UGT2B17
MHC
MAPT
GRCh37
(hg19)
7
alternate
haplotypes
at
the
MHC
Alternate
loci
released
as:
FASTA
AGP
Alignment
to
chromosome
http://genomereference.org

6. MHC
(chr6)
Chr
6
representa%on
(PGF)
Alt_Ref_Locus_2
(COX)

7. Which
Picture
Do
You
Believe?
A
B
23241623 23246895 23246895
50
30 40
30
E E
20 C C
Coverage
Coverage
A A
G G
T T
− −
20
M M
10
10
0 0
23.24165 23.24175 23.24185 23.24195
23.24170 23.24180 23.24190 23.2470 23.2471 23.2472 23.2469 23.2470 23.2471 23.2472 23.2473 23.2474
Position (Mb) Position (Mb)

8. CDC/NCBI Clinical NGS RM Project
q Two human cell lines (NA19240, NA12878)
q Existing AND new sequence data (NGS & Sanger) from 36 clinical gene
panels, WES, WGS (volunteer labs)
q Assess data quality (coverage, quality scores, etc); accept data which
meets predefined criteria
q NCBI will analyze and host data files online for public access. Will
display:
q All data sets with metrics (coverage, platform, software, etc)
q Consensus sequence track- regions of “high, medium or low”
sequence confidence indicated
q Develop guidance for using online data files as tools for test validation
and NGS trouble-shooting
q Publish manuscript of this process and the findings
q This data is available to NIST
Office of Surveillance, Epidemiology, and Laboratory Services
Laboratory Science, Policy and Practice Program Office

9. CDC/NCBI Clinical NGS RM Project
Participants: § Sivakumar Gowrisankar - Partners
§ Subramanian Ajay - Illumina § Srinka Ghosh- Complete Genomics
§ Tina Hambuch- Illumina § Jay Kaufman- Complete Genomics
§ Elaine Lyon- ARUP § Richard Leach- Complete Genomics
§ Rong Mao - ARUP § Shashi Kulkarni- Wash. U
§ Karl Voelkerding- ARUP § Elaine Mardis- Wash U.
§ Nazneen Aziz- CAP § Savita Shrivastava – Wash U.
§ Ephram Chin- Baylor § Marc Salit- NIST
§ Victor Wei Zhang - Baylor § Justin Zook- NIST
§ Cristina Da Silva - Emory § Richa Agarwala - NCBI
§ Madhuri Hegde- Emory § Deanna Church - NCBI
§ John Compton- GeneDx § Donna Maglott – NCBI
§ Soma Das- U. Chicago § Jim Ostell - NCBI
§ Dan Farkas- Sequenome § Chris O’Sullivan – NCBI
§ Matt Ferber- Mayo § Wendy Rubinstein - NCBI
§ Ed Highsmith- Mayo § Steve Sherry- NCBI
§ §
Manohar Furtado- Life Technologies Chunlin Xiao – NCBI
§ Ute Geigenmuller – Harvard § Lorraine Toji- Coriell
§ Birgit Funke- Partners § Lisa Kalman- CDC

10. The
Disclaimer:
The findings and conclusions in this
report are those of the author and do not
necessarily represent the official position
of the Centers for Disease Control and
Prevention/the Agency for Toxic
Substances and Disease Registry.
Lisa Kalman, PhD LKalman@cdc.gov

11. ACMG
Efforts
regarding
WGS/WES
1. Technical
guidelines
for
NextGenera%on
sequencing
(from
Lab
QA
Commicee).
1. Addresses
Targeted
mul%-­‐gene
panes,
WES,
and
WGS
2. Content,
method,
data
analysis,
variant
filtering,
sequencing
of
regions
with
homology,
companion
technologies
and
result
confirma%on)
3. Ini%al
Valida%on
(test,
plalorm)
4. Data
analysis
op%miza%on
5. Metrics
and
performance
parameters
(analy%c
sensi%vity/specificity,
false
posi%ve/nega%ve,
clinical
sensi%vity,
assay
robustness/precision,
limits
of
detec%on)
6. Ongoing
valida%on
of
modifica%ons
of
test,
plalorm,
analysis
pipeline
7. Reference
materials
for
QC
and
PT.
Includes
warning
about
using
cell
lines.
Includes
possibility
of
using
simulated
electronic
sequence
(for
non-­‐wet
lab
component).
2. Development
of
model
consent
for
WGS/WES
3. List
of
“secondary
findings”
that
should
be
reported
4. Collabora%on
amongst
ACMG/CAP/AMP/ASHG
to
develop
recommended
terminology
for
variant
classifica%on
in
rela%on
to
disease
risk/causa%on
5. New
CPT
codes
–
will
gene%c
tests
be
paid
on
the
Clinical
Lab
or
Physician
Fee
Schedules,
or
both

12. Horizon
Discovery
–
reference
standards
for
Next
Genera6on
Sequencing
1. HorizonDx
combines
three
core
capabili6es
• Highly
accurate
gene
engineering
technology
à
generates
isogenic
human
cell
lines
• FFPE
%ssue
modelling
capability
à
FFPE
blocks
containing
defined
cell
ra%os
• World-­‐class
molecular
characteriza%on
à
droplet
digital
PCR,
STR
and
SNP6
2. HorizonDx
developing
a
mul6-­‐plex
reference
standard
for
NGS
• Combining
>10
clinically
relevant
oncogene
muta%ons
into
a
single
gDNA/FFPE
standard
• Staggered
allele
burdens
from
ranging
from
1-­‐25%
• First
commercially
available
NGS
standard
3. The
case
for
including
MCF10a
as
a
reference
genome
• Normal
cell
line,
well
characterized
and
highly
u%lized
for
cancer
research
• Would
pave
the
way
for
the
crea%on
of
a
disease
reference
genome
,
or
analyte
specific
reference
material
which
offers
high
prac%cal
u%lity
• Horizon
has
>100
knock-­‐in/knock-­‐out
cell
lines
in
MCF10a
background
to
leverage
into
the
consor%um
To
find
out
more:
visit:
www.horizondx.com
or
contact
Joshua
Kapp
at
j.kapp@horizondiscovery.com
12

13. RNA-Sequencing Standards Groups
1. FDA: Cluster
Samples
Sequencing Quality Control (SeQC)-eeded
Internal
Controls
N
SOPs
Essen6al
Beau6fully
(Sadly)
by
Prep
SOLiD, Illumina
Helicos, 454, Across
Test
Sites
2. ABRF:Reference
Study: 454, IonTorrent (PGMHas
Make
Enough
NGS Aligner
Claims
are
Every
PlaUorm
& Proton) Illumina,iren’s
Call
Biosciences
Material
a
S
Pacific Pros/Cons
Biological
Noise
Chemical/
Op%cal
Informa%c

14. Base
Modifica%ons
of
DNA:
5-­‐methylcytosine
known
biological
5-­‐hydroxymethylcytosine
8-­‐oxoguanine
glucosylated
5-­‐hydroxymethylcytosine
importance
4-­‐methylcytosine
6-­‐methyladenine
8-­‐oxoadenine
5-­‐formylcytosine
5-­‐carboxycytosine
β-­‐D-­‐Glucosyl-­‐hydroxymethyluracil
(J
base)
Phosphorothioa%on
(backbone)
1-­‐methyladenine
3-­‐methylcytosine
Inosine
5-­‐hydroxycytosine
O6-­‐methylguanine
O4-­‐methylthymine
5-­‐hydroxyuracil
5-­‐hydroxymethyluracil
The
four
ribonucleo%des
(backbone)
RNA:
6-­‐methyladenosine
Forterre
and
Grosjean,
2009
1-­‐methyladenosine