Recent studies in non-invasive biomarker research have demonstrated the potential of using cell-free nucleic acids isolated from blood plasma to serve as surrogates for solid tumors. Somatic mutations representing the tumors could be successfully detected from cell-free DNA (cfDNA) and cell-free RNA (cfRNA), providing new tumor assessment methods in addition to tissue biopsy. However, the low amount of circulating tumor fragments in the blood presents significant challenges for accurate variant detection with NGS assays. Moreover, utilization of both cfDNA and cfRNA requires methods capable of interrogating both types of analytes to maximize the utility of each blood sample.

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Richard Chien*, Ru Cao, Yanchun Li, Kris Lea, Jian Gu, Varun Bagai, Jeff Schageman, Khalid Hanif, Dima Brinza*, Ruchi
Chaudhary*, Kunal Banjara*, Dalia Dhingra*, Janice K. Au-Young*, Kelli S. Bramlett. Thermo Fisher Scientific, 2130 Woodward St.,
Austin, TX 78744, *180 Oyster Point Blvd. ,South San Francisco, CA 94080
An Efficient NGS Workflow for Liquid Biopsy Research Using a
Comprehensive Assay Panel to Assess Cell-Free Total Nucleic Acid
RESULTSABSTRACT
Recent studies in non-invasive biomarker research have
demonstrated the potential of using cell-free nucleic acids
isolated from blood plasma to serve as surrogates for solid
tumors. Somatic mutations representing the tumors could be
successfully detected from cell-free DNA (cfDNA) and cell-free
RNA (cfRNA), providing new tumor assessment methods in
addition to tissue biopsy. However, the low amount of
circulating tumor fragments in the blood presents significant
challenges for accurate variant detection with NGS assays.
Moreover, utilization of both cfDNA and cfRNA requires
methods capable of interrogating both types of analytes to
maximize the utility of each blood sample.
We demonstrate the feasibility for a comprehensive cell-free
total nucleic acid (cfTNA) NGS workflow (Pan-Cancer Cell
Free Research Assay) that utilizes an extensive multi-gene
panel to survey high-value variants relevant to multiple tumor
types for liquid biopsy research. The method includes a novel
sample preparation to simultaneously isolate cfDNA and
cfRNA, followed by library preparation and sequencing on Ion
Torrent™ platforms. 20 ng of input cell-free nucleic acid was
used to prepare libraries. Multiple libraries were pooled
together for templating on Ion Chef™ and sequenced on Ion
S5™.
We have generated and streamlined an efficient sample-to-
report NGS workflow using a broad single-pool multiplex assay
panel to query more than 800 tumor driver and resistance
hotspots with high sensitivity and specificity. The assay’s limit
of detection at 0.1% allelic frequency enables accurate
detection of low-abundance tumor variants from blood samples
for liquid biopsy research. The broad content coverage of the
panel encompasses hotspot SNVs and INDELs, CNVs, and
gene fusions relevant to multiple tumor types, as well as
expanded coverage of TP53 exon regions for TP53 mutation
analysis. The different variant types were reliably detected in
verified control samples harboring known variants, highlighting
the capability of the assay to perform comprehensive mutation
assessment. Sequencing on the Ion S5™ delivered > 95% on-
target reads and uniform amplification with deep sequencing
depth (> 40,000x).
We demonstrate the ability to utilize isolated cell-free total
nucleic acid for accurate detection of high-value variants
implicated in multiple tumors Ion Torrent™ NGS.
INTRODUCTION
Liquid biopsy research contributes to precision medicine
initiatives and enables minimally invasive and inexpensive
sampling when compared with traditional tissue biopsy.
However, the low amount of circulating tumor fragments in the
blood presents significant challenges for accurate variant
detection with NGS assays. Moreover, utilization of both cfDNA
and cfRNA requires methods capable of interrogating both
types of analytes to maximize the utility of each blood sample.
We describe here a two days sample-to-report workflow that
utilizes cfTNA with a single-pool Pan-Cancer Cell Free
Research Assay that surveys oncology variants across multiple
tumor types for the detection of SNV/indels, gene Fusions, and
CNVs.
MATERIALS AND METHODS
Plasma preparation: Blood samples were collected into BD K2
EDTA tubes and Streck Cell-Free DNA BCT tubes following
manufacturer’s instructions by a third-party vendor. plasma
from blood sample was obtained by centrifugation at 1600 x g
for 10 min at 4C, followed by another spin at 6,000 x g for 30
min at 4 C to remove any residual blood cells.
cfTNA isolation: cfTNA was isolated from ~4-mL of plasma
using MagMAX Cell-Free TNA Isolation Kit following
manufacture’s guideline.
SNV reference control: Internal 0.1% or 0.5% cfDNA reference
materials were used to evaluate SNV. Variant frequencies from
the reference materials were verified with dPCR.
CNV reference control: cfDNA from CNV positive cell lines
were isolated and titrated into normal donor plasma cfTNA
background and copy number status was verified with dPCR.
Fusion references control: Seraseq™ Fusion RNA Mix v2 or
internal fusion ctrl were titrated into normal donor plasma
cfTNA background to achieve expected Fusion frequency.
Library preparation, Templating and Sequencing: Library
preparation was performed using the Pan-Cancer Cell Free
Research Assay. Ion ChefTM System and Ion 540TM Kit-Chef
were used for templating, followed by sequencing on Ion S5
system. 4-plex library pool ran on a Ion 540 chip.
Data analysis: Data analysis was performed in Torrent SuiteTM
5.6 and Ion Reporter 5.6.
Table 1 The Pan-Cancer Cell Free Research Assay Content
Figure 1 The Pan-Cancer Cell Free Research Assay Workflow
CONCLUSIONS
 We have successfully generated a NGS work flow that
simultaneously analyzes single-nucleotide variants, indels,
copy number variations, as well as gene fusions across
56 genes associated with a number of cancers. including
but not limited to lung cancer, breast cancer, and colon
cancer
 The NGS work flow is optimized with MagMAX Cell-Free
TNA Isolation Kit that allow both cfDNA and cfRNA
isolated from one single blood tube
 The NGS work flow allow an efficient sample-to-report
workflow within 2 days, which is compatible with
Oncomine™ knowledgebase that allows customers easy
access to the variant reports.
 The NGS workflow using a broad single-pool multiplex
assay panel to query more than 800 tumor driver and
resistance hotspots, the LOD of 0.1% can be achieved
with sensitivity of >89% and specificity of >99%
 For TP53 mutations(~80% coverage of full length gene),
an LOD of 0.5% can be achieved.
 For CNV targets, detection as low as 1.3 fold amplification
can be achieved with sensitivity and specificity of >99%
 For fusion detection, as low as 1% RNA fusions in cfTNA
(internal ctrl) can be achieved.
TRADEMARKS/LICENSING
For Research Use Only. Not for use in diagnostic
procedures.
© 2017 Thermo Fisher Scientific Inc. All rights reserved. All
trademarks are the property of Thermo Fisher Scientific and
its subsidiaries unless otherwise specified. TaqMan is a
registered trademark of Roche Molecular Systems, Inc., used
under permission and license. Seraseq is a registered
trademark of SeraCare Life Sciences, used under permission
and license.
The assay enable a blood sample to report workflow completed in 2
days, which detected SNV, CNV and fusions using cfTNA ( cfDNA and
cfRNA) from the same blood tube.
1B Summary of targeting limitation of detection for the assay
content
To evaluate CN status, we titrated cfDNA isolated from CN positive cell
lines that harbored CCND1, EGFR, ERBB2, FGFR1, FGFR2, MET
MYC into normal donor plasma cfTNA background down to 1.3~1.6
fold. CNV reference controls were verified with dPCR assays. 8
replicates from each CNV control were tested. The assay sensitivity
and specificity in the run are >99% using formula described previously.
Single Pool design (DNA & RNA)
Covers:
• Key oncology hotspot mutations
• Gene Fusions
• CNVs
• MET exon 14 skipping
Hotspot SNV/Indel
• 0.1% AF LOD with 20 ng input
TP53 extensive coverage SNV/Indel
• 0.5% AF
CNV detection
• 1.4x fold change
Fusion detection
• 1% RNA fusions in cfTNA
Fusion_ID Reads Families
CD74-ROS1 3022 326
EML4-ALK 6020 720
ETV6-NTRK3 7417 884
FGFR3-BAIAP2L1 1840 246
FGFR3-TACC3 2071 315
KIF5B-RET 2856 381
LMNA-NTRK1 6659 832
MET exon 14 skipping 8585 1172
NCOA4-RET 9572 1170
SLC34A2-ROS1 2003 226
SLC45A3-BRAF 4960 641
TMPRSS2-ERG 724 90
TPM3-NTRK1 7209 849
1A List of genes covered in the Assay
Table 2 Detection Sensitivity and Specificity of SNV
Figure 2 Sequencing Performance of the Pan-Cancer
Cell Free Research Assay
A representative sequencing result from the Ion 540 chip with 4 cfTNA
isolated from 4 individual healthy donor blood plasma samples.
Oncomine™
cfTNA
Research Assay
Sample Library Prep Templating &
Sequencing
Analysis Lab-created
Report
Ion Torrent™
Chef™/S5™
Torrent Suite™
Ion Reporter™
Oncomine™
Knowledgebase
MagMAX™
Technology
TNA
Isolation
cfTNA isolation
from blood High
yield, automation-
ready.
minimal input
requirement,
high
multiplexing
Ion S5™ Fast accurate
sequencing, flexible
throughput
Detection of
variants at
frequency >0.1%
with specificity
>99%
Annotation and
reporting with
large
compendium of
onco-genomic
data
Blood Sample Custom Report
Single tube of
whole blood
Table 3 Detection Sensitivity and Specificity of CNV
Table 4 The Pan-Cancer Cell Free Research Assay
Detected All 13 Expected Fusions with Commercial
RNA References
The Pan-Cancer Cell Free Research Assay was designed to detect
>800 hotpots in 56 genes related to various solid tumors, 12 CNV
genes and 11 fusion genes (1A). It is a single pool design that allow
utilizing both cfDNA and cfTNA from the same sample simultaneously
detecting SNV, CNV and gene fusions and aiming to achieve LOD as
listed in 1B. AF: allelic frequency
Figure 3 Verification of CNV Positive Control with
Orthogonal dPCR Assay
Here is a representative CN ctrl verification example using MET CNV
ctrl. Orthogonal dPCR assays were performed to quantify MET CN
status using multiple TaqMan® probes targeting to MET. dPCR assays
showed consistent CN calling results as NGS sequencing.
0
0.5
1
1.5
2
2.5
dPCR NGS
1.3X
1.8x
Fold
CNV fold
expected
NGS sequencing (n=8) Sensitivity Specificity
MYC 1.3x 1.34 1.35 1.38 1.34 1.36 1.35 1.38 1.36 100% 100%
MET 1.3x 1.30 1.29 1.33 1.32 1.31 1.32 1.32 1.32 100% 100%
FGFR2 1.4x 1.42 1.42 1.45 1.39 1.43 1.41 1.41 1.41 100% 100%
FGFR1 1.4x 1.50 1.47 1.46 1.47 1.52 1.48 1.49 1.50 100% 100%
ERBB2 1.4x 1.49 1.51 1.52 1.52 1.53 1.50 1.49 1.49 100% 100%
CCND1 1.6x 1.58 1.56 1.59 1.65 1.60 1.57 1.54 1.50 100% 100%
EGFR 1.7x 1.72 1.66 1.72 1.69 1.64 1.71 1.72 1.70 100% 100%
Seraseq™ Fusion RNA Mix v2 that harbored 13 expected fusions
with known RNA copies was titrated (10%) into normal donor plasma
cfTNA , all fusions could be detected as listed in the table above.
Internal 1%RNA fusion ctrl was used for fusion LOD test (internal
data).
We utilized 0.1% and 0.5% ( for TP53 extended coverage) fragmented
AcroMetrix Oncology Hotspot control material (MM) to determine SNV
sensitivity and specificity. The allelic frequency of MM was verified with
dPCR assay. Sensitivity and specificity measurements were carried out
with multiple independent instruments (four S5 sequencing systems and
four Ion Chef templating instruments) and three individuals users.
Following equations were used to calculate sensitivity and specificity:.
SENSITIVITY = TP/(TP+FN)%
SPECIFICITY = TN/(TN+FP)%
Samples
Users and
replicates
TP Sensitivity
Average
Sensitivity
FP Specificity
Average
Specificity
0.5%MM
user1rep1 103 99.0%
99.7%
0 100.0%
100.0%
user1rep2 103 99.0% 0 100.0%
user2rep1 104 100.0% 0 100.0%
user2rep2 104 100.0% 0 100.0%
user3rep1 104 100.0% 0 100.0%
user3rep2 104 100.0% 0 100.0%
0.1%MM
user1rep1 101 97.1%
89.2%
0 100.0%
99.9%
user1rep2 101 97.1% 0 100.0%
user1rep3 92 88.5% 0 100.0%
user1rep4 96 92.3% 0 100.0%
user1rep5 88 84.6% 0 100.0%
User1rep6 84 80.8% 0 100.0%
User2rep1 93 89.4% 0 100.0%
User2rep2 97 93.3% 1 99.9%
user2rep3 94 90.4% 0 100.0%
user2rep4 94 90.4% 0 100.0%
user2rep5 83 79.8% 0 100.0%
user2rep6 96 92.3% 0 100.0%
user3rep1 90 86.5% 0 100.0%
user3rep2 91 87.5% 0 100.0%
user3rep3 90 86.5% 0 100.0%
user3rep4 89 85.6% 0 100.0%
user3rep5 93 89.4% 0 100.0%
user3rep6 97 93.3% 0 100.0%