AACR 2017: #5391
Low Frequency Variant Detection and Tissue-of-Origin
Exploration Using Liquid Biopsies
J.
1
Lenhart ,
1Swift
A.
1
Wood ,
S.
1
Sandhu ,
C.
1
Schumacher ,
K.
1
Cunningham ,
L.
1
Kurihara ,
and T.
1
Harkins
Biosciences, 58 Parkland Plaza, Suite 100, Ann Arbor, MI 48103, Tel: 734.330.2568
Abstract
Accel-NGS 2S Workflow with MIDs
The promise of liquid biopsy assays lie in the non-invasive monitoring of diseases, such as cancer, through cellfree DNA (cfDNA) or circulating tumor cell DNA. This may assist in advancing early-stage diagnosis and
improving the ultimate prognosis while simultaneously monitoring treatment response over time. Since these
materials are often limited, most liquid biopsy assays incorporate targeted sequencing to enable cost-effective
deep coverage of target loci for detection of low frequency pathogenic variants, yet a critical aspect in attaining
the necessary sensitivity is an assay that produces uniform, comprehensive coverage from low DNA input
quantities. We have developed a liquid biopsy workflow to enable low frequency variant detection from a 10 mL
blood draw using the Promega Maxwell® RSC combined with Swift Biosciences Accel-NGS® 2S library
preparation methodologies.
Sample
ü  Broad input range: 10 pg-1 µg
0.4%
2
5 cm ovarian ‘borderline’ serous content (cancer-like)
1.1%
ü  No heat steps
3
Recurrent pT2, pN0 mammary carcinoma, 2.15 cm
2.4%
ü  Single temperature incubations
4
pT1/pN1 pancreatic adenocarcinoma with neoadjuvant therapy
3.6%
5
Metastatic colon cancer to the liver (previously treated)
4.4%
6
14 cm ovarian ‘borderline’ serous content (cancer-like)
18.0%
ü  Increased library complexity
7
Colon-cancer, non-resectable adenocarcinoma T4a by imaging
18.0%
ü  Balanced coverage of AT-/GC-rich regions
8
Metastatic colorectal adenocarcinoma with liver metastasis, 2 cm primary
43.4%
ü  5' and 3' end repair enable use of damaged DNA
•  Swift has introduced MIDs, paired with Accel-NGS 2S.
•  MIDs enable identification of unique library molecules.
•  MIDs prevent strand and fragmentation duplicates from being
removed during de-duplication, which preserves library complexity.
Sample 2
Sample 3
1%
1%
1%
cfDNA X
cfDNA A
ALLELE FREQUENCY
Chr: Position
99%
99%
cfDNA X
cfDNA B
cfDNA X
Sample 4
Sample 5
1%
0.5%
cfDNA C
cfDNA
99%
99.5%
gDNA A
gDNA A
gDNA B
gDNA B
cfDNA Spike-In Variants
Automated
cfDNA
Purification
ALLELE FREQUENCY
Chr: Position
Figure 1. Automated cfDNA purification using the Promega Maxwell RSC. A 10 mL whole blood draw was
collected from patients immediately proceeding tumor resection. Specifically, 10 mL of whole blood was collected
and stored in Streck cell-free DNA BCT vials and shipped to Swift Biosciences at room temperature for immediate
processing. Upon sample arrival, 300 µL of whole blood was processed using the Promega Maxwell RSC and the
Whole Blood kit to purify normal DNA. Next, plasma was purified by centrifugation and the entire sample was
processed using the Promega Maxwell RSC cfDNA Plasma kit.
Tissue of
Primary Tumor
B
Metastatic
Yield (pg)
Integrity
Score
Monomer Peak
Mode
Lung
Yes
11200
0.339
174
Bladder
No
32000
0.093
167
Liver
No
20400
0.186
172
Pancreas
Yes
12000
0.273
175
Breast
Yes
12400
0.271
175
Liver
No
8000
0.205
172
Control
NA
24000
0.162
167
Control
NA
8800
0.1403
X
Control
NA
7640
0.264
175
Control
NA
11760
0.294
175
Liver
No
514116
0.392
178
Kidney
No
37814
0.667
172
Colon
Yes
138413
0.216
165
Colon
Yes
174317
0.392
164
Bowel
No
21527
0.418
X
Ovarian
Yes
53942
0.738
172
X
98084
0.615
176
Gallbladder
No
90384
0.468
172
Colon
Yes
52091
0.637
168
2: 212244718
12: 25361074
12:25361142
12: 25361646
12: 40688695
12: 115108136
Sample 1
Expected
Observed
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.05%
1.15%
1.40%
1.39%
0.71%
0.90%
Sample 2
Sample 3
Expected Observed Expected Observed
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
0.87%
1.16%
0.97%
1.40%
0.97%
1.96%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
0.77%
1.01%
0.66%
0.59%
0.55%
0.70%
2: 212243011
2:212244761
2:212245090
2: 212245489
3: 176738798
3: 176739663
3: 176741730
5: 38475507
5: 38481335
7: 106547469
7: 106547921
7: 26224668
7: 55238464
7: 55238874
10: 8116598
11: 32409625
11: 32410002
11: 32410337
11: 32452240
17: 70121339
17: 70122108
19: 1224934
19: 1225054
19: 1228191
20: 31024274
20: 31025535
X: 41093413
Sample 4
Sample 5
Expected
Observed
Expected
Observed
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.0%
1.1%
0.9%
0.5%
1.3%
1.1%
1.2%
0.7%
1.4%
0.8%
1.0%
0.5%
0.9%
0.9%
0.8%
1.1%
1.4%
1.1%
1.1%
1.1%
0.9%
1.1%
0.6%
0.8%
1.1%
1.0%
1.1%
0.8%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.5%
0.6%
0.3%
0.4%
0.7%
0.6%
0.7%
0.4%
0.7%
0.6%
0.8%
0.6%
0.8%
0.8%
0.8%
0.5%
0.7%
0.5%
0.6%
0.4%
0.9%
0.8%
0.6%
0.7%
0.7%
0.8%
1.2%
0.5%
Figure 3. Identifying low frequency variants in cfDNA using Accel-NGS 2S Hyb Library Kit. cfDNA
was extracted from blood of four individuals with unique genetic backgrounds and Coriell gDNA samples
from different genetic backgrounds were obtained. To determine the effect of MIDs on low frequency
variant calling, sample spike-ins were performed at 1% or 0.5% frequency into 10 ng cfDNA or 100 ng
gDNA. Libraries were prepared with the Swift Accel-NGS 2S Hyb Library Kit with MIDs, enriched with the
IDT xGen® Pan-Cancer Panel that covers an 800kB target containing 127 genes, and sequenced on an
Illumina® HiSeq® to a minimum of 8000x coverage. A consensus sequence was generated for each MID
family (BMFtools) and data were analyzed for homozygous SNPs present in the spike-in sample only. 6/6
known variants were present in all three 1% cfDNA samples and 27/27 known variants were present in
both 1% and 0.5% gDNA samples depicting the power of MIDs for low frequency variant calling.
Accel-NGS Methyl-Seq DNA Library Kit
[bp]
C
ALU
Alu
115
Alu
247
Figure 2. Purification and quantitative analysis of purified cfDNA. A. Composition of quantitative metrics
associated with all purified cfDNAs. B. Purified cfDNA was run on the Agilent Bioanalyzer to determine approximate
composition. Most cfDNA migrated at ~170bp representing a nucleosome monomer, but also contained some slower
migrating species (350 and 510bp) representing a dimer and trimer respectively. C. Accurate qPCR quantification of
cfDNA is imperative for successful library preparation. Fluorometric methods such as Qubit® do not quantify amplifiable
DNA and cannot distinguish cfDNA from high molecular weight (HMW) genomic DNA (gDNA). A qPCR assay targeting
both 115bp and 247bp regions of the Alu repeat elements can quantify amplifiable DNA. The Alu115 primers quantify
both cfDNA and HMW gDNA, while the Alu247 primers quantify only HMW gDNA. The ratio of 247/115 determines a
DNA integrity score; the expected score for HMW gDNA is 1, and the expected score for cfDNA is between 0.10 and
0.65, but can vary with cancer types. This assay is based on Hao et al, Br J Cancer 2014 Oct 14; 111(8); 1482-9.
Figure 6. Genome-wide methylation status of sample 8.
This Circos plot represents the methylation status of 1 Mb
bins across chromosomes 1-22 for Sample 8. Sun et al.,
2015 PNAS
gDNA Spike-In Variants
Sample 1
99%
A
Table 1. Genome-wide percent hypomethylation of cancer samples. WGBS was performed on 8 cfDNA
cancer samples and 5 healthy controls. Using 5 ng of input cfDNA and 10 million mapped reads per sample
provided enough coverage to identify genome-wide hypomethylation status. Percent hypomethylation was
calculated by comparing the methylation density (MD) of 1 Mb bins to the average of the 5 healthy control
samples. Bins were assigned as hypomethylated if MD was > 3 SD lower than the average MD. Sun et al.,
2015 PNAS
Identification of cfDNA Variants Down to 0.5%
Promega Maxwell RSC Automated cfDNA Extraction
Hypomethylation
Fallopian tube high-grade papillary serous carcinoma pT3c N1 with 2 nodes
involved by micrometasasis
Control Spike-In Experiments
Plasma
Separation
Pathology
1
ü  No adapter titration across input-range
In parallel, we have developed a workflow to determine if the epigenetic status of cell-free DNA can identify
tissue-of-origin. This workflow utilizes the Accel-NGS Methyl-Seq DNA Library Kit to enable unbiased
characterization from low (5 ng) cfDNA inputs. Through whole genome bisulfite sequencing, using a priori
knowledge of differentially methylated regions characteristic of different human tissues, we can identify the
predominant tissue source of cfDNA in blood.
Sample
Collection
Sequence the Cancer Methylome from 5 ng cfDNA
ü  With-bead protocol for single-tube workflow
Briefly, whole blood samples were collected in Streck cell-free DNA BCT vials from patients with late stage
cancer and cfDNA was extracted with the Promega Maxwell RSC. This instrument yielded DNA outputs ranging
from 8 to 32 ng, with a size profile defined by a predominant peak of ~170bp and a mean Alu repeat qPCR
integrity score of 0.22 [0.09-0.34], characteristic of high quality cell-free DNA lacking cellular DNA content. A total
of 20 ng cfDNA was used to make an Accel-NGS 2S Hyb library followed by hybridization capture using Agilent
SureSelect Human All Exon probes. The Accel-NGS 2S Hyb Library Kit exhibits a 90% library conversion rate
with cfDNA and provides high complexity libraries with uniform target coverage. In addition, molecular barcodes
were incorporated to label each library molecule uniquely prior to PCR amplification. These molecular barcodes
were utilized for accurate removal of PCR duplicates while simultaneously preserving naturally occurring
fragmentation and strand duplicates to maximize data recovery. Secondly, these barcoded molecules were
grouped to generate consensus sequences after removal of false positives originating from PCR and sequencing
errors. Variant calling was performed using Vardict and Lofreq enabling highly sensitive and precise detection of
variants down to a 0.5% allele frequency.
X
V.
1
Makarov
Figure 4. Workflow of the Accel-NGS Methyl-Seq library
preparation. The Accel-NGS Methyl-Seq Library Kits enable users to
make libraries from bisulfite-converted samples by using Swift’s
Adaptase™ technology. Unlike other library methods, the Adaptase
technology can generate library molecules from single-stranded DNA
fragments, which allow researchers to recover more of their input
DNA from difficult samples compared to other commercially-available
products. Non-uracil containing library products are shown in light blue.
For the Accel-NGS Methyl-Seq Kit, bisulfite conversion is performed
prior to library construction. The lightning bolts represent bisulfiteinduced fragmentation, NGS adapters are depicted in plum and blue,
and non-uracil containing library products are shown in light blue.
Sample ID
Concentration
(nM) of Library
maxwell_cfDNA_1
17.95
maxwell_cfDNA_2
13.11
maxwell_cfDNA_3
6.00
maxwell_cfDNA_4
10.90
maxwell_cfDNA_5
12.39
maxwell_cfDNA_6
6.57
maxwell_cfDNA_8
12.42
maxwell_cfDNA_9
6.23
maxwell_cfDNA_10
6.75
Figure 5. Made WGBS libraries from
purified cfDNA.
Several cfDNA
samples were used to construct whole
genome bisulfite (WGBS) libraries with
low cfDNA inputs (5-15 ng total) using
the Accel-NGS Methyl-Seq DNA Library
Kit. Yields for each library are shown.
Tissue-of-Origin Studies of ctDNA
Figure 7. Exploring tissue-specific methylation patterns to identify tissue-of-origin of circulating tumor
DNA. cfDNA originating from most liquid biopsies predominantly has cfDNA originating from the lymphoid and
myeloid tissues. For advanced cancer diseases, large amounts of tumor-derived DNA (ctDNA) can be
represented within the cfDNA, and depending on the origin of the primary tumor, may represent a significant
proportion of the cfDNA. Previous studies have shown that the different human organ systems/tissues contain
unique differentially methylated regions (DMRs). Using cfDNA originating from blood samples from patients
with known tumor origins, we look to identify if we can isolate tissue-specific DMRs. Specifically, can we
differentiate the DMRs from lymphoid and myeloid tissues?
Here, we have begun to validate the use of the Accel-NGS Methyl-Seq DNA Library Kit to identify tissuespecific DMRs in cfDNA. The high conversion rate offered by this kit will allow sensitive detection of low
frequency/minority fractions of cfDNA DMR patterns originating from the tumor. By increasing complexity of
the WGBS library, trace tissues may be identified with increased confidence, allowing for exploration on the
utility of using cfDNA to not only identify ctDNA, but to identify its tissue-of-origin.
Current status: Several cfDNA samples were used to construct whole genome bisulfite (WGBS) libraries
with low cfDNA inputs (5-15 ng total). Each library has been currently sequenced to ~10X coverage using the
Illumina sequencing platform. The current status of this project is in data analysis and DMR identification
within each cfDNA.
WGBS cfDNA
Libraries
Sequenced to
~10X Coverage
Identify Methylation
Patterns (DMRs) in
Each cfDNA Library
(Progress: data
analysis ongoing)
Conclusions
•  The Promega Maxwell RSC purified high quality cfDNA from whole blood samples obtained from patients
with advanced cancer.
•  The Accel-NGS 2S Hyb DNA Library Kit used alongside MID technology allows for accurate variant calling
down to 0.5%.
•  Using just 5 ng of input cfDNA and 10 million reads, Accel-NGS Methyl-Seq is able to construct libraries
from cancer samples and enables methylation status calls for these samples.
•  Preliminary experiments are currently underway to identify tissue-of-origin of ctDNA through methylation
patterns in Accel-NGS Methyl-Seq DNA libraries.
Swift Biosciences, Inc.
58 Parkland Plaza, Suite 100 • Ann Arbor, MI 48103 www.swiftbiosci.com
© 2017, Swift Biosciences, Inc. The Swift logo and Adaptase are trademarks and Accel-NGS is a registered trademark of Swift Biosciences. This product is for Research Use Only. Not for use in diagnostic procedures. Maxwell is a registered trademark of Promega, Inc.
Qubit is a registered trademark of Thermo Fisher Scientific Inc. xGen is a registered trademark of Integrated DNA Technologies, Inc. Illumina and HiSeq are registered trademarks of Illumina, Inc. 17-1436, 04/17
www.swiftbiosci.com