Abstract 3960: Combined circulating tumour cell (CTC) and circulating tumor DNA (ctDNA) analysis of blood from patients with pancreatic cancer

Abstract

Background
The challenge to improve outcomes for patients diagnosed with advanced pancreatic cancer remains very real with only small improvements in median survival gained by the use of systemic chemotherapy and little improvement in 5-year survival over the past decades. The advent of next generation sequencing (NGS) of tumor nucleic acids has opened up the possibility of improving outcomes through personalized therapies selected on the basis of tumor genetics.
Whilst NGS biomarkers can be measured in tumor biopsies sampled shortly before and after treatment this is often not practical for ethical, logistical and simple lack of availability. To circumvent problems associated with tumor sampling we have developed and evaluated blood-borne nucleic acids biomarkers for patients diagnosed with advanced pancreatic cancer.
Approach
We developed a NGS circulating free DNA (cfDNA) analysis pipeline based on the generation of whole genome NGS libraries followed by sequencing of over 600 cancer-associated genes using Agilent SureSelect. Using whole blood collected with Streck cell free DNA blood collection tubes (cfDNA BCT) we optimised combined circulating tumor cell (CTC) enrichment and cfDNA isolation. Quantitative measurements of KRAS mutations using both NGS and droplet digital PCR (ddPCR) were used to compare the tumor component present in both CTCs and cfDNA. We evaluated the overall approach using External Quality Assessment (EQA) controls and over 50 advanced pancreatic cancer patient samples. For CTC analysis we also compared epitope dependent (CellSearch) and independent (Parsortix) enrichment.
Results
After applying our NGS pipeline to 5 EQA genomic controls we identified all 14 known mutations correctly indicating high sensitivity and specificity. Both ddPCR and NGS identified KRAS mutations in patient cfDNA with a higher success rate seen for ddPCR consistent with the higher sensitivity of this methodology. From the NGS output additional mutations were detected in samples which either harboured or lacked detectable KRAS mutations. Consistent with previous observations CellSearch CTCs were detected at low levels in around 20% of patients with a similar frequency seen in initial analysis of CTCs obtained by epitope independent enrichment (Parsortix). In general KRAS mutations were detected at a higher level in patient cfDNA compared to enriched CTCs although some patients showed detectable CTC KRAS mutations with no KRAS mutations detected in their cfDNA by either ddPCR or NGS. Ongoing analysis is aimed at establishing if the molecular observations correlate with clinical outcome.
Conclusion
Combined CTC enrichment and cfDNA isolation is readily achievable using a single Streck cfDNA BCT. Results indicate that combined CTC and cfDNA analysis is more sensitive than either approach alone.
Citation Format: Ged Brady, Dominic Rothwell, Mahmood Ayub, Nigel Smith, Sumitra Mohan, Jakub Chudziak, Kyaw Aung, Richard Hubner, Crispin Miller, Alison Backen, Hui Sun Leong, Sakshi Gulati, Chang Sik Kim, Angela Lamarca, Mairéad McNamara, Juan Valle, Caroline Dive. Combined circulating tumour cell (CTC) and circulating tumor DNA (ctDNA) analysis of blood from patients with pancreatic cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3960.

Full text

1
SCIENTIFIC REPORTS | (2019) 9:11610 | https://doi.org/10.1038/s41598-019-47489-7
www.nature.com/scientificreports
Analysis of circulating cell-free DNA
identies KRAS copy number gain
and mutation as a novel prognostic
marker in Pancreatic cancer
Sumitra Mohan1, Mahmood Ayub1, Dominic G. Rothwell1, Sakshi Gulati1, Bedirhan Kilerci1,
Antoine Hollebecque
1, Hui Sun Leong2, Nigel K. Smith1, Sudhakar Sahoo2, Tine Descamps1,
Cong Zhou
1, Richard A. Hubner3, Mairéad G. McNamara
3,4, Angela Lamarca
3,
Juan W. Valle
3,4, Caroline Dive
1 & Ged Brady1
Serial biopsy of pancreatic ductal adenocarcinoma (PDAC), to chart tumour evolution presents a
signicant challenge. We examined the utility of circulating free DNA (cfDNA) as a minimally invasive
approach across a cohort of 55 treatment-naïve patients with PDAC; 31 with metastatic and 24 with
locally advanced disease. Somatic mutations in cfDNA were detected using next generation sequencing
in 15/24 (62.5%) and 27/31 (87%) of patients with locally advanced and metastatic disease, respectively.
Copy number changes were detected in cfDNA of 10 patients of whom 7 exhibited gain of chromosome
12p harbouring KRAS as well as a canonical KRAS codon 12 mutation. In multivariable Cox Regression
analysis, we show for the rst time that patients with KRAS copy number gain and KRAS mutation have
signicantly worse outcomes, suggesting that this may be linked to PDAC progression. The simple
cfDNA assay we describe will enable determination of the presence of KRAS copy number gain and
KRAS mutations in larger studies and clinical trials.
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with <7% 5-year survival1 and increasing
worldwide incidence2. Poor patient outcomes are attributed to several factors, including late diagnosis, chemo-
therapy resistance and the absence of druggable targets to improve patient outcomes3. Obtaining tumour biopsies
is challenging and carbohydrate antigen 19-9 (CA 19-9), the only approved circulating biomarker for routine
clinical management of PDAC (National Comprehensive Cancer Network [NCCN] guidelines) is limited by
sub-optimal sensitivity and specicity. More recently, circulating cell free DNA (cfDNA) has been proposed as
a minimally invasive alternative to traditional blood-based protein biomarkers and invasive tissue biomarkers
for many solid cancer types, including PDAC4,5. A previous study detected KRAS mutations in cfDNA of 58.9%
of patients with PDAC with distant metastasis and 18.2% of patients with locally advanced disease6. In this pilot
study, we evaluated targeted KRAS sequencing and broad next-generation sequencing (NGS) analysis of 641
cancer-associated genes in the cfDNA of 55 patients with PDAC to evaluate the potential clinical utility of cfDNA
in PDAC (Fig.1A).
Results
A total of 55 treatment-naïve patients with PDAC were identied (between Feb 2011 to Apr 2014); 24 with locally
advanced disease and 31 with metastatic disease. e clinical details including age, gender, performance status
and metastatic sites are provided in the Supplementary Table1.
1Clinical Experimental Pharmacology Group, Cancer Research UK Manchester Institute, University of Manchester,
Alderley Park, SK10 4TG, Macclesfield, UK. 2Computational Biology Support, Cancer Research UK Manchester
Institute, University of Manchester, Alderley Park, M20 4BX, Maccleseld, UK. 3Medical Oncology Department, The
Christie NHS Foundation Trust; Division of Cancer Sciences, University of Manchester, M20 4BX, Manchester, United
Kingdom. 4Division of Cancer Sciences, University of Manchester, M20 4BX, Manchester, UK. Juan W. Valle, Caroline
Dive and Ged Brady jointly supervised this work. Correspondence and requests for materials should be addressed to
G.B. (email: gerard.brady-2@manchester.ac.uk)
Received: 8 April 2019
Accepted: 12 July 2019
Published: xx xx xxxx
OPEN

2
SCIENTIFIC REPORTS | (2019) 9:11610 | https://doi.org/10.1038/s41598-019-47489-7
www.nature.com/scientificreports
www.nature.com/scientificreports/
No somatic mutations or copy number alterations were detected in 16 non-cancer controls (Table1). No
signicant dierences were observed in yield of cfDNA detected between the 31 patients with metastatic and 24
with locally advanced PDAC (p-value = 0.19; Fig.2). From cfDNA NGS, both CNA and somatic mutations were
elevated in the patients with metastatic disease compared to the patients with locally advanced disease (p-values
of 0.0164 and 0.0049, respectively, Fig.2B,C). Somatic mutations were detected in 87% (27/31) and 54% (13/24)
of the samples from patients with metastatic and locally advanced disease, respectively. Known non-synonymous
activating KRAS mutations, conrmed by ddPCR, were detected in 35% (11/31) and 12.5% (3/24) of samples
from patients with metastatic and locally advanced disease respectively. In addition to the 14 mutations detected
by NGS, a further seven KRAS mutations (four metastatic, three locally advanced) were detected using ddPCR,
which were below the 2.5% VAF (Variant Allele frequency) threshold used for NGS analysis (Fig.1B). In keeping
with previous studies, NGS of cfDNA from the patiens with metastatic disease also identied canonical TP53 and
KMT2D mutations at frequencies of 29% (9/31) and 16% (5/31) respectively6 (Fig.1B).
Measurable copy number alterations (CNA) were detected in 10 of the 55 patients’ cfDNA samples (nine met-
astatic, one locally advanced), of whom seven exhibited a gain in chromosome 12p that harbours KRAS (Fig.1B).
All seven PDAC cfDNA samples with copy number gain (CNG) of KRAS also exhibited non-synonymous somatic
mutations in KRAS (Fig.1B and Supplementary Table1).
Kaplan-Meier analysis of overall survival (OS) based on KRAS mutation alone (7/55), KRAS mutation and
CNG (7/55) and with KRAS wild-type (34/55), revealed best prognosis for patients with KRAS wild-type (median
survival 10.6 months), followed by patients with KRAS mutation without CNG (median survival 5.5 months).
e worst prognosis was associated with the combination of a KRAS mutation and CNG (median survival 2.5
RUNX1T1
SMARCA4
TMPRSS2
PCDHGA5
PDCD1LG2
KRAS
TP53
TTN
KMT2D
KDM5A
ABI1
MYC
PAX2
PIK3C2B
PTPRC
FGFR4
HOXA11
NUP214
PDE4DIP
PMS2
RECQL4
RIMS2
SLC45A3
SMAD4
TCF4
TET1
WT1
SEPT6
ACSL6
AFF1
AFF4
ARID1A
ATM
ATR
BCL6
BCL9
CBL
CDKN2A
CLTCL1
EPHA10
EPHA5
ERBB4
ERCC3
EZH2
FANCA
GPC3
IDH1
KAT6A
KMT2A
KMT2C
LRRK2
MCL1
MECOM
MED12L
MUC16
NOTCH2
PCM1
PIK3CA
WDR36
ZNF521
ACVR1B
ALDH2
AXIN2
CCND1
DNMT3A
EML4
FGFR1
GPR113
MAP2K4
MRE11A
MSH6
NIN
PAX7
PBRM1
PCDHA3
PIK3R5
PML
SARDH
SF3B2
SPECC1
SPEN
TET2
TGFBR2
TOP2A
WHSC1
CNBP
HECW1
P1
P6
P10
P11
P15
P17
P19
P20
P21
P22
P26
P27
P29
P31
P33
P34
P35
P37
P43
P44
P50
P51
P52
P55
P2
P3
P4
P5
P7
P8
P9
P12
P13
P14
P16
P18
P23
P24
P25
P28
P30
P32
P36
P38
P39
P40
P41
P42
P45
P46
P47
P48
P49
P53
P54
Mutation +GainofCopyNumber
Mutation +LossofCopy Number
Mutation
Gain of Copy Number
Loss Of Copy Number
PS
1
0
(N=32)
(N=7)
2.5
reference
(0.99-6.4)
0.053
Mutation + CN
G
Mutation + CNL
Mutation
CNG
CNL
p < 0.0001
0.00
1.00
Time (Months)
Percent Survival
Mutation Only
CNG + Mutation
WT
Time (Months)
Whole Blood Sample
Whole Genome
Library
Plasma/cfDNA
GermlineDNA
Copy Number
Analysis (CNA)
Somatic Mutation
Analysis
Low Depth Whole
Genome Sequencing
High DepthTargeted
Sequencing
Combine with
Mutational Data
ConfirmKRASMUT
with ddPCR
cfDNA cfDNA+ Germline
KRAS CNG
Highest VAF
Liver
PS
1
0
1
0
2+
1
0
(N=7)
(N=48)
(N=55)
(N=20)
(N=35)
(N=16)
(N=32)
(N=7)
3.5
reference
1.0
2.8
reference
4.2
2.5
reference
(1.19 - 10.2)
(1.01 - 1.1)
(1.28 - 6.2)
(1.48 - 11.9)
(0.99 - 6.4)
0.023*
0.005**
0.01*
0.007**
0.053
#Events: 55
Global p-value (Log-Rank): 1.679e-07
AIC: 306.91; Concordance Index: 0.77
12 510
AB
D
C
0.75
0.50
0.25
010 20 30 40
010 20 30 40
41 22 810
7000 0
7200 0
Distant Metastasis Locally Advanced
Figure 1. (A) Sample Workow. is owchart explains the workow used in this study starting from whole
blood samples collected in this study to the analysis performed. (B) Combined copy number and mutational
analysis of cfDNA. Combined mutational and copy number plot for the 85 genes that were positive for at least 1
mutation across all 55 cfDNA samples analysed. Boxes coloured orange represent mutation and a copy number
gain (CNG), yellow mutation and copy number loss (CNL), green mutation, red CNG, and blue for CNL. (C)
Kaplan-Meier analysis of the overall survival according to KRAS mutation alone (7/55), KRAS mutation and
copy number gain (7/55), along with KRAS wild-type (41/55) in 55 patients revealed best prognosis for patients
with KRAS wild-type, with a median survival of 10.6 months, followed by patients with KRAS mutation alone
with a median survival of 5.6 months. Patients with both the KRAS mutation and copy number gain had the
worst prognosis with a median survival of 2.5 months (overall Log-rank test p-value = < 0.0001). KRAS WT vs
KRAS MUT only Log-rank p-value = 0.0610, KRAS WT vs KRAS CN gain + MUT Log-rank p = value = 0.0012
and KRAS MUT only vs KRAS CN gain + MUT Log-rank p-value = < 0.0001. (D) Hazard ratios for the factors
that were used in a multivariable analysis.

3
SCIENTIFIC REPORTS | (2019) 9:11610 | https://doi.org/10.1038/s41598-019-47489-7
www.nature.com/scientificreports
www.nature.com/scientificreports/
months, Log-rank p-value < 0.0001; Fig.1C,D). Univariate analysis identied highest VAF (any gene), KRAS
CNG, performance status (PS) and presence of liver metastases as signicant factors for shorter survival with a
p-value < 0.05. Stepwise multivariable analysis (Table1) identied KRAS CNG and mutation as an independent
predictor for shorter survival.
Discussion
In this pilot study of 55 patients with PDAC, we applied NGS and ddPCR to cfDNA to establish which readouts, if
any, are linked to clinical outcomes. Although we see a relatively short median survival of 7.99 months compared
to the 19.77 months reported in a TCGA study7, this most likely reects dierences in staging with the TCGA
cohort comprising operable localised disease whereas our cohort includes patients with locally advanced and
metastatic disease, resulting in a shorter median survival, in line with those reported by other groups8,9. Analysis
of cfDNA from each patient revealed the presence of a canonical KRAS somatic mutation, which was determined
by ddPCR and was found to be 38% (21/55) overall; 48% (15/31) in metastatic disease and 25% (6/24) in locally
advanced disease, in keeping with other published studies6. Although our detection rate of 38% for the presence
of a KRAS mutation in patient cfDNA is in line with other reports, there is considerable variation in reported
frequencies (27~93%)10,11 which may reect the methodologies employed, as well as the variability of KRAS allelic
ratios in the tumour10 and the low ctDNA burden associated with pancreatic cancer12. Analysis of a larger cohort
with a consistent specied cfDNA methodology is required to assess the aect of KRAS variation on the accuracy
of prognosis.
As expected, from the threshold of detection used for the targeted NGS in this study (2.5%)13, only 14/21
ddPCR positive samples were found to harbour targeted NGS somatic KRAS mutations (Supplementary Table1).
However, by extending the NGS analysis to an additional 640 genes, somatic mutations were detected in 71%
(39/55) in all samples; 84% (26/31) in metastatic disease and 50% (12/24) in locally advanced disease. e most
striking novel observation that emerged from this study was that >10% of patients with PDAC harboured both a
KRAS mutation and a KRAS CNG, and that the latter correlated with a worsened prognosis. Although amplied
mutated KRAS has been reported in non-small cell lung cancer (NSCLC) and is also associated with poor clinical
outcome14, this is the rst report in PDAC. In addition to identifying CNG of KRAS, we also noted four cases
where TP53 mutations were accompanied by copy number loss (CNL), suggesting that further analysis of a larger
patient group may also identify CNL as a prognostic biomarker.
Parameter At risk group HR [95% CI] p-value
Univariate analysis
Gender Male (vs Female) 1.68 [0.97, 2.89] 0.064
Age Continuous 0.99 [0.97, 1.02] 0.545
ECOG Performance status 1 (vs 0) 1.77 [0.77, 4.08] 0.179
>=2 (vs)) 4.34 [1.72, 10.96] 0.002
ORR Response (vs no response) 0.31 [0.13, 0.76] 0.010
Metastasis
Number of metastatic sites >=2 sites (vs <2 sites) 1.31 [0.66, 2.62] 0.44
Liver metastasis Yes (vs No) 3.05 [1.70, 5.51] <0.001
Lung metastasis Yes (vs No) 0.77 [0.35, 1.67] 0.505
Other sites Yes (vs No) 1.61 [0.68, 3.83] 0.283
WCC$Continuous 1.18 [1.11, 1.25] <0.001
Neutrophils$Continuous 1.19 [1.11, 1.26] <0.001
Lymphocytes Continuous 0.72 [0.46, 1.12] 0.143
LDH Continuous 1.001 [1.000, 1.003] 0.003
Ca 19.9 (Log2) Continuous 1.07 [0.99, 1.16] 0.091
CNA Continuous 7.09 [3.19, 15.78] <0.001
Mutational burden
Number of mutations$Continuous 1.06 [1.01, 1.12] 0.028
Number of KRAS mutations$Continuous 1.11 [1.07, 1.16] <0.001
KRAS mutations present Yes (vs No) 3.46 [1.76, 6.77] <0.001
KRAS copy number gain Yes (vs No) 10.94 [3.85, 31.08] <0.001
Highest VAF Continuous 1.07 [1.04, 1.10] <0.001
Multivariable analysis
ECOG Performance status 1 (vs 0) 2.51 [0.98, 6.38] 0.053
>=2 (vs 0) 4.20 [1.48, 11.94] 0.007
Metastasis Liver metastasis Yes (vs No) 2.83 [1.28, 6.24] 0.010
Mutational burden KRAS copy number gain Yes (vs No) 3.47 [1.19, 10.17] 0.023
Highest VAF Continuous 1.05 [1.01, 1.08] 0.005
Table 1. Univariate and multivariable Cox regression analysis for prediction of OS. Abbreviations: ORR,
objective response rate (clinical outcome variable); VAF: variant allele frequency; ECOG, Eastern Cooperative
Oncology Group; WCC: white cell count; $, Excluded from stepwise model building due to collinearity.

4
SCIENTIFIC REPORTS | (2019) 9:11610 | https://doi.org/10.1038/s41598-019-47489-7
www.nature.com/scientificreports
www.nature.com/scientificreports/
We now have the opportunity to verify these initial results by examining additional patient cfDNAs from the
on-going Precision-Panc clinical trial, and serial measurements may inform response to treatment15.
Our results demonstrate cfDNA analysis can be used in advanced disease to identify patients with worse
prognosis who may benet from more aggressive chemotherapy. In addition, the identication of KRAS CNG and
mutation as a poor prognostic factor, could also help to identify patients with resectable disease with higher risk
of early tumour relapse, who may benet from additional staging imaging before surgery (i.e. Magnetic resonance
imaging of the liver or 18 uorodeoxyglucose (FDG)-positron emission tomograph) or potential neo-adjuvant.
Materials and Methods
Non-Cancer volunteer and patient blood sample collection. Patients diagnosed with advanced
treatment-naïve PDAC were prospectively recruited. Baseline blood samples (before treatment initiation) were
collected in Cell-Free™ DNA BCTs (Streck, Omaha, NE), or BD Vacutainer® K2EDTA tubes, following receipt
of informed consent in compliance with the Declaration of Helsinki and Good Clinical Practice under eth-
ics approval number 07/H1014/96 (approved by Internal Review and Ethics Board of the Manchester Cancer
Research Centre BioBank).
Circulating cell free DNA preparation. Plasma and cfDNA were isolated as previously described16.
Germline DNA was isolated from EDTA whole blood, using QIAmp Blood Mini Kit (Qiagen, Hilden, Germany)
as per manufacturer’s instructions.
NGS library preparation and sequencing. Whole genome sequencing (WGS) of cfDNA and corre-
sponding germline DNA from the patients as well as non-cancer controls were carried out using the Accel-NGS®
2 S Plus DNA Library Kit as previously described16.
Targeted NGS analysis. Targeted NGS of 641 cancer-associated genes was carried out using Agilent
SureSelectXT as described previously13.
Somatic mutation detection from targeted re-sequencing data. ree mutation callers were used:
MuTect (version 1.1.5); VarScan (version 2.3.9) and Biomedical Genomics Workbench 4.1 (CLC Bio, Qiagen).
Single nucleotide variant (SNV) calls were accepted, if identied by both MuTect and Biomedical Genomics
Workbench and indels accepted if identied by both VarScan and Biomedical Genomics Workbench (Fig.1B).
HMMcopy (version 1.8.0) was used to call regions as gained or lost from WGS16.
Droplet digital PCR. Droplet digital PCR (ddPCR) was carried out using a QX200 ddPCR system (Bio-Rad)
with ddPCRTM KRAS Screening multiplex kit17.
Statistical analyses. Mann-Whitney t-tests were used to compare cfDNA metrics (cfDNA in ng/ml of
plasma, Percent genome amplied [PGA] and Highest VAF) between patients with locally advanced disease and
patients with distant metastases. Factors associated with mutational burden and standard clinical and biochemi-
cal factors were subjected to Kaplan-Meier survival analysis and univariate Cox proportional hazards regression
to predict overall survival (OS), considering the proportionality and linearity assumptions. OS was dened as the
time in months between date of rst diagnosis of malignancy and time of death. Univariately signicant parame-
ters (5% signicance level) were included in a multivariable Cox regression analysis (bidirectional stepwise selec-
tion based on Akaike information criterion; exclusion of collinear parameters and clinical outcome variable).
Statistical analysis was performed using the computing environment R (R Development Core Team, 2005).
Ethics approval and consent to participate. Blood samples were collected from patients with PDAC
following receipt of informed consent in compliance with the Declaration of Helsinki and Good Clinical Practice
under ethics 07/H1014/96, aer approval from the Internal Review and the Ethics Boards of e Manchester
Cancer Research Centre BioBank.
Figure 2. Comparison between patients with distant metastasis and locally advanced disease for (A) Yield of cf
DNA in ng/ml of plasma (B). Percent Genome Amplied (PGA) and (C) highest VAF.

5
SCIENTIFIC REPORTS | (2019) 9:11610 | https://doi.org/10.1038/s41598-019-47489-7
www.nature.com/scientificreports
www.nature.com/scientificreports/
Data Availability
All the data generated or analysed during this study are included in this published article, or are available from the
corresponding author upon reasonable request.
References
1. Ferlay, J. et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer
136, E359–386, https://doi.org/10.1002/ijc.29210 (2015).
2.  ahib, L. et al. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the
United States. Cancer es 74, 2913–2921, https://doi.org/10.1158/0008-5472.can-14-0155 (2014).
3. Paulson, A. S., Tran Cao, H. S., Tempero, M. A. & Lowy, A. M. erapeutic advances in pancreatic cancer. Gastroenterology 144,
1316–1326, https://doi.org/10.1053/j.gastro.2013.01.078 (2013).
4. Heitzer, E. et al. Tumor-associated copy number changes in the circulation of patients with prostate cancer identied through whole-
genome sequencing. Genome Med 5, 30, https://doi.org/10.1186/gm434 (2013).
5. Mohan, S. et al. Changes in colorectal carcinoma genomes under anti-EGF therapy identied by whole-genome plasma DNA
sequencing. PLoS Genet 10, e1004271, https://doi.org/10.1371/journal.pgen.1004271 (2014). PGENETICS-D-13-02785 [pii].
6. Taai, E. et al. Clinical utility of circulating tumor DNA for molecular assessment in pancreatic cancer. Scientic eports 5, 18425,
https://doi.org/10.1038/srep18425
7. Nicolle, . et al. Prognostic Biomarers in Pancreatic Cancer: Avoiding Errata When Using the TCGA Dataset. Cancers 11, 126,
https://doi.org/10.3390/cancers11010126 (2019).
8. Peran, I., Madhavan, S., Byers, S. W. & McCoy, M. D. Curation of the Pancreatic Ductal Adenocarcinoma Subset of the Cancer
Genome Atlas Is Essential for Accurate Conclusions about Survival-elated Molecular Mechanisms. Clin Cancer es 24, 3813–3819,
https://doi.org/10.1158/1078-0432.ccr-18-0290 (2018).
9. Siegel, . L., Miller, . D. & Jemal, A. Cancer statistics, 2017. CA: A Cancer Journal for Clinicians 67, 7–30, https://doi.org/10.3322/
caac.21387 (2017).
10. L ennerz, J. . & Stenzinger, A. Allelic ratio of AS mutations in pancreatic cancer. e oncologist 20, e8–9, https://doi.org/10.1634/
theoncologist.2014-0408 (2015).
11. Le Calvez-elm, F. et al. AS mutations in blood circulating cell-free DNA: a pancreatic cancer case-control. Oncotarget 7,
78827–78840, https://doi.org/10.18632/oncotarget.12386 (2016).
12. Mouliere, F. et al. Enhanced detection of circulating tumor DNA by fragment size analysis. Science Translational Medicine 10,
eaat4921, https://doi.org/10.1126/scitranslmed.aat4921 (2018).
13. othwell, D. G. et al. Utility of ctDNA to support patient selection for early phase clinical trials: the TAGET study. Nat Med, https://
doi.org/10.1038/s41591-019-0380-z (2019).
14. Sasai, H. et al. Evaluation of ras gene mutation and copy number gain in non-small cell lung cancer. J orac Oncol 6, 15–20,
https://doi.org/10.1097/JTO.0b013e31820594f0 (2011).
15. Graham, J. S. et al. PIMUS-001: An adaptive phase II study of FOLFOX-A (FOLFOX and nab-paclitaxel) versus AG (nab-paclitaxel
and gemcitabine) in patients with metastatic pancreatic cancer, with integrated biomarer evaluation (ISCTN75002153) – Part of
Precision-Panc. J Clin Oncol 36, TPS4158–TPS4158, https://doi.org/10.1200/JCO.2018.36.15_suppl.TPS4158 (2018).
16. Williamson, S. C. et al. Vasculogenic mimicry in small cell lung cancer. Nature Communications 7, 13322, https://doi.org/10.1038/
ncomms13322 (2016).
17. Janu, F. et al. Multiplex ASG12/G13 mutation testing of unamplied cell-free DNA from the plasma of patients with advanced
cancers using droplet digital polymerase chain reaction. Ann Oncol 28, 642–650, https://doi.org/10.1093/annonc/mdw670 (2017).
Acknowledgements
We sincerely thank the patients and their families for provision of blood samples for research. Funding for this
study was from Cancer Research UK (CRUK) via the core CRUK Manchester Institute grant (C5759/A27412),
the CRUK Manchester Centre (C5759/A25254), the CRUK Manchester Experimental Cancer Medicines Centre
(A20465) and the NIHR Manchester Biomedical research Centre. Sumitra Mohan’s salary and consumable costs
were funded via CRUK Precision Panc grant C480/A25235. Sample collection and analysis was undertaken via
the Pancreatic Cancer Research Fund (PCRF) 2012 Project Grant. AL received funding from European Society
for Medical Oncology (ESMO) Fellowship Programme, Spanish Society of Medical Oncology (SEOM) Fellowship
Programme, American Society of Clinical Oncology (ASCO) Conquer Cancer Foundation Young Investigator
Award and e Christie Charity.
Author Contributions
G.B., C.D., J.W.V. and A.L. designed the study. J.W.V., A.L., R.H. and MMN. recruited and consented the patients,
and collected blood samples. A.L. provided clinical data. M.A., D.G.R., G.B. and C.D. conceived and designed the
experiments. M.A., S.M., N.S. and A.H. performed the experiments. M.A., S.M., G.B., A.L., H.S.L., S.S, P.S, B.K.
and S.G. analysed the data. A.L., T.D. and C.Z., performed statistical analyses. S.M, M.A., G.B., C.D., A.L. and
J.W.V. interpreted the data. S.M., M.A., G.B, C.D., A.L. and J.W.V. prepared the manuscript. All authors reviewed
the manuscript.
Additional Information
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-019-47489-7.
Competing Interests: AL received travel and educational support from Ipsen, Pzer, Bayer, AAA, Sirtex
Medical, Novartis, Mylan and Delcath Systems; speaker honoraria from Merck, Pzer and Ipsen; advisory
honoraria from EISAI and Nutricia; she is a member of the Knowledge Network and NETConnect Initiatives
funded by Ipsen. JV reports Consulting or Advisory role for Ipsen, Novartis, AstraZeneca, Merck, Delcath
Systems, Agios, Pzer, PCI Biotech, Incyte, Keocyt, QED, Pieris Pharmaceuticals, Genoscience Pharma,
Mundipharma EDO; Honoraria from Ipsen; and Speakers’ Bureau for Novartis, Ipsen, Nucana and Imaging
Equipment Limited; all outside the scope of this work. CD acts in a consultant or advisory role for Biocartis and
AstraZeneca and receives research grants/support from AstraZeneca, Astex Pharmaceuticals, Bioven, Amgen,
Carrick erapeutics, Merck AG, Taiho Oncology, GSK, Bayer, Boehringer Ingelheim, Roche, BMS, Novartis,
Celgene, Epigene erapeutics Inc; all outside the scope of work. MMN has received research grant support
from Servier, Ipsen and NuCana. She has received travel and accommodation support from Bayer and Ipsen and

6
SCIENTIFIC REPORTS | (2019) 9:11610 | https://doi.org/10.1038/s41598-019-47489-7
www.nature.com/scientificreports
www.nature.com/scientificreports/
speaker honoraria from Pzer, Ipsen and NuCana. She has served on advisory boards for Celgene, Ipsen, Sirtex
and Baxalta; all outside the scope of this work. Other authors have no conict of interests to declare.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional aliations.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre-
ative Commons license, and indicate if changes were made. e images or other third party material in this
article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons license and your intended use is not per-
mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the
copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
© e Author(s) 2019