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Circulating cell-free tumor DNA (cfDNA) analysis of 50-genes by next-
generation sequencing (NGS) in the prospective MOSCATO trial
Running title: cfDNA next-generation sequencing in MOSCATO trial
Cécile Jovelet1†ŧ, Ecaterina Ileana1,2†, Marie-Cécile Le Deley3,4,5, Nelly Motté1, Silvia
Rosellini3, Alfredo Romero1, Celine Lefebvre8, Marion Pedrero8, Noémie Pata-Merci6,
Nathalie Droin6, Marc Deloger7, Christophe Massard2, Antoine Hollebecque2, Charles Ferté9,
Amélie Boichard1, Sophie Postel-Vinay2,8, Maud Ngo-Camus2, Thierry De Baere10, Philippe
Vielh11, Jean-Yves Scoazec1,5,11, Gilles Vassal12, Alexander Eggermont5,9, Fabrice André5,8,9,
Jean-Charles Soria2,5,8*, Ludovic Lacroix 1,8,11,13*
1 Laboratoire de Recherche Translationnelle et Centre de Ressources Biologiques, AMMICA,
INSERM US23/CNRS UMS3655, Gustave Roussy, France
2 Département d’Innovation Thérapeutique et d’Essais Précoces, Gustave Roussy, Villejuif, France
3 Département de Biostatistiques et Epidémiologie, Gustave Roussy, Villejuif, France
4 INSERM U1018, Gustave Roussy, Villejuif, France
5 Faculté de Médecine, Université Paris Sud, Le Kremlin-Bicêtre France
6 Plateforme de Génomique Fonctionnelle, AMMICA, INSERM US23/CNRS UMS3655, Gustave
Roussy, France
7 Plateforme de bioinformatique, AMMICA, INSERM US23/CNRS UMS3655, Gustave Roussy,
France
8 INSERM U981, Gustave Roussy, Villejuif, France
9 Département d’Oncologie médicale, Gustave Roussy, Villejuif, France
10 Département de Radiologie, Gustave Roussy, Villejuif, France
11 Département de Biologie et Pathologie Médicales, Gustave Roussy, Villejuif, France
12 Direction de la Recherche Clinique, Gustave Roussy, Villejuif, France
13 Université Paris Sud, Faculté de Pharmacie, Châtenay-Malabry, France.
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† Both authors contributed equally as first authors
ŧ Corresponding author: Cécile Jovelet cecile.jovelet@gustaveroussy.fr
Laboratoire de Recherche Translationnelle
114 rue Edouard Vaillant
94800 Villejuif, France
0033 1 42 11 43 64
* Both authors shared the senior authorship
Conflict of interest
The authors report no conflicts of interest.
Keywords: Next generation sequencing, Circulating free DNA, Phase-I clinical trials
Statement of Translational Research:
Molecular screening using next generation sequencing (NGS) of tumor-derived
nucleic acids in plasma or circulating cell-free DNA (cfDNA), only requires a blood
sample, and appears as an attractive non-invasive alternative to DNA analysis
extracted from tissue sample (tDNA). cfDNA based analysis might also overcome the
issue of sampling bias. This article confirms the feasibility of NGS mutations
screening based on cfDNA samples on a large cohort of phase I clinical trial. If
cfDNA’s specificity was very high, sensitivity appeared limiting for using this
surrogate analysis in all patients. We consequently developed a sensitivity prediction
score, which allowed appropriately selecting patients for cfDNA analysis. The
analysis of cfDNA using NGS represents an attractive noninvasive option for patients
in which a tumor molecular profile is required.
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ABSTRACT
Purpose: Liquid biopsies based on circulating cell-free DNA (cfDNA) analysis are
described as surrogate samples for molecular analysis. We evaluated the
concordance between tumor DNA (tDNA) and cfDNA analysis on a large cohort of
patients with advanced or metastatic solid tumor, eligible for phase I trial and with
good performance status, enrolled in MOSCATO 01 trial (Clinical trial
NCT01566019).
Experimental Design: Blood samples were collected at inclusion and cfDNA was
extracted from plasma for 334 patients. Hot-spot mutations were screened using next
generation sequencing for 50 cancer genes.
Results: Among the 283 patients with tDNA-cfDNA pairs, 121 had mutation in both,
99 in tumor only, 5 in cfDNA only and for 58 patients no mutation was detected,
leading to an 55.0% estimated sensitivity (95%CI, 48.4%-61.6%) at the patient level.
Among the 220 patients with mutations in tDNA, the sensitivity of cfDNA analysis was
significantly linked to the number of metastatic sites, albumin level, tumor type, and
number of lines of treatment. A sensitivity prediction score could be derived from
clinical parameters. Sensitivity is 83% in patients with a high score (≥8). In addition,
we analyzed cfDNA for 51 patients without available tissue sample. Mutations were
detected for 22 patients, including 19 oncogenic variants and 8 actionable mutations.
Conclusions:
Detection of somatic mutations in cfDNA is feasible for prescreening phase I
candidates with a satisfactory specificity; overall sensitivity can be improved by a
sensitivity score allowing to select patients for whom cfDNA constitutes a reliable
non-invasive surrogate to screen mutations.
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INTRODUCTION
Personalized medicine in oncology, which has seen a rapid development in the last
decade due to advances in cancer genomic research and technology, consists of
proposing a treatment tailored to the individual patient's molecular profile (1). This
assumes that the identified genomic mutations are driver alterations, and that
matched targeted drugs are available (2). With the use of high-throughput molecular
technologies, the feasibility of such approach has been demonstrated in several pilot
trials (3,4) as well as in large scale studies (5).
In order to characterize the genomic alterations, tissue from primary tumor or
metastasis is generally used for molecular analysis. In the absence of suitable tissue
samples from surgical or endoscopic resections, targeted biopsy is required.
However, biopsy techniques present ethical and logistical challenges, due to their
invasiveness, morbidity, cost, variable accessibility of the tumor sites, as well as
potential sampling bias due to intratumor genetic heterogeneity (6,7). In contrast,
tumor-derived nucleic acids in plasma (also known as circulating cell-free tumor DNA
- cfDNA), only requires a blood sample, can be collected easily and repeatedly, and
may be a non-invasive alternative to tissue biopsy (8).
cfDNA is composed of small circulating DNA fragments shed into the plasma
following apoptosis or necrosis of tumor cells, or through spontaneous release from
cancer tissue, and possibly circulating tumor cells (CTCs) (9,10). The detection and
clinical use of cfDNA have long remained challenging due to the mixture between
tumor circulating free DNA and normal circulating free DNA, the low levels of cfDNA,
and the difficulty to accurately quantify the number of DNA fragments. However,
recent technological advances now enable the detection, quantification and analysis
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of cfDNA (11), thereby opening up possibilities for clinical use. Added to the minimal
invasiveness of blood sampling, the attractiveness of liquid biopsy also consists in
avoiding cost and delay linked to the tissue biopsy.
The potential applications of cfDNA are currently under extensive investigations, and
include diagnosis, patient selection for targeted therapies, prognostic assessment,
monitoring of treatment response and resistance, as well as screening for acquired
mutations at the time of resistance (10,12). Recent studies have shown a correlation
between cfDNA levels and treatment response in breast cancer (13), the ability of
cfDNA to identify mutations associated with acquired resistance (14), and its potential
for diagnosis and patient selection of targeted therapy (15).
The ongoing MOSCATO 01 (Molecular Screening for Cancer Treatment
Optimization) trial (Clinical trial NCT01566019) is a prospective molecular screening
program of patients eligible for phase I trials, which uses high-throughput
technologies (Next Generation Sequencing (NGS) and Comparative Genome
Hybridization (CGH)) to match patients’ tumor molecular profile with targeted therapy
and evaluates the clinical benefit of such approach (16,17).
In the current study, we evaluated whether detection of mutations in cfDNA is a
reliable alternative to tissue biopsies for molecular screening, by determining the
performance of cfDNA analysis to detect mutations as compared to tumor tissue
(tDNA).
PATIENTS AND METHODS
Patients and sample collection
Patients referred to the Drug Development Department (DITEP) at the Gustave
Roussy Cancer Center were eligible for inclusion in the MOSCATO 01 trial if they
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had an advanced or metastatic solid tumor progressive after at least one line of
standard therapy, with a good performance status (ECOG=0 or 1) and a primary
tumor or metastasis accessible to biopsy. The study was approved by the local
institutional review board and all patients provided written informed consent for
genetic analysis of their tumor and plasma samples prior to participation in this study.
CT-scan or ultrasound-guided biopsies were performed in metastatic or primary
tumor sites to carry out a comprehensive molecular characterization. The percentage
of tumor cells out of all nucleated cells was assessed on a 3μm section after a
hematoxylin and eosin staining by a senior pathologist. Samples with at least 10% of
tumor cells were considered for further tDNA analysis through NGS; at least 30% of
tumor cells within the sample was required for CGH analysis. Patients with less than
10% of tumor cells on the tissue sample underwent only cfDNA analysis. A weekly
molecular tumor board reviewed all the NGS and CGH results and determined the
most relevant targeted therapy available through early clinical trials according to the
molecular profile. Treatment efficacy was evaluated by Response Evaluation Criteria
in solid tumors (RECIST) version 1.1.(18). Peripheral blood samples were collected
just before the tissue biopsy.
Extraction and quantification of cfDNA
Blood samples (3 to 10 ml) were collected in EDTA-tubes (BD Vacutainer - Beckton,
Dickinson and Company, Franklin Lakes, NJ) and centrifugated for 10 minutes at
1000 g within 4 hours of the blood draw. The supernatant containing the plasma was
further centrifugated at 14,000 g for 10 minutes at room temperature and stored at
−80°C until analysis. Initially, the plasma were selected based on their availability,
and then, consecutively.
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DNA was extracted from 500 µl of plasma using the QIAamp DSP virus kit (Qiagen),
according to the manufacturer’s instructions, and resuspended in 40 µL of AVE
buffer. A real-time quantitative PCR TaqMan™ assay targeting GAPDH was used to
measure plasma DNA concentration.
tDNA was extracted from the fresh frozen biopsy sample using the AllPrep DNA/RNA
Mini Kit (Qiagen) and quantified with Qubit 2.0 (Life technologies).
Identification of somatic genomic mutations by NGS
Targeted sequencing libraries were generated using the Ion AmpliSeq Library kit 2.0
according to the manufacturer’s instructions (Life Technologies, Darmstadt,
Germany). Tumor and plasma samples were analyzed independently with Cancer
Hotspot Panel v2 (CHP2) targeting 50 cancer genes covered by 207 amplicons (Life
Technologies). For this study, any other genomic alterations found with other biopsy
analysis (additional NGS panel, CGH, FISH, etc.) performed in the MOSCATO trial
were not considered.
The primers used for amplification were partially digested by FuPa enzyme. The
digested product was then ligated with adapters and barcodes, then amplified and
purified using Agencourt beads. The quantity of the libraries was assessed using the
Qubit 2.0 Fluorometer. An equal amount of each library were pooled and amplified
with the Ion OneTouch 2 system by the emulsion polymerase chain reaction (PCR)
with the Ion PGMTM Template OT2 200 Kit (Life Technologies). The enrichment was
then performed with the Ion One Touch ES (Enrichment System). The enriched Ion
Spheres were loaded into a 316v.2 Ion Sequencing Chip. Sequencing was made
using Ion Personal Genome Machine (PGM, Life Technologies) using real-time
measurement of the hydrogen ions produced during DNA replication. The
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sequencing data was analyzed with the Torrent Suite Variant Caller 4.2 software and
reported somatic variants were compared to the reference genome hg19. The
variants were called if >5 reads supported the variant and/or total base depth >50
and/or variant allele frequency >1% was observed. All the variants identified were
visually controlled on .bam files using Alamut v2.4.2 software (Interactive
Biosoftware, Rouen, France). All the germline variants found in 1000 Genomes
Project or ESP (Exome Sequencing Project database) with frequency >0.1% were
removed. All somatic mutations were annotated, sorted and interpreted by an expert
molecular biologist according to available databases (COSMIC, TCGA) and medical
literature. Analyses of tDNA and cfDNA were performed and interpreted
independently.
For validation of our analysis method, we used a Quantitative DNA Reference
Standard control (Horizon Diagnostics, Cambridge,UK) harboring 16 mutations in 9
different genes. 10ng of DNA Reference Standard were spiked in 1 mL of control
plasma (pool of healthy patients). Two independent analyses were performed with
CHP2 panel using the cfDNA analysis protocol. All the mutations detected in the
plasma were analyzed down to variant with 1% allele frequency. Nevertheless, the
1% allele frequency threshold could not be considered for all the samples in our
analysis, as the assay sensitivity depends on the coverage depth which is variable
between samples and genomic positions.
MiSeq resequencing analysis (Illumina), using CHP2 library amplicons, was
performed.
Statistical analysis
All patients with available tDNA and cfDNA results were included in the main
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analysis. We estimated sensitivity and specificity of cfDNA analysis compared to
tDNA considered as the reference test. The analysis was performed both at the gene
level, with possibly multiple mutated genes per patient, and at the patient level.
Kappa agreement coefficient was estimated at the gene level in the subset of
patients who were consecutively included. Among patients with mutations found in
tumor, the cfDNA result was classified as fully concordant if all mutations identified in
tumor were also found in cfDNA, and partially concordant if only part of the mutations
found in tumor were identified in cfDNA. At the patient level, the sensitivity of cfDNA
was computed as the probability of a positive concordant cfDNA result (fully or
partially concordant) in patients for whom mutations were found in tumor.
We then modeled this probability of a positive concordant cfDNA result in patients
whose mutations were found in tumor tissue, and built a sensitivity prediction score
using a logistic regression. All variables associated with a p-value <0.15 in
univariable model were then evaluated in multivariable analysis using a backward
procedure. We first considered only patient characteristics at study entry (age,
gender, ECOG performance status, primary tumor site, number of metastatic sites,
LDH level, albumin level and number of prior lines of treatment), in order to build a
clinical score, available before tumor biopsy and DNA extraction. We also evaluated
the diagnostic value of the RMH score, which is a score developed by the team from
Royal Marsden Hospital for prognosis purpose in the setting of oncology phase 1
trials score (19). We then evaluated the additional value of cfDNA concentration
adjusted on the clinical score. Continuous variables (age, LDH and albumin levels,
number of metastatic sites and prior lines of treatment, RMH score, cfDNA
concentration) were entered in the model assuming a loglinear relationship, after
check of the shape of the relation in the model. Models were compared using Akaike
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Information Criterion (AIC)(20). Calibration and discriminative ability of the final
model were evaluated using Hosmer-Lemeshow test (21) and Area Under Curve
(AUC) (22). We set the p-value threshold at 0.05 in the multivariable analysis. The
final model was validated using the 10-fold cross validation approach. All estimates
are given with their 95% confidence intervals (95%CI) and tests are two-sided.
All analyses were performed with SAS v9.3 (SAS Institute, Cary, NC).
RESULTS
Patient characteristics
From November 2011 to October 2014, 669 heavily pretreated patients with
metastatic or advanced solid tumors were enrolled in the MOSCATO 01 trial (Figure
1). Among the 600 patients for whom a biopsy was performed, a total of 334 (56%)
patients had a cfDNA analysis. All plasma samples have been processed within 4
hours after the blood sampling, ensuring a good homogeneity of all the pre-analytical
and analytical steps in our cohort. Evaluable NGS results were obtained by both
tDNA and cfDNA analysis in 283 patients (85%, main analysis set). The first 178
patients were selected based on the plasma availability and knowing mutational
status in tumor, whereas from June 2014, all consecutive patients enrolled in
MOSCATO 01 trial (N=105) had a cfDNA analysis. For 51 additional patients without
tissue samples available for molecular testing (due to the low content in tumor cells in
biopsy samples), only cfDNA analysis was performed. Those patients were not
included in statistical analysis.
Patient and disease baseline characteristics (N=283) are summarized in Table 1.
Patients had a median age at biopsy of 58 years (range: 18-79). Thoracic,
gastrointestinal, breast, head and neck, gynecologic and urologic cancer, cancer
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were the most frequent tumor locations, with 19%, 18%, 15%, 14%, 9%, and 7%,
respectively. 95% of the patients were metastatic and were heavily pretreated with a
median number of 3 previous lines of treatment (range: 0-11). The median cfDNA
concentration found in plasma was 18.1 ng/mL (range: <0.1-727.0 ng/mL). As
illustrated in Figure 2, cfDNA concentration was significantly higher in patients with a
gastrointestinal tumor than in patients with other tumor sites (median: 30.4 versus
16.2, p-value=0.02).
Mutations analysis
Overall, 283 patients had tumor and plasma samples collected at the same time-
point which were analyzed independently using the same CHP2 panel. Mean depth
for the cfDNA NGS analysis was x492. A total of 347 mutations were identified in
tDNA and 195 mutations were identified in cfDNA. In tDNA, at least 1 mutation was
detected in 220 patients (78%) (1 mutation in 129 patients, 2 to 4 in 91 patients).
Mutations are detailed in Supplementary Table 1.
The most frequently mutated genes in tumor (N>3) were TP53 (47.3% of patients),
KRAS (17.3%), PIK3CA (13.1%), EGFR (5.3%), CDKN2A (4.2%), APC (3.9%),
PTEN (3.9%), SMAD4 (3.5%), AKT1 (2.5%), STK11 (2.5%), CTNNB1 (2.1%),
FBXW7 (2.1%), KIT (1.8%), ERBB2 (1.4%), RB1 (1.4%) and BRAF (1.4%). In
cfDNA, at least 1 mutation was detected in 134 patients (47%) (median= 1 mutation
per patient, range 1-4 mutations). The most frequent mutations (n>3) found in cfDNA
were in TP53 (23.3%), KRAS (11.0%), PIK3CA (6.0%), APC (3.2%), PTEN (3.2%),
EGFR (2.8%), ERRB2 (2.5%), SMAD4 (2.1%), CTNNB1 (1.8%), CDKN2A (1.4%),
AKT1 (1.4%), KIT (1.4%) and RB1 (1.4%) genes. No correlation was observed
between allele frequency observed in tumor tissue and in plasma.
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The cfDNA analysis of the 51 patients without tDNA analysis revealed at least one
mutation in 22 patients (43.1%). Among the 29 mutations found, 19 were pathogenic
variants and included 8 variants of therapeutic interest. Several of these cfDNA
analysis have been performed in parallel to tDNA analysis of patients recently
enrolled in MOSCATO 01 trial and the results were discussed in dedicated molecular
tumor board. Also, cfDNA analysis could be performed in the same conditions (turn-
around time and cost) as tDNA analysis.
In order to confirm mutations identified in cfDNA with the PGM-Ion Torrent analysis,
MiSeq resequencing analysis (Illumina) was performed using the CHP2 library
amplicons on a set of 83 patients. The mean depth for the cfDNA analysis with
MiSeq was x748 and 91 mutations were identified. The MiSeq analysis allowed the
detection of 16 additional mutations which were not identified in cfDNA analysis by
PGM-Ion Torrent, with 11 of them (69%) present in the tDNA analysis. Conversely,
PGM-Ion Torrent analysis identified 6 mutations in cfDNA, which were also found in
the tDNA, but which could not be detected with MiSeq sequencing of cfDNA. Overall,
MiSeq and PGM sequencing provided concordant results for the cfDNA analysis.
Concordance between tDNA and cfDNA
Among the 347 mutations identified in tDNA from 283 patients, 173 mutations were
identified in both tDNA and cfDNA, and 174 mutations in tDNA, but not in cfDNA.
Results for the main genes are illustrated in Figure 3-A. Moreover, 22 mutations
were found in cfDNA, but not in tDNA, providing additional information. The
sensitivity of using NGS for 50 targeted hot-spot genes analysis in cfDNA compared
to tDNA was 49.9% (95%CI, 44.6-55.1%) and the specificity was 99.8% (95%CI,
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99.8-99.9%). Sensitivity in the five most frequent mutated genes (TP53, KRAS,
PIK3CA, EGFR and APC, representing 75.9% of all cfDNA mutations, was 47%,
59%, 43%, 53% and 46%, respectively. The Kappa coefficient calculated on the 105
consecutive patients was 0.60 (95%CI, 0.50-0.69).
At the patient level, NGS analysis allowed us to observe: (i) 58 patients without any
mutation found in tumor or in cfDNA; (ii) 5 patients with mutations found in cfDNA but
not in tumor; (iii) 220 patients with mutations found in tumor and plasma. Among
these latter, the mutations found in plasma were fully concordant for 87 patients
(39.5%) and partially concordant for 34 patients (15.5%), whereas the mutations
found in tumor were not found in the cfDNA for the 99 other patients (45%), leading
to an estimated sensitivity of 55.0% (95%CI, 48.4-61.6%) at the patient level (Figure
3-B).
Table 2 details the modeling of cfDNA positivity among the 220 patients with
mutations found in the tumor, including 121 patients with fully or partially concordant
cfDNA result. The sensitivity of cfDNA test significantly increased with the number of
metastatic sites (Figure 4-A, p-value=0.0006 in multivariable model), a decreased
albumin level (Figure 4-B, p-value=0.0007) and the number of prior treatment lines
(Figure 4-C, p-value=0.047); it was also significantly associated with the primary
tumor site (Figure 4-D, p-value=0.002). The sensitivity was higher in breast and
gastrointestinal cancers and lower in patients with head and neck or urologic cancer.
The final multivariable analysis clinical model, based on these four clinical
characteristics, had a good predictive power as indicated by the Area Under Curve
(AUC) equal to 0.78 (Supplementary Figure 1), as well as a good goodness of fit
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(Hosmer and Lemeshow test, p-value=0.34). Using the 10-fold cross validation, the
estimated prediction error was 0.205.
The relationship between the derived sensitivity prediction score and the probability
of a positive cfDNA result is illustrated on the Figure 4-E. The estimated sensitivity of
cfDNA was 28% when focusing on the patients with a low score (first third of the
distribution), which mainly corresponded to patients with less advanced disease
and/or patients with head and neck or urologic cancer. It is noticeable that cfDNA
was positive in only 1 of the 22 patients with the lowest score values (the first decile
which includes 21 patients with head and neck cancer). By contrast, sensitivity of
cfDNA is 83% in patients with a high score value (last third of the distribution),
reaching 94% (17/18) in the highest decile that is mainly represented by patients with
advanced breast or gastrointestinal cancer.
The strong relationship between LDH level and cfDNA positivity observed in
univariable analysis vanished in multivariable analysis (OR=1.33, 95%CI, 0.98 to
1.81, p-value=0.07), because LDH level was significantly correlated with the other
variables included in the model (Spearman correlation coefficient between LDH and
clinical score = 0.36, p-value<.0001).
The RMH score was significantly associated with cfDNA positivity. However, the
performance of the multivariable model described hereinabove was better than that
of the model based only on RMH score (Akaike Information Criterion, 254.2 for the
final clinical multivariable model, versus 264.5 for the model based only on RMH
score, when both models include the 212 patients with no missing data for RMH
score).
The positivity of cfDNA result was significantly higher with increasing cfDNA
concentration (p-value=0.003 in univariable analysis and 0.045 in multivariable
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analysis). However, the added value of cfDNA concentration to the multivariable
clinical model was marginal: the AUC increased from 0.78 to 0.79.
DISCUSSION
In this study, we show that use of NGS for cfDNA analysis is feasible in routine
clinical settings for molecular characterization in patients with locally advanced or
metastatic solid tumors, eligible for phase I trials and displaying a good performance
status. To our knowledge, this study reports the largest patient cohort used to
evaluate the feasibility of NGS gene panel screening using plasma samples matched
with analysis of tumor samples issued for various tumor types obtained from 283
patients. This is also the first study reporting a sensitivity prediction score enabling
better selection of patients for whom liquid biopsy could be a good surrogate of tissue
biopsy and the one for whom the cfDNA NGS analysis will not be contributive.
Overall, the specificity was above 95%, but the sensitivity of cfDNA analysis was
estimated at only 55% (121/220) in the patients with at least one mutation found in
tDNA. The observed discordance could be due to several reasons: poor mutation
detection in cfDNA due to low cfDNA concentrations in the bloodstream (23);
presence of the mutation in allele frequencies below the detection limit of our
method; true disease heterogeneity (24); and poor DNA quality (25), true genetic
differences between cfDNA (released mainly from necrotic cells) and tDNA. Ongoing
technological advances are likely to improve the cfDNA analysis method and further
increase the concordance between tDNA and cfDNA. For low cfDNA levels, on first
hand, we chose to use the limited quantity of plasma available through the
prospective collection set up. Carrying out cfDNA from larger plasma volume could
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increase the levels of cfDNA for molecular analysis for some cases. For several
samples, even using four times the volume of plasma may lead to better results;
indeed, we have tested 45 additional cases using 2mL of plasma and observed 49%
of concordance (data not shown). On the other hand, using alternative sequencing
method as MiSeq with better coverage did not increase the number of mutations
detected. Nevertheless, our results should be also considered in a context of novel
and more sensitive techniques. For example, methodologies based on digital PCR
are making cfDNA analyses possible even in cases where only low levels of cfDNA
are present, with a threshold of detection of 0.01% or lower (11). In addition,
important progress has been made using sequence-specific assays that target
predefined mutations and detect extremely rare alleles (26). False negative results
(mutation found in tDNA but not in cfDNA) may be further reduced by a re-estimation
of the background frequency noise and the minimum allele frequency threshold.
Another approach could be to prioritize screening of limited numbers of mutations
with highly sensitive techniques instead of large tumor type-agnostic gene panel.
However, whatever the threshold of detection, a sufficient DNA concentration or DNA
copy number is needed, but was not always observed in our cohort, with DNA
concentration range from less than 0.1 ng/mL to 727 ng/mL.
Even if our cohort of phase I patients could be considered as biased by low
representation of some tumor types (e.g. melanoma), our results could be compared
with several prior studies investigating the concordance of cfDNA and tDNA in
different tumor types with varying results. Rothé et al. found a sensitivity of cfDNA
estimated at 82% in 11 patients with mutated metastatic breast cancer (27). Another
study found a sensitivity rate of 58% in 50 mutated metastatic lung cancer patients
(28). The estimated sensitivity of cfDNA in patients with breast or lung cancer in our
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series (74% and 55% respectively) is consistent with the published results. In a
recent study based on 39 patients with different types of tumor harboring mutations,
Frenel et al. estimated sensitivity of cfDNA at 59% (23/39), similar to our overall
result (55%). (29).
Lebofsky et al. compared the cfDNA and tDNA using NGS technology in 34 patients
with 18 different tumor types, enrolled in the phase II SHIVA trial. Among the 18
patients with paired samples and mutations found in metastasis biopsies, cfDNA
analysis was concordant in 17 cases, leading to a very high sensitivity rate (97% at
the gene level, 98% at the patient level) (15). Several factors may explain the
apparent discrepancy between our result and the SHIVA trial results, in addition to
the play of chance in this small sample size series: patient characteristics were
different, with, in their series compared to ours, more breast and GI cancers, less
head and neck or urologic cancer, more patients with a number of metastatic sites
greater than 2. In addition, most plasma samples (31/34) were collected after biopsy,
often several months after the initial biopsy, when the disease could be more
advanced.
Part of the disparity in sensitivity rates of cfDNA reported by different studies could
also be explained by the different procedures that are used in laboratories at both
pre-analytical (blood collection, processing, storage, baseline of patients) and
analytical phases (DNA extraction, quantification and analysis) (11,24,30,31). In
order to introduce the liquid biopsy in the clinical management of cancer patients, it is
important that these parameters be standardized for consensus analysis and
reporting (32).
Most of the published studies had a lower number of patients tested and the method
of patient selection is not described in detail. Moreover, the performance status and
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the disease’s characteristics are generally poorly reported in most of the published
studies. Those points limit the possibility of comparison with our cohort of patients
having mainly performance status and a low RMH score at the time of sample
collection that appear to be crucial for paired cfDNA and tDNA molecular status
concordance.
In the current study, we identified mutations only in the cfDNA analysis and not in
tDNA in few patients. This point was consistent with previous data (26-29). This
suggests that cfDNA analysis using NGS could be also used as a complementary
analysis to the tDNA in order to better evaluate the tumor heterogeneity (7). In
addition, we analyzed the cfDNA of 51 patients without tDNA because of low tumor
cellularity (less than 10% of tumor cell) and we found one or more mutations in 22
patients (43%), including 8 pathogenic variants of therapeutic interest. This data
highlights the feasibility and the added value of cfDNA analysis for patients for whom
tissue biopsy is not available. Nevertheless, due to relatively high rate of false
negative result, it is important to identify patients for whom biopsy contribution is
essential.
We showed that the sensitivity of cfDNA analysis was significantly associated with
the primary tumor site and increased with the number of metastatic sites, the number
of prior lines of treatment and a decrease of albumin level. For a practical use in
selecting patients for whom cfDNA could be used as a surrogate molecular screening
maker, we have built, for the first time, a sensitivity prediction score. Patients with a
high score are more likely to have a contributive result from cfDNA analysis. Also, our
findings suggest that for those patients, cfDNA is a valuable surrogate material for
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19
molecular screening by NGS analysis, whereas tumor biopsy analysis will be
recommended in patients with a low sensitivity prediction score.
Therefore, molecular characterization based on cfDNA analysis could be performed
in the same conditions as tDNA analysis (turn-around time and cost). cfDNA based
analysis could be used for diagnosis and treatment decisions, even for patients for
whom tumor biopsy is not feasible, which represent a significant proportion of
patients with metastatic cancers, or when the quality of the histologic sample is too
poor for molecular analysis. But further studies on prospective validation cohorts are
required to determine whether an optimized version of the assay may be applicable
in routine clinical practice as a liquid biopsy.
Nevertheless, there are still improvements possible to our study, beside pre-
analytical points discussed previously. We evaluated only the cfDNA at baseline
compared with the tDNA regardless of the tumor types and progression rate before
the inclusion in the MOSCATO trial. Further cfDNA kinetics analysis (i.e. analysis at
multiple time-point samples) and correlation of the cfDNA results with the clinical
outcome is warranted.
Despite its inconveniences, tumor biopsy remains the most efficient diagnostic tool at
present. However, several major advantages of cfDNA justify further investigation
and development as a tool to detect tumor-specific genomic alterations in the
circulation, beyond what is currently possible with tumor biopsy. Firstly, the possibility
of cfDNA analysis even with low levels of cfDNA may lead to early detection of
cancer (11). Secondly, even when biopsy is technically feasible, the minimally
invasive, more convenient cfDNA quantification would be more acceptable to patients
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than biopsy. Thirdly, cfDNA analysis facilitates monitoring of molecular alterations
associated with tumor response or acquired resistance, while sparing the patient
from repeated biopsies and their potential harms.
In conclusion, our results on a consistent cohort of patients suggest that targeted
NGS for the detection of somatic mutations in cfDNA could be applicable in clinical
for patient eligible for phase I clinical trials and bearing advanced or metastatic solid
tumors. We have established a score that allows us to select patients for whom the
analysis of cfDNA using NGS offers an attractive non-invasive option to screen
mutations and to selected patients for whom it will likely be recommended to have
analysis performed on tumor tissue.
GRANT SUPPORT
This work was supported by grant INCa-DGOS-INSERM 6043.
ACKNOWLEDGEMENTS
The authors acknowledge clinical team, including Yohann Loriot, Rastislav Bahleda,
Anas Gazzah, Andrea Varga, Eric Angevin for their implication in enrollment of
patient in MOSCATO trial, and Aljosa Celebic for Data Management.
The authors acknowledge laboratory and bioinformatic teams, including Mélanie
Laporte, Isabelle Miran, Ludovic Bigot, Stéphanie Coulon, Marie Breckler, Catherine
Richon, Aurélie Honoré, Magali Kernaleguen, Glawdys Faucher, Zsofia Balogh,
Jonathan Sabio, Lionel Fougeat, Marie Xiberras, Leslie Girard, Lucie Herard,
Catherine Lapage, Guillaume Meurice, Romy Chen-Min-Tao, Yannick Boursin for
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their support in the preanalytic processing of samples storage and for tumor sample
analysis of the patients included in MOSCATO 01 trial.
The authors specially acknowledge Yuki Takahashi for her editing support.
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Table 1: Patient and tumor characteristics
Frequency (%) Total N=283
Age at study entry Median [Range] 58 [18 ; 79] Gender Male 149 (52.7%) Female 134 (47.3%) Primary tumor site Breast 41 (14.5%) Gastrointestinal 50 (17.7%) Gynecologic 26 (9.2%) Head and Neck 39 (13.8%) Thoracic 54 (19.1%) Urologic 21 (7.4%) Other1 52 (18.4%) ECOG Performance Status (64 MD) 0 100 (45.7%) 1 112 (51.1%) 2 7 (3.2%) Number of previous lines of therapy Median [Range] 3 [0 ; 11] Number of metastatic sites 0 15 (5.3%) 1 101 (35.7%) 2 87 (30.7%) 3 51 (18%) > 3 29 (10.2%) LDH level (UI/L) (7 MD) Median [Range] 220 [14 ; 6980] Albumin level (g/L) (3 MD) Median [Range] 36 [22 ; 47] RMH score(10 MD)2 0 102 (37.4%) 1 106 (38.8%) 2 53 (19.4%) 3 12 (4.4%) cfDNA Concentration (ng/mL) (2 MD) Median [Range] 18.1 [< 1 ; 727] Number of mutated genes /patient 0 63 (22.3%) 1 129 (45.6%) >1 91 (32.2%)
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MD: Missing Data 1 Other tumor site: prostate (N=18), cancer of unknown primary (N=9), testis (N=5), thyroid (N=4), bone (N=4), skin (N=3), penis (N=2), peritoneum (N=2), connective tissue and soft tissue (N=3), lip (N=1), scalp and neck (N=1). 2 RMH Score: Prognostic score developed by the team from Royal Marsden Hospital in the setting of oncology phase I trials (ref Arkenau JCO 2009), based on LDH level, albumin level and number of sites of metastases, weighted equally: LDH normal (0) versus LDH > upper limit of normal (1), albumin > 35 g/L (0) versus albumin < 35g/L (1), and sites of metastases < 2 (0) versus sites of metastases > 2 (1). The prognostic score for each individual was computed as the sum of the three components.
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Table 2: Modeling of the cfDNA positivity in the 220 patients with mutations found in the tumor. Univariable analysis Multivariable analysis
Clinical model (1) OR (95%CI) P-value OR (95%CI) P-value Age at study entry 1.01 [0.99 - 1.04] 0.24 Gender 0.10
Female 1 (ref) Male 0.63 [0.37 - 1.09]
ECOG performance status
0.83
0 1 (ref) 1 0.87 [0.47 - 1.60] 2 1.32 [0.23 - 7.62]
Primary tumor site 0.0004 0.002 Breast 1 (ref) 1 (ref) Gastrointestinal 0.83 [0.29 - 2.39] 1.42 [0.42 - 4.78] Gynecologic 0.51 [0.15 - 1.70] 0.54 [0.14 - 2.08] Head and Neck 0.07 [0.02 - 0.27] 0.15 [0.03 - 0.64] Thoracic 0.42 [0.15 - 1.20] 0.66 [0.19 - 2.25] Urologic 0.16 [0.04 - 0.62] 0.14 [0.03- 0.62] Other 0.53 [0.18 - 1.57] 0.82 [0.24 - 2.76]
Number of metastatic sites
1.79 [1.35 - 2.36] <0.0001 1.79 [1.28- 2.49] 0.0006
LDH level (OR associated with a 100 UI/L increase)
1.77 [1.26 - 2.50] 0.001
Albumin level (OR associated with a 10 g/L increase)
0.39 [0.20 - 0.77] 0.006 0.26 [0.12- 0.57] 0.0007
RMH score <0.0001 0 1 (ref) 1 3.19 [1.66 - 6.12] 2 7.00 [2.99 - 16.4] 3 16.0 [1.90 -135]
Number of prior lines of treatment
1.26 [1.08 - 1.46] 0.003 1.21 [1.002- 1.46] 0. 0474
Concentration of cfDNA (OR associated with a 10 ng/mL increase)
1.14 [1.05 - 1.24] 0.003
(1) The clinical model described in the table includes the four clinical variables found significantly associated with the cfDNA positivity (p-value<0.05 in multivariable analysis): primary tumor site, number of metastatic sites, albumin level and number of prior lines of treatment. Overall 217 patients are included in this model due to missing value for Albumin level in 3 patients.
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30
Figure Legends Figure 1: Participant flow diagram
Figure 2: cfDNA plasma concentration according to primary tumor site Concentration values are reported in ng/mL of plasma; Bar corresponds to the median.
Figure 3: Concordance between tumor biopsies (tDNA) and plasma (cfDNA)
molecular analysis.
Concordance between tDNA and cfDNA for the 283 patients with paired
samples; at gene level for the 16 most frequently mutated genes (among 50)
(A) and at the patient level (B) (T+ mutation in tumor biopsy, P+ mutation in
plasma sample ; T- no mutation in tumor biopsy and no mutation in plasma samples)
Figure 4: cfDNA result in patients with mutations found in the tumor, according to
Albumin level (A), number of metastatic site (B), Number of previous lines of therapy
(C) and Primary tumor site (D), and sensitivity prediction score (E). Panel D: H&N=Head and Neck, Urol.=Urologic, Thor.=Thoracic, G.I.=Gastrointestinal,
Gyne.=Gynecologic
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Breast
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intestin
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d Neck
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UrologicOther
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10
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50100
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B -
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Published OnlineFirst January 12, 2016.Clin Cancer Res Cecile Jovelet, Ecaterina Ileana, Marie-Cecile Le Deley, et al. MOSCATO trialnext-generation sequencing (NGS) in the prospective Circulating cell-free tumor DNA (cfDNA) analysis of 50-genes by
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