circulating cell-free tumor dna (cfdna) analysis of 50-genes ......2016/01/12 · author manuscript...

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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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Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on January 12, 2016; DOI: 10.1158/1078-0432.CCR-15-2470

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† Both authors contributed equally as first authors

ŧ Corresponding author: Cécile Jovelet [email protected]

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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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




20

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




21

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.




22

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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%)




28

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.




29

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.




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




Figure 1

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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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