Research Article
High-Throughput Genomics and Clinical
Outcome in Hard-to-Treat Advanced Cancers:
Results of the MOSCATO 01 Trial
Christophe Massard1, Stefan Michiels2, Charles Ferté1, Marie-Cécile Le Deley2, Ludovic Lacroix3,
Antoine Hollebecque1, Loic Verlingue1, Ecaterina Ileana4, Silvia Rosellini5, Samy Ammari6, Maud Ngo-Camus1,
Rastislav Bahleda1, Anas Gazzah1, Andrea Varga1, Sophie Postel-Vinay1, Yohann Loriot1, Caroline Even7,
Ingrid Breuskin7, Nathalie Auger8, Bastien Job9, Thierry De Baere10, Frederic Deschamps10, Philippe Vielh11,
Jean-Yves Scoazec8, Vladimir Lazar12, Catherine Richon13, Vincent Ribrag14, Eric Deutsch15, Eric Angevin1,
Gilles Vassal16, Alexander Eggermont17, Fabrice André18, and Jean-Charles Soria19
SIGNIFICANCE: This study suggests that high-throughput genomics could improve outcomes in a
subset of patients with hard-to-treat cancers. Although these results are encouraging, only 7% of the
successfully screened patients benefited from this approach. Randomized trials are needed to validate
this hypothesis and to quantify the magnitude of benefit. Expanding drug access could increase the
percentage of patients who benefit. Cancer Discov; 7(6); 586–95. ©2017 AACR.
See related commentary by Schram and Hyman, p. 552.
1
Drug Development Department (DITEP), Gustave Roussy, Université ParisSud, Université Paris-Saclay, Villejuif, France. 2Gustave Roussy, Université
Paris-Saclay, Service de biostatistique et d’épidémiologie, Villejuif, France;
CESP, INSERM, Fac. de médecine–Univ. Paris-Sud, Université Paris-Saclay
Villejuif, France. 3Laboratoire de Recherche Translationnelle et Centre de
Ressources Biologiques, AMMICA, INSERM US23/CNRS UMS3655, Gustave Roussy, France; Département de Biologie et Pathologie médicales,
Gustave Roussy, Villejuif, France; INSERM Unit U981, Gustave Roussy,
Villejuif, France; Faculté de Médecine, Kremlin-Bicêtre, Université Paris
Sud, France; Department of Medical Oncology, Gustave Roussy, Villejuif,
France. 4Drug Development Department (DITEP), Gustave Roussy, Villejuif, France; Laboratoire de Recherche Translationnelle et Centre de Ressources Biologiques, AMMICA, INSERM US23/CNRS UMS3655, Gustave
Roussy, France. 5Service de Biostatistique et d’Epidémiologie, Gustave
Roussy, Villejuif, France. 6Département de Radiologie, Gustave Roussy,
Villejuif, France. 7Département de Cancérologie Cervico Faciale, Gustave
Roussy, Villejuif, France. 8Département de Biologie et Pathologie médicales, Gustave Roussy, Villejuif, France. 9Plateforme de Bioinformatique,
UMS AMMICA, Gustave Roussy, Villejuif, France. 10Département de Radiologie interventionnelle, Gustave Roussy, Villejuif, France. 11Département de
Biologie et Pathologie médicales, Gustave Roussy, Villejuif, France; Laboratoire de Recherche Translationnelle et Centre de Ressources Biologiques,
AMMICA, INSERM US23/CNRS UMS3655, Gustave Roussy, France. 12Functional Genomics Unit, Gustave Roussy, Villejuif, France; Département de
586 | CANCER DISCOVERY
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Biologie et Pathologie médicales, Gustave Roussy, Villejuif, France. 13Functional Genomics Unit, Gustave Roussy, Villejuif, France; Laboratoire de
Recherche Translationnelle et Centre de Ressources Biologiques, AMMICA,
INSERM US23/CNRS UMS3655, Gustave Roussy, France. 14Drug Development Department (DITEP), Department of Medical Oncology, Gustave
Roussy, Université Paris-Saclay, Villejuif, France. 15Department of Radiation Oncology, Drug Development Department (DITEP), INSERM U1030,
Molecular Radiotherapy, Gustave Roussy, Université Paris-Saclay, Villejuif,
France; University Paris Sud, Université Paris-Saclay, Le Kremlin-Bicêtre, France. 16Direction de la Recherche Clinique, Gustave Roussy, Villejuif, France. 17Gustave Roussy, Université Paris-Saclay, Villejuif, France.
18
INSERM Unit U981, Gustave Roussy, Villejuif, France; Faculté de Médecine, Kremlin-Bicêtre, Université Paris Sud, France; Department of Medical
Oncology, Gustave Roussy, Villejuif, France. 19Drug Development Department (DITEP), Inserm Unit U981, Université Paris Saclay, Université ParisSud, Gustave Roussy, Villejuif, France.
Note: Supplementary data for this article are available at Cancer Discovery
Online (http://cancerdiscovery.aacrjournals.org/).
Corresponding Author: Fabrice André, Institut Gustave Roussy, 114 Rue
Edouard Vaillant, Villejuif 94805, France. Phone: 33-1-42-11-43-71; Fax:
33-1-42-11-52-74; E-mail: fandre@igr.fr
doi: 10.1158/2159-8290.CD-16-1396
©2017 American Association for Cancer Research.
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High-throughput genomic analyses may improve outcomes in patients with advanced
cancers. MOSCATO 01 is a prospective clinical trial evaluating the clinical benefit of
this approach. Nucleic acids were extracted from fresh-frozen tumor biopsies and analyzed by array
comparative genomic hybridization, next-generation sequencing, and RNA sequencing. The primary
objective was to evaluate clinical benefit as measured by the percentage of patients presenting progression-free survival (PFS) on matched therapy (PFS2) 1.3-fold longer than the PFS on prior therapy (PFS1).
A total of 1,035 adult patients were included, and a biopsy was performed in 948. An actionable molecular alteration was identified in 411 of 843 patients with a molecular portrait. A total of 199 patients
were treated with a targeted therapy matched to a genomic alteration. The PFS2/PFS1 ratio was >1.3 in
33% of the patients (63/193). Objective responses were observed in 22 of 194 patients (11%; 95% CI,
7%–17%), and median overall survival was 11.9 months (95% CI, 9.5–14.3 months).
abstract
Oncogenic drivers are genomic alterations that lead to malignant transformation and cancer progression (1, 2). Targeting
oncogenic drivers improves outcomes in patients with metastatic cancers. For example, ALK and EGFR inhibitors have
been shown to improve outcomes in patients presenting with
ALK translocations or EGFR activating mutations (3, 4). Nevertheless, the number of approved targeted therapies that are
matched to a genomic alteration remains rare. As of 2016,
only nine genomic alterations are assessed in daily practice
in patients with metastatic cancers to decide about targeted
therapies. In the last 5 years, sequencing efforts have generated considerable advances in the knowledge of cancer biology.
First, although only a few genetic somatic abnormalities are
tested in oncology, it is believed that >400 genes could drive
tumor progression (5). Second, most of the frequent cancers
present a large number of very rare targetable genomic alterations (6). Several cases reported in the literature support the
concept that targeting a rare genomic event could translate into
patient benefit. As an illustration, a patient with an activating
mTOR mutation presented an outlier response to everolimus
(7). Third, some oncogenic drivers, like BRAF mutations or
ERBB2 amplifications, are shared across several diseases (8, 9),
and their targeting could improve outcomes (10, 11).
These considerations have generated the hypothesis that
testing a large number of genes across all tumor types could
improve outcomes in patients with “hard-to-treat” advanced
cancers. New advances in biotechnologies allow testing of a
large number of gene abnormalities in individuals with good
analytic validity (12). Several trials have shown that these
technologies can be delivered in the context of clinical initiatives (13, 14). Nevertheless, there is as yet no evidence that
this approach improves outcome. In the present single-center
single-arm open-label trial, we have evaluated the clinical
benefit of testing a large panel of genes in patients with
advanced “hard-to-treat” cancers.
RESULTS
Study Flow and Screening Phase
The study flow is reported in Fig. 1. Overall, 1,035 adult
patients gave signed informed consent between December 2011
and March 2016 for the MOSCATO trial (NCT01566019), a
successful tumor biopsy was performed in 948 of them, and
a molecular portrait was obtained in 843 (89%). Patient characteristics are reported in Table 1. The most frequent tumor types
included digestive cancer (n = 197), lung cancer (n = 170), urological cancer (n = 158), breast cancer (n = 135), and head and neck
cancers (n = 111). The main sites of biopsy were liver (n = 353),
lung (n = 203), lymph nodes (n = 153), and bone metastases
(n = 44). The following complications occurred: pneumothorax (n = 13), hemorrhage (n = 2), and others (n = 13).
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INTRODUCTION
Massard et al.
RESEARCH ARTICLE
Patients included
n = 1,110
Pediatric patients n = 75
Adults included
n = 1,035
Successful tumor biopsy
n = 948
Screen failure n = 87
No biopsiable lesion: n = 28
SAE: n = 25
Consent withdrawal: n = 11
Clinical deterioration or death: n = 5
Other: n = 11
Missing: n = 7
No NGS nor CGH: n = 105
Molecular portrait (NGS or CGH)
n = 843
No actionable target: n = 432
Actionable target, n = 411
Received matched treatment
n = 199
Evaluable for PFS2/PFS1
n = 193
Rapid clinical deterioration: n = 64
Other protocol: n = 45
Waiting for treatment: n = 37
Exclusion criteria: n = 21
Trial not open or missing slot: n = 17
Absence of progressive disease: n = 6
Patient or physician refusal: n = 11
Concomitant illness: n = 2
Unknown: n = 9
PFS1 missing: n = 5
PFS2<1.3*PFS1 and not yet progressed
n=1
Figure 1. Study flow.
All patients recovered after medical treatment without any
deaths related to biopsy procedure.
The techniques used to obtain molecular portraits for
843 patients at the start of the MOSCATO trial included
targeted sequencing (feasible for 837 patients) and array comparative genomic hybridization (aCGH) analysis (feasible for
745 patients). During the course of the trial, RNA sequencing (RNA-seq; 427 patients) and whole-exome sequencing
were added (166 patients). Genomic reports were validated
by a molecular geneticist and discussed during a weekly
multidisciplinary molecular tumor board dedicated to the
MOSCATO trial. Actionability of the target was defined by
the molecular board. An actionable target was detected in
411 patients (49% of the 843 patients with a molecular portrait). In some patients, actionable targets were identified by
multiple tests. Actionable targets were identified by targeted
sequencing in 255 patients, by aCGH in 252 patients, by IHC
in 17 patients, and by FISH in 16 patients. The median time
from signed consent to biopsy and from biopsy to molecular
board (encompassing sequencing and aCGH analyses) was 19
days [interquartile range (IQR), 6–33 days] and 21 days (IQR,
14–27 days), respectively. We did not observe any meaningful
difference in time from biopsy to molecular tumor board
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between the 64 patients with clinical deterioration (median
of 21 days; IQR, 19–26) and the other patients (median, 21
days; IQR, 14–26 days).
Patient Characteristics in the Treatment Phase
One hundred ninety-nine patients received a therapy
matched to the genomic alteration in the subsequent line of
therapy, based on CGH (105 patients), targeted sequencing
(85 patients), and MET-positive IHC (9 patients). This represents 19% of the patients who consented (95% CI, 17%–22%)
and 24% of the patients for whom a molecular portrait was
successfully obtained. Reasons for not providing targeted
therapies in the other 212 patients with a targetable alteration
are outlined in Fig. 1. Patient characteristics of the matched
treatment population are reported in Table 1. Patients received
a median of 4 prior lines of treatment for advanced disease
(1–14). The median Royal Marsden Hospital (RMH) score was
1 (0–3). The vast majority of the patients received matched
therapy in the context of phase I/II trials (n = 149, 75%). The
matched therapies used in the trial are listed in Table 2. One
hundred twenty-seven patients were treated with single-agent
targeted therapy, and 72 patients received a combination
(chemotherapy in 42 cases; targeted therapy in 30).
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NGS + CGH: n = 739 / NGS alone: n = 98 / CGH alone: n = 6
Genomics to Improve Cancer Outcome
RESEARCH ARTICLE
Table 1. Patient characteristics
Sucessfully biopsied
patients, N = 948 (%)
Age at inclusion
N
Median
Range
948
57
19–86
Matched treatment
population, N = 199 (%)
199
57
28–86
485 (51%)
463 (49%)
85 (43%)
114 (57%)
ECOG performance status
0
1
2
Missing
348 (46%)
396 (52%)
13 (2%)
191
88 (50%)
86 (49%)
3 (2%)
22
Tumor type
Head and neck
Digestive
Lung
Bone
Melanoma
Mesothelioma and soft tissue
Breast
Gynecological, female
Urological
Central nervous system
Thyroid and other endocrine glands
Ill-defined primary tumor
111 (12%)
197 (21%)
170 (18%)
11 (1%)
12 (1%)
26 (3%)
135 (14%)
83 (9%)
158 (16%)
1 (0%)
8 (1%)
36 (4%)
15 (8%)
46 (23%)
32 (16%)
0 (0%)
3 (2%)
4 (2%)
38 (19%)
24 (12%)
29 (15%)
0 (0%)
2 (1%)
6 (3%)
Metastasis at inclusion
No (locally advanced)
Yes
Missing
60 (6%)
887 (94%)
1
7 (4%)
192 (96%)
0
Number of metastatic sites
Median
Range
Missing
Number of previous therapies for advanced disease
Median
Range
Missing
RMH prognostic score
N
Median
Range
Missing
The actionable genomic alterations were located in 53
genes; 98 were amplifications and 23 deletions or loss, 103
mutations, 18 were translocations, and 8 were based on IHC.
The genes that contained molecular alterations that were the
actual targets of the chosen matched treatment are reported
in Fig. 2 (and according to tumor type in Supplementary
Fig. S1). The most frequent targeted alterations were PIK3CA
mutations and amplifications (n = 32), ERBB2 mutations
and amplifications (n = 24), PTEN mutations and deletions
2
0–9
1
2
0–9
0
4
0–15
19
4
1–14
0
907
1
0–3
41
194
1
0–3
5
(n = 15), FGFR1 mutations and amplifications (n = 13), EGFR
mutations and amplifications (n = 13), and NOTCH1/2/3/4
mutations, amplifications, or translocations (n = 12).
Efficacy
The primary endpoint could be evaluated in 193 patients
(PFS1 was not available in 5 patients, and 1 patient had PFS2/
PFS1 < 1.3 without progressive disease under matched treatment during follow-up). In total, 33% of the patients (63 of 193)
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Sex
Male
Female
Massard et al.
RESEARCH ARTICLE
Table 2. Subgroup analyses
Fisher test for
difference in
proportions
No. patients with
PFS2/PFS1 > 1.3
No. patients
(n = 193)
P(PFS2/PFS1
> 1.3)
95% CI
19%–58%
28%–60%
14%–32%
28%–66%
P = 0.15
P = 0.67
Category
Level of evidence
1
2
3
4
10
18
21
14
27
41
95
30
37%
44%
22%
47%
RMH score
RMH 0
RMH 1
RMH 2
RMH 3
26
25
9
2
79
79
29
2
33%
32%
31%
100%
23%–44%
22%–43%
15%–51%
16%–100%
Melanoma
Breast
Head and neck
Thyroid and other
endocrine glands
Ill-defined primary
tumor
Digestive
Gynecological,
female
Lung
Urological
Mesothelioma and
soft tissue
0
13
8
0
3
36
15
2
0%
36%
53%
0%
0%–71%
21%–54%
27%–79%
0%–84%
0
5
0%
0%–52%
17
4
45
24
38%
17%
24%–53%
5%–37%
9
10
2
32
28
3
28%
42%
67%
14%–47%
19%–65%
9%–99%
P = 0.73
Therapy class
ALK
AR
Cell cycle
DNA damage
EGFR
ERBB2
FGFR
IDH
IGF1R
KIT
MAPK
MDM2
MET
NOTCH
PI3K–AKT–mTOR
2
2
1
0
2
17
7
1
1
1
2
0
3
6
18
6
5
5
2
12
26
24
2
1
1
12
2
11
25
59
33%
40%
20%
0%
17%
65%
29%
50%
100%
100%
17%
0%
27%
24%
31%
4%–78%
5%–85%
1%–72%
0%–84%
2%–48%
44%–83%
13%–51%
1%–99%
3%–100%
3%–100%
2%–48%
0%–84%
6%–61%
9%–45%
19%–44%
P = 0.72
Year of inclusion
2011
2012
2013
2014
2015
0
15
11
21
16
3
49
38
59
44
0%
31%
29%
36%
36%
0%–71%
18%–45%
15%–46%
24%–49%
22%–52%
P = 0.95
Tumor type
presented a PFS2/PFS1 > 1.3 (two-sided 95% CI, 26%–39%).
The null hypothesis that the true probability is lower than or
equal to 15% is rejected with a one-sided P value of <0.001. In
a preplanned secondary analysis, we directly compared the
paired failure times PFS2 and PFS1 through a χ2 test (15).
The null hypothesis of equality of the paired survival times
was rejected in favor of PFS2 (χ2 = 8.25 with 1 df, P = 0.004). A
Kaplan–Meier plot of the PFS2/PFS1 ratio is reported in Fig.
3A. Individual PFS1 and PFS2 of the patients are reported in
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Fig. 3B, and an alternative display of these times in Supplementary Fig. S2. We tested in a sequential manner the null hypothesis in the subgroup of 27 patients for whom target treatment
was considered level of evidence A: 37% of the patients (10 of
27) presented a PFS2/PFS1 > 1.3 (two-sided 95% CI, 19%–58%).
The null hypothesis that the true probability is lower than or
equal to 15% is again rejected (one-sided P = 0.004).
Responses by RECIST 1.1 criteria among the 194 patients
with non-missing PFS1 included 2 complete responses (1%)
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Variable
Genomics to Improve Cancer Outcome
RESEARCH ARTICLE
32
PIK3CA
ERBB2
24
PTEN
15
FGFR1
13
EGFR
13
NOTCH
12
RAS
10
MET
10
FGF
9
8
RB1
6
FBXW7
5
CDK
5
MDM2
4
ERBB3
4
AR
4
ALK
4
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Target
RAF
3
AKT1
PIK3CB
2
NOTCH2
2
IDH1
2
FGFR3
2
FGFR2
2
TSC1
1
ROS1
1
LKB1
1
KIT
1
IGF1R
1
DNA repair
1
CDKN2A
1
ATR
1
0
10
20
30
Number of patients
Figure 2. Genes that contain the molecular alterations matched with therapies (n = 199).
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RESEARCH ARTICLE
1.0
patients), FGFR inhibitor (n = 4 patients), EGFR inhibitor
(n = 3 patients), ALK inhibitor (n = 3 patients), and MAPK or
PI3K–AKT–mTOR inhibitors (n = 2 patients). The majority of
patients without RECIST data had an early clinical deterioration (28 of 39). The median follow-up for progression-free
survival on matched therapy or PFS2 was 20 months. The
6-month estimated PFS2 was 16% (95% CI, 12%–22%), and
median PFS2 was estimated at 2.3 months (95% CI, 1.9–2.7
months). The 1-year estimated overall survival (OS) probability was 49% (95% CI, 42%–58%) and the estimated median OS
was 11.9 months (95% CI, 9.5–14.3 months).
0.6
0.4
Subgroup Analyses
0.2
Probability
0.8
A
0.0
B
0
1
194
77
2
3
PFS2/PFS1
37
+
+
Patients ranked by descending PFS2/PFS1
+
+
18
4
5
11
8
DISCUSSION
+
+
+
+
+
+
+
Period
PFS1
PFS2
+
0
10
20
30
PFS1 + PFS2 (months)
40
Figure 3. Efficacy on primary endpoint. A, Kaplan–Meier curve of PFS2/
PFS1. Crosses denote censored data. Green line denotes PFS2/PFS1 > 1.3.
B, Individual PFS1 and PFS2 times, ordered by descending PFS2/PFS1
(n = 194). Crosses denote censored data. Patients above the blue horizontal
line have PFS2/PFS1 > 1.3.
and 20 partial responses (10%) for an overall response rate of
11% (95% CI, 7%–17%), 100 stable disease (52%), 33 progressive disease (17%), and 39 not available (20%; Fig. 4). One
patient with breast cancer and an FBXW7 mutation has presented a complete response after treatment with a NOTCH
inhibitor. Moreover, one patient with a HER-positive biliary
cancer was treated with a trastuzumab and chemotherapy
combination and has also presented a complete response.
Among the 20 patients who presented a partial response, the
following treatments were provided: HER inhibitors (n = 8
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There are controversies about whether the use of highthroughput genomics could improve outcome in patients
with hard-to-treat cancers. In the present study, we have
shown that tumor sequencing improves outcome in 33% of
patients with advanced cancers. Previous trials had evaluated
the efficacy of multigene molecular abnormality screening
approaches to personalized therapy. In the SAFIR01 trial
(14), only 30% of the treated patients presented an objective response or stable disease. In the SHIVA randomized
trial (13), no significant improvement in progression-free
survival could be observed in the precision medicine arm as
compared with the standard-of-care arm. There are several
reasons that could explain why the MOSCATO trial is positive, whereas previous ones failed. First, 75% of the patients
received last-generation targeted therapies in the context of
phase I/II trials. It is well established that only therapies that
hit the target with high bioactivity can improve outcome. As
an illustration, vemurafenib dramatically improves outcome
of BRAFV600-mutant melanoma, whereas sorafenib, a weak
BRAF inhibitor, does not (16). Second, it is very likely that the
MOSCATO trial team benefited from the experience acquired
in previous precision medicine trials (13, 17, 18–23).
In the present nonrandomized study, the ratio PFS2/PFS1
was chosen as a primary endpoint. Von Hoff first proposed
this endpoint and concluded that a ratio >1.3 would indicate
a treatment benefit. This is supported by the observation that
PFS decreases over the lines of therapy in the natural course
of the disease (24). In that regard, in a study that included
934 patients with metastatic breast cancer, median PFS times
were 9.3, 5.5, 4.9, 4.1, and 0.2 months in the first, second, third,
fourth, and fifth lines of therapy, respectively (25). This endpoint has been successfully used to develop oxaliplatinum and
to find optimal dosing of imatinib, but has been criticized to be
of little value when the within-patient correlation between consecutive PFS times in the natural course of the disease is low
(26). Although this study reports, for the first time, evidence
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In order to explore which patients could derive more benefit from this approach, we retrospectively assessed the PFS2/
PFS1 ratio according to post hoc subgroups. The benefit of
sequencing was not significantly different according to the
RMH score (exact test for difference in proportions, P = 0.67),
drug family (P = 0.72), year of inclusion (P = 0.95), disease
(P = 0.66), and level of evidence for the target gene molecular
abnormality (P = 0.15; Table 2).
Genomics to Improve Cancer Outcome
RESEARCH ARTICLE
50
0
In the present trial, we have shown that the molecular
screening strategy led to a matched targeted treatment for
19% of successfully screened patients, and 33% of those
matched-treated patients or 7% of those successfully screened
obtained a PFS ratio above the predefined threshold. Next
steps include the validation of clinical utility in randomized
trials, better defining which patients could derive benefit
from this approach, and the further development of new
methods to detect cancer drivers in individuals.
METHODS
Study Design and Procedure
Figure 4. Maximum percent change from baseline in the sum of
the diameters of target lesions of the patients treated with matched
treatments. The change from baseline in the target lesion diameter is
shown for patients who had measurable disease at baseline according to
RECIST, version 1.1, and either a post-baseline measurement or an early
clinical deterioration, (n = 170). Patients with early clinical deterioration (n = 16) are arbitrarily put at the maximum observed increase. Bars
are color-coded according to most frequent target families. The “other”
category includes AR, DNA repair, HER3, IDH, IGF1R, MDM2, and cellcycle families.
that sequencing a large panel of genes improves outcome of a
subset of patients, it does not therefore provide level I evidence
that this approach improves outcome in the general population of patients with metastatic cancers. Indeed, the quantification of the impact of such an approach in the overall population
requires randomized controlled trials. SAFIR02_Breast and
SAFIR02_lung trials (NCT02299999 and NCT02117167) are
currently addressing such questions. The other limitation
of the current trial is the lack of comparison with patients,
treated by the same targeted therapies, whose tumors do not
have matched genomic abnormalities. Nevertheless, it is unlikely
that this would explain the efficacy observed in the current trial,
because efficacy of unmatched targeted therapies has so far been
observed only with CDK4 and mTOR inhibitors.
There are several ways to further improve the efficacy of
precision medicine. We first need better tools to identify the
relevant genomic alterations. In the present study, although
RNA-seq and whole-exome sequencing were performed in
427 and 166 patients, respectively, they were not useful to
drive patients to therapy. Further trials that include immunotherapeutics and therapies targeting translocations will better
assess the utility of these technologies. Also, RNA-seq could
be useful to define pathway activation and target expression,
but this was not assessed in the current study. Second, we
need to better define which patients derive more benefit from
this approach. Several studies have suggested that patients
with high genomic instability (27) and/or subclonal alterations are less likely to derive benefit from targeted therapies.
Finally, there is a need to develop combinations, especially in
patients who present multiple driver alterations (28).
The Molecular Screening for Cancer Treatment Optimization trial
(MOSCATO; NCT01566019) is a monocentric, prospective clinical
trial. The trial accrued patients between December 2011 and March
2016. This study aimed to show that high-throughput genomics
improve a progression-free survival ratio in patients with advanced
cancers. Patients were eligible if they presented a locally advanced,
unresectable, or metastatic cancer that had progressed during at least
one line of prior therapy. Only patients who were considered noncurable by a multidisciplinary board were eligible for the trial. Patients
who presented with a genomic alteration that rendered them eligible
for a targeted therapy approved in the indication were not eligible for
this trial. Children were eligible for the trial, but were not included
in the population of patients where the primary endpoint was
evaluated as prespecified in the protocol. This study is registered with
ClinicalTrials.gov, MOSCATO 01 (NCT01566019). All patients gave
signed informed consent for the trial and genomic analyses. This
trial protocol was approved by an institutional review committee and
done in accordance with the Declaration of Helsinki.
Genomic Analyses
After having given signed informed consent, the patient underwent
either a biopsy or a tumor resection. The tumor cellularity was assessed
by a senior pathologist on a hematoxylin and eosin slide from the
same biopsy core than the one used for nucleic acid extraction and
molecular analysis. In case of tumor cellularity >30%, the association
of aCGH and targeted sequencing was performed, completed by RNAseq analysis for cases collected after 2014 and whole-exome sequencing
for selected cases, when the quality and the quantity of nucleic acid
were sufficient. In cases with tumor cellularity with 10% to 30%, only
targeted sequencing was performed. Tumor DNA, RNA, and germline
DNA from whole-blood samples [used for whole-exome sequencing
(WES)] were extracted using, respectively, AllPrep DNA/RNA Mini Kit
and DNeasy Blood and Tissue Kit according to the manufacturer’s
instructions. The molecular analysis using targeted sequencing and
aCGH was carried out as described previously (29, 30). Briefly, targeted
sequencing was performed using Personal Genome Machine (Ion
Torrent PGM, ThermoFisher Scientific) with Ion AmpliSeq multiple
genes panels, based on multiplex-PCR. For biopsies collected from
May to November 2012, the targeted gene panel consisted of the Ion
AmpliSeq Cancer Panel (CP1) covering 190 amplicons in 40 cancer
genes (ThermoFisher Scientific). For biopsies collected from December
2012 to September 2013, the targeted gene panel consisted of the Ion
AmpliSeq Cancer Hotspot Panel v2 covering 207 amplicons in 50 cancer genes (ThermoFisher Scientific). Finally, for biopsies collected after
September 2013, the targeted gene panel (MOSC3) covered 75 critical
oncogenes or tumor suppressor genes using Ion AmpliSeq custom
design (details available in Supplementary Methods), combining 1,218
amplicons with the CHP2 panel. The library preparation as well as the
sequencing analysis were performed according to the manufacturer’s
recommendation for Ion AmpliSeq Workflow. The bioinformatic
analysis was performed with Torrent Suite software, variantCaller
June 2017
CANCER DISCOVERY | 593
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−100
Change from baseline (%)
100
Target family
ALK
EGFR
FGFR
HER2
MAPK
MET
NOTCH
Other
PI3K–AKT–mTOR
Massard et al.
RESEARCH ARTICLE
Genomic Report and Treatment Decision
The genomic analysis report (TGS, aCGH, RNA-seq, and WES) provided by the molecular geneticist was discussed during a weekly multidisciplinary molecular tumor board dedicated to the MOSCATO trial, and
a matched therapy was decided accordingly. Actionability of the target
was defined by the molecular board. Actionability was defined by the
availability of drugs that hit either the target or the pathway activated by
the target. Patients who were offered a matched targeted therapy during
the multidisciplinary molecular board and who received it constituted the
population where primary endpoint would be evaluated. In some patients
presenting an actionable genomic alteration, no therapy was proposed at
the time of the molecular tumor board, but they could have been further
treated with a matched therapy outside MOSCATO trial.
Statistical Hypotheses and Analyses
The primary endpoint was defined as the PFS2/PFS1 ratio or
growth modulation index (15), in which the progression-free survival
on matched therapy (PFS2) was compared with the progressionfree survival for the most recent therapy on which the patient had
just experienced progression (PFS1). Progression-free survival on
matched treatment (PFS2) was defined as the time from start of treatment to progression, as defined by RECIST 1.1, clinical progression,
or death from any cause. Progression-free survival on prior therapy
(PFS1) was defined as the time from start of the last prior treatment
to progression as defined by RECIST 1.1 or clinical progression. The
null hypothesis was that the PFS ratio >1.3 for 15% of patients or
fewer (17). In order to reject this null hypothesis with 90% power at
a one-sided significance level of 0.05, and assuming the true propor594 | CANCER DISCOVERY
June 2017
tion of patients with a PFS ratio >1.3 was equal to 24%, a total of 165
evaluable patients was required. The primary endpoint was tested by
a one-sided exact test. We also tested sequentially the PFS ratio in the
subgroup of patients for which the target treatment was considered
level of evidence A according to recommendation (32), which was
scored by an expert blinded from clinical outcome. It was also prespecified as secondary analysis in the protocol to directly compare the
paired failure times PFS2 and PFS1 through a χ2 test with 1 degree
of freedom in a loglinear model for correlated failure times (15).
Secondary endpoints were the number of patients who received a
matched treatment to describe feasibility, PFS2, OS, and best overall
response (RECIST 1.1). OS was defined as the time from inclusion to
death, irrespective of the cause. Patients still alive at the last visit were
censored at the date of last follow-up. The Kaplan–Meier method
was used to construct survival curves. The median follow-up was
calculated by the reverted Kaplan–Meier method. Exploratory post hoc
subgroup analyses were performed for the primary outcome using
exact tests for differences in proportions. Statistical analyses were
performed in SAS 9.4 and R 3.2.2.
Disclosure of Potential Conflicts of Interest
E. Deutsch reports receiving commercial research grants from
Roche Genentech, AZD, Lilly, and Servier, and is a consultant/advisory board member for Merck Pfizer and EISAI. A.M. Eggermont
is a consultant/advisory board member for Actelion, Bayer, BMS,
GSK, Haliodx, MSD, Nektar, Novartis, Pfizer, and Sanofi. J.-C. Soria
is a consultant/advisory board member for AstraZeneca, GSK, Lilly,
MSD, Pfizer, Pharmamar, Pierre Fabre, Roche, Sanofi, and Servier.
No potential conflicts of interest were disclosed by the other authors.
Authors’ Contributions
Conception and design: S. Michiels, M.-C. Le Deley, L. Lacroix,
A. Hollebecque, G. Vassal, F. André, J.-C. Soria
Development of methodology: S. Michiels, C. Ferté, M.-C. Le Deley,
L. Lacroix, R. Bahleda, T. De Baere, J.-Y. Scoazec, V. Lazar, J.-C. Soria
Acquisition of data (provided animals, acquired and managed
patients, provided facilities, etc.): C. Massard, C. Ferté, L. Lacroix,
A. Hollebecque, L. Verlingue, E. Ileana, S. Ammari, M. Ngo-Camus,
R. Bahleda, A. Gazzah, A. Varga, S. Postel-Vinay, Y. Loriot, C. Even,
I. Breuskin, N. Auger, T. De Baere, F. Deschamps, P. Vielh, J.-Y. Scoazec,
C. Richon, V. Ribrag, E. Deutsch, E. Angevin, A. Eggermont,
J.-C. Soria
Analysis and interpretation of data (e.g., statistical analysis,
biostatistics, computational analysis): C. Massard, S. Michiels,
C. Ferté, M.-C. Le Deley, L. Lacroix, A. Hollebecque, L. Verlingue,
S. Rosellini, S. Ammari, R. Bahleda, Y. Loriot, N. Auger, B. Job,
F. Deschamps, P. Vielh, A. Eggermont, F. André, J.-C. Soria
Writing, review, and/or revision of the manuscript: C. Massard,
S. Michiels, C. Ferté, M.-C. Le Deley, L. Lacroix, A. Hollebecque,
A. Gazzah, A. Varga, S. Postel-Vinay, Y. Loriot, C. Even, P. Vielh,
J.-Y. Scoazec, V. Lazar, V. Ribrag, E. Angevin, A. Eggermont, F. André,
J.-C. Soria
Administrative, technical, or material support (i.e., reporting
or organizing data, constructing databases): C. Massard, C. Ferté,
M.-C. Le Deley, L. Lacroix, E. Ileana, E. Angevin, G. Vassal, J.-C. Soria
Study supervision: C. Ferté, M.-C. Le Deley, L. Lacroix, G. Vassal,
J.-C. Soria
Acknowledgments
The authors acknowledge the global clinical team for its involvement
in the enrollment of patients in MOSCATO trial, as well as all relevant
personnel from Gustave Roussy Direction de la Recherche who made
possible this academic-sponsored trial (Aurélie Abou-Lovergne, Lisa
Lambert, Thibaud Motreff, and Delphine Vuillier). Maud Ngo-Camus,
Aljosa Celebic, and Katty Malekzadeh are thanked for Clinical Data
www.aacrjournals.org
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(ThermoFisher Scientific) completed by our own filtering and annotation pipeline. Each retained variant was reviewed by a molecular
geneticist and classified as pathogenic variant, unknown pathogenicity
variant, or probably nonpathogenic variant (details available in Supplementary Methods). The median coverage depth on retained variants
is over 700 reads, offering a sensitivity down to 5% of allelic frequency.
The whole-genome aCGH, SurePrint G3 Human aCGH Microarray
4×180K, Agilent Technologies) was performed as described previously
(30). The microarray scanning was performed using default parameters, and quantification of Cy5 and Cy3 signals was extracted with
Feature Extraction v10.5.1.1 (Agilent Technologies), then analyzed
and annotated with our own bioinformatic pipeline, described in Supplementary Methods. The aCGH analysis was mainly used to detect
tumor gene amplifications and/or deletions (an amplifications >×0.7
log2 ratio and deletions < 0.5 log2 ratio) and was discussed during
the tumor board. The alterations with a length less than 10 Mb were
considered of interest. When needed, CGH results were confirmed
by FISH analysis. Between 2014 and March 2016, CGH arrays were
substituted by Cytoscan technology to quantify copy-number alterations. Whole-exome analysis and RNA-seq analysis were performed on
a limited number of cases by IntegraGen pattern platform (IntegraGen), starting from the same nucleic acid prepared for targeted gene
sequencing (TGS) or aCGH. Analysis protocols used for WES and
RNA-seq are reported in Supplementary Methods.
All molecular analysis results from targeted sequencing, aCGH, WES,
or RNA-seq were reviewed one by one by a molecular geneticist responsible for generating a molecular report highlighting annotated molecular abnormalities to be discussed during the molecular tumor board.
The genomic data from this trial are available in the GENIE database.
In addition, we performed IHC for phospho-MET in order to drive
patients to MET inhibitors. The IHC analysis targeting MET and
phospho-MET was performed by the clinical pathology laboratory as
described previously (31) using antihuman c-MET, clone SP44 (reference M3444, Spring Bioscience) or clone CVD13 (reference 18-2257,
Invitrogen) and anti-phospho-MET (Tyr1234/1235) clone D26 (reference 3077, Cell Signaling Technologies).
Genomics to Improve Cancer Outcome
Management/Data Management. Dorota Gajda, Dienabou Sylla, and
Adrien Allorant are thanked for their statistical assistance. The authors
acknowledge laboratory and bioinformatic teams, including Amélie
Boichard, Mélanie Laporte, Isabelle Miran, Nelly Motté, 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,
Marc Deloger, Celine Lefebvre, and Marion Pedrero, for their support
in the preanalytic processing of samples, storage, and for tumor sample
analysis (wet lab and bioinformatic) of the patients included in the
MOSCATO 01 trial. The authors especially acknowledge Yuki Takahashi for her editing support and Nelly Hainault for final submission.
RESEARCH ARTICLE
14.
15.
16.
17.
Grant Support
Received December 12, 2016; revised January 6, 2017; accepted
March 7, 2017; published OnlineFirst April 1, 2017.
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