Article

Genomics to select treatment for patientswith metastatic breast cancer
https://doi.org/10.1038/s41586-022-05068-3
Received: 24 October 2021
Accepted: 3 July 2022
Published online: 7 September 2022
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Fabrice Andre1,2,3,4,34 ✉, Thomas Filleron5,34, Maud Kamal6,34, Fernanda Mosele2,
Monica Arnedos1, Florence Dalenc7, Marie-Paule Sablin6,8, Mario Campone9,
Hervé Bonnefoi10, Claudia Lefeuvre-Plesse11, William Jacot12, Florence Coussy13,
Jean-Marc Ferrero14, George Emile15, Marie-Ange  Mouret-Reynier16, Jean-Christophe Thery17,
Nicolas Isambert18, Alice Mege19, Philippe Barthelemy20, Benoit You21, Nawale Hajjaji22,
Ludovic Lacroix23, Etienne Rouleau23, Alicia Tran-Dien2,3,24, Sandrine Boyault25,
Valery Attignon25, Pierre Gestraud26, Nicolas Servant26, Christophe Le Tourneau6,
Linda Larbi Cherif6, Isabelle Soubeyran27, Filippo Montemurro28, Alain Morel29,
Amelie Lusque5, Marta Jimenez30, Alexandra Jacquet30, Anthony Gonçalves31,35,
Thomas Bachelot32,35 & Ivan Bieche33,35

Cancer progression is driven in part by genomic alterations1. The genomic
characterization of cancers has shown interpatient heterogeneity regarding driveralterations2, leading to the concept that generation of genomic profiling in patientswith cancer could allow the selection of effective therapies3,4. Although DNA
sequencing has been implemented in practice, it remains unclear how to use its
results. A total of 1,462 patients with HER2-non-overexpressing metastatic breast
cancer were enroled to receive genomic profiling in the SAFIR02-BREAST trial. Twohundred and thirty-eight of these patients were randomized in two trials (nos.
NCT02299999 and NCT03386162) comparing the efficacy of maintenance treatment5
with a targeted therapy matched to genomic alteration. Targeted therapies matched
to genomics improves progression-free survival when genomic alterations areclassified as level I/II according to the ESMO Scale for Clinical Actionability of
Molecular Targets (ESCAT)6 (adjusted hazards ratio (HR): 0.41, 90% confidence
interval (CI): 0.27–0.61, P < 0.001), but not when alterations are unselected using
ESCAT (adjusted HR: 0.77, 95% CI: 0.56–1.06, P = 0.109). No improvement inprogression-free survival was observed in the targeted therapies arm (unadjusted HR:
1.15, 95% CI: 0.76–1.75) for patients presenting with ESCAT alteration beyond level I/II.
Patients with germline BRCA1/2 mutations (n = 49) derived high benefit from olaparib(gBRCA1: HR = 0.36, 90% CI: 0.14–0.89; gBRCA2: HR = 0.37, 90% CI: 0.17–0.78). This
trial provides evidence that the treatment decision led by genomics should be driven
by a framework of target actionability in patients with metastatic breast cancer.

Cancer is a disease whose development is at least partially driven by
germline and/or somatic genetic alterations located on oncogenes ortumour suppressor genes1. Thanks to the advent of next-generation
sequencing (NGS), DNA analyses have shown that the genomic driversof cancer can differ between patients2. This observation led to thedevelopment of cancer precision medicine, in which a comprehensive
genomic profile is generated in each patient and a targeted therapy
is given accordingly3,4. Questions remain on how to use the results
obtained from genomic profiling in daily practice, for treatment decision. The SAFIR02-BREAST trial is a prospective randomized trial thatcompared targeted therapies matched to genomic alterations with thestandard of care—that is, maintenance chemotherapy5—in patients withmetastatic breast cancer (mBC). Here we show that targeted therapies
matched to genomics improves progression-free survival (PFS) whengenomic alterations are classified as level I/II according to the European
Society of Medical Oncology (ESMO) Scale for Clinical Actionabilityof Molecular Targets (ESCAT)6 (adjusted HR: 0.41, 90% CI: 0.27–0.61,
P < 0.001), but not when alterations are classified beyond level II (unadjusted HR: 1.15, 95% CI: 0.76–1.75). This trial provides evidence that thetreatment decision led by genomics should be driven by a frameworkof target actionability in patients with mBC.
After the pilot study (SAFIR01) showed the feasibility of multi-genetechnologies at a national level7, the phase II trial (MOSCATO) suggested that, in 33% of patients, PFS was increased by therapy matched
to a genomic alteration detected by a multi-gene panel8. Other trials
reported consistent results, that around 20% of patients would gain
access to new drugs and one-third of them would derive some benefit
from such drug access9. Although these results show that sequencing

A list of affiliations appears at the end of the paper.

Nature | Vol 610 | 13 October 2022 | 343

Article
Alpelisib and fulvestrant
SAFIR-PI3K
R
R
Frozen or FFPE fresh
biopsy, frozen or FFPE
biopsy <12 months or
ctDNA sample

SAFIR02BREAST
targeted

SAFIR02-BREAST
screening phase• Patients withmetastatic, HER2non-overexpressing
breast cancer• Resistant toendocrine therapy(if HR+)• Previously treatedwith a maximum of
one line ofchemotherapy

R

2:1

Hierarchical testing
Step 1: PFS in ESCAT I/II(n = 115)

Maintenance CT

Step 2: PFS in ITT (n = 238)

Targeted therapy matched
to genomics*

After a predefined numberof events was reached in
ESCAT I/II

2:1

NGS and CGH
array

Stratification:• First- or second-line CT• CR/PR or SD• Group of genomic
alteration (A,B,C,D)

In a preplanned pooled
analysis of SAFIR02-BREAST
and SAFIR-PI3K

Maintenance CT

YesCR, PR, SD after
six to eight CT cycles(or four cycles if
stopped for toxicity)

Stratification:• First- or second-line CT• CR/PR or SD

Targetablemolecular
alteration?
Durvalumab 10 mg kg–1
every 14 daysNon = 1,462
R
SAFIR02-BREASTIMMUNO

2:1

Previously reported23

Maintenance CT*olaparib, capivasertib, vistusertib, AZD8931, vandetanib, bicalutamide, AZD4547, selumetinib
Fig. 1 | Design of the trial. Patients were selected on the basis of having
received fewer than two lines of chemotherapy (CT) in the metastatic setting,and to present with mBC with no overexpression of HER2. After patients had
signed an informed consent, a biopsy was done with the exception of (1)patients who had undergone a biopsy <12 months before inclusion and (2)patients for which a biopsy was not feasible. In the latter case, a plasma sample
was obtained for analysis of circulating tumour DNA. Multi-gene sequencing
and comparative genomic hybridization (CGH) arrays were performed as
described in Methods. In patients eligible for germline sequencing,

determination of germline BRCA1/2 was also performed. Patients received
between six and eight cycles of chemotherapy. For patients who presented
with stable disease (SD) or an objective response (partial (PR) or complete(CR) response), and genomic alteration targetable by a drug available in thetrial, randomization between matched targeted therapy and maintenancechemotherapy was proposed. The primary endpoint of the trial was PFS in apooled analysis of SAFIR02-BREAST and SAFIR-PI3K. The trial had hierarchicaltesting, starting with the population of patients with ESCAT I/II alterations
followed by the overall population.

technologies present good analytical validity and could improve outcome in a subset of patients, their impact and how to use the results
in daily practice remain unclear. As an example, matching molecularalterations with drugs approved in another disease did not improveoutcome in a randomized trial10. To prioritize genomic alterations fortreatment decision, scales of evidence for genomic alterations havebeen developed. As an example, ESMO has created ESCAT6, a scalethat includes six levels ranked according to the level of evidence of thematched genomic alteration/drug. Level I means that the drug matchedto the genomic alteration has been proved to be effective; level II meansthat the drug matched to the genomic alteration has been associatedwith preliminary evidence of efficacy. Beyond level II, the efficacy ofthe matched drug/genomic alteration is hypothetical at best. In the
SAFIR02-BREAST trial in patients with mBC, we evaluated the clinicalutility of the identification of therapeutic targets by genomics, together
with the utility of the method in interpretation of the genomic report.
The study design is shown in Fig. 1.

alterations are reported in Extended Data Figs. 1 and 2, respectively. Twohundred and thirty-eight (16%) patients were subsequently randomized
between maintenance chemotherapy (n = 81) and targeted therapy (n = 157).
The clinical characteristics of the 238 patients randomized, together withthe drugs used in the maintenance setting, are reported in Table 1.
The first step in the statistical plan was to analyse the efficacy oftargeted therapies in patients presenting with an ESCAT I/II genomic
alteration. Of the 115 patients with a genomic alteration classified as
ESCAT I/II, 57 presented with BRCA1/2 alterations, three with a PALB2
alteration, 31 with a PIK3CA mutation, 16 with an AKT1 mutation, fivewith a PTEN mutation and/or deletion and three with an ERBB2 mutation.
Eight patients presented with a somatic-only BRCA1/2 genomic alteration. BRCA genomic alteration was determined by germline sequencing in 13 patients (including six for which NGS/comparative genomic
hybridization (CGH) was not feasible). Adverse events were pooled for
ESCAT I/II, and intent-to-treat (ITT) populations and are reported in Supplementary Data 1. No new safety signal was detected. After a medianfollow-up of 21.4 months (90% CI: 17.9–27.6), analysis of the primary endpoint in patients with ESCAT I/II showed a significantly longer PFS in thetargeted therapy arm than in the control arm. Median PFS was 9.1 months(90% CI: 7.1–9.8) in the experimental arm and 2.8 months (90% CI:
2.1–4.8) in the control arm (adjusted HR = 0.41, 90% CI: 0.27–0.61,
P < 0.001) (Fig. 2a). We further assessed the efficacy of matching an
ESCAT I/II genomic alteration with targeted therapy within subgroups(Extended Data Fig. 3). The efficacy of matched targeted therapies was not

Results
Between April 2014 and October 2019, 1,462 patients with human epidermal
growth factor receptor-2 (HER2)-negative mBC signed informed consent
to perform a biopsy and/or a genomic test in SAFIR02-BREAST, and were
included in the trial. Out of 1,462 screened patients, 646 presented witha targetable genomic alteration. The CONSORT diagram and genomic
344 | Nature | Vol 610 | 13 October 2022

Table 1 | Patient characteristics
Overall population (n = 238)

ESCAT I/II population (n = 115)

Arm A (n = 157)

Arm B (n = 81)

P value

Arm A (n = 75)

Arm B (n = 40)

P valueTNBC

32 (21%)19 (25%)0.54

19 (28%)13 (35%)0.68

HR+/HER2–

116 (77%)

56 (75%)50 (71%)24 (65%)

Breast cancer subtypes of primary tumour

HER2+

3 (2%)0

1 (1%)

0

Missing

6

6

5

3

Yes

93 (59%)45 (56%)No

64 (41%)36 (44%)Yes

73 (46.5%)

34 (42%)

No84 (53.5%)

47 (58%)

Four or five10 (6%)

5 (6.5%)5 (7%)

3 (8%)

Six to eight

146 (93%)75 (92.5%)

70 (93%)36 (90%)Nine

1 (1%)

1 (1%)0

1 (2%)

Under three

77 (49%)43 (53%)Three or more

80 (51%)38 (47%)Yes

101 (64%)

53 (65%)No

56 (36%)28 (35%)First

132 (84%)

69 (85%)

Second25 (16%)12 (15%)

Partial/complete response

74 (47%)38 (47%)

Stable disease

83 (53%)43 (53%)

IGF1R amplification (n = 3), TSC1/2 mutation (n = 3), STK11 deletion(n = 1), RPTOR amplification (n = 1) and AZD2014

7 (4%)

1 (1%)

AKT1 mutation and AZD5363

12 (8%)

Previous chemotherapy in metastatic setting
0.58

50 (67%)24 (60%)25 (33%)16 (40%)0.48

Previous hormonotherapy in metastatic setting
0.50

37 (49%)16 (40%)38 (51%)24 (60%)0.34

Number of cycles of chemotherapy received at randomization
1.00

0.52

Number of metastatic sites at screening
0.55

36 (48%)20 (50%)39 (52%)20 (50%)0.84

Liver metastasis at screening
0.87

46 (61%)23 (57.5%)

29 (39%)17 (42.5%)

0.69

Line of chemotherapy at randomization
0.82

65 (87%)38 (95%)10 (13%)

2 (5%)

0.21

Disease status at randomization
0.97

34 (45%)18 (45%)41 (55%)22 (55%)NANA

4 (5%)

12 (16%)4 (10%)

0.97

Matched genomic alteration/targeted therapy decision by MTB–PTEN mutation and/or deletion and AZD5363

8 (5%)

2 (2%)

4 (5%)

1 (3%)PIK3CA (n = 36), PIK3R1 (n = 2); mutations/PIK3CB (n = 1), AKT1 (n = 1),
AKT3 (n = 1), PDPK1 (n = 1); amplifications and AZD5363

30 (19%)12 (15%)NA

NA

FGFR1 (n = 19), FGFR2 (n = 1), FGF4 (n = 4), FGFR3 (n = 1); amplifications/
FGFR2 (n = 1), FGFR4 (n = 1); mutations and AZD4547

17 (11%)10 (12%)

NA

NA

ERBB2 mutation and AZD8931

1 (0.5%)2 (2%)

1 (1%)

2 (5%)EGFR mutation/amplification (n = 3) or ERBB3 mutation (n = 2) andAZD89312 (1%)

3 (4%)NANA

FRS2 amplification (n = 10), NF1 mutation (n = 7), KRAS mutation (n = 4), 17 (11%)
BRAF amplification (n = 1), BRAF mutation (n = 1) and selumetinib

6 (7%)NANA

VEGFA amplification (n = 3), RET mutation (n = 1), EGFR amplification(n = 1), KDR mutation (n = 1) and vandetanib

3 (4%)NANA

3 (2%)AR amplification and bicalutamide0

1 (1%)NA

NA

BRCA1 mutation and olaparib

11 (7%)

10 (12%)

11 (15%)10 (25%)

BRCA2 mutation and olaparib

26 (16%)10 (12%)26 (35%)10 (25%)

PALB2 mutation and olaparib

1 (0.5%)2 (2%)

1 (1%)

2 (5%)

ATR/ATM mutation/deletion and olaparib

2 (1%)

4 (5%)NANA

PIK3CA mutation and alpelisib

20 (13%)

11 (14%)20 (27%)

11 (27%)–

ESCAT ranking
Continued

Nature | Vol 610 | 13 October 2022 | 345

Article
Overall population (n = 238)

ESCAT I/II population (n = 115)

Arm A (n = 157)

Arm B (n = 81)

P value

Arm A (n = 75)

Arm B (n = 40)

P valueI49 (31%)31 (38%)0.22

49 (65%)31 (77%)0.18

II

26 (17%)9 (11%)

26 (35%)9 (22%)

III

15 (10%)4 (5%)NA

NA

IV

27 (17%)18 (22%)

NA

NA

X

10 (6%)

9 (11%)

NA

NA

NM

30 (19%)10 (12%)NA

NA

PaclitaxelNA

21 (33%)NA

12 (36%)

CapecitabineNA

13 (21%)NA

5 (15%)

BevacizumabNA

8 (13%)

NA

6 (18%)

Chemotherapy administered in the maintenance setting (n = 63)
NA

Paclitaxel + bevacizumabNA

7 (11%)NA

3 (9%)OtherNA

14 (22%)NA

7 (21%)

NA

NM, not matched; NA, not applicable.

different between patients with ESCAT I (HR = 0.53, 90% CI: 0.34–0.82)
and ESCAT II genomic alterations (HR = 0.38, 90% CI: 0.18–0.78).
Patients with germline BRCA1/2 mutations derived high benefit fromolaparib (gBRCA1: HR = 0.36, 90% CI: 0.14–0.89; gBRCA2: HR = 0.37, 90%
CI: 0.17–0.78). There was no heterogeneity between genomic alterations
regarding treatment efficacy (interaction test, P = 0.26) (Extended Data
Fig. 3). When patients with gBRCA1/2 alterations were excluded from theanalysis (n = 66), HR for progression or death was 0.64 (90% CI: 0.39,
1.06). Although median PFS was 1.8 (90% CI: 1.4–NR (not reached)) and2.3 months (90% CI: 1.8–2.8) in the control arms of cohorts AZD5363 andolaparib, it was 8 months (90% CI: 3.2–15.5) in the cohort alpelisib. This
could reflect the fact that a large number of patients received effective
chemotherapy in the maintenance setting in this cohort (paclitaxel,n = 4, 44%; capecitabine, n = 4, 44%; cyclophosphamid and epirubicin,
n = 1, 11%). Targeted therapies matched to ESCAT I/II genomic alterations
had no impact on overall survival (adjusted HR = 0.94, 90%CI: 0.57–1.55,
P = 0.831) when given in the maintenance setting.
Because genomics had an impact on PFS in the subgroup of patientswith ESCAT I/II, we moved the statistical analysis to the second step. In
the overall population, with median follow-up of 24.7 months (95% CI:
17.9–30.6), there was no significant difference in PFS between the twoarms (adjusted HR: 0.77, 95% CI: 0.56–1.06, P = 0.109). Median PFS in thecontrol arm was 2.9 (95% CI: 2.3–4.8) and 5.5 (95% CI: 4.0–6.9) months
in the targeted therapy arm (Fig. 2b). ESCAT classification was highly
predictive for the benefit of targeted therapies matched to genomic
alterations (interaction test, P = 0.004; Extended Data Fig. 4). Targeted
therapies matched to genomic alterations were not effective in patients
without ESCAT I/II alteration (HR: 1.15, 95% CI: 0.76–1.75). In this subgroup, median PFS was 2.8 (95% CI: 1.6–4.2) and 3.1 (95% CI: 1.8–5.7) inpatients treated with targeted therapy and maintenance chemotherapy,
respectively (P = 0.49) (Fig. 2c). We could not detect any evidence that agenomic alteration ranked III/IV was associated with outlier sensitivity
to a matched therapy. Indeed, only one patient with an ESCAT III/IV
alteration and treated with targeted therapy had PFS > 12 months.
Taking advantage of the large number of patients included in the trial,
we then performed exploratory biomarker analyses. In multivariable
analyses that included age and grade, number and site of metastases, TP53
mutations (adjusted HR: 1.87, 95% CI: 1.49–2.34, P < 0.001), homologous
recombination deficiency (HRD) (adjusted HR: 1.31, 95% CI: 1.05–1.63,
P = 0.017) and PIK3CA mutations (adjusted HR: 1.25, 95% CI: 1.00–1.56,
P = 0.052) were associated with poor overall survival from inclusion in the
population of patients presenting with hormone receptor (HR)+/HER2–
mBC (n = 574 for CGH and 614 for NGS; Supplementary Data 2).
We then focused the analyses on patients who presented with a
BRCA1/2 alteration, treated with olaparib and for which a copy number analysis was done on the tumour sample (n = 31). In this group of
346 | Nature | Vol 610 | 13 October 2022

patients, loss of heterozygosity (LOH) on BRCA1 and/or BRCA2 wasassociated with longer PFS (HR = 0.32, 95% CI: 0.14, 0.73, P = 0.0049)(Fig. 3a). LOH on BRCA1/2 was associated with higher HRD (P < 0.001).
A high HRD score (≥42) was associated with a better outcome (HR = 0.32,
95% CI: 0.12, 0.83, P = 0.013) (Fig. 3b). Two patients presenting withan AKT1 mutation and treated with capivasertib had PFS censoredat 24 and 27 months, respectively. Two patients with PTEN alterationand treated with capivasertib had PFS of 12 and 23 months, respectively; one of these patients had Cowden syndrome and was previously
reported11. The second patient presented with two somatic PTEN mutations associated with a hotspot loss-of-function mutation on PIK3R1, agene coding the regulatory subunit of PI3K12. Patients presenting witha double PIK3CA mutation (n = 5) and treated with alpelisib and fulvestrant had a lower hazard of progression (HR = 0.34, 95% CI: 0.07, 1.66)
compared with patients having a single mutation (n = 10). One patientwith a double PIK3CA mutation and treated with alpelisib and fulvestrant was censored at 30 months. Finally, we assessed which genes
were more frequently amplified or deleted in mBC compared withprimary tumours. We first analysed the 45 genes previously reported
by Curtis13 to present recurrent copy number alterations in early-stage
breast cancers. In patients with HR+/HER2– mBC (n = 565), 14 of thesegenes presented copy number alterations more frequently in mBC ascompared with early-stage breast cancer (n = 2,162) (Fig. 3c). When
we analysed the entire genome, five additional genes were more frequently amplified in HR+/HER2– mBC as compared with early-stage
breast cancers (Fig. 3c). TERT (telomerase reverse transcriptase) was
amplified in 5.8% of HR+/HER2– mBC. No gene was significantly more
frequently amplified or deleted in metastatic triple-negative breast
cancer (TNBC) (n = 361) as compared with early-stage TNBC (n = 471).

Discussion
Although progress is providing newer sequencing tools that potentiallyovercome technological hurdles, our study shows that the tools forclinical interpretation of sequencing are pivotal in achieving tangible
clinical benefits. In the current study we tested ESCAT as a framework
for actionability610 and found that DNA sequencing should lead to treatment administration only for patients presenting with genomic alterations ranked level I/II. This could extrapolate to the tier I and II evidence
from the Association for Molecular Pathology14 and to levels 1–3A from
OncoKB15. The finding that such frameworks constitute the pillar for aneffective implementation of cancer precision medicine is an argument
for investment in the development of databases that inform clinicalteams about the relevance of matched drug–genomic alterations15,16.
Pioneer publications on cancer precision medicine have reported
the vision that modelling biology in each patient, on the basis of DNA

aa

PFS in patients with ESCAT I/II genomic alterations (n = 115)

PFS in patients with BRCA1/2 genomic alteration andtreated with olaparib according to LOH of BRCA

1.00

0.75

HR adjusted for stratification factors:
0.41 (90% CI: 0.27, 0.61)
P < 0.001

0.50

At least one LOH BRCA1 or BRCA2
Not LOH BRCA1/2

0.75

PFS

PFS

1.00

Maintenance chemotherapy
Targeted therapy matched togenomic alteration

HR = 0.32 (95% CI: 0.14, 0.73)

0.50

0.25

0.25
0
0

6

12

18

24

Maintenancechemotherapy

40

9

5

0

0

0

Targeted therapy
matched togenomic alteration

75

46

22

9

6

3

b

30
36
Months

42

48

54

60

0

0

0

0

0

1

1

1

1

0

0
0

6

12

18

24

Not LOH BRCA1/2 10
LOH BRCA1/2 21

5
17

1
10

0
5

0
3

b

PFS in the overall population (n = 238)

0
2

0
1

42

48

54

60

0
1

0
1

0
1

0
0

PFS in patients with BRCA1/2 genomic alteration andtreated with olaparib according to HRD score

1.00

1.00
HRD < 42
HRD ≥ 42

0.75

Maintenance chemotherapy
Targeted therapy matched togenomic alteration

0.50

HR adjusted for stratification factors:
0.77 (95% CI: 0.56, 1.06)
P = 0.109

0.75

PFS

PFS

30 36
MonthsHR = 0.32 (95% CI: 0.12, 0.83)

0.50

0.25

0.25
0

1

1

1

1

0

HRD < 42
HRD ≥ 42

c

0.25

0
0

6

12

18

24

30
36
Months

Maintenance 41
chemotherapy

9

2

2

1

1

Targeted therapy 82
matched togenomic alteration

20

4

2

1

0

42

48

54

60

1

0

0

0

0

0

0

0

0

0

Fig. 2 | PFS according to treatment arm and ESCAT ranking. a–c, PFS inpatients with ESCAT I/II (a), in the overall population (b) and with alteration
beyond ESCAT I/II (c), according to treatment arm. HR and P values were
estimated using a Cox proportional-hazards model adjusted to stratification
factors in patients with ESCAT I/II (a) and in the overall population (b) (primaryobjectives), and are unadjusted in patients with alteration beyond ESCAT I/II (c).
HR is reported, together with 90% CI for the ESCAT I/II population and 95% for
the overall and non-ESCAT I/II populations. All statistical tests were two-sided.
No adjustment was made for multiple comparisons.

sequencing, would allow identification of therapeutic targets at theindividual level4,17. Our study suggests that, besides the companion
diagnostics validated in therapeutic trials, we do not yet have the biological knowledge to identify a target in each individual. Indeed, in
the SAFIR02-BREAST trial there was no efficacy of targeted therapies
when genomic alterations were classified beyond level II. This finding is

30 36Months

42

48

54

60

6
25

2
20

1
10

0
5

0
3

0
2

0
1

0
1

0
1

0
0

0
1

Discovery genes
HR+/Her2– mBC
HR+/Her2– eBC

25
20

Homozygous
deletions

15
10
5
0

TRIM46

Unadjusted HR:
1.15 (95% CI: 0.76, 1.75)

0.50

24

30

FASLG (TNFSF18/TNFSF4)

Percentage focal amplificationor homozygous deletion

PFS

0.75

18

Recurrent CNA previously
reported in primary tumours

35
Maintenance chemotherapy
Targeted therapy matched togenomic alteration

12

Genes more frequently amplified or deleted in metastatic
HR+/Her2– breast cancers as compared with early stage

PFS in patients presenting genomic alteration beyond ESCAT I/II (n = 123)
1.00

6

SOCS1

3

0

ST3GAL1(NDRG1/WISP1)

7

0

TERT

11

0

RAD21

26

0

GJC2

66

0

PTEN

Targeted therapy 157
matched togenomic alteration

1

ZNF2171

0

CDKN2A

1

60

BIRC5

2

54

SEMG1

7

48

RPS6KB11842

NDUFC2-KCTD14

Maintenance 81
chemotherapy

c

30
36
Months

POLD4

24

CCND1

18

MYC

12

SEPHS1

6

ZNF703 (FGFR1)

0

Fig. 3 | Exploratory analyses on biomarkers. a, PFS according to the presenceof LOH on BRCA, as defined in Methods. b, PFS according to the presence of
HRD: a cut-off was predefined at 42, as reported in Methods. PFS was assessedin patients with BRCA1/2 genomic alteration, treated with olaparib and forwhich a copy number analysis had been done on tissue. HR from the Cox
proportional-hazards model is reported, together with 95% CI. c, The 19 genes
found to present more frequently with a focal amplification or deletion in
HR+/HER2– mBC (n = 565) as compared with early-stage HR+/HER2– breast
cancers (early breast cancer (eBC)) (n = 2,162). The frequencies of copy number
alterations reported in the figure are calculated from SAFIR02-BREAST (mBC)
and TCGA (eBC). Genes were considered significant at P < 0.05 in comparison
between SAFIR02-BREAST and all three early-stage BC datasets (TCGA,METABRIC and PACS04).

Nature | Vol 610 | 13 October 2022 | 347

Article
corroborated by preclinical data suggesting that DNA sequencing alone is
not a tool suitable for the development of predictors of drug sensitivity18.
The future in the field of cancer modelling will be to integrate, in eachpatient, data derived from different sources. As example, in the DREAM
project, integration of RNA expression with DNA sequencing improved
prediction for drug sensitivity18. Other research efforts are currently
evaluating the use of organoids to model cancer biology and predict
drug sensitivity19,20.
Interestingly, the current trial suggests a high efficacy of olaparib givenin the maintenance setting (HR = 0.36, 90% CI: 0.14–0.89) and HR = 0.37
(90% CI: 0.17–0.78) in patients with gBRCA1 and gBRCA2, respectively),
whereas its benefit was more limited in the OlympiaD trial in which it wasadministered as frontline therapy (HR = 0.58, 95% CI: 0.43–0.80)21. Thisfinding suggests that there is a rationale to testing poly-(ADP-ribose)polymerase (PARP) inhibitors as maintenance therapy.
The current trial presents several limitations. First, it did not test a
specific platform or technology of sequencing but rather the utility ofmultiple genomic tests and the method used to interpret these. This iscommon and is a contingency in clinical trials in which, over the timerequired to complete enrolment, newer or constantly refined sequencing platforms are implemented. Second, patients with germline BRCA
mutations derive high benefit from matched targeted therapy and one
could argue that the results from the ESCAT I/II group were driven by
this population. Nevertheless, subgroup analysis shows that targeted
therapies matched to genomic alterations also reduce the risk of progression when excluding patients with germline BRCA1/2 mutations(HR = 0.64, 90% CI: 0.39, 1.06). Third, the design allowed the inclusionof only those patients who presented with sensitivity to chemotherapy.
This could have biased the population toward a group of patients who
do not present with genomic alterations involved in resistance. Fourth,some drugs could be considered suboptimal. As an example, neratinib,
for which efficacy has been extensively reported22, would have beena better choice to target HER2 as compared with AZD8931. Finally,
the design itself did not properly test precision medicine because thecontrol arm included maintenance chemotherapy.
The SAFIR02-BREAST trial suggests that the use of genomics improves
the outcome of patients who present with a match drug/alteration
ESCAT I/II, but not for those presenting with ESCAT beyond I/II . Reporting
the results of genomics in the context of a framework of target actionability
should therefore be considered as a standard of care. The results of the trial
should be interpreted with caution, because a large part of the benefit
observed with matched targeted therapies was derived from patients
presenting with BRCA1/2 alteration and the small sample size does
not allow exploration of the actionability of new genomic alterations.

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acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi.org/10.1038/s41586-022-05068-3.
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Springer Nature or its licensor holds exclusive rights to this article under a publishingagreement with the author(s) or other rightsholder(s); author self-archiving of the accepted
manuscript version of this article is solely governed by the terms of such publishingagreement and applicable law.
© The Author(s), under exclusive licence to Springer Nature Limited 2022
Department of Medical Oncology, Gustave Roussy, Villejuif, France. 2INSERM U981, Gustave
Roussy, Villejuif, France. 3PRISM Center for personalized medicine, Gustave Roussy, Villejuif,
France. 4Medical School, Université Paris Saclay, Kremlin Bicetre, France. 5Department of
Biostatistics, Institut Claudius Regaud, IUCT oncopole, Toulouse, France. 6Department ofDrug Development and Innovation, Institut Curie, Saint Cloud, France. 7Department ofMedical Oncology, Institut Claudius-Regaud IUCT oncopole and University of Paul Sabatier,Toulouse, France. 8Department of Medical Oncology, Institut Curie, Paris, France. 9Institut deCancérologie de l’Ouest – René Gauducheau, Saint Herblain, University of Angers, Angers,
France. 10Department of Medical Oncology, Institut Bergonié INSERM U1218 and Université ofBordeaux, Bordeaux, France. 11Department of Medical Oncology, Centre Eugène Marquis,
Rennes, France. 12Department of Medical Oncology, Institut du Cancer de Montpellier, Institut
de Recherche en Cancérologie de Montpellier INSERM U1194 and Montpellier University,
Montpellier, France. 13Department of Medical Oncology, Institut Curie, Saint-Cloud, France.
1

14
Department of Medical Oncology, Centre Antoine Lacassagne, University Côte d’Azur, Nice,
France. 15Department of Medical Oncology, Centre François Baclesse, Caen, France.
16
Department of Medical Oncology, Centre Jean Perrin, Clermont-Ferrand, France.
17
Department of Medical Oncology, Centre Hennri Becquerel, University of Medicine ofRouen, Rouen, France. 18Department of Medical Oncology, Centre Georges François Leclerc,
Dijon, France. 19Institut Sainte Catherine, Avignon, France. 20Institut de Cancérologie
Strasbourg Europe, Strasbourg, France. 21Department of Medical Oncology, Institut deCancérologie des Hospices Civils de Lyon, Université Claude Bernard Lyon 1, Lyon, France.
22
Department of Medical Oncology, Centre Oscar Lambret INSERM U1192 PRISM Laboratory
and University of Lille, Lille, France. 23Cancer Genetics Laboratory, Department of Pathology
and Medical Biology, Gustave Roussy, Villejuif, France. 24Bioinformatic Core Facility, UMS
AMMICA, Gustave Roussy, Villejuif, France. 25Department of Translational Research andInnovation, Centre Léon Bérard, Lyon, France. 26Bioinformatics and Computational Systems
Biology of Cancer, PSL Research University, Mines Paris Tech, INSERM U900, Paris, France.
27
Unit of Molecular Pathology – Department of Biopathology, Institut Bergonié, Bordeaux,France. 28Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy. 29Department of Innate
Immunity and Immunotherapy, Institut de Cancérologie de l’Ouest – Centre Paul Papin,
Angers, France. 30Unicancer, Paris, France. 31Department of Medical Oncology, Institut
Paoli-Calmettes, Marseille, France. 32Department of Medical Oncology, Centre Léon Bérard,
Lyon, France. 33Department of Genetics, Institut Curie, INSERM U1016, Université Paris Cité,
Paris, France. 34These authors contributed equally: Fabrice Andre, Thomas Filleron, Maud
Kamal. 35These authors jointly supervised this work: Anthony Goncalves, Thomas Bachelot,
Ivan Bieche. ✉e-mail: fabrice.andre@gustaveroussy.fr

Methods
Patients and study design
SAFIR02-BREAST (no. NCT02299999), a prospective trial testing theclinical utility of genomics, was run between 2014 and 2021 (Supplementary Data 3 and 4). The study design is reported in Fig. 1. Patientswere eligible if they presented with HER2– mBC and had received a maximum of one line of chemotherapy in the metastatic setting. Patients
presenting with HR+ breast cancer had to be resistant to endocrine
therapy (tamoxifen or aromatase inhibitor). Patients who were eligible signed an informed consent for biopsy and genomic analyses
within SAFIR02-BREAST. Patients were given a genomic test on either abiopsy of metastasis (n = 1,163) or a plasma sample obtained before thethird cycle of chemotherapy (n = 125 between 2017 and 2019). Patients
received conventional chemotherapy on the basis of the decision of
the investigator. When genomic results were available, a national
molecular tumour board (MTB) allocated the patient to a targeted
therapy available in the trial (listed in Table 1). After six to eight cycles ofchemotherapy, patients who did not present with progressive disease
were randomized between the targeted therapy matched to genomic
alteration as decided by the MTB, or maintenance of chemotherapy
for a duration decided by the investigator. From December 2017, a
specific protocol (SAFIR-PI3K, no. NCT03386162; Supplementary
Data 5)) was opened for patients with HR+/HER2– mBC presenting
with a hotspot PIK3CA mutation detected in SAFIR02-BREAST. This
protocol randomized alpelisib (300 mg) combined with fulvestrant(500 mg monthly) versus chemotherapy, and data were preplanned
to be merged with SAFIR02-BREAST. All patients who entered the randomized part of SAFIR02-BREAST and SAFIR-PI3K signed informed
consent. The SAFIR02-BREAST trial was approved by the French ethics
committee, CPP, on 13 December 2013 (no. 2013-09-07) and the Frenchhealth authorities, ANSM, on 14 January 2014 (no. 2013-001652-36).
The SAFIR-PI3K trial was approved by CPP on 7 July 2017 (no. 2-17-21)
and by ANSM on 19 July 2017 (no. 2017-000154-19). SAFIR-PI3K was
introduced in the SAFIR02-BREAST trial in 2017 and was approved by
CPP on 17 September 2017 (no. 2013-09-07 MS10) and by ANSM on
22 December 2017 (no. 2013-001652-36 MS10). An Independent Data
Monitoring Committee (IDMC) that included six members monitored
the study every 6 months.
Molecular testing and reporting
ER (estrogen receptor), PR (progesteron receptor) and HER2 (humanepidermal growth factor receptor 2) were determined locally. The last
status available was used to define molecular subtypes.
A tumour sample was obtained either prospectively after signing the informed consent or retrospectively if the last biopsy was
performed <12 months before inclusion. When it was not possible
to perform biopsy or when tumour samples could not be analysed(<30% tumour cells for frozen sample, <10% for formalin-fixed,
paraffin-embedded (FFPE) samples), circulating tumour DNA wasused to perform genomic profiling. Methods for DNA extraction arepreviously reported23. Five core facilities performed the genomic analyses. CytoScan and OncoScan FFPE Assays Kits (Affymetrix, Thermo
Fisher Scientific) were used to determine copy number alterationsin fresh tumour DNA and FFPE or ctDNA samples, respectively. Twogene panels were used during the trial: the first assessed 50 genes andwas used between 2014 and 2017; the second assessed 65 genes andwas used after 2017 (list of genes is given in Supplementary Material
and Study Protocol, Supplementary Data 4). DNA (10 ng) was used toperform the initial PCR step (17 cycles). Amplicons were then partiallydigested using the enzyme FuPA to eliminate extremities corresponding to primer sequences. The digested product was ligated with adaptors and barcodes, then amplified and purified. Adaptors contained
specific indices (barcodes) different for each sample so that libraries from different samples could be combined before sequencing.

Quality and quantity assessment of DNA libraries was achieved using a
Qubit 2.0 Fluorometer (Thermo Fisher Scientific) and/or BioAnalyzer
profiling. After equimolar pooling of libraries, the final solution was
sequencing using either a MiSeq instrument (Illumina), an Ion Torrent
PGM or an Ion Torrent GeneStudio S5 (Thermo Fisher Scientific),
depending on the various regional molecular cancer genetics platforms. A depth of coverage of >100 reads was required for variant calling, with thresholds of 5% for the alternate allele for calling of SNVs/
mutations and 10% for indels. Raw reads were aligned on the reference
human genome hg19, and variants were annotated using ANNOVAR
and the following databases: COSMIC68, dbSNP137, 1000 Genomes,
ESP6500 and RefGene annotations. Only non-synonymous variants
not observed in >0.1% of the population (1000 Genomes, ESP6500 andgnomAD) were identified as mutations. All somatic mutations wereannotated, sorted and interpreted (pathogenic, probably pathogenic
or unlikely to be pathogenic) by an expert in molecular biology according to available databases (Cosmic, TCGA, cancer Hotspot, OncoKB).
Only truncating mutations (frameshift deletion and insertion, stop
gain, splicing alteration) and known mutations (missense variants andin-frame deletion and insertion) from available databases (Cosmic,
TCGA, cancer Hotspot, OncoK) were retained for tumour suppressor
gene pathogenic variants. All missense variants and in-frame deletions and insertions known to be hotspot mutations from available
databases (Cosmic, TCGA, cancer Hotspot, Onco) were retained foroncogene pathogenic variants.
Copy number variations (CNVs) from CytoScan and OncoScan
Affymetrix were defined using the R package rCGH24. Briefly, log2
relative ratios (LRRs) were calculated and centralization of the profile determined the baseline (two copies being the neutral level)from which copy number analysis (CNA) was estimated. Breakpoints
in LRR continuity were identified by segmenting the profile. Thesesegments were further used to determine potential gains or losses,
using the following scale: copy = 0, homozygous deletion; copy = 1,
loss; copy = 2, copy neutral; 3 ≤ copy ≤4, gain; copy > 4, amplification. The type of LOH state was defined using the R package EaCoN,
available at github (https://github.com/gustaveroussy/EaCoN). HRD
score25 was determined using HRDetect. Based on the CNV profile,
HRDetect measures the frequency of large-scale LOH, telomericallelic imbalance and large-scale transition events to determine HRD
score. A cut-off of 42 was selected for HRDetect before analyses26.
To perform comparison of CNAs between mBC and early-stage BC(TCGA2, METABRIC13 and PACS04, a prospective trial testing adjuvant
chemotherapy27), only focal amplifications and homozygous deletions were considered. For both early-stage BC and mBC samples,
we excluded those that (1) were ctDNA; (2) failed during processing;(3) had a flat (low-dynamic) CGH profile; (4) were considered too noisy(a threshold of 1,500 segments by profile was used to considered asample as noisy); or (5) showed ERBB2 amplifications. Based on theseparameters, we compared 926 mBC versus 2,633 early-stage BC. CNA
was performed in the same way for early-stage BC as for mBC. Focally
amplified genes were defined as those fully included in a DNA segment<10 Mb and with copy number six or greater. Homozygous deleted
genes were defined as those fully or partially included in a DNA segment with a copy number of 0. For each focal amplified region we
identified gene(s) located in the smallest common focal amplified
DNA segment (SCFADS). If several genes located were in this SCFADS,
we have indicated those from the OncoKB Cancer Gene List (https://www.oncokb.org/cancerGenes). The frequency of amplification and/or deletion in SAFIR02-BREAST was compared with that of the three
early-stage BC datasets. Genes were considered significant at P < 0.05
in the comparison between SAFIR02-BREAST and all three early-stage
BC datasets (TCGA, METABRIC, PACS04). For TNBC, only TCGA andMETABRIC were used for comparison.
The genomic results of each patient were discussed during a bimonthly
MTB, when allocation to a targeted therapy arm was decided.

Article
Treatment and follow-up
The experimental arm included eight drugs in SAFIR02-BREAST (capivasertib (AZD5363) 960 mg, 4 days on/3 days off; vistusertib (AZD2014)
100 mg, continuous dosing; AZD8931 80 mg, continuous dosing;
AZD4547 160 mg, 2 weeks on/1 week off; olaparib 600 mg, continuous
dosing; selumetinib 150 mg, continuous dosing; bicalutamide 150 mg,
continuous dosing; vandetanib 300 mg, continuous dosing); and one
combination (alpelisib 300 mg d–1 combined with fulvestrant 500 mg
monthly) in SAFIR-PI3K. Recommendations for dose reduction arereported in V1 of the SAFIR02-BREAST protocol (Supplementary Data 3)
and in the SAFIR-PI3K protocol (Supplementary Data 5). Treatments
were given until either progressive disease or unacceptable toxicity,
as defined by the investigator. In the control arm, the investigator continued the chemotherapy given during the first six to eight cycles. The
plan was to continue chemotherapy until either disease progression or
unacceptable toxicity. The decision to stop chemotherapy was given at
the discretion of the investigator. The patient was censored if a switch
was performed from investigational drug or chemotherapy to anotherdrug in the maintenance setting (with the exception of tamoxifen or
aromatase inhibitors in patients with endocrine-resistance HR+ mBC).
Matched drug–genomic alterations, together with ESCAT ranking andnumbers of patients, are reported in Table 1. An assessment of drug
efficacy was done every 6 weeks for the first 6 months, and thereafter
every 9 weeks. Response was defined according to RECIST1.1 criteria.
Based on recommendations from the IDMC, clinical progressions werealso included as events for primary endpoint.
Statistical analyses
The primary objective of the SAFIR02-BREAST trial was to show thattargeted therapies guided by genomic analysis improve PFS as compared with standard maintenance using cytotoxic therapy. The primary endpoint of the SAFIR02-BREAST trial is PFS in a pooled analysis
of SAFIR02-BREAST and SAFIR-PI3K protocols. The initial protocol(Supplementary Data 3) was aimed at comparison of genomic-driven
targeted therapies with maintenance chemotherapy in the ITT population. This protocol was amended in October 2020 following therelease of the ESCAT ranking system, to perform hierarchical testing
as described below (approved by CPP on 23 December 2020 (no. 201309-07 MS17), ANSM on 13 November 2020 (no. 2013-001652-36 MS17)
and the IDMC). ESCAT I/II alterations were those reported in 2019 by
Condorelli et al.28, updated with Tung et al.29 for PALB2 and somatic
BRCA1/2 alterations (ESCAT II)29.
Comparisons of PFS between arms were planned to be tested witha hierarchical fixed-sequence procedure in prespecified populations:step 1 in the ESCAT I/II population with a two-sided level of 10% significance, followed by step 2 in the ITT population at a two-sided level of
5% significance. Statistical significance was required at step 1 before
formal testing of step 2, otherwise comparison in the ITT population
was considered exploratory. Full details about the hierarchical procedure are provided in Supplementary Data (protocol and statisticalanalysis plan, Supplementary Data 4).
For the primary objective of PFS in the ESCAT I/II population, 85 eventsof tumour progression or death would be needed in this subgroup ofpatients (with 90% power, a two-sided significance level of 10% and a
2:1 randomization ratio) to detect a HR of 0.51 (increase in median PFS
from 3.00 to 5.88 months). For the ITT population, 205 events of tumour
progression or death would be needed (with 80% power, a two-sided
significance level of 5% and a 2:1 randomization ratio) to detect a HRof 0.66 (increase in median PFS from 3.00 to 4.54 months). To observe
the number of events required, we estimated that the trial needed torandomize 240 patients with at least 110 in ESCAT I/II categories. Basedon PFS observed during the chemotherapy phase and the number ofgenomic abnormalities identified in SAFIR01 (ref. 6), we planned toscreen 1,460 patients to achieve these objectives.

Primary endpoints were planned for analysis on the ESCAT I/II and
ITT populations (targeted substudy 1) when the required number of
events had been reached (n = 85 for ESCAT I/II and n = 205 for overall
population). In agreement with the IDMC, it was decided to analyse theprimary endpoint of the study after 85 events in ESCAT I/II and after
195 events in the ITT population. Treatment allocation was performed
using the minimization method as implemented in the randomization
module of eCRF (ennov Clinical).
The primary endpoint (PFS) and secondary endpoint (overall survival(OS)) were analysed with a Cox regression model, adjusted for the variables used for stratification of randomization (line of chemotherapy,
disease status at randomization and group of genomic alteration). The
magnitude of the treatment effect was estimated with the adjusted
HR and its 90% CI on the ESCAT I/II population and 95% CI on the ITT
population. The Kaplan–Meier approach was used to estimate survival
rates for each treatment arm. For subgroup analyses, treatment differences were tested using the log-rank test and HR estimated with an
unadjusted Cox proportional-hazards model. Statistical significanceof the interaction between treatment effect and a covariate was tested
using a Cox proportional-hazards model fitted with the covariate, thetreatment arm and an interaction term between the treatment arm andcovariate. For exploratory biomarker analyses regarding TP53, HRD
and PIK3CA mutations, OS was defined as the time from inclusion todeath and was estimated using the Kaplan–Meier method. Univariable
and multivariable analyses were performed using the log-rank test andCox proportional-hazards model, respectively. Factors with P ≤ 0.2 inunivariable analysis were included in multivariable analysis. Statistical analyses were carried out using Stata software v.16 (StataCorp).

Reporting summary
Further information on research design is available in the Nature
Research Reporting Summary linked to this article.

Data availability
Genomic data and modalities for access are available at
EGAS00001005584 and https://nextcloud.gustaveroussy.fr/s/JXLt7taZs8EtBF7.
23. Bachelot, et al. Durvalumab compared to maintenance chemotherapy in metastatic
breast cancer: the randomized phase II SAFIR02-BREAST IMMUNO trial. Nat. Med. 27,
250–255 (2021).
24. Commo, F. et al. rCGH: a comprehensive array-based genomic profile platform for
precision medicine. Bioinformatics 32, 1402–1404 (2016).
25. Davies, H. et al. HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on
mutational signatures. Nat. Med. 23, 517–525 (2017).
26. Telli, M. L. et al. Homologous recombination deficiency (HRD) score predicts response toplatinum-containing neoadjuvant chemotherapy in patients with triple-negative breast
cancer. Clin. Cancer Res. 22, 3764–3773 (2016).
27. Carene, D. et al. Association between FGFR1 copy numbers, MAP3K1 mutations, andsurvival in axillary node-positive, hormone receptor-positive, and HER2-negative earlybreast cancer in the PACS04 and METABRIC studies. Breast Cancer Res. Treat. 179,
387–401 (2020).
28. Condorelli R et al, Genomic alterations in breast cancer: level of evidence foractionability according to ESMO Scale for Clinical Actionability of molecular Targets(ESCAT). Ann. Oncol. 30, 365–373 (2019)29. Tung NM et al, TBCRC 048: Phase II Study of Olaparib for Metastatic Breast Cancer andMutations in Homologous Recombination-Related Genes. J. Clin. Oncol. 38, 4274–4282(2020)
Acknowledgements We thank the patients and their families, as well as the investigators(especially V. Massard, T. l’Haridon, L. Venat-Bouvet, F. Del Piano, X. Tchiknavorian, N. Dohollou
and C. Alliot) and their staff involved in SAFIR02-BREAST and SAFIR-PI3K. We also thank the
UNICANCER team involved in both trials and C. Honfo Ga for data management. We also thankmembers of the IDMC (N. Turner, S. Loibl, S. Novello, E. Felip, S, Litiere and D. Beltram). The
study was funded by Fondation ARC, AstraZeneca, the IHU-B programme (PRISM) and theBreast Cancer Research Foundation. AstraZeneca also supported targeted therapy(capivasertib, AZD2014, AZD8931, AZD4547, olaparib, selumetinib, bicalutamide andvandetanib) supply and distribution to the study sites. Novartis supported the supply oftargeted therapy (alpelisib).
Author contributions F.A., T.B., T.F. and M.J. designed the study. F.A. and T.B. were thecoordinators of the SAFIR02-BREAST trial. F.A. and A.G. were the coordinators of theSAFIR-PI3K trial. M.J. and A.J. operated the trial. T.B., M.A., F.D., M.-P.S., M.C., H.B., C.L.-P., W.J.,
F.C., J.-M.F., C.L.-P., M.-A.M.R., J.-C.T., N.I., A. Mege, P.B., B.Y., N.H. and A.G. included patients inthe trial. I.B., L.L., E.R., S.B., V.A., I.S., M.K., C.L., N.S., P.G., L.L.C. and A. Morel performed
genomic analyses. A.T.-D. collected genomic data and stored them on EGA. F.A., T.B.,
F. Mosele, T.F., A.L., I.B., M.J., F. Montemurro and A.J. wrote the manuscript. All authors haveread and agreed on the content of the manuscript.
Competing interests F.A. received research funding and served as a speaker/advisor(compensated by the hospital) for Pfizer, Roche, Lilly, Daiichi Sankyo, AstraZeneca andNovartis. T.B. received research funding and served as a speaker/advisor (compensatedby the hospital) for Roche, Novartis, Pfizer, Seattle Genetics, Lilly and AstraZeneca.
M.A. received research funding and served as a speaker/advisor (compensated by the hospital)
for Novartis, AstraZeneca, Seattle Genetics, Abvie and Pfizer. M.C. received researchfunding and served as a speaker/advisor (compensated by the hospital) for AstraZeneca,
Novartis, Abbvie, Sanofi, Lilly, Pfizer, Sandoz, ACCORD, G1 Therapeutic, Pierre Fabre
Oncology, Servier, Roche, Daiichi and Gilead. F.D. received research funding and served
as a speaker/advisor (compensated by the hospital) for Roche, Novartis, Lilly, Pfizer, Eisai,
MSD and AstraZeneca. C.L.-P. received research funding and served as a speaker/advisor(compensated by the hospital) for AstraZeneca, Roche and Pfizer. A.G. received researchfunding and served as a speaker/advisor (compensated by the hospital) for AstraZeneca,
Pfizer, Novartis, Roche and MSD. M.-A.M.R. received research funding and served as aspeaker/advisor for Pfizer, Novartis, Lilly, Roche, MSD and Myriad. W.J. received researchfunding and served as a speaker/advisor for AstraZeneca, BMS, Daiichi Sankyo, Eisai,
Lilly France, MSD, Novartis, Pfizer and Roche. B.Y. received research funding and served

as a speaker/advisor for MSD, AstraZeneca, GSK-TESARO, BAYER, Roche-Genentech,ECS Progastrine, Novartis, LEK, Amgen, Clovis Oncology, Merck Serono, BMS, SEAGEN
and Myriad. P.B. served as a speaker/advisor for Roche, BMS, IPSEN, MSD, Bayer, Amgen,
Esai, Janssen Cilag, Pfizer, Novartis and Astellas and received honoraria from Seagen.
N.H. received research funding from Novartis and Pfizer, and served as a consultant for
AstraZeneca, Roche, Novartis and Pfizer. N.I. received research funding and served asa speaker/advisor (compensated by the hospital) for Ipsen and Transgene. J.-C.T. received
research funding and served as a speaker/advisor (compensated by the hospital) for Pfizer
and AstraZeneca. J.-M.F. received research funding and served as a speaker/advisor for
Pfizer and Esai. T.F. consulted for Cellectis (compensated by the hospital). The following
authors declared no competing interests: F. Mosele, L.L., G.E., A. Mege, F.C., M.-P.S.,
I.B., E.R., I.S., V.A., S.B., A. Morel, A.T.-D., A.L., H.B., M.J., A.J., L.L.C., M.K., P.G. and N.S.
C.L. participated in advisory boards for MSD, BMS, Merck Serono, GSK, AstraZeneca,
Nanobiotix, Roche, Rakuten, Seattle Genetics and Celgene. F. Montemurro received
consultancy fees from Roche, AstraZeneca, Daiichi Sankyo, SeaGen, MSD, Eli Lilly,
Pierre Fabre and Novartis, and travel grants from Roche.
Additional information
Supplementary information The online version contains supplementary material available athttps://doi.org/10.1038/s41586-022-05068-3.
Correspondence and requests for materials should be addressed to Fabrice Andre.
Peer review information Nature thanks Alicia Okines and the other, anonymous, reviewer(s)for their contribution to the peer review of this work.
Reprints and permissions information is available at http://www.nature.com/reprints.

Article

Extended Data Fig. 1 | CONSORT diagram of the trial.

Extended Data Fig. 2 | Genomic alterations identification in patients with HR+/Her2- (left panel) or TNBC (right panel). The analysis focuses on the 50 genesthat were included in the first panel and on the copy number analyses.

Article

Extended Data Fig. 3 | Subgroup analysis regarding efficacy of targeted
therapy on Progression free survival, in patients presenting an ESCAT I/II
alteration. The figure reports unadjusted Hazard Ratio (diamonds) and 90%
confidence intervals (error bars) estimated using a Cox proportional hazard
model in each subgroup for progression or death according to clinical andbiological variables. P-value for interaction between treatment arm and eachvariable was estimated using a Cox proportional hazard model fitted with thetreatment arm, the variable and an interaction term between treatment armand variable. All statistical tests were two sided. No adjustment was made formultiple comparisons. *: A: tyrosine kinase, B: PI3K/mTOR pathway, C: MEK
pathway, D: DNA repair.

Extended Data Fig. 4 | Subgroup analysis regarding efficacy of targeted
therapy on progression free survival, in the intent-to-treat population. The
figure reports unadjusted Hazard Ratio (diamonds) and 95% confidence intervals(error bars) estimated using a Cox proportional hazard model in each subgroupfor progression or death according to clinical and biological variables. P-value forinteraction between treatment arm and each variable was estimated using a Cox
proportional hazard model fitted with the treatment arm, the variable and aninteraction term between treatment arm and variable. All statistical tests were
two sided. No adjustment was made for multiple comparisons. *: A: tyrosine
kinase, B: PI3K/mTOR pathway, C: MEK pathway, D: DNA repair.