# Comprehensive Genome Profiling in Patients With Metastatic Non-Small Cell Lung Cancer: The Precision Medicine Phase II Randomized SAFIR02-Lung Trial

Fabrice Barlesi, Pascale Tomasini, Maryam Karimi, Stefan Michiels, Judith Raimbourg, Catherine Daniel, Henri Janicot, Anne Madroszyk, Clarisse Audigier-Valette, Elisabeth Quoix, et al.

► To cite this version:

Fabrice Barlesi, Pascale Tomasini, Maryam Karimi, Stefan Michiels, Judith Raimbourg, et al. Comprehensive Genome Profiling in Patients With Metastatic Non-Small Cell Lung Cancer: The Precision Medicine Phase II Randomized SAFIR02-Lung Trial. Clinical Cancer Research, 2022, 28 (18), pp.4018-4026. 10.1158/1078-0432.CCR-22-0371. hal-04084975

HAL Id: hal-04084975

https://amu.hal.science/hal-04084975v1

Submitted on 5 Jun 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1 Comprehensive genome profiling in patients with metastatic non-small cell lung cancer:

2 the precision medicine phase 2 randomized SAFIR02-Lung trial

3

4 Running title: Phase 2 randomized SAFIR02-Lung trial

5

6 Authors

7 Fabrice BARLESI¹,², Pascale TOMASINI², Maryam KARIMI³,⁴, Stefan MICHIELS³,⁴,
8 Judith RAIMBOURG⁵, Catherine DANIEL⁶, Henri JANICOT⁷, Anne MADROSZYK⁸,
9 Clarisse AUDIGIER-VALETTE⁹, Elisabeth QUOIX¹⁰, Julien MAZIERES¹¹, Denis MORO-
10 SIBILOT¹², Eric DANSIN¹³, Olivier MOLINIER¹⁴, Hugues MOREL¹⁵, Eric PICHON¹⁶,
11 Alexis CORTOT¹⁷, Josiane OTTO¹⁸, François CHOMY¹⁹, Pierre-Jean SOUQUET²⁰, Nicolas
12 CLOAREC²¹, Etienne GIROUX LEPRIEUR²², Ivan BIECHE²³, Ludovic LACROIX²⁴,
13 Sandrine BOYAULT²⁵, Valery ATTIGNON²⁵, Isabelle SOUBEYRAN²⁶, Alain MOREL²⁷,
14 Alicia TRAN-DIEN²⁸, Alexandra JACQUET²⁹, Filippo Gustavo DALL'OLIO¹, Marta
15 JIMENEZ²⁹, Jean-Charles SORIA¹, Benjamin BESSE¹

16

17 1. Department of Medical Oncology, Gustave Roussy, Villejuif, FRANCE;

18 2. Aix Marseille University, CNRS, INSERM, CRCM, APHM, Multidisciplinary Oncology & Therapeutic Innovations Department, Marseille, FRANCE;

19 3. Office of Biostatistics and Epidemiology, Gustave Roussy, Villejuif, FRANCE;

20 4. Oncostat U1018, Inserm, University Paris-Saclay, labeled Ligue Contre le Cancer, Villejuif, FRANCE

21 5. Department of Medical Oncology, ICO- Centre René Gauducheau, Nantes, FRANCE;

22 6. Department of Medical Oncology, Institut Curie, Paris, FRANCE;

23 7. Department of Medical Oncology, Centre Hospitalier Universitaire de Clermont-Ferrand - Hôpital Gabriel Montpied, Clermont-Ferrand, FRANCE;

24 8. Department of Medical Oncology, Institut Paoli-Calmettes, Marseille, FRANCE ;

25 9. Department of Medical Oncology, CHI de Toulon - Hôpital Sainte-Musse, Toulon, FRANCE ;

26 10. Department of Pneumology, CHRU Strasbourg - Nouvel Hôpital Civil, Strasbourg, FRANCE;

27 11. Centre Hospitalier Universitaire de Toulouse - Hôpital Larrey, Toulouse, FRANCE;

28 12. Department of Physiology and Pneumology, CHU de Grenoble-Alpes, Grenoble, FRANCE

29 13. Department of Medical Oncology, Centre Oscar Lambret, Lille, FRANCE ;

30 14. Department of Pneumology, CH du Mans, Le Mans, FRANCE ;

31 15. Department of Pneumology and thoracic oncology, CHR d'Orléans, Orléans, FRANCE;

32 16. Department of Pneumology, Hôpital Bretonneau, Tours, FRANCE ;

33 17. Univ. Lille, CHU Lille, Lille, FRANCE;

34 18. Department of onco-pneumology, Centre Antoine Lacassagne Nice, FRANCE;

35 19. Department of Medical Oncology, Institut Bergonié, Bordeaux, FRANCE;

36 20. Department of Pneumology, CH de Lyon Sud, Lyon, FRANCE;

37 21. Department of Onco-Hematology, Centre Hospitalier Henri Duffau, Avignon, FRANCE;

38 22. Department of onco-pneumology, Hôpital Ambroise Paré, Boulogne Billancourt, FRANCE;

39 23. Department of Genetics, Institut Curie and University of Paris, Paris, FRANCE,

40 24. Department of Medical Biology and Pathology, BMO unit, AMMICa UMS3655/US23, Gustave Roussy cancer campus, Villejuif, FRANCE.
47

48

49

50

51

52

53

54

55

56

57

Correspondence to

58

59

60

61

62

63

64

65

66

67

**Competing Interests statement:**

68

69

70

71

Fabrice BARLESI received personal fees from Astra-Zeneca, Bayer, Bristol-Myers Squibb, Boehringer-Ingelheim, Eli Lilly Oncology, β. Hoffmann-La Roche Ltd, Novartis, Merck, MSD, Pierre Fabre, Pfizer and Takeda.

72

73

74

75

76

Pascale TOMASINI received Consulting fees from Roche, AZ, Takeda, BMS, AbbVie and also Payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Roche, AZ, Takeda, BMS, AbbVie. She received Supports for attending meetings and/or travel from AZ, BMS, Takeda.

77

78

Maryam KARIMI: NO DISCLOSURE

79

80

81

Stefan MICHIELS has Participated on a Data Safety Monitoring Board for Sensorion, Biophytis, Servier, Yuhan

82

83

Judith RAIMBOURG: NO DISCLOSURE
84

85 Catherine DANIEL: NO DISCLOSURE

86

87 Henri JANICO received supports for attending meetings from Boehringer and MSD

88

89 Anne MADROSZYK: NO DISCLOSURE

90

91 Clarisse AUDIGIER-VALETTE received payment for expert testimony from ROCHE AMGEN AZ

92 MSD BMS PFIZER LILLY TAKEDA ABBVIE and supports for ate,di, meetings from ROCHE

93 AMGEN AZ MSD BMS PFIZER LILLY TAKEDA ABBVIE

94

95 Elisabeth QUOIX received payements (speaker's Bureau) from Chigaï and BMS, supports for

96 attending meeting from BMS, Roche, Takeda

97

98 Julien MAZIERES received Personal fees from Roche, Astra Zeneca, Pierre Fabre, Takeda, BMS,

99 MSD, Hengrui, personal fees BLUEPRINT, DAIICHI, Novartis.

100

101 Denis MORO-SIBILOT received grants for Roche, AstraZeneca and Boehringer; payments from

102 BMS, MSD, AstraZeneca, Roche, Pfizer, takeda, Boehringer, Lilly

103

104 Eric DANSIN: NO DISCLOSURE

105

106 Olivier MOLINIER has Participated on a Data Safety Monitoring Board or Advisory Board

107 for Astra Zeneca, BMS, TAKEDA, MSD.

108 Hugues MOREL received Support for attending meetings and/or travel from BOEHRINGER

109 and has participated on a Data Safety Monitoring Board or Advisory Board for TAKEDA

110 Eric PICHON received Payment or honoraria from Roche, Takeda, BMS, AstraZeneca; Support for

111 attending meetings from Takeda, BMS

112

113 Alexis CORTOT received Grants or contracts from Astra-Zeneca, Roche, Merck, Novartis;

114 Consulting fees from Astra-Zeneca, MSD, Novartis, Roche; honoraria from Astra-Zeneca,

115 BMS, MSD, Pfizer, Novartis, Takeda, Roche, Johnson & Johnson; Support for attending
116 meetings and/or travel from Astra-Zeneca, BMS, MSD, Pfizer, Novartis, Takeda, Roche. Ha
117 has participated to Data safety Monitoring Board or Advisory Board for Astra-Zeneca, BMS,
118 MSD, Pfizer, Novartis, Takeda, Roche, Johnson & Johnson

119 Josiane OTTO: NO DISCLOSURE

120

121 François CHOMY received payments from AstraZeneca, BMS, Takeda

122

123 Pierre-Jean SOUQUET received Grants or contracts, Consulting fees, honoraria, Support for attending meetings from AstraZeneca and has participated on a Data Safety Monitoring Board or Advisory Board for AstraZeneca

126 Nicolas CLOAREC: NO DISCLOSURE

127

128 Etienne GIROUX LEPRIEUR received Grants or contracts from BMS; Payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from
129 AstraZeneca, Bristol-Myers-Squibb, MSD, Roche; Support for attending meetings and/or
130 travel from AstraZeneca, Bristol-Myers-Squibb, MSD, Roche, Takeda. He has participated
131 on a Data Safety Monitoring Board or Advisory Board for AstraZeneca, Bristol-Myers-
132 Squibb, Novartis, Roche, Takeda a,d have had a fiduciary role for IFCT (unpaid)
133

134

135 Ivan BIECHE: NO DISCLOSURE

136

137 Ludovic LACROIX: NO DISCLOSURE

138

139 Sandrine BOYAULT: NO DISCLOSURE

140

141 Valery ATTIGNON: NO DISCLOSURE

142

143 Isabelle SOUBEYRAN: NO DISCLOSURE

144

145 Alain MOREL has participated to an Advisory board for French National Cancer Institute (INCa)
146

147 Alicia TRAN-DIEN: NO DISCLOSURE

148

149 Alexandra JACQUET: NO DISCLOSURE

150

151 Filippo Gustavo DALL'OLIO: NO DISCLOSURE

152

153 Marta JIMENEZ: NO DISCLOSURE

154

155 Jean-Charles SORIA In the last 2 years consultancy fees and shares from:Relay Therapeutics;

156 Gritstone bio. Board of Directors Hookipa Pharmaceuticals 2018-2021. Full time employee at

157 AstraZeneca 2017-2019; Full time employee at Amgen August 2021-present

158 Benjamin BESSE received Sponsored Research at Gustave Roussy Cancer Center from

159 4D Pharma, Abbvie, Amgen, Aptitude Health, AstraZeneca, BeiGene, Blueprint Medicines,

160 BMS, Boehringer Ingelheim, Celgene, Cergentis, Cristal Therapeutics, Daiichi-Sankyo, Eli

161 Lilly, GSK, Inivata, Janssen, Onxeo, OSE immunotherapeutics, Pfizer, Roche-Genentech,

162 Sanofi, Takeda, Tolero Pharmaceuticals.

163

164

165
166 **Translational relevance**

167 To our knowledge, UNICANCER SAFIR02-Lung / IFCT 1301 study is the first randomized

168 phase 2 trial to evaluate the use of NGS-based molecular profiling to guide treatment

169 selection for switch maintenance therapy in metastatic NSCLC. SAFIR 02-Lung study

170 demonstrated that serial collection of tissue biopsies and blood samples followed by NGS

171 analysis is feasible in a large number of centers, including both large university hospitals as

172 well as smaller community hospitals. This study highlighted that non-EGFR, non-ALK NGS-

173 guided maintenance therapy is feasible, albeit it did not lead to a substantial clinical benefit

174 beyond durvalumab for PD-L1 positive patients.

175
176 **Abstract**

177

178 **Purpose:** Targeted therapies (TT) and immune checkpoint blockers (ICB) have
179 revolutionized the approach to non-small cell lung cancer (NSCLC) treatment in the era of
180 precision medicine. Their impact as switch maintenance therapy based on molecular
181 characterization is unknown.

182 **Experimental design:** SAFIR02-Lung was an open-label, randomized, phase 2 trial,
183 involving 33 centers in France. We investigated eight TT (substudy-1) and one ICB
184 (substudy-2), compared to standard-of-care as a maintenance strategy in advanced EGFR,
185 ALK wild-type (wt) NSCLC patients without progression after first line chemotherapy, based
186 on high-throughput genome analysis. The primary outcome was progression-free survival
187 (PFS).

188 **Results:** Among the 175 patients randomized in substudy-1, 116 received TT (selumetinib, 189 vistusertib, capivasertib, AZD4547, AZD8931, vandetanib, olaparib, savolitinib) and 59 190 standard-of-care. Median PFS was 2.7 months (95% CI 1.6-2.9) with TT vs. 2.7 months (1.6- 191 4.1) with standard-of-care (HR 0.97, 0.7-1.36; p=0.87). There were no significant differences 192 in PFS within any molecular subgroup. In substudy-2, 183 patients were randomized, 121 193 received durvalumab and 62 standard-of-care. Median PFS was 3.0 months (2.3-4.4) with 194 durvalumab versus 3.0 months (2.0-5.1) with standard-of-care (HR=0.86; 0.62-1.20; 195 p=0.38). Preplanned subgroup analysis showed an enhanced benefit with durvalumab in 196 patients with PD-L1 Tumor Proportion Score (TPS) ≥ 1%, (n=29, HR=0.29; 0.11-0.75) as 197 compared to PD-L1 <1% (n=31, HR=0.71, 0.31-1.60, interaction p= 0.036).

198 **Conclusions:** Molecular profiling can feasibly be implemented to guide treatment choice for
199 the maintenance strategy in EGFR/ALK wt NSCLC; in the current study it did not lead to
200 substantial treatment benefits beyond durvalumab for PD-L1 ≥ 1 patients.
201

202 **Clinical trial registration:** NCT02117167

203

204

205

206
207 **Introduction**

208

209 The advent of precision medicine has dramatically revolutionized the landscape of cancer

210 treatment¹. With an increasing number of molecular alterations susceptible to targeted

211 treatment, next-generation sequencing (NGS) is progressively replacing sequential strategies

212 in several cancer types, including non-small cell lung cancer (NSCLC), as it allows

213 sequencing of a high number of nucleotides in a short timeframe at an affordable cost².

214 Several studies have confirmed the feasibility of implementing NGS in therapeutic decision-

215 making in advanced cancer patients. In the SAFIR01 trial³, a targetable abnormality was

216 identified in 69% of 251 breast cancer patients for whom high throughput genome analysis

217 was performed. In the MOSCATO trial, a molecular portrait with NGS found an actionable

218 alteration in 411 out of 843 patients with various advanced cancers, and 199 of them received

219 a targeted therapy matched to a genomic alteration. Whilst the objective response rate (ORR)

220 of 11% reported in the MOSCATO study does not reach the impressive antitumor results

221 seen with EGFR tyrosine kinase inhibitors and ALK and ROS1 inhibitors, nonetheless

222 improved outcomes were reported in a subset of patients⁴.

223 Maintenance therapy either with the same agent used during induction phase (continuation

224 maintenance) or with different agent (switch maintenance) is an option for patients with

225 unselected advanced NSCLC, after 4 cycles of first-line platinum-based chemotherapy.

226 Pemetrexed⁵, gemcitabine⁶ or targeted therapy such as erlotinib⁷ has been shown to delay

227 recurrence in this setting. To date, the role of targeted agents and immune checkpoint

228 blockers (ICBs) in molecularly-selected NSCLC patients as maintenance treatment based on

229 a broad NGS panel, has not been addressed. We performed a prospective phase 2 randomized

230 study to evaluate the use of NGS-based molecular profiling to guide treatment selection for

231 maintenance therapy in this metastatic NSCLC.
232 Methods

233 Trial design and molecular profiling

234 SAFIR02-Lung is a French phase 2 interventional randomized open-label trial comparing
235 several targeted therapies in NSCLC patients with a corresponding actionable genomic
236 alteration and immunotherapy in patients who do not present an actionable genomic alteration
237 found with centralized molecular analysis or have contraindication to targeted agents as
238 decided by the molecular tumor board based on the clinical , versus standard of care,
239 following first-line chemotherapy. The first patient was randomized in substudy 1 on July 10,
240 2014 and the last patient on May 7, 2019, and in substudy 2, the first patient was randomized
241 on the January 26, 2016 and the last on April 3, 2019. The SAFIR02-Lung trial is
242 summarized in Figure 1.

243 Eligible patients have EGFR and ALK wildtype chemonaïve NSCLC with metastatic relapse
244 or stage IV disease at diagnosis or stage IIIB disease not amenable to surgery or radiotherapy.
245 For substudy 1, patients had to have a targetable alteration, as identified by the molecular
tumor board, and stable or responding disease after four cycles of chemotherapy. Patients not
246 eligible for substudy 1, including those without a targetable alteration, who had stable or
247 responding disease after four cycles of platinum-based chemotherapy were eligible for
248 substudy 2. In order to identify patient with a targetable alteration, a fresh biopsy prior to the
249 initiation of first-line platinum-based chemotherapy or at least before the third cycle was
250 required. If the biopsy was unusable, an archived formalin-fixed paraffin-embedded (FFPE)
251 biopsy or FFPE cytoblock or frozen biopsy was acceptable. When neither fresh or archived
tissue was suitable for the study (<30% tumor cells for frozen and < 10% for FFPE or
252 insufficient size) and the patient could not undergo a new biopsy (e.g., inaccessible location,
253 bone disease as the sole site, or safety concerns), circulating tumor DNA (ctDNA) on blood
254 samples obtained before third cycle was a tertiary option.
255
256
Array comparative genomic hybridization (CGH) was performed using five platforms either with Affymetrix CytoScan® assays for fresh tumor DNA or Affymetrix OncoScan® assays for FFPE or ctDNA samples. NGS was performed with Ion Torrent PGM, or Illumina MiSeq/MiniSeq, or AmpliSeq technology, using a panel of approximately 70 genes (Supplementary material; Study protocol – Appendix 4).

Copy number variations (CNV) from CytoScan and OncoScan were defined using the R package rCGH (v1.16.0 under R v3.6.3). Log2 relative ratios were calculated before centralization of the profile to set the baseline from which copy number alterations were estimated (two copies being the neutral level). Break points in the log2 relative ratio continuity were identified by profile segmentation. These segments were used to determine a potential gain or loss (scale in copy number (CN) CN=0: homozygous deletion – CN=1: loss – CN=2: copy neutral – CN=3-4: gain – CN=5 or more: amplification)

All cases were discussed during a bi-monthly national molecular tumor board meeting.

Eligible patients were allocated to substudy 1 according to the protocol (Supplementary material; Study protocol – Appendix 5 and 6).

272

273 Randomization

In substudy 1, patients with an actionable alteration corresponding to categories A to D were randomized in a 2:1 ratio using minimization to the corresponding targeted treatment or standard of care. All other patients were randomized in a 2:1 ratio to substudy 2, to receive durvalumab or standard of care, irrespective of their PD-L1 status, using minimization.

For substudy 1, randomization was stratified by histological subtype (squamous vs. non-squamous), by chemotherapy tumor response (stable disease vs. tumor response), by smoker/non-smoker, and by molecular alteration category (defined as Category A: HER2 or RET aberration or FGFR1 amplification OR FGFR2 mutation OR FGFR3 mutation or any
282 other aberration that is not in B, C, D; Category B: no HER2, RET, FGFR1-2-3, LKB1

283 aberration but KRAS OR BRAF mutation; Category C: no HER2, RET, FGFR1-2-3, LKB1,

284 KRAS, BRAF aberration but PIK3CA mutation OR PIK3CA amplification OR PTEN loss OR

285 PTEN mutation OR AKT1 mutation; Category D: no HER2, RET, FGFR1-2-3, KRAS, BRAF,

286 PIK3CA, PTEN, AKT1, aberration but LKB1 mutation). For substudy 2, randomization was

287 stratified by histological subtype (squamous vs. non-squamous), by tumor response (stable

288 disease vs. tumor response), and by smoker/non-smoker status.

289 The trial was approved by the local ethics committee (CPP Ile de France 2 on 9 November

290 2013; 2013-08-04) and the French health authorities (17 October 2013; 130975A-12), and

291 was conducted in accordance with the Declaration of Helsinki, current International

292 Conference on Harmonization guidelines and all applicable regulatory and ethical

293 requirements. All patients provided written informed consent for biopsies, randomization and

294 use of their biological samples for research purposes. The study protocol and statistical

295 analysis plan are available as Supplementary Information.

296

297 **Treatment and follow-up**

298 Eligible patients received four cycles of platinum-based chemotherapy according to local

299 standard practice. In the interventional part of the study, patients without disease progression

300 were randomized and treated either with matched targeted therapy, durvalumab or standard

301 maintenance therapy until disease progression or unacceptable toxicity. The following

302 targeted therapies were administered in substudy 1: selumetinib 75 mg twice daily (bid),

303 vistusertib 50 mg bid, capivasertib 480 mg bid, AZD4547 80 mg bid, AZD8931 40 mg bid,

304 vandetanib 300 mg once daily (qd), olaparib 300 mg bid, savolitinib 600 mg qd (or 400 mg

305 qd for patients with body weight less than 50 kg) or vemurafenib 960 mg bid plus

306 cobimetinib 60 mg qd; in substudy 2, durvalumab (10 mg/kg every 2 weeks) was used.
307 Treatment efficacy was monitored by a computed tomography scan every six weeks during
308 the initial six months after randomization to maintenance, and every nine weeks thereafter.

309

310 **Outcomes**

311 In both substudies, the primary endpoint was progression-free survival (PFS), defined as the
312 time from randomization until the date of objective radiological disease progression (assessed
313 via RECIST 1.1 8), clinical progression or death (by any cause in the absence of disease
314 progression) regardless of whether the patient withdraws from therapy or receives another
315 anticancer therapy prior to disease progression. Patients who had not progressed or died at the
316 time of analysis were censored at the time of the latest date of assessment from their last
317 evaluable efficacy assessment. However, if the patient progressed or died after two or more
318 missing visits, the patient was censored at the time of the latest evaluable assessment. The
319 secondary endpoints were overall survival (OS) and Overall response rate (ORR). OS was
320 defined as the time from randomization to death due to any cause. Patients still alive at the
321 time of analysis (including lost to follow-up) were censored at the last known alive date and
322 patients without post-baseline survival information were censored at day one. ORR was
323 defined as the percentage of patients with at least one tumor assessment demonstrating a
324 complete response (CR) or partial response (PR) using RECIST v1.1 criteria.

325 Safety data were graded according to the National Cancer Institute Common Terminology
326 Criteria for Adverse Events (v4.03).

327

328 **Statistical analysis**

329 For substudy 1, the primary objective was to demonstrate that a targeted therapeutic approach
330 guided by genomic analysis (arm A1) improves PFS compared to non-targeted maintenance
331 therapy (arm B1). Assuming exponential survival, in order to detect an increase in median
332 PFS from 4 months (arm B1) to 6 months (arm A1) (hazard ratio [HR] of 0.66) with 80%
333 power at a two-sided significance level of 0.05 using the logrank test and a 2:1 randomization
334 (arm A1: arm B1), 205 events were required. Assuming a 72-month enrollment period with
335 uniform accrual, and 12 months additional follow-up, a total of 230 patients were planned to
336 be randomized.

337 For substudy 2, the primary objective was to demonstrate that maintenance with
338 immunotherapy (durvalumab, arm A2) improves PFS compared to standard maintenance
339 therapy (arm B2). Assuming exponential survival, in order to detect an increase in median
340 PFS from 4 months (Arm B2) to 6.5 months (Arm A2), corresponding to a HR of 0.62, with
341 80% power at a two-sided significance level of 0.05 using the logrank test and a 2:1
342 randomization (arm A2: arm B2), 155 events were required. Assuming a 54-month enrolment
343 period with uniform accrual, and an additional 5.5 months follow-up, a total of 180 patients
344 were planned to be randomized.

345 Efficacy endpoints were analyzed in the intention-to-treat population, defined as all
346 randomized patients analyzed in the treatment group they were assigned to. Safety results
347 were to be summarized by the treatment patients actually received.

348 For each substudy, baseline characteristics (age at randomization, gender, ECOG
349 performance status, histological subtype, stage, number of metastatic sites at screening, liver
350 metastasis status, bone metastasis status) were summarized by treatment arm using
351 descriptive statistics. Continuous variables were summarized using median and QR (1st; 3rd
352 quartiles). Qualitative variables were summarized using counts and percents.

353 For each substudy and for PFS and OS, a Cox regression model was used to compare the
354 progression-free survival and overall survival between the two treatment arms, adjusted for
355 the stratification factors from the randomization. An adjusted hazard ratio (HR) between the
356 two treatment arms (ie, the magnitude of treatment effect) was estimated by this model and
its 95% confidence interval (CI). The Kaplan-Meier approach was used to estimate survival rates for each treatment arm. For substudy 1, each individual experimental drug was compared separately to standard maintenance therapy using Kaplan-Meier approach and log-rank test including only those patients that were oriented to that particular drug.

Preplanned subgroups were PDL1≥1% vs PDL1<1% (only for substudy 2), histological subtype (squamous vs nonsquamous), smoking status (smoker/non-smoker), initial tumor response after chemotherapy (Stable disease vs Tumor response), molecular alterations (categories A, B, C, D from randomization), age (<65, 65-74 and ≥75 years old), gender, performance status (ECOG score 0 vs ≥1), presence of brain metastasis, number of metastases (≤3 vs >3) and presence of KRAS mutation (only for substudy 2). Forest plots were used for visual representation, and test for interactions. One unplanned subgroup analysis was performed in substudy 2 according to the presence of copy number alterations.

Overall response rate was assessed by treatment arm and the comparison between arms was performed using Chi-square or Fisher's exact test. A two-sided significance level of 0.05 was applied separately for each substudy. All statistical analyses were performed using SAS.

### Role of the funding source

The study funders did not play any role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication. They provided usual information regarding the adequate use of their products and patients' selection in the study.

### Data sharing statement:

The data generated in this study are available upon request from the sponsor (Unicancer).
382 **Results**

383 Overall, 999 patients were included in SAFIR02-Lung in 33 centers across France between
384 July 2014 and May 2019, and molecular profiling was successful in 863. All 863 cases were
385 discussed in the study's molecular tumor board, while 138 were excluded (n= 48: no biopsy
386 performed or procedure failure; n = 88: non usable sample as <30% tumor cells or fragment
387 too small). Out of 394 patients with a molecular alteration eligible for inclusion in substudy
388 1, 219 resulted not eligible to randomization due to progression (n = 118), death (n = 23),
389 patient decision (n = 11) and other (n = 67), while out of the 365 patients included in
390 substudy 2, 182 were not eligible for randomization due to progression (n = 99), death (n =
391 14), patient decision (n = 10) and other reasons (n = 59). Figure 1 presents a CONSORT
392 diagram according to the two substudies. Substudy 1 was prematurely closed due to slow
393 recruitment.

394

395 **Impact on survival of matched targeted therapy as maintenance therapy**

396 175 patients were randomized in substudy 1 following chemotherapy (Category A, n=48;
397 Category B, n=99; Category C, n=12; Category D, n=16). Table 1 presents baseline
398 characteristics of the randomized patients; 59% were male, 44% had an ECOG performance
399 status of 0, and the majority (87%) had an adenocarcinoma histology. Response to first-line
400 induction chemotherapy was reported in 47 patients (27%). Overall, 116 patients were
401 randomized to targeted treatment (65 selumetinib, 18 vistusertib, 9 capivasertib, 8 AZD4547,
402 6 AZD8931, 5 vandetanib, 4 olaparib, 1 savolitinib) and 59 to standard of care (54
403 pemetrexed, 4 gemcitabine and 1 erlotinib). A median of seven cycles of targeted treatment
404 (IQR 5;14) was administered and four cycles (IQR 2;8) of standard of care.
405 In total, 112 of the 113 (99.1%) patients who received targeted treatment and 56 out of 57
406 (98.2%) treated with standard of care permanently discontinued treatment, with the main
407 reason for discontinuation being disease progression (132 radiological and 5 clinical

408 progression) in 137 patients (81.6%). Representativeness of study participants is summarized

409 in table S1. Adverse events are described in Supplementary material (section 1 of sub-study 1

410 statistical report), and are consistent with previous reports.

411 The median follow-up for PFS was 42.0 months (95% CI 18.8–42.0). At the cut-off date

412 (October 12th, 2020), 168 (96%) patients had progressive disease or had died. The median

413 PFS was 2.7 months (95% CI 1.6–2.9) in patients who received targeted treatment and 2.7

414 months (95% CI 1.6–4.1) with standard of care (adjusted HR= 0.97, 95% CI 0.7–1.36;

415 p=0.87; Figure 2).

416 Median follow-up for overall survival (OS) was 27.1 months (95% CI 26.0–29.6). Overall,

417 115 (66%) patients had died at the cut-off date. Median OS was 14.3 months for targeted

418 treatment (95% CI 11.0–18.3) and 14.1 months (95% CI 8.0–30.9) for standard of care

419 (adjusted HR = 1.03; 95% CI 0.69–1.55; p=0.87; Figure 2).

420 Planned exploratory analyses by study drug for selumetinib, vistusertib and capivasertib did

421 not reveal significant differences between targeted treatment and standard of care in terms of

422 PFS or OS (Supplementary figure 1). Survival curves for AZD8931, AZD4547, olaparib,

423 savolitinib and vandetanib were not estimated due to the small number of patients treated

424 with these drugs. Other planned subgroup analyses for PFS and OS are detailed in the

425 Supplementary material – section 2 of sub study 1 statistical report and supplementary figure

426 2 and 3.

427 Objective response rates were not statistically significantly different between targeted

428 treatment (5.3%) and standard of care (10.5%; p=0.22).

429

430 *Impact on survival of durvalumab as maintenance treatment*
183 patients were randomized in substudy 2, 49 of whom had an response to induction
chemotherapy, 62% were male, 37% had a performance ECOG status of 0, and the majority
(89%) had an adenocarcinoma histology (Table 2). A total of 121 patients were randomized
to durvalumab and 62 to standard of care (57 received treatment, 51 pemetrexed, 5
gemcitabine and 1 other). A median of 8 cycles of durvalumab were administered (IQR 3;
19), versus a median of 4 cycles (IQR 2; 8) of standard of care.

In total, 109 of the 121 (92%) patients who received durvalumab and all 57 (100%) treated
with standard of care permanently discontinued treatment, with the main reason being disease
progression, which was reported in 126 patients (75.9%, 122 radiological and 4 clinical
progression). Adverse events are described in Supplementary material (section 1 of sub-study
2 statistical report), and are consistent with previous reports.

The median follow-up time for PFS was 23.8 months (95% CI 19.6–28.8). At the cut-off
date (February 5th, 2020), 162 (89%) patients had progressive disease or had died. Median
PFS was 3.0 months (95% CI 2.3–4.4) in patients randomized to durvalumab and 3.0 months
(95% CI 2.0–5.1) in patients randomized to standard of care (HR adjusted for stratification
factors = 0.86; 95% CI 0.62–1.20; p=0.38).

Planned subgroup analyses for PFS are provide in the supplementary figure 4. Out of 81
patients for whom PD-L1 expression tested, 29 had positive PD-L1 (≥1%), 31 negative
(<1%) and 21 patients missing measurement. An enhanced benefit with durvalumab vs
standard-of-care was observed in patients with PD-L1 ≥ 1%, (n=29, HR=0.29; 0.11-0.75) as
compared to PD-L1 <1% (n=31, HR=0.71, 0.31-1.60, interaction p= 0.036, Figure 3,
supplementary Figure 4).

Unplanned exploratory analyses for the presence of copy number alterations or a *KRAS*
mutation did not identify enhanced treatment effects (Supplementary material, section 2 in
sub-study 2 statistical report).
456 Median follow up for OS was months 21.6 months (95% CI 19.6–24.3). A total of 107

457 patients had died at the cut-off time (58.5%). Median OS was 17.0 months in patients

458 randomized to durvalumab (95% CI 12.1–19.5) and 14.9 months (95% CI 10.5–22.0) with

459 standard of care (HR adjusted for stratification factors = 0.93; 95% CI 0.61–1.40; p=0.73).

460 Planned subgroup analyses for OS are provide in the supplementary figure 5.

461 Exploratory subgroup analysis in terms of OS found a stronger benefit for durvalumab

462 compared to standard of care arm in PD-L1 ≥ 1% patients HR= 0.32 (95% CI 0.12–0.83) as

463 compared to PD-L1 ≤ 1 patients HR= 1.20 (95% CI 0.48–2.99, interaction p =0.039, Figure

464 3, supplementary figure 5). No significant difference was identified in terms of ORR (14.3%

465 for durvalumab vs 3.6% for standard of care, p=0.10).

466

467 Discussion
468

469 Until recently, platinum-based chemotherapy administered for four cycles followed by

470 maintenance treatment has been considered as the standard frontline treatment for advanced

471 NSCLC. This evolved with the advent of chemo-immunotherapy combinations, however to

472 date the use of targeted agents or ICB selected on the basis of a broad NGS panel to guide the

473 maintenance therapy for advanced NSCLC has not been reported. Our study showed that

474 maintenance treatment selected this way, either targeted therapy or immune-checkpoint

475 blockade, did not result in any improvement over standard of care, albeit a signal of immune

476 checkpoint blockade activity was seen in patients with PD-L1 ≥ 1% tumors. It is important to

477 highlight that overall, this study demonstrated that serial collection of tissue biopsies and

478 blood samples followed by NGS analysis is feasible in a large number of centers, including

479 both large university hospitals as well as smaller community hospitals.

480 Other studies have evaluated the efficacy of multigene molecular abnormality screening

481 approaches to personalized therapy in later lines of therapy. In the SAFIR01 trial³ only 30%
482 of the treated breast cancer patients presented an objective response or stable disease. The
483 MOSCATO trial reached its primary objective of evaluating clinical benefit as measured by
484 the percentage of patients presenting PFS on matched therapy that was 1.3-fold longer than
485 PFS on prior therapy⁴. In the SHIVA randomized trial⁹ no significant improvement in PFS
486 was observed in the precision medicine arm compared with the standard of care arm.

487 Substudy 1 evaluating targeted therapies supports the feasibility of NGS for decision-making
488 on a large scale, albeit it did not reach its original planned number of patients or events and
489 did not identify an important benefit of the approach on clinical outcome. Anyway it should
490 be also considered that we excluded *EGFR* and *ALK* positive patients and not all the
491 molecular alterations considered are true oncogenes or are targeted by a selective inhibitor.

492 Finally, current knowledge has progressed since the SAFIR02-Lung study was initiated, and
493 available drugs are more active and could be more promising for further evaluation in this
494 setting. For example, we used vandetanib for *RET* translocation, savolitinib in *MET* exon 14
495 splice site mutation and selumetinib in *KRAS* mutation positive patients, that are no longer
496 considered the treatments of choice in these cancers, and new agents with higher activity are
497 or will soon be available, such as selpercatinib¹⁰, capmatinib¹¹ and sotorasib¹². In particular,
498 sotorasib has recently shown promising activity in a phase 1 trial including pretreated
499 NSCLC patients with a *KRAS* p.G12C mutation, although the ORR and PFS (32.2% and 6.3
500 months, respectively) are not comparable to those seen with EGFR inhibitors in patients with
501 *EGFR* mutations, or with ALK inhibitors in patients with *ALK* rearrangements. A similar
502 rationale could lead to consideration of new agents that could be not enough promising to
503 replace chemoimmunotherapy in the front-line, while the maintenance setting could be an
504 interesting strategy to explore.

505 Our immunotherapy substudy also highlighted that the therapeutic landscape has evolved
506 relative to the time of the study design, with immune checkpoint blockade moving from the
second-line to the first-line treatment following the results of the KEYNOTE-024 study with pembrolizumab monotherapy in patients with PD-L1 ≥ 50%¹³ and KEYNOTE-189¹⁴ and KEYNOTE-407¹⁵ for pembrolizumab in combination with platinum-based chemotherapy for non-squamous and squamous NSCLC.

Moreover, due to the study design such that only those patients who are not suitable for substudy 1 are able to enroll in substudy 2, our population treated with durvalumab vs.

standard of care is highly selected and different subpopulations were excluded from enrollment. For example, patients with KRAS mutation positive cancer were only included in substudy 2 if they met the exclusion criteria for substudy 1, and a recent analysis showed that these patients particularly benefit from ICI¹⁶.

An interesting finding comes from the preplanned subgroup analysis of PD-L1 ≥ 1% patients, in which durvalumab resulted in a significantly larger benefit on PFS and OS compared to standard of care. This is consistent with the available evidences from clinical trials, where the magnitude of benefit obtained with ICB increased proportionally with the increase in PD-L1 TPS¹³,¹⁷,¹⁸. This finding also highlights how precision medicine and biomarker analysis is an important step in selecting the right patients to be treated with new agents.

This strategy could be of interest in patients who are candidates for combination chemo-immunotherapy however there are safety concerns or other reasons for excluding a combination regimen (e.g., pre-existing autoimmune disease) for which it could be difficult to attribute a toxicity to chemotherapy or immunotherapy.

Few patients, including in the US and the majority of European countries, receive the results of frontline NGS testing before starting any treatment, including in the case of all-comer patients receiving combined chemotherapy and immunotherapy. In this context, the SAFIR02-Lung trial illustrates how it could be both feasible and also potentially important,
to identify actionable targets, especially as highly active drugs for *MET*, *RET*, *NTRK*, *KRAS*, and other alterations are now available.

In conclusion, our study showed the feasibility of using NGS for decision-making within the maintenance strategy in NSCLC, albeit it did not translate into an increased clinical outcome as compared with standard of care. Further studies should evaluate new targeted agents in molecularly-selected patients and restrict the use of anti-PD-1/PD-L1 to PD-L1 ≥ 1% patients.

<table><caption>Table 1. Baseline characteristics of patients randomized in substudy 1 (targeted therapy)</caption><thead><tr><th>Variable</th><th></th><th>Total<br>N = 175</th><th>Arm A<br>N=116<br>(targeted treatment)</th><th>Arm B<br>N=59<br>(standard of care)</th></tr></thead><tbody><tr><td rowspan="3">Age at<br>randomiza-<br>tion</td><td>&lt; 65 years</td><td>131 (74.9%)</td><td>91 (78.4%)</td><td>40 (67.8%)</td></tr><tr><td>65-74 years</td><td>39 (22.3%)</td><td>21 (18.1%)</td><td>18 (30.5%)</td></tr><tr><td>&gt; 74 years</td><td>5 (2.9%)</td><td>4 (3.4%)</td><td>1 (1.7%)</td></tr><tr><td rowspan="2">Gender</td><td>Male</td><td>104 (59.4%)</td><td>70 (60.3%)</td><td>34 (57.6%)</td></tr><tr><td>Female</td><td>71 (40.6%)</td><td>46 (39.7%)</td><td>25 (42.4%)</td></tr><tr><td rowspan="3">Smoking<br>habits</td><td>Current</td><td>92 (52.6%)</td><td>64 (55.2%)</td><td>28 (47.5%)</td></tr><tr><td>Former</td><td>75 (42.9%)</td><td>47 (40.5%)</td><td>28 (47.5%)</td></tr><tr><td>Never</td><td>8 (4.6%)</td><td>5 (4.3%)</td><td>3 (5.1%)</td></tr><tr><td rowspan="2">ECOG per-<br>formance<br>status</td><td>0</td><td>77 (44.0%)</td><td>45 (38.8%)</td><td>32 (54.2%)</td></tr><tr><td>1</td><td>98 (56.0%)</td><td>71 (61.2%)</td><td>27 (45.8%)</td></tr><tr><td rowspan="4">Histological<br>subtype</td><td>Adenocarcinoma</td><td>153 (87.4%)</td><td>102 (87.9%)</td><td>51 (86.4%)</td></tr><tr><td>Large cell carci-<br>noma</td><td>1 (0.6%)</td><td>0</td><td>1 (1.7%)</td></tr><tr><td>Squamous cell<br>carcinoma</td><td>20 (11.4%)</td><td>14 (12.1%)</td><td>6 (10.2%)</td></tr><tr><td>Other</td><td>1 (0.6%)</td><td>0</td><td>1 (1.7%)</td></tr></tbody></table>
<table><caption>Table 2. Baseline characteristics of patients randomized in substudy 2 (immunotherapy)</caption><tbody><tr><td rowspan="4">Stage</td><td>IB</td><td>3 (1.7%)</td><td>2 (1.7%)</td><td>1 (1.7%)</td></tr><tr><td>IIIB</td><td>8 (4.6%)</td><td>7 (6.0%)</td><td>1 (1.7%)</td></tr><tr><td>IV</td><td>162 (92.6%)</td><td>106 (91.4%)</td><td>56 (94.9%)</td></tr><tr><td>Unknown</td><td>2 (1.1%)</td><td>1 (0.9%)</td><td>1 (1.7%)</td></tr><tr><td rowspan="3">Number of metastatic sites at screening</td><td>Mean (sd)</td><td>2.9 (1.4)</td><td>2.9 (1.3)</td><td>3.0 (1.6)</td></tr><tr><td>Median (Q1 ; Q3)</td><td>3 (2 ; 4)</td><td>3 (2 ; 4)</td><td>3 (2 ; 4)</td></tr><tr><td>Min ; Max</td><td>0 ; 8</td><td>0 ; 8</td><td>1 ; 7</td></tr><tr><td rowspan="3">Liver metastasis</td><td>0 lesions</td><td>148 (84.6%)</td><td>100 (86.2%)</td><td>48 (81.4%)</td></tr><tr><td>1 lesion</td><td>12 (6.9%)</td><td>8 (6.9%)</td><td>4 (6.8%)</td></tr><tr><td>≥2 lesions</td><td>15 (8.6%)</td><td>8 (6.9%)</td><td>7 (11.9%)</td></tr><tr><td rowspan="4">Bone metastasis</td><td>0 lesions</td><td>109 (62.3%)</td><td>72 (62.1%)</td><td>37 (62.7%)</td></tr><tr><td>1 lesion</td><td>20 (11.4%)</td><td>14 (12.1%)</td><td>6 (10.2%)</td></tr><tr><td>≥ 2 lesions</td><td>43 (24.6%)</td><td>28 (24.1%)</td><td>15 (25.4%)</td></tr><tr><td>Effusion</td><td>3 (1.7%)</td><td>2 (1.7%)</td><td>1 (1.7%)</td></tr></tbody></table>

<table><thead><tr><th></th><th></th><th>Overall N=183</th><th>Arm A N=121 (durvalumab)</th><th>Arm B N=62 (standard of care)</th></tr></thead><tbody><tr><td rowspan="3">Age at randomization</td><td>&lt; 65 years</td><td>113 (61.7%)</td><td>78 (64.5%)</td><td>35 (56.5%)</td></tr><tr><td>65-74 years</td><td>58 (31.7%)</td><td>35 (28.9%)</td><td>23 (37.1%)</td></tr><tr><td>≥ 75 years</td><td>12 (6.6%)</td><td>8 (6.6%)</td><td>4 (6.5%)</td></tr><tr><td rowspan="2">Gender</td><td>Male</td><td>114 (62.3%)</td><td>73 (60.3%)</td><td>41 (66.1%)</td></tr><tr><td>Female</td><td>69 (37.7%)</td><td>48 (39.7%)</td><td>21 (33.9%)</td></tr><tr><td rowspan="3">Smoking habits</td><td>Current</td><td>92 (52.6%)</td><td>64 (55.2%)</td><td>28 (47.5%)</td></tr><tr><td>Former</td><td>75 (42.9%)</td><td>47 (40.5%)</td><td>28 (47.5%)</td></tr><tr><td>Never</td><td>8 (4.6%)</td><td>5 (4.3%)</td><td>3 (5.1%)</td></tr><tr><td rowspan="2">ECOG performance status</td><td>0</td><td>67 (36.6%)</td><td>48 (39.7%)</td><td>19 (30.6%)</td></tr><tr><td>1</td><td>116 (63.4%)</td><td>73 (60.3%)</td><td>43 (69.4%)</td></tr><tr><td rowspan="2">Histological subtype</td><td>Adenocarcinoma</td><td>162 (88.5%)</td><td>107 (88.4%)</td><td>55 (88.7%)</td></tr><tr><td>Large cell carci-</td><td>6 (3.3%)</td><td>5 (4.1%)</td><td>1 (1.6%)</td></tr></tbody></table>

540
541
542
<table><tbody><tr><td></td><td>noma</td><td></td><td></td><td></td></tr><tr><td></td><td>Other</td><td>2 (1.1%)</td><td>1 (0.8%)</td><td>1 (1.6%)</td></tr><tr><td></td><td>Squamous cell carcinoma</td><td>13 (7.1%)</td><td>8 (6.6%)</td><td>5 (8.1%)</td></tr><tr><td rowspan="5">Stage</td><td>IA</td><td>1 (0.5%)</td><td></td><td>1 (1.6%)</td></tr><tr><td>IIB</td><td>1 (0.5%)</td><td>1 (0.8%)</td><td></td></tr><tr><td>IIIA</td><td>1 (0.5%)</td><td>1 (0.8%)</td><td></td></tr><tr><td>IIIB</td><td>10 (5.5%)</td><td>7 (5.8%)</td><td>3 (4.8%)</td></tr><tr><td>IV</td><td>170 (92.9%)</td><td>112 (92.6%)</td><td>58 (93.5%)</td></tr><tr><td rowspan="5">Number of metastatic sites at screening</td><td>Mean</td><td>2.7</td><td>2.8</td><td>2.6</td></tr><tr><td>SD</td><td>1.32</td><td>1.41</td><td>1.10</td></tr><tr><td>Median</td><td>3.0</td><td>3.0</td><td>3.0</td></tr><tr><td>Q1 - Q3</td><td>2 - 3</td><td>2 - 4</td><td>2 - 3</td></tr><tr><td>Min - Max</td><td>1 - 7</td><td>1 - 7</td><td>1 - 6</td></tr><tr><td rowspan="4">Bone</td><td>0 Lesions</td><td>109 (59.6%)</td><td>69 (57.0%)</td><td>40 (64.5%)</td></tr><tr><td>1 Lesion</td><td>23 (12.6%)</td><td>18 (14.9%)</td><td>5 (8.1%)</td></tr><tr><td>≥2 Lesions</td><td>50 (27.3%)</td><td>33 (27.3%)</td><td>17 (27.4%)</td></tr><tr><td>Effusion</td><td>1 (0.5%)</td><td>1 (0.8%)</td><td></td></tr><tr><td rowspan="3">Liver</td><td>0 lesions</td><td>161 (88.0%)</td><td>103 (85.1%)</td><td>58 (93.5%)</td></tr><tr><td>1 lesion</td><td>11 (6.0%)</td><td>9 (7.4%)</td><td>2 (3.2%)</td></tr><tr><td>≥ 2 lesions</td><td>11 (6.0%)</td><td>9 (7.4%)</td><td>2 (3.2%)</td></tr><tr><td rowspan="3">Tumor cell PD-L1 (n = 81)</td><td>&lt; 1%</td><td>31</td><td>20</td><td>11</td></tr><tr><td>≥ 1%</td><td>29</td><td>22</td><td>7</td></tr><tr><td>Missing</td><td>21</td><td>14</td><td>7</td></tr></tbody></table>

543

544

545 Acknowledgements

546 We thank the patients and their families as well as all of the investigators and their staff

547 involved in SAFIR02-Lung trial.

548 We are grateful to the members of the Independent Data Monitoring Committee: Pr Nicholas

549 TURNER, Pr Sibylle LOIBL, Dr Silvia NOVELLO, Dr Enriqueta FELIP, Ms Saskia

550 LITIERE, Ms Delphine BELTRAM.

551 We thank IFCT for its expertise and advice in thoracic oncology throughout the study.
552 This research was conducted with support from Fondation ARC and from an Investigator

553 Sponsored Study Program supported by AstraZeneca. We thank Sarah McKenzie for the

554 editing work.
555 Figure legends

556

557 Figure 1: CONSORT (Consolidated Standards of Reporting Trials) diagram for the SAFIR

558 02-Lung trial

559

560 Figure 2: Progression Free Survival (PFS) and Overall Survival (OS) in substudy 1 and 2.

561 Figure 2a – PFS for targeted therapy vs standard of care in substudy 1. Figure 2b OS for

562 targeted therapy vs standard of care in substudy 1. Figure 2c PFS for durvalumab vs

563 standard of care in substudy 2. Figure 2d OS for durvalumab vs standard of care in substudy

564 2.

565

566 Figure 3

567 Progression Free Survival (PFS) and Overall Survival (OS) for durvalumab vs standard of

568 care in substudy 2 according to PD-L1 expression. Figure 2a – PFS for durvalumab vs

569 standard of care in PD-L1 negative patients. Figure 2b OS for durvalumab vs standard of

570 care in PD-L1 negative patients. Figure 2c PFS for durvalumab vs standard of care in PD-

571 L1 positive patients. Figure 2d OS for durvalumab vs standard of care in PD-L1 positive

572 patients.

573

574

575
576 References

577

578 1. Koeppel F, Bobard A, Lefebvre C, et al. Added value of whole-exome and transcriptome sequencing for clinicalmolecular screenings of advanced cancer patients with solid tumors. *Cancer J (United States)*. 2018;24(4):153-162.
doi:10.1097/PPO.0000000000000322

582 2. Mosele F, Remon J, Mateo J, et al. Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: a report from the ESMO Precision Medicine Working Group. *Ann Oncol.* 2020;31(11):1491-1505.
doi:10.1016/j.annonc.2020.07.014

586 3. André F, Bachelot T, Commo F, et al. Comparative genomic hybridisation array and DNA sequencing to direct treatment of metastatic breast cancer: A multicentre, prospective trial (SAFIR01/UNICANCER). *Lancet Oncol.* 2014;15(3):267-274.
doi:10.1016/S1470-2045(13)70611-9

590 4. Massard C, Michiels S, Ferté C, et al. High-throughput genomics and clinical outcome in hard-to-treat advanced cancers: Results of the MOSCATO 01 trial. *Cancer Discov.* 2017;7(6):586-595. doi:10.1158/2159-8290.CD-16-1396

593 5. Ciuleanu T, Brodowicz T, Zielinski C, et al. Maintenance pemetrexed plus best supportive care versus placebo plus best supportive care for non-small-cell lung cancer: a randomised, double-blind, phase 3 study. *Lancet*. 2009;374(9699):1432-1440. doi:10.1016/S0140-6736(09)61497-5

597 6. Pérol M, Chouaid C, Pérol D, et al. Randomized, phase III study of gemcitabine or erlotinib maintenance therapy versus observation, with predefined second-line treatment, after cisplatin-gemcitabine induction chemotherapy in advanced non-small-cell lung cancer. *J Clin Oncol.* 2012;30(28):3516-3524.
601 doi:10.1200/JCO.2011.39.9782

602 7. Cappuzzo F, Ciuleanu T, Stelmakh L, et al. Erlotinib as maintenance treatment in advanced non-small-cell lung cancer: A multicentre, randomised, placebo-controlled phase 3 study. *Lancet Oncol.* 2010;11(6):521-529. doi:10.1016/S1470-2045(10)70112-1

606 8. Schwartz LH, Litière S, De Vries E, et al. RECIST 1.1 - Update and clarification: From the RECIST committee. *Eur J Cancer.* 2016;62:132-137.

608 doi:10.1016/j.ejca.2016.03.081

609 9. Le Tourneau C, Delord JP, Gonçalves A, et al. Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): A multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. *Lancet Oncol.* 2015;16(13):1324-1334. doi:10.1016/S1470-2045(15)00188-6

613 10. Drilon A, Oxnard GR, Tan DSW, et al. Efficacy of Selpercatinib in RET Fusion-Positive Non-Small-Cell Lung Cancer. *N Engl J Med.* 2020;383(9):813-824. doi:10.1056/nejmoa2005653

616 11. Wolf J, Seto T, Han J-Y, et al. Capmatinib (INC280) in METΔex14 -mutated advanced non-small cell lung cancer (NSCLC): Efficacy data from the phase II GEOMETRY mono-1 study. *J Clin Oncol.* 2019;37(15_suppl):9004-9004. doi:10.1200/jco.2019.37.15_suppl.9004

620 12. Hong DS, Fakih MG, Strickler JH, et al. KRAS G12C Inhibition with Sotorasib in Advanced Solid Tumors. *N Engl J Med.* 2020;383(13):1207-1217. doi:10.1056/NEJMoa1917239

623 13. Reck M, Rodríguez-Abreu D, Robinson AG, et al. Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer. *N Engl J Med.* 2016;375(19):1823-1833. doi:10.1056/NEJMoa1606774
626 14. Gandhi L, Rodríguez-Abreu D, Gadgeel S, et al. Pembrolizumab plus Chemotherapy in Metastatic Non-Small-Cell Lung Cancer. *N Engl J Med*.

628 2018;378(22):NEJMoa1801005. doi:10.1056/NEJMoa1801005

629 15. Paz-Ares L, Luft A, Vicente D, et al. Pembrolizumab plus Chemotherapy for Squamous Non-Small-Cell Lung Cancer. *N Engl J Med*. 2018;379(21):2040-2051.
630
631 doi:10.1056/NEJMoa1810865

632 16. Sun L, Tan M, Cohen RB, Langer CJ, Mamtani R, Aggarwal C. Association Between
633 KRAS Variant Status and Outcomes With First-line Immune Checkpoint Inhibitor-
634 Based Therapy in Patients With Advanced Non-Small-Cell Lung Cancer. *JAMA*
635 *Oncol*. April 2021. doi:10.1001/jamaoncol.2021.0546

636 17. Borghaei H, Paz-Ares L, Horn L, et al. Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. *N Engl J Med*. 2015;373(17):1627-1639.
637
638 doi:10.1056/NEJMoa1507643

639 18. Herbst RS, Baas P, Kim D-WW, et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. *Lancet*. 2016;387(10027):1540-1550. doi:10.1016/S0140-6736(15)01281-7

643

644

645
<figure><img src="image_2.png" /><figcaption>Figure 1</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>Figure 2</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Figure 3</figcaption></figure>