# Disease-free survival as surrogate for overall survival in esophageal cancer: An individual patient data meta-analysis of neoadjuvant chemotherapy and chemoradiotherapy

Nicolas Cabrit, Maurice Cheugoua-Zanetsie, Jayne Tierney, Pierre Thirion, Matthew Nankivell, Kathryn Winter, Hong Yang, Bas Wijnhoven, Dewi Vernerey, B. Mark Smithers, et al.

► To cite this version:

Nicolas Cabrit, Maurice Cheugoua-Zanetsie, Jayne Tierney, Pierre Thirion, Matthew Nankivell, et al. Disease-free survival as surrogate for overall survival in esophageal cancer: An individual patient data meta-analysis of neoadjuvant chemotherapy and chemoradiotherapy. European Journal of Cancer, 2025, 218, 10.1016/j.ejca.2025.115292. hal-05029227

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Submitted on 16 Apr 2025

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ARTICLE INFO

Keywords:

Preoperative

Chemotherapy

Radiotherapy

Esophageal cancer

Gastroesophageal junction

Individual patient data network meta-analysis

Surrogate endpoint

A B S T R A C T

**Background:** The use of surrogate endpoints may expedite the reporting of study outcomes of clinical trials. The validity of disease-free survival (DFS) as a surrogate for overall survival (OS) in the neoadjuvant treatment of esophageal (E) or gastroesophageal junctional (GEJ) carcinomas remains uncertain.

**Objective:** To evaluate DFS as a surrogate end-point for OS in E/GEJ using the meta-analytical approach

**Design, setting, and participants:** individual patient data from an international meta-analysis on operable locally advanced E/GEJ, which including randomized trials comparing at least two of the neo-adjuvant treatment strategies: upfront surgery (S), chemotherapy followed by surgery (CS), and/or chemoradiotherapy followed by surgery (CRS).

**Main outcomes and measures:** Individual (Kendall's tau) and trial-level (R2) correlations between DFS and OS were estimated using a Clayton copula.

**Results:** DFS and OS data were available for a total of 4518 pts: 2222 pts included in CS vs S, 1908 pts in CRS vs S, and 388 in CS vs CRS comparisons. 3440 patients had a DFS event and 3303 patients died. Kendall's tau was 0.73 [95 % CI 0.71 – 0.75] and R2 trial-level correlation was 0.95 [0.84 – 0.99] for CS vs S, Kendall's tau was 0.76 [0.74 – 0.77] and R2 was 0.96 [0.87 – 0.99] for CRS vs S, Kendall's tau was 0.87 [0.78 – 0.92] and R2 was 0.93 [0.43 – 1] for CRS vs CS. In a multistate model, the median time in the recurrence state was shorter in older vs more recent trials: mean time of 10.8 [10.2 – 11.4] vs 16.5 months [15.4–17.6].

**Conclusions and relevance:** DFS is a validated surrogate endpoint for OS in trials evaluating neoadjuvant chemotherapy or chemoradiotherapy in E/GEJ. DFS may be more useful as an endpoint when delays between recurrences and death become larger.

1. Introduction

In 2020, there were worldwide 600,000 new esophageal cancer cases (8th incidence rank) and cancer-related mortality remains high with 544,000 deaths (6th mortality rank) [1]. In locally advanced stages (UICC stage II and III) treatment usually includes neoadjuvant treatment and surgery, but the optimal neoadjuvant treatment remains debated [2, 3]. Two options of neoadjuvant treatment strategies, preoperative chemotherapy or preoperative chemo-radiotherapy have been evaluated, mostly compared to upfront surgery through numerous trials [4,5], and still remain under investigation [6–9], Few trials have compared directly the two neo-adjuvant strategies, but the ESOPEC trial suggests a statistically significant overall survival benefit of pre-operative chemo-therapy over pre operative chemo-radiotherapy in patients with adenocarcinoma [10]. However, both preoperative strategies remain recommended by European and American societies.

In oncology, overall survival is considered the gold standard endpoint for the evaluation of treatment efficacy, but its observation can be cost and time-challenging. The use of a validated surrogate endpoint for overall survival (OS) that is available with shorter follow-up may expedite the reporting of clinical trials and save costs. However, the chosen surrogate outcome must be a robust substitute for the gold-standard clinical outcomes. The highest level of evidence of validation of surrogate endpoints is obtained by individual patient data meta-analysis which allows evaluation of correlation at both the patient and trial levels [11]. In the neoadjuvant treatment of esophageal cancer, the most studied surrogate endpoint for OS is disease-free survival (DFS). Ajani et al. [12] and Petrelli et al. [13] evaluated DFS as a surrogate for OS in esophageal and gastro-esophageal junction cancer but did not use individual patient data, while Ronellenfitsch et al. [14] evaluated DFS as a surrogate for OS using individual patient data from a limited set of highly selected trials in the adenocarcinoma subgroup (8 trials including 1126 patients). The present study uses the recently performed comprehensive individual patient data-based MANATEC02 meta-analysis [2] of the effect of chemotherapy followed by surgery (CS) vs surgery (S), chemoradiotherapy followed by surgery (CRS) vs S, and of CRS vs CS for locally advanced esophageal and gastro-esophageal junction (E/GEJ) cancer. The primary objective was to evaluate DFS as a surrogate for OS for various neoadjuvant chemo(radio)therapy regimens, including the comparison between neoadjuvant chemotherapy and neoadjuvant chemoradiotherapy.

2. Methods

2.1. Patients and studies

This study was preplanned in the protocol of the network meta-analysis conducted by the MANATECcollaborative group (for the protocol see https://www.gustaveroussy.fr/sites/default/files/meta-analysis-protocolole-ma-f.pdf and https://www.gustaveroussy.fr/sites/default/files/nma-protocol-valide.pdf, PROSPERO registration CRD42018107158). Eligible randomized trials compared CS versus S, CRS versus S or CS versus CRS, in first-line treatment of a locally advanced adenocarcinoma or squamous cell carcinoma of the esophagus or gastro-esophageal junction, and closed accrual by the end of 2015. The MANATEC-02 meta-analysis [2] included 26 trials (4985 patients) with available individual patient data (IPD): 13 trials (2478 patients) on CS versus S, 12 trials (2222 patients) on CRS versus S and 4 trials (497 patients) on CRS versus CS. Among the 26 trials in the MANATEC-02 meta-analysis [2], only those who provided DFS data were included in the current analysis.

2.2. Endpoints

Overall survival was defined as time from random assignment until death from any cause. Patients were censored at the last date of follow-up they were known to be alive. Disease-free survival was defined as time from random assignment to treatment until recurrence or death from any cause. Patients without a DFS event were censored at the date of last follow-up.

2.3. Statistical analysis

In the preplanned analyses, the three types of comparison, (CS vs S, CRS vs S and CRS vs CS) were analyzed separately. An additional analysis was performed using all trial data. As described by Burzykowski et al. [15], correlation was estimated at the patient and trial level: the correlation at the patient level was estimated by Kendall's tau and the correlation at the trial level was estimated by correlation coefficient R². We used a two-step copula approach (Clayton and Placket copulas, not adjusted for measurement error) and a one-step Poisson model, provided by surrosurv [16] package in R, to estimate Kendall's tau and R². The R² was considered excellent if higher than 0.9, very good if higher than 0.75, good if higher than 0.5, moderate if higher than 0.25, and poor otherwise as in Rotolo et al. [17] and Mauguen et al. [18]. A bootstrap
approach was used to calculate confidence intervals for the R² au tau in the different models. The bootstrap resampling was stratified on the trial meaning that in each sample the same number of trials (with the same number of patients) were available. One thousand samples were drawn, and the reported quantities were the mean bootstrap values, and the observed 2.5 and 97.5 quantiles of the distribution for 95 % confidence intervals. The surrogate Threshold Effect (STE) was calculated, corresponding to the minimal treatment effect to observe on the surrogate (DFS) to predict a non-null effect of the treatment on the true endpoint (OS). Survival curves for OS and DFS were drawn with Kaplan Meier estimates. To explore if there was a time trend across the trials included in this meta-analysis between the recurrence and death and how much sooner recurrences can be observed as compared to mortality, we used a illness-death or multi-state Markov model to estimate mean time in the “recurrence” state (also called sojourn time), in each trial. Patients are in an initial “health” state and will experience a death as terminal or absorbing event, which may or may not be preceded by an intermediate “recurrence” event. Patients in the recurrence state cannot return to the health state.

### 3. Results

Data on DFS were not available for 467 patients (9.4 %). Four trials did not provide any data on recurrence: one trial for the CS vs S comparison (36 patients), two trials for the CRS vs S comparison (210 patients) and one four arm trial which participated to all comparison (217 patients). Finally, four patients in the included trials had data issues and were excluded. In total, individual data of 4518 patients from 22 trials was analyzed (46 patients in the smallest trial and 802 in the largest): 2222 patients in CS versus CS comparison (10 trials), 1908 patients in CRS versus S (9 trials) and 388 in CRS versus CS (3 trials). Among these cases, 3440 (76.1 %) patients had a recurrence, and 3303 (73.1 %) died during follow-up. Median follow-up times were 65.5 months (95 % CI 61.3–71.4) for CS vs S, 73.4 months (71.5–76.4) for CRS vs S, 95.7 months (73–114) for CRS vs CS, and 72 months (69.3–74.3) for the overall population. Table 1 presents the cumulative OS and DFS events at 1,2 and 5 years.

Estimated survival curves of DFS and OS according to the different comparisons are shown in Figs. 1 and 2. Overall, for CS vs S, median OS was 16.6 months [95 % CI 15.8 – 17.4] and median DFS was 12.0 [11.4–12.9]. For CRS vs S median OS was 30.1 months [27.8–33.2] and median DFS was 21.9 months [20–24.7]. For CS vs CRS, median OS was

<table><caption>**Table 1**
Cumulative event number of OS and DFS at 1, 2 and 5 years from randomization (n = 4518).</caption><thead><tr><th>Comparison / Event</th><th>1 year<sup>1</sup></th><th>2 years<sup>1</sup></th><th>5 years<sup>1</sup></th><th>Total<sup>1</sup></th></tr></thead><tbody><tr><td>CS vs S<br>Death</td><td>867 (49 %)</td><td>1346<br>(76 %)</td><td>1710<br>(96 %)</td><td>1779<br>(100 %)</td></tr><tr><td>Recurrence or<br>death<br>CRS vs S<br>Death</td><td>1113<br>(61 %)</td><td>1517<br>(83 %)</td><td>1768<br>(97 %)</td><td>1829<br>(100 %)</td></tr><tr><td>Recurrence or<br>death<br>CS vs CRS<br>Death</td><td>473 (36 %)</td><td>828 (66 %)</td><td>1171<br>(93 %)</td><td>1260<br>(100 %)</td></tr><tr><td>Recurrence or<br>death<br>Overall<br>Death</td><td>717 (54 %)</td><td>1001<br>(75 %)</td><td>1258<br>(94 %)</td><td>1339<br>(100 %)</td></tr><tr><td>Recurrence or<br>death</td><td>95 (36 %)</td><td>171 (65 %)</td><td>243 (92 %)</td><td>264 (100 %)</td></tr><tr><td>Overall<br>Death</td><td>134 (49 %)</td><td>205 (75 %)</td><td>256 (94 %)</td><td>272 (100 %)</td></tr><tr><td>Recurrence or<br>death</td><td>1435<br>(43 %)</td><td>2345<br>(71 %)</td><td>3124<br>(95 %)</td><td>3303<br>(100 %)</td></tr><tr><td>Overall<br>Death</td><td>1964<br>(57 %)</td><td>2723<br>(79 %)</td><td>3282<br>(95 %)</td><td>3440<br>(100 %)</td></tr></tbody></table>

28.6 months [25.5–33.6] and median DFS was 21.9 months [17.3–27.4].

The different individual and trial-level correlations of the models by treatment comparison, and overall, are provided in Table 2. Regarding individual-level correlation, depending on the model and comparison, Kendall's tau values ranged from 0.70 to 0.87 which indicates good individual level correlation. Regarding trial-level correlation, depending on the model and comparison choice, R2 values ranged from 0.93 to 1 which is excellent. The Surrogate Threshold effects ranged from 0.81 to 0.88; with all three models giving an STE of 0.86 in the overall sample as minimum hazard ratio on DFS to predict a non-null effect of OS in a future trial.

The mean time in the recurrence state was 5.3 months at minimum for the Songkla trial (inclusion started in 1988) and 22.3 months at maximum for FFCD-9901 (inclusion started in 2000). For trials before 1995, mean time in the recurrence state was 10.8 months (95 %CI: 10.2 – 11.4) and was 16.5 months (95 %CI: 15.4 – 17.6) in trials after 1995, Fig. 3.

### 4. Discussion

It has often been advocated that the use of surrogate endpoints as primary endpoint could ameliorate the efficiency of randomized clinical trials or have ethical and practical advantages [19]. Yet, the chosen surrogate outcome must first be shown to be a robust substitute for the gold standard. The present study aimed to validate DFS as a surrogate for OS in the neoadjuvant pre-operative setting for cancer of the esophageal and gastro-esophageal junction across pathological subtypes and includes a comparison of preoperative chemotherapy vs. preoperative chemoradiotherapy. At the patient level, the individual correlation values between DFS and OS were good; when a patient has a longer DFS it also should have a longer OS. At the trial level, the correlation between treatment effects on DFS and treatment effects on OS was excellent: and in the regression model a strong effect on DFS can predict a strong effect on OS. The conclusions were very similar across the 3 treatment comparisons and for the overall analysis.

A strength of the present study is that our results are based on individual patient data meta-analysis approach which is considered as the highest methodological level of evidence and allowing the analysis of data in a standardized fashion. We used a widespread methodology evaluating both patient-level and trial-level correlations. To date, MANATEC-02 is the largest IPD-based meta-analysis on the subject.

Several limitations of our study should be emphasized. The first is that some of the clinical trials occurred before 2000 and there have been many changes in the global care of cancer patients has changed since then. Because of the semi-competing risk nature of the DFS endpoints, the first observed events may have already been deaths. This is illustrated by the lower mean time in the recurrence state for the oldest trials. Also, data from 467 patients (9.4 % of patients from the meta-analysis) was excluded for lack of recurrence information.

The correlation between DFS and OS is underpinned by a strong clinical and biological rationale in the context of neoadjuvant treatments. Yet, other factors such as death not related to cancer or the availability of other effective treatment when recurrence occurs may influence this correlation. Although DFS has not been validated (yet) as an optimal surrogate for OS in all cancers, it has been validated as a surrogate for OS in gastric cancer in the adjuvant setting, which is relevant given the biological similarities with esophageal and gastro-esophageal junction cancer.

Concerning the applicability of our results, one can argue that the short time between recurrence events and death in these relatively aggressive cancers reduces the potential interest of DFS as a surrogate. Using the illness-death modeling approach, we identified a relevant increase of mean time in the “recurrence” state over the years, with a mean sojourn time of above one year in the recent trials. Such delays are probably relevant when planning a trial. Legitimately one could

<footnote n="1">¹ Data are N (percent)</footnote>
<figure><img src="image_2.png" /><figcaption>Fig. 1. Kaplan Meier curves of estimated DFS and OS according to the different comparisons. CS: Chemotherapy followed by surgery, S: Surgery, CRS: Chemoradiotherapy followed by surgery.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Fig. 2. Treatment effects on Overall Survival (hazard ratios, on a logarithmic scale) vs treatment effects on Disease-Free Survival. CS: Chemotherapy followed by surgery, S: Surgery, CRS: Chemoradiotherapy followed by surgery. Trial size: N, number of patients. Shaded area corresponds to 95 % confidence bounds.</figcaption></figure>
<table><caption>**Table 2**
Summary of individual and trial level surrogate model evaluations of DFS/OS by treatment comparison, and overall.</caption><thead><tr><th rowspan="2">Models</th><th colspan="3">CS vs S<br>10 trials, 2222 patients</th><th colspan="3">CRS vs S<br>9 trials, 1908 patients</th><th colspan="3">CRS vs CS<br>3 trials, 388 patients</th><th colspan="3">Overall<br>22 trials, 4518 patients</th></tr><tr><th>Kendall's tau</th><th>R<sup>2</sup></th><th>STE</th><th>Kendall's tau</th><th>R<sup>2</sup></th><th>STE</th><th>Kendall's tau</th><th>R<sup>2</sup></th><th>STE</th><th>Kendall's tau</th><th>R<sup>2</sup></th><th>STE</th></tr></thead><tbody><tr><td>Clayton copula,<br>unadjusted</td><td>0.73(0.71;<br>0.75)</td><td>0.95 (0.84;<br>0.99)</td><td>0.81</td><td>0.76 (0.74;<br>0.77)</td><td>0.96 (0.87;<br>0.99)</td><td>0.88</td><td>0.87 (0.78;<br>0.92)</td><td>0.93<br>(0.43; 1)</td><td>NA</td><td>0.75 (0.74;<br>0.76)</td><td>0.95 (0.89;<br>0.99)</td><td>0.86</td></tr><tr><td>Plackett copula,<br>unadjusted</td><td>0.82 (0.75;<br>0.84)</td><td>0.95 (0.86;<br>0.99)</td><td>0.81</td><td>0.76 (0.75;<br>0.78)</td><td>0.96 (0.88;<br>0.99)</td><td>0.88</td><td>0.84 (0.79;<br>0.89)</td><td>0.93<br>(0.42; 1)</td><td>-</td><td>0.77 (0.75;<br>0.84)</td><td>0.95 (0.89;<br>0.99)</td><td>0.86</td></tr><tr><td>Poisson model</td><td>0.70 (0.68;<br>0.72)</td><td>0.98 (0.86;<br>1)</td><td>0.81</td><td>0.73 (0.71;<br>0.74)</td><td>1.00 (1; 1)</td><td>0.82</td><td>0.78 (0.74;<br>0.82)</td><td>0.97<br>(0.29; 1)</td><td>-</td><td>0.70 (0.69;<br>0.72)</td><td>0.98 (0.90;<br>1)</td><td>0.86</td></tr></tbody></table>

Unadj: unadjusted, CS: Chemotherapy followed by surgery, S: Surgery, CRS: Chemoradiotherapy followed by surgery, STE: Surrogate Threshold effects. Mean of bootstrap estimate (95 % confidence interval in brackets). NA: too few trials to calculate.

<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Mean sojourn time in recurrence state by trial using an illness-death model. The blue line corresponds to linear regression weighted by trial size, the shaded area to 95 % confidence bounds. The vertical bar represents the 95 % confidence interval for each estimate.</figcaption></figure>

question if the strong correlation between DFS and OS will remain if the time between recurrence and death increases further by novel therapeutic options either in the adjuvant or metastatic setting [20–23]. It should also be emphasized that our surrogacy results cannot be automatically extrapolated to therapies with different mechanisms of action such as immune checkpoint inhibitors [24] or targeted therapies.

In conclusion, DFS appeared an excellent surrogate for OS in esophageal and gastro-esophageal junction cancer treated by neoadjuvant chemotherapy or chemoradiotherapy and could be used as the primary endpoint in future trials evaluating these treatments.

## Funding

This study was funded by the French government Programme Hospitalier de Recherche Clinique en Cancérologie (PHRC-K18-187).

## CRediT authorship contribution statement

**BOONSTRA Jurjen:** Writing – review & editing, Validation, Investigation, Data curation.
**NILSSON Magnus:** Writing – review & editing, Validation, Investigation.
**PIESSEN Guillaume:** Writing – review & editing, Validation, Investigation, Data curation.
**Michiels Stefan:** Writing – original draft, Validation, Project administration, Methodology, Formal analysis, Data curation, Conceptualization.
**B. Mark SMITHERS:** Writing – review & editing, Validation, Investigation, Data curation.
**FARON Matthieu:** Writing – original draft, Validation, Project administration, Methodology, Formal analysis, Data curation, Conceptualization.
**VATHAIRE Florent de:** Writing – review & editing, Validation, Investigation, Data curation.
**CUNNINGHAM David:** Writing – review & editing, Validation, Investigation, Data curation.
**LAW Simon:** Writing – review & editing, Validation, Investigation, Data curation.

**Writing – review & editing, Validation, Investigation, Data curation.**
**YCHOU Marc:** Writing – review & editing, Validation, Investigation, Data curation.
**VALMASONI Michele:** Writing – review & editing, Validation, Investigation, Data curation.
**URBA Susan:** Writing – review & editing, Validation, Investigation, Data curation.
**STAHL Michael:** Writing – review & editing, Validation, Investigation, Data curation.
**YANG Hong:** Writing – review & editing, Validation, Investigation, Data curation.
**TEPPER Joel:** Writing – review & editing, Validation, Investigation, Data curation.
**CHEUGOUA-ZANETSIE Maurice:** Writing – original draft, Validation, Project administration, Methodology, Formal analysis, Data curation, Conceptualization.
**LORDICK Florian:** Writing – review & editing, Validation, Investigation, Data curation.
**CABRIT Nicolas:** Writing – review & editing, Validation, Investigation, Data curation.
**THOMAS Janine:** Writing – review & editing, Validation, Investigation, Data curation.
**WILLIAUME Danièle:** Writing – review & editing, Validation, Investigation, Data curation.
**WINTER Kathryn:** Writing – review & editing, Validation, Investigation, Data curation.
**PAOLETTI Xavier:** Writing – review & editing, Validation, Supervision, Conceptualization.
**NANKIVELL Matthew:** Writing – review & editing, Validation, Investigation, Data curation.
**BURMEISTER Bryan:** Writing – review & editing, Validation, Supervision, Conceptualization.
**THI-RION Pierre:** Writing – review & editing, Validation, Supervision, Conceptualization.
**GEBSKI Val:** Writing – review & editing, Validation, Supervision, Conceptualization.
**TIERNEY Jayne:** Writing – review & editing, Validation, Supervision, Conceptualization.
**VERNEREY Dewi:** Writing – review & editing, Validation, Investigation, Data curation.
**DUCREUX Michel:** Writing – review & editing, Validation, Investigation, Data curation.
**WIJNHOVEN Bas:** Writing – review & editing, Validation, Investigation, Data curation.
**PIGNON Jean-Pierre:** Writing – original draft, Validation, Project administration, Methodology,
Formal analysis, Data curation, Conceptualization. FU Jianhua: Writing – review & editing, Validation, Supervision, Conceptualization. SANDICK Johanna van: Writing – review & editing, Validation, Supervision.

Declaration of Competing Interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: FL: institutional grants from: Astra Zeneca, Beigene, BMS, Daiichi San-kyo and Gilead, and personal fees from: Amgen, ArtTempi. Astellas, Astra Zeneca, Bayer, Biontech, BMS, Boehringer Ingelheim, Daiichi Sankyo, Eli Lilly, Gilead, Elsevier, Incyte, Medscape, MedUpdate, Merck Serono, MSD, PAGE, Roche, Servier, StreamedUp!, VJ Oncology, all outside the scope of the submitted work. SM: personal fees for Scientific Committee Study member: Roche, for Data and safety monitoring member of clinical trials: IQVIA, Kedrion, Biophytis, Servier, Yuhan, outside the scope of the submitted work The other authors reported no COI.

Appendix: MANATEC-02 Collaborative Group

MANATEC-02: Individual patient data Meta-Analysis of chemotherapy or chemoradiotherapy as NeoAdjuvant Treatment of Esophageal or gastroesophageal junction Carcinoma

Project management group

Matthieu Faron, Armel Maurice Cheugoua-Zanetsie, Jean Pierre Pignon, Pierre Blanchard, Michel Ducreux, Stefan Michiels

Advisory board

Pierre Thirion, Jayne Tierney, Val Gebski, Bryan H. Burmeister, Xavier Paoletti, Johanna Van Sandick, Jianhua Fu

Investigators

Chanvitan Apinop (Prince of Songkla University, Songkla, Thailand),
Anne Arezina (Alliance for Clinical Trial in Oncology, Chicago, IL),
Emilie Barbier (Fédération Francophone de Cancérologie Digestive,
Dijon, France), Gary Bass (Connolly Hospital Blanchardstown, Dublin,
Ireland), Pierre Blanchard (Gustave Roussy, Villejuif, France), Jurjen
Boonstra (Erasmus University Medical Center, Rotterdam, the
Netherlands), Jean-François Bosset (Chru Jean Minjoz, Besançon,
France), Bryan H. Burmeister (Princess Alexandra, Brisbane, Australia),
Armel Maurice Cheugoua-Zanetsie (Gustave Roussy, Villejuif, France),
David Cunningham (Royal Marsden Hospital, London, United
Kingdom), Michel Ducreux (Gustave Roussy, Villejuif, France), Matthieu
Faron (Gustave Roussy, Villejuif, France), Jianhua Fu (Sun Yat-Sen
University Cancer Center, Guangzhou, China), Val Gebski (Nhmrc,
Sydney, Australia), D. J. Girling (MRC, London, United Kingdom), David
Kelsen (Memorial Sloan Kettering Cancer Center, New York, NY), Fre-
drik Klevebro (Karolinska University Hospital, Stockholm, Sweden),
Simon Law (Queen Mary Hospital, Hong-Kong SAR, China), Elisabeth Le
Prise (Centre Eugene Marquis, Rennes, France), Florian Lordick (Uni-
versity of Leipzig Medical Center, Leipzig, Germany), Tanaphon Mai-
pang (Prince of Songkla University, Songkla, Thailand), Murielle Mauer
(EORTC, Brussel, Belgium), Stefan Michiels (Gustave Roussy, Villejuif,
France), Jennifer Moughan (NRG Oncology Statistics Data Management
Center, Philadelphia, PA), Matthew Nankivell (MRC, London, United
Kingdom), Knut Nygaard (Aker University Hospital, Oslo, Norway),
Patrick Owens (Saint Luke's Hospital, Dublin, Ireland), Xavier Paoletti
(Institut Curie, Paris, France), Guillaume Piessen (CHU de Lille, Lille,
France), Jean Pierre Pignon (Gustave Roussy, Villejuif, France), Jean-
Luc Raoul (Centre Eugene Marquis, Rennes, France), Jack Roth (MD

Anderson, Houston, TX), Alberto Ruol (Università Degli Studi di Padova, Padova, Italia), Peter Schlag (Association Surgical Oncology der Deutsch, Germany), Christoph Schumacher (Technische University München, Munich, Germany), Joel Shapiro (Erasmus University Medical Center, Rotterdam, the Netherlands), B. Mark Smithers (University of Queensland, Brisbane, Australia), Michael Stahl (Evang. Kliniken Essen-Mitte, Essen, Germany), Joel Tepper (University of North Carolina School of Medicine, Chapel Hill, NC), Pierre Thirion (St Luke, Dublin, Ireland), Janine M. Thomas (Princess Alexandra Hospital, Woolloomugaba, Australia), Jayne Tierney (MRC, London, United Kingdom), Susan Urba (University of Michigan Medical Center, Ann Arbor, MI), Michele Valmasoni (Padova University Hospital, Padova, Italy), Ate van der Gaast (Erasmus, Rotterdam, the Netherlands), Johanna van Sandick (The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands), Bas Wijnhoven (Erasmus University Medical Center, Rotterdam, the Netherlands), Danièle Willaume (Centre Eugène Marquis, Rennes, France), Kathryn Winter (NRG Oncology Statistics and Data Management Center, Philadelphia, PA), John Wong (Queen Mary Hospital, University of Hong Kong, Hong Kong, China), Hong Yang (Sun Yat-Sen University Cancer Center, Guangzhou, China), Marc Ychou (Val d'Aurelles, Montpellier, France)

**Appendix A. Supporting information**

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.ejca.2025.115292.

Data availability

The central analysis team at Gustave Roussy does not have the authorization to share the meta-analytic database and data requests need to be made with the individual trial groups.

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