Approche Bayésienne de la survie dans les essais cliniques pour les cancers rares

Bayesian approach augments the information provided by the trial itself by incorporating external information into the trial analysis. In addition, this approach allows the results to be expressed in terms of probability of some treatment effect, which is more informative and interpretable than a p-value and a confidence interval. In addition, the frequent reduction of an analysis to a binary interpretation of the results (significant versus non-significant) is particularly harmful in rare diseases.In this context, the objective of my work was to explore the feasibility, constraints and contribution of the Bayesian approach in clinical trials in rare cancers with a primary censored endpoint. A review of the literature confirmed that the implementation of Bayesian methods is still limited in the analysis of clinical trials with a censored endpoint.In the second part of our work, we developed a Bayesian design, integrating historical data in the setting of a real clinical trial with a survival endpoint in a rare disease (osteosarcoma). The prior incorporated individual historical data on the control arm and aggregate historical data on the relative treatment effect. Through a large simulation study, we evaluated the operating characteristics of the proposed design and calibrated the model while exploring the issue of commensurability between historical and current data. Finally, the re-analysis of three clinical trials allowed us to illustrate the contribution of Bayesian approach to the expression of the results, and how this approach enriches the frequentist analysis of a trial.L'approche Bayésienne permet d’enrichir l'information apportée par l'essai clinique, en intégrant des informations externes à l'essai. De plus, elle permet d’exprimer les résultats directement en termes de probabilité d’un certain effet du traitement, plus informative et interprétable qu’une p-valeur et un intervalle de confiance. Par ailleurs, la réduction fréquente d’une analyse à une interprétation binaire des résultats (significatif ou non) est particulièrement dommageable dans les maladies rares. L’objectif de mon travail était d'explorer la faisabilité, les contraintes et l'apport de l'approche Bayésienne dans les essais cliniques portant sur des cancers rares lorsque le critère principal est censuré. Tout d’abord, une revue de la littérature a confirmé la faible implémentation actuelle des méthodes Bayésiennes dans l'analyse des essais cliniques avec critère de survie.Le second axe de ce travail a porté sur le développement d’un essai Bayésien avec critère de survie, intégrant des données historiques, dans le cadre d’un essai réel portant sur une pathologie rare (ostéosarcome). Le prior intégrait des données historiques individuelles sur le bras contrôle et des données agrégées sur l’effet relatif du traitement. Une large étude de simulations a permis d’évaluer les caractéristiques opératoires du design proposé, de calibrer le modèle, tout en explorant la problématique de la commensurabilité entre les données historiques et actuelles. Enfin, la ré-analyse de trois essais cliniques publiés a permis d’illustrer l'apport de l'approche Bayésienne dans l'expression des résultats et la manière dont cette approche permet d’enrichir l’analyse fréquentiste d’un essai

Bayesian Approach to Survival in Clinical Trials in Rare Cancers

L'approche Bayésienne permet d’enrichir l'information apportée par l'essai clinique, en intégrant des informations externes à l'essai. De plus, elle permet d’exprimer les résultats directement en termes de probabilité d’un certain effet du traitement, plus informative et interprétable qu’une p-valeur et un intervalle de confiance. Par ailleurs, la réduction fréquente d’une analyse à une interprétation binaire des résultats (significatif ou non) est particulièrement dommageable dans les maladies rares. L’objectif de mon travail était d'explorer la faisabilité, les contraintes et l'apport de l'approche Bayésienne dans les essais cliniques portant sur des cancers rares lorsque le critère principal est censuré. Tout d’abord, une revue de la littérature a confirmé la faible implémentation actuelle des méthodes Bayésiennes dans l'analyse des essais cliniques avec critère de survie.Le second axe de ce travail a porté sur le développement d’un essai Bayésien avec critère de survie, intégrant des données historiques, dans le cadre d’un essai réel portant sur une pathologie rare (ostéosarcome). Le prior intégrait des données historiques individuelles sur le bras contrôle et des données agrégées sur l’effet relatif du traitement. Une large étude de simulations a permis d’évaluer les caractéristiques opératoires du design proposé, de calibrer le modèle, tout en explorant la problématique de la commensurabilité entre les données historiques et actuelles. Enfin, la ré-analyse de trois essais cliniques publiés a permis d’illustrer l'apport de l'approche Bayésienne dans l'expression des résultats et la manière dont cette approche permet d’enrichir l’analyse fréquentiste d’un essai.Bayesian approach augments the information provided by the trial itself by incorporating external information into the trial analysis. In addition, this approach allows the results to be expressed in terms of probability of some treatment effect, which is more informative and interpretable than a p-value and a confidence interval. In addition, the frequent reduction of an analysis to a binary interpretation of the results (significant versus non-significant) is particularly harmful in rare diseases.In this context, the objective of my work was to explore the feasibility, constraints and contribution of the Bayesian approach in clinical trials in rare cancers with a primary censored endpoint. A review of the literature confirmed that the implementation of Bayesian methods is still limited in the analysis of clinical trials with a censored endpoint.In the second part of our work, we developed a Bayesian design, integrating historical data in the setting of a real clinical trial with a survival endpoint in a rare disease (osteosarcoma). The prior incorporated individual historical data on the control arm and aggregate historical data on the relative treatment effect. Through a large simulation study, we evaluated the operating characteristics of the proposed design and calibrated the model while exploring the issue of commensurability between historical and current data. Finally, the re-analysis of three clinical trials allowed us to illustrate the contribution of Bayesian approach to the expression of the results, and how this approach enriches the frequentist analysis of a trial

Bayesian survival analysis in clinical trials:what methods are used in practice?

Background Bayesian statistics are an appealing alternative to the traditional frequentist approach to designing, analysing, and reporting of clinical trials, especially in rare diseases. Time-to-event endpoints are widely used in many medical fields. There are additional complexities to designing Bayesian survival trials which arise from the need to specify a model for the survival distribution. The objective of this article was to critically review the use and reporting of Bayesian methods in survival trials. Methods A systematic review of clinical trials using Bayesian survival analyses was performed through PubMed and Web of Science databases. This was complemented by a full text search of the online repositories of pre-selected journals. Cost-effectiveness, dose-finding studies, meta-analyses, and methodological papers using clinical trials were excluded. Results In total, 28 articles met the inclusion criteria, 25 were original reports of clinical trials and 3 were re-analyses of a clinical trial. Most trials were in oncology (n = 25), were randomised controlled (n = 21) phase III trials (n = 13), and half considered a rare disease (n = 13). Bayesian approaches were used for monitoring in 14 trials and for the final analysis only in 14 trials. In the latter case, Bayesian survival analyses were used for the primary analysis in four cases, for the secondary analysis in seven cases, and for the trial re-analysis in three cases. Overall, 12 articles reported fitting Bayesian regression models (semi-parametric, n = 3; parametric, n = 9). Prior distributions were often incompletely reported: 20 articles did not define the prior distribution used for the parameter of interest. Over half of the trials used only non-informative priors for monitoring and the final analysis (n = 12) when it was specified. Indeed, no articles fitting Bayesian regression models placed informative priors on the parameter of interest. The prior for the treatment effect was based on historical data in only four trials. Decision rules were pre-defined in eight cases when trials used Bayesian monitoring, and in only one case when trials adopted a Bayesian approach to the final analysis. Conclusion Few trials implemented a Bayesian survival analysis and few incorporated external data into priors. There is scope to improve the quality of reporting of Bayesian methods in survival trials. Extension of the Consolidated Standards of Reporting Trials statement for reporting Bayesian clinical trials is recommended

Incorporating individual historical controls and aggregate treatment effect estimates into a Bayesian survival trial: a simulation study

Abstract Background Performing well-powered randomised controlled trials (RCTs) of new treatments for rare diseases is often infeasible. However, with the increasing availability of historical data, incorporating existing information into trials with small sample sizes is appealing in order to increase the power. Bayesian approaches enable one to incorporate historical data into a trial’s analysis through a prior distribution. Methods Motivated by a RCT intended to evaluate the impact on event-free survival of mifamurtide in patients with osteosarcoma, we performed a simulation study to evaluate the impact on trial operating characteristics of incorporating historical individual control data and aggregate treatment effect estimates. We used power priors derived from historical individual control data for baseline parameters of Weibull and piecewise exponential models, while we used a mixture prior to summarise aggregate information obtained on the relative treatment effect. The impact of prior-data conflicts, both with respect to the parameters and survival models, was evaluated for a set of pre-specified weights assigned to the historical information in the prior distributions. Results The operating characteristics varied according to the weights assigned to each source of historical information, the variance of the informative and vague component of the mixture prior and the level of commensurability between the historical and new data. When historical and new controls follow different survival distributions, we did not observe any advantage of choosing a piecewise exponential model compared to a Weibull model for the new trial analysis. However, we think that it remains appealing given the uncertainty that will often surround the shape of the survival distribution of the new data. Conclusion In the setting of Sarcome-13 trial, and other similar studies in rare diseases, the gains in power and accuracy made possible by incorporating different types of historical information commensurate with the new trial data have to be balanced against the risk of biased estimates and a possible loss in power if data are not commensurate. The weights allocated to the historical data have to be carefully chosen based on this trade-off. Further simulation studies investigating methods for incorporating historical data are required to generalise the findings

Phase II and biomarker study of programmed cell death protein 1 inhibitor nivolumab and metronomic cyclophosphamide in paediatric relapsed/refractory solid tumours: Arm G of AcSé-ESMART, a trial of the European Innovative Therapies for Children With Cancer Consortium

International audienc

Phase I or II Study of Ribociclib in Combination With Topotecan-Temozolomide or Everolimus in Children With Advanced Malignancies: Arms A and B of the AcSé-ESMART Trial

International audiencePURPOSE AcSé-ESMART is a proof-of-concept, phase I or II, platform trial, designed to explore targeted agents in a molecularly enriched cancer population. Arms A and B aimed to define the recommended phase II dose and activity of the CDK4/6 inhibitor ribociclib with topotecan and temozolomide (TOTEM) or everolimus, respectively, in children with recurrent or refractory malignancies. PATIENTS AND METHODS Ribociclib was administered orally once daily for 16 days after TOTEM for 5 days (arm A) or for 21 days with everolimus orally once daily continuously in a 28-day cycle (arm B). Dose escalation followed the continuous reassessment method, and activity assessment the Ensign design. Arms were enriched on the basis of molecular alterations in the cell cycle or PI3K/AKT/mTOR pathways. RESULTS Thirty-two patients were included, 14 in arm A and 18 in arm B, and 31 were treated. Fourteen patients had sarcomas (43.8%), and 13 brain tumors (40.6%). Main toxicities were leukopenia, neutropenia, and lymphopenia. The recommended phase II dose was ribociclib 260 mg/m 2 once a day, temozolomide 100 mg/m 2 once a day, and topotecan 0.5 mg/m 2 once a day (arm A) and ribociclib 175 mg/m 2 once a day and everolimus 2.5 mg/m 2 once a day (arm B). Pharmacokinetic analyses confirmed the drug-drug interaction of ribociclib on everolimus exposure. Two patients (14.3%) had stable disease as best response in arm A, and seven (41.2%) in arm B, including one patient with T-acute lymphoblastic leukemia with significant blast count reduction. Alterations considered for enrichment were present in 25 patients (81%) and in eight of nine patients with stable disease; the leukemia exhibited CDKN2A/B and PTEN deficiency. CONCLUSION Ribociclib in combination with TOTEM or everolimus was well-tolerated. The observed activity signals initiated a follow-up study of the ribociclib-everolimus combination in a population enriched with molecular alterations within both pathways

PemBOv trial: Pembrolizumab plus bevacizumab with or without pegylated liposomal doxorubicin-based chemotherapy in patients with platinum-resistant ovarian cancer.

5575 Background: Few platinum resistant ovarian cancer (PROvC) patients respond to anti-PD1 monotherapy (ORR 7.6%) with little impact on survival (OS 10.1 mo). Among responders the median duration of response is impressive (18.7 mo) (Hamanishi 2021). Methods: We have evaluated the combination of pembrolizumab (200mg), with bevacizumab (400mg) for 6 cycles plus minus peglyated liposomal doxorubicin (PLD) q3w in PROvC patients with no limit in previous treatment lines, allowed to be previously treated with bevacizumab. An initial safety run evaluated the dual combination of pembrolizumab plus PLD (cohort A). The triple combination was evaluated at MTD-1 and at MTD of PLD (30mg/m 2 q3w) (cohort C). The dual combination of pembrolizumab + bevacizumab was run in parallel (cohort B). This is an open label phase I trial with a modified toxicity probability interval design. The evaluation criteria endpoints were safety and efficacy. Pharmacokinetics of bevacizumab were evaluated. NCT03596281 Results: A total of 47 patients (pts) were enrolled between January 2019 and February 2021. Median age was 70 years (38-77). 30/12 pts (63.8/25.5%) had an initial FIGO stage III/IV, 44 pts (93.6%) had a HGSOC. 40 pts (85.1%) underwent surgery, out of which 13 pts (32.5%) had a primary debulking. BRCA mutations were present in 9 pts (19.1%). Pts had a median of 3 previous treatment lines (0-13), including pretreatment with antiangiogenic agents in 36 (76.6%) and PARP inhibitors in 21 pts (44.7%). No DLT was reported. Grade 3/4 treatment-related adverse events were reported in 2 pts (30%), 4 (20%) and 11 (50%) in cohorts A, B and C respectively. The ORR was 0, 26.3 (95% CI 6.5-46.1) and 30% (9.9-50.1) with a DCR of 0, 78.9 and 75% in cohorts A, B and C respectively. According to investigator assessment, the median PFS was 2.1, 4.7 and 4.8 mo (table). The blinded independent central review is currently under evaluation. A large inter-patient variability in bevacizumab plasma concentrations was observed among patients. The 400 mg flat dosing achieved residual concentrations similar to that of 5 mg/kg Q2W or 7.5 mg/kg q3w (51± 30 μg/ml in cohort B and 63 ± 55 μg/ml in cohort C (p>0.05) after C1). Overall, 22 % of pts of cohort B and 18 % of cohort C showed trough levels below the targeted threshold (i.e. < 25 μg/ml). Correlative studies are ongoing. Conclusions: Short-term flat dose bevacizumab potentiates the response to anti-PD1 therapy even in the absence of chemotherapy in heavily pre-treated PROvC patients. The long-term treatment with bevacizumab could potentially improve the outcome. The combination of anti-PD-1 plus anti-angiogenic agents should be a backbone for the treatment of PROvC patients. Clinical trial information: NCT03596281. [Table: see text