Correlations between objective response rate and survival-based endpoints in first-line advanced non-small cell lung Cancer: A systematic review and meta-analysis

Goring, Sarah; Varol, N.; Waser, Nathalie; Popoff, Evan; Lozano‐Ortega, Greta; Lee, Adam F.; Yuan, Yong; Eccles, Laura J.; Tran, Phuong; Penrod, John R.

doi:10.1016/j.lungcan.2022.06.009

Cited by 8 publications

(9 citation statements)

References 89 publications

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“…Another published NSCLC meta-analysis has also demonstrated different trends in the relationship between OS and ORR by treatment class, with a larger OS benefit per unit of ORR benefit in IO-based therapy versus chemotherapy trials than in chemotherapy versus chemotherapy trials. 12 As with this earlier report and the present analysis, numerous other studies confirm positive ORRto-OS associations in PD(L)-1 treated NSCLC. [8][9][10][11] A further recent meta-analysis reported covariates similar to those in the present analyses but did not allow prediction of new trial results with composition of these covariates.…”

Section: Discussionsupporting

confidence: 92%

“…Hence, a model‐based meta‐analysis (MBMA) approach holds great potential to provide more robust treatment comparisons by integrating external trial data from multiple sources and quantifying the effect of differing trial populations and treatments 5–7 . In NSCLC, multiple prior meta‐analyses have identified important correlations in early efficacy outcomes, 8 such as overall response rate (ORR) and overall survival (OS) for PD(L)‐1 inhibitors 9–12 …”

Section: Introductionmentioning

confidence: 99%

“…[5][6][7] In NSCLC, multiple prior meta-analyses have identified important correlations in early efficacy outcomes, 8 such as overall response rate (ORR) and overall survival (OS) for PD(L)-1 inhibitors. [9][10][11][12] The objective of this study is the development of a comprehensive MBMA framework in NSCLC for predicting efficacy of regimens that combine novel agents with PD-1 inhibitors, namely pembrolizumab and nivolumab (alone or in combination with chemotherapy), using ORR, OS, and OS-ORR correlation data. Several illustrative applications are highlighted.…”

Section: Introductionmentioning

confidence: 99%

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Model‐based meta‐analysis of non‐small cell lung cancer with standard of care PD‐1 inhibitors and chemotherapy for early development decision making

Turner

Wada

Zhou

et al. 2023

CPT Pharmacom & Syst Pharma

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Single‐arm cohorts/trials are often used in early phase oncology programs to support preliminary clinical activity assessments for investigational products, administered alone or in combination with standard of care (SOC) agents. Benchmarking clinical activity of those combinations against other treatments, including SOC, requires indirect comparisons against external trials, which presents challenges including cross‐study differences in trial populations/other factors. To facilitate such nonrandomized comparisons, we developed a comprehensive model‐based meta‐analysis (MBMA) framework to quantitatively adjust for factors related to efficacy in metastatic non‐small cell lung cancer (mNSCLC). Data were derived from 15 published studies assessing key programmed cell death protein‐1 (PD‐1) inhibitors pembrolizumab (n = 8) and nivolumab (n = 7), representing current SOC in mNSCLC. In the first stage, a mixed‐effects logistic regression model for overall response rate (ORR) was developed accounting for effects of various population covariates on ORR. The ORR model results indicated an odds ratio (OR) of 1.02 for squamous versus non‐squamous histology and OR of 1.20 for PD‐ligand 1 tumor proportion score (TPS) per every 10% increase of TPS level. Next, a nonparametric mixed‐effects model for overall survival (OS) was developed with ORR/other clinical covariates as input. Subsequently, MBMA simulations of relevant hypothetical scenarios involving single‐arm trial design predicted OS hazard ratios as a function of ORR with matched patient characteristics. Findings from this MBMA and derived parameter estimates can be generally applied by the reader as a framework for interpreting efficacy data from early phase trials to support ORR‐based go/no‐go decisions and futility rules, illustrated through examples in this report.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model‐based meta‐analysis of non‐small cell lung cancer with standard of care PD‐1 inhibitors and chemotherapy for early development decision making

Turner

Wada

Zhou

et al. 2023

CPT Pharmacom & Syst Pharma

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“…Second, for the trial level, there must be a strong association between the treatment effect of the surrogate endpoint and that of the true endpoint [19]. However, the individual level data are usually not available, so we use arm level data as an alternative, which can also be seen in many other studies [20][21][22][23]. To describe the correlation strength between surrogate and true endpoints in this study, we divided values of the correlation coefficient into five levels, as shown in Table 1.…”

Section: Data Synthesis and Analysismentioning

confidence: 99%

Validating ORR and PFS as surrogate endpoints in phase II and III clinical trials for NSCLC patients: difference exists in the strength of surrogacy in various trial settings

et al. 2022

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Objective This study aims to systematically validate the performance of surrogate endpoints in phase II and III clinical trials for NSCLC patients under various trial settings. Methods A literature search retrieved all registered phase II and III trials of NSCLC patients in which OS, with at least one of ORR and PFS, were reported. Associations between surrogate and true endpoints were assessed on two levels. On the arm level, three pairs of correlations, i.e., ORR vs. median OS, ORR vs. median PFS, and median PFS vs. median OS, were analysed using Spearman’s rho. On the trial level, similarly, three pairs of correlations, i.e., ΔORR vs. HR of OS, ΔORR vs. HR of PFS, and HR of PFS vs. HR of OS, were analysed using Spearman’s rho and weighted linear regression model respectively. Finally, sensitivity analyses were performed to explore surrogacy under various trial settings. Results At arm level, three pairs of correlations are all high (Spearman’s rho = 0.700, 0.831, 0.755, respectively). At trial level, there is a low correlation between ΔORR and HR of OS, a high correlation between ΔORR and HR of PFS and a moderate correlation between HR of PFS and HR of OS (Spearman’s rho = 0.462, 0.764, 0.584, respectively). In the sensitivity analysis, we find correlations between surrogate and true endpoints vary by different trial settings. It is noteworthy that the strength of surrogacy of these intermediate endpoints in targeted therapy is greater than that in immunotherapy. Conclusion According to the arm-level and trial level-analysis, we suggest that in phase II and III trials of targeted therapy and immunotherapy for NSCLC patients: 1) ORR lacks validity for the surrogacy of OS, excluding in first-line therapy, and 2) ORR may be an appropriate surrogate endpoint for PFS, and 3) PFS may be considered a modest surrogacy for OS, with better performance in first-line therapy trials. Moreover, to provide more convincing evidence of surrogacy of the surrogate endpoints, patient-level analyses are in desperate need.

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“…Similarly, it is difficult to link dose effect on PFS to OS when estimated separately. The potential inconsistencies in the results make it challenging to gain a reliable and integrated understanding of the underlying dose/exposure‐efficacy relationship 2 …”

Section: Introductionmentioning

confidence: 99%

A multistate modeling and simulation framework to learn dose–response of oncology drugs: Application to bintrafusp alfa in non‐small cell lung cancer

Liu

Grisic

Krishnan

et al. 2023

CPT Pharmacom & Syst Pharma

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The dose/exposure‐efficacy analyses are often conducted separately for oncology end points like best overall response, progression‐free survival (PFS) and overall survival (OS). Multistate models offer to bridge these dose‐end point relationships by describing transitions and transition times from enrollment to response, progression, and death, and evaluating transition‐specific dose effects. This study aims to apply the multistate pharmacometric modeling and simulation framework in a dose optimization setting of bintrafusp alfa, a fusion protein targeting TGF‐β and PD‐L1. A multistate model with six states (stable disease [SD], response, progression, unknown, dropout, and death) was developed to describe the totality of endpoints data (time to response, PFS, and OS) of 80 patients with non‐small cell lung cancer receiving 500 or 1200 mg of bintrafusp alfa. Besides dose, evaluated predictor of transitions include time, demographics, premedication, disease factors, individual clearance derived from a pharmacokinetic model, and tumor dynamic metrics observed or derived from tumor size model. We found that probabilities of progression and death upon progression decreased over time since enrollment. Patients with metastasis at baseline had a higher probability to progress than patients without metastasis had. Despite dose failed to be statistically significant for any individual transition, the combined effect quantified through a model with dose‐specific transition estimates was still informative. Simulations predicted a 69.2% probability of at least 1 month longer, and, 55.6% probability of at least 2‐months longer median OS from the 1200 mg compared to the 500 mg dose, supporting the selection of 1200 mg for future studies.

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Correlations between objective response rate and survival-based endpoints in first-line advanced non-small cell lung Cancer: A systematic review and meta-analysis

Cited by 8 publications

References 89 publications

Model‐based meta‐analysis of non‐small cell lung cancer with standard of care PD‐1 inhibitors and chemotherapy for early development decision making

Model‐based meta‐analysis of non‐small cell lung cancer with standard of care PD‐1 inhibitors and chemotherapy for early development decision making

Validating ORR and PFS as surrogate endpoints in phase II and III clinical trials for NSCLC patients: difference exists in the strength of surrogacy in various trial settings

A multistate modeling and simulation framework to learn dose–response of oncology drugs: Application to bintrafusp alfa in non‐small cell lung cancer

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