Abstract
PURPOSE
Many immune checkpoint inhibitors (ICIs) have been approved on the basis of tumor response end points in nonrandomized trials, including objective response rate (ORR) and duration and depth of response. We aimed to assess the validity of these end points as surrogate end points for overall survival (OS) in patients with advanced solid tumors treated with ICIs at trial and treatment arm levels.
METHODS
ICI trials in advanced solid cancers published between January 1, 2000, and March 31, 2020, were included. Correlations between ORR, durable response (DR) of ≥ 6 months, complete response (CR), and OS were assessed for treatment comparisons (trial-level) and for patients receiving ICI (arm-level), using weighted linear regression.
RESULTS
Sixty-three trials were eligible, including 58 randomized controlled trials and 20 nonrandomized controlled trials (78 ICI arms and 30,815 patients). The majority were phase III (63%), and OS was the most common primary end point (40%). In relative treatment comparisons, correlations between ORR risk ratio and OS hazard ratio (HR), 6-month DR ratio and OS HR, and CR ratio and OS HR were r = 0.58, r = 0.62, and r = 0.42, respectively. Exploratory studies in melanoma, non–small-cell lung cancer, and other tumors showed similar results, although 6-month DR ratio was strongly correlated with OS HR (r = 0.89). Within ICI arms only, correlations between ORR and 12-month OS, 6-month DR and 12-month OS, and CR and 12-month OS were r = 0.76, r = 0.84, and r = 0.50, respectively, in all eligible trials.
CONCLUSION
Relative measures of tumor response (ORR, 6-month DR, and CR) are poor surrogate end points for OS in ICI studies. However, ORR and 6-month DR are prognostic of 12-month OS in ICI studies supporting their use for screening activity of novel agents in early-phase nonrandomized trials.
INTRODUCTION
There is a need for validated surrogate end points that reliably predict for long-term survival in cancer clinical trials. This exercise helps to accelerate drug development and support early regulatory approval. For immune checkpoint inhibitors (ICIs), where a substantial improvement in patient outcome has been observed, there is a shift in the strength of evidence required for regulatory approvals, which has affected the design and selection of end points in clinical trials.1 Drug regulatory agencies have accepted surrogate outcomes, including tumor response, to meet the demand for rapid access to novel drugs with apparent clinical benefit.2,3 As of July 2019, the US Food and Drug Administration has approved 58 indications for seven ICIs across 15 cancer types (plus one tumor-agnostic indication), all on the basis of nonrandomized trials with primary end points that included objective response rate (ORR) and/or duration of response.4
CONTEXT
Key Objective
Significant tumor response in nonrandomized trials has led to drug approval for many immunotherapies, but this may not reflect significant overall survival (OS) benefit. This systematic review and meta-analysis examined the validity of response, including duration and depth of response, as surrogate end points for OS. It included 63 immunotherapy trials in advanced cancers.
Knowledge Generated
The pooled relative comparisons of tumor response and duration and depth of response showed poor correlation with OS, with the exception of 6-month durable response and OS in non–small-cell lung cancer. Objective response rate and 6-month durable response are prognostic of 12-month OS. Complete response is a poor prognostic and surrogate end point for OS.
Relevance
OS remains the most clinically meaningful end point in immunotherapy trials. The prognostic nature of duration and response rate make them useful in screening for novel agents in early-phase nonrandomized trials.
Whenever a drug approval is based on intermediate outcomes, there is a safety net requirement for subsequent confirmatory studies.5,6 Unfortunately, timely drug withdrawal does not necessarily occur if or when postapproval trials report no significant overall survival (OS) benefit compared with the initially reported results in early-phase trials.4,7-9 Thus, as more ICI trials are completed, it is critical to use these additional long-term data to revisit the lingering question of the validity of using tumor response end points as surrogate end points for OS. We need to confirm if these response end points can be relied on for drug development and approval decisions, which will subsequently inform their interpretation in clinical practice.
Computed tomography scans are performed routinely to assist clinicians in the assessment of response to treatment, and the widely held perception is that response is associated with clinical benefit including OS. If we presume that tumor response is prognostic of OS, the key questions to address are how reliable are response rates in predicting OS (eg, 12-month OS) and by how much do differences in response translate to differences in OS (magnitude of treatment effect)?
The unique adaptive memory of T-cell response from ICIs may stimulate a durable clinical response.10 In the Keynote-001 study of the anti–programmed death-1 (PD-1) ICI, pembrolizumab in metastatic melanoma, 16% of participants achieved complete response (CR) as a measure of depth of response; of these, 90% remained alive and disease-free after 2 years, indicative of durability of response.11 Deep and durable responses (DRs) from ICIs have also been reported in non–small-cell lung cancers (NSCLCs)12,13 and renal cell carcinoma.14,15 However, the validity of these two surrogate end points have yet to be systematically evaluated across all ICI trials of different tumors.
The primary aim of this systematic review and meta-analysis is to assess the validity of ORR and duration and depth of response as surrogate end points for OS in patients with advanced solid tumors treated with ICIs. We aimed to evaluate duration of response with DR of ≥ 6 months (6-month DR) and depth of response with CR as defined by RECIST v1.1.16 The secondary aim is to develop and validate a prediction model for 12-month OS using ORR.
METHODS
We searched electronic bibliographic databases (MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials) and conference abstracts through the ASCO and European Society for Medical Oncology websites for eligible studies published between January 2000 and March 2020. Search terms included atezolizumab, avelumab, durvalumab, nivolumab, pembrolizumab, ipilimumab, tremelimumab, cytotoxic T-cell lymphocyte-4 (CTLA-4), PD-1, programmed death-ligand 1 (PD-L1), and checkpoint inhibitor.
Studies were eligible if they assessed ICIs (anti–PD-1, anti–PD-L1, or anti–CTLA-4) in unresectable locally advanced or metastatic solid tumors and reported outcomes for both ORR and OS. Both randomized controlled trials (RCTs) and non-RCTs were included. We excluded studies in neoadjuvant, adjuvant, or maintenance settings and trials comparing different doses of the same ICI, local injection therapies, or other novel ICIs not targeting anti–PD-1, anti–PD-L1, or anti–CTLA-4. We excluded studies combining ICI with other therapies such as chemotherapy, targeted therapies, radiotherapy, or other locally directed therapies as combination therapies may introduce a different mechanism of action on tumors. We excluded studies with a sample size of 80 patients or less because small studies provide less precise estimates of OS for our analysis.
Data Extractions and Statistical Analyses
Two authors (P.-S.K. and W.-H.Y.) screened the studies independently and extracted the following data: trial phase, sample size, treatment arms, line of therapy, tumor histology, reported primary outcome(s), and results of outcomes including CR, ORR (both CR and partial response), duration of response, and OS hazard ratio (HR) for the comparison of the experimental ICI treatment versus control. Types of tumor response assessment have also been collected (RECIST v1.116 or others); ORR risk ratio was calculated as the ratio of ORR in the control versus experimental ICI arm.
We retrieved data on proportions of patients with CR as the best tumor response and also landmark efficacy end points including 6-month DR, 12-month OS, and 24-month OS. Data were extracted from the Kaplan-Meier curves using Digitizelt software (version 2.0) if they were not reported in the studies. Any discrepancies were resolved by consensus. Six-month DR was defined as the proportion of patients who had a continuous response for ≥ 6 months. Twelve-month and 24-month OS were the proportions of patients who remained alive at 12 and 24 months, respectively. The 6-month DR ratio was that of control versus experimental ICI arm. We expressed CR ratio in the same fashion. The median and interquartile range of all outcomes were also calculated.
Correlation by Relative Treatment Comparisons
Using all eligible RCTs, we assessed the correlations between treatment arms of RCTs (trial level), with ORR risk ratio versus OS HR, 6-month DR ratio versus OS HR, and CR risk ratio versus OS HR, using weighted linear regression. The strength of correlations was expressed as the correlation coefficient r, with values close to 1 indicating strong associations. Exploratory analyses were performed separately in melanoma, NSCLC, and other cancers.
Correlation Within ICI Arms
Within ICI arms of all included studies (RCTs and non-RCTs), we assessed the correlations between 6-month DR and 12-month OS and that between CR and 12-month OS. These analyses were performed using weighted linear regression. A sensitivity analysis of correlations between response (ORR, 6-month DR, and CR) and 24-month OS was also performed.
Twelve-Month OS Prediction in ICI Arms on the Basis of ORR
As a secondary end point, we aimed to predict the 12-month OS using ORR with a regression model. We divided the data sets into (1) model development cohort consisting of RCTs conducted from January 2000 to June 2019 and (2) validation cohort consisting of RCT and non-RCT studies conducted from July 2019 to March 2020. Using the model development cohort data set, a linear regression model, adjusted for tumor histology types (melanoma, NSCLC, and others), was developed by using the ordinary least-squares approach to examine the relationships between ORR and 12-month OS rate. In the validation cohort data set, we obtained a predicted 12-month OS rate by applying the reported ORR to the regression model. The performance of this model was assessed by comparing the actual versus model-predicted 12-month OS rate; we assessed visually for calibration using plots of actual versus model-predicted 12-month OS rates.
We used STATA version 15 statistical software (StataCorp) for all linear regression analyses weighted by sample size.
RESULTS
Baseline Characteristics
We identified 63 eligible trials (PRISMA, Data Supplement) comprising data from 78 ICI treatment arms and 30,815 patients (including 18,084 in experimental ICI arms, Data Supplement). Overall, 58 (74%) trials were RCTs and 20 (27%) were non-RCTs. Thirteen trials (17%) were multi-arm studies (both RCTs and non-RCTs). OS was the most common primary end point (40%), and 15 of the 63 RCTs had coprimary end points. Sixty trials assessed tumor response using RECIST v1.1,16 one used immune-related response criteria,17 and two used modified RECIST for mesothelioma.18
Table 1 summarizes the characteristics of included trials. The anti–PD-1 and anti–PD-L1 ICIs included were pembrolizumab, nivolumab, atezolizumab, durvalumab, and avelumab. The CTLA-4 ICIs were ipilimumab and tremelimumab. Of the 78 ICI arms, 14 were doublet ICIs, combining nivolumab with ipilimumab, durvalumab with tremelimumab, or talimogene laherparepvec with ipilimumab. The majority were phase III RCTs. There were 15 tumors types, predominantly NSCLC and melanoma. Of note, one study was tumor-agnostic, selecting patients on the basis of microsatellite instability–high or mismatch repair–deficient tumors.
TABLE 1.
Summary of Studies
The median for ORR risk ratio, 6-month DR ratio, CR risk ratio, and OS HR were 0.66, 0.51, 0.34, and 0.78, respectively. The median for ORR, CR, 6-month DR, and 12-month OS in ICI arms were 20%, 3%, 18%, and 51%, respectively (Data Supplement). The median sample size per ICI arm was 198 patients (IQR, 119-314), and the minimum follow-up ranged from 4 to 48 months.
Correlations of Surrogates With OS in Relative Treatment Comparisons
In relative comparisons, the correlation coefficient between ORR risk ratio and OS HR was r = 0.58 (n = 54; Fig 1A). The fitted line of scatterplots of this comparison was almost flat, indicating a wide range of ORR risk ratio but narrow range of OS HR. We observed similar results in the comparisons of 6-month DR ratio with OS HR (r = 0.62; Fig 1B) and CR risk ratio with OS HR (r = 0.42; Fig 1C).
FIG 1.
Correlations among (A) ORR risk ratio, (B) 6-month DoR ratio, (C) CR risk ratio, and OS HR. Linear regression plots were generated with best-fitted lines—observed correlation (solid line) and ideal line (dotted line). Each data point represents a study from each ICI comparison. Size of circles indicates sample size of each ICI study. CR, complete response; DR durable response; HR, hazard ratio; ICI, immune checkpoint inhibitor; ORR, objective response rate; OS, overall survival; r, correlation coefficient.
In the exploratory analyses, correlations between ORR risk ratio and OS HR were not different compared with all tumors combined (melanoma: r = 0.51; NSCLC: r = 0.66; and others: r = 0.60; Data Supplement). Correlations between CR risk ratio and OS HR were similarly poor (melanoma: r = 0.18; NSCLC: r = 0.16; and others: r = 0.36; Data Supplement).
There were only 18 trials that were evaluable for treatment effects on 6-month DR ratio. The correlations between 6-month DR ratio and OS in melanoma, NSCLC, and others were r = NE, r = 0.89, and r = 0.54, respectively (Data Supplement), compared with all tumors combined (r = 0.62).
Correlations of Surrogates With 12-Month OS Within ICI Arm Only
Within the ICI arms of the included trials, the correlation coefficient between ORR and 12-month OS was r = 0.76 (Fig 2A). We observed a similar result for the correlation between 6-month DR and 12-month OS (r = 0.84; Fig 2B). In contrast, the correlation coefficient between CR and 12-month OS was r = 0.50 (Fig 2C).
FIG 2.
Correlations at arm level. (A) ORR versus 12-month OS. (B) Six-month DR versus 12-month OS. (C) CR versus 12-month OS. Linear regression plots were generated with best-fitted lines—observed correlation (solid line) and ideal line (dotted line). Each data point represents a study from each ICI arm. Size of circles indicates sample size of the ICI arm of the study. 6-month DR, proportions of patients who had a tumour response at 6 months; CR, complete response; DR, durable response; ICI, immune checkpoint inhibitor; ORR, objective response rate; OS, overall survival; r, correlation coefficient.
In sensitivity analyses, we assessed the correlations between all three tumor response end points and 24-month OS and found similar results (r = 0.79 for ORR, r = 0.89 for 6-month DR, and r = 0.65 for CR; Data Supplement).
Development and Validation of a Regression Model to Predict 12-Month OS
In the model development data set, matched data for ORR and 12-month OS were available from 44 ICI arms. The regression equation for the relationship between ORR and 12-month OS rate, accounting for different tumor types, was as follows: 12-month OS rate = (1.012324 × ORR) + 0.3006825 + (0 × melanoma) + (0.0021079 × NSCLC) − (0.038521 × other tumors).
When this model was applied to the validation data set, the predicted 12-month OS closely agreed with the observed 12-month OS (r = 0.63; Fig 3), showing strong prediction of this ORR model for 12-month OS.
FIG 3.
Observed versus predicted OS12 by ORR. Linear regression plots were generated with best-fitted lines—observed correlation (solid line) and ideal line (dotted line). Each data point represents a study from each ICI arm. Size of circles indicates sample size of the ICI arm of the study. ICI, immune checkpoint inhibitor; ORR, objective response rate; OS, overall survival; OS12, 12-month OS rate; r, correlation coefficient.
DISCUSSION
We reviewed immunotherapy trial data over 20 years and found that widely used tumor response end points, namely objective response, duration of response, and CR, were poor surrogate end points for OS at trial level. However, for patients who received immunotherapy (arm-level analysis), ORR and 6-month DR were prognostic for 12- and 24-month OS. We also demonstrate that ORR can reliably predict for 12-month OS and therefore has a useful role in early-phase studies to screen for potentially promising agents. In contrast, CR rate was not prognostic of 12-month OS.
To consider the implications of these findings, it is important to understand the additional evidence required for a surrogate versus a prognostic end point. Prentice19 requires two criteria: (1) tumor response should meet the requirements for a prognostic end point by accurately predicting OS, regardless of the treatment received (referred to as individual-level surrogacy). It allows the tumor response to treatment to be used as a surrogate for OS in an individual and can be assessed through single-arm studies. (2) The relative effect of immunotherapy versus control on tumor response should fully capture the relative effect on OS (trial-level surrogacy). It allows the trial treatment effect on tumor response to be used as a surrogate for the trial treatment effect on OS and requires comparative studies to demonstrate a strong correlation between relative measures of these two end points. This is therefore a much more stringent requirement to meet.19
Why are some highly prognostic end points not also good surrogate end points? Meeting the above two criteria is not just a statistical box-ticking exercise but they also help in making this distinction. Patients who have sufficient (at least 30% per RECIST criteria)16 and durable tumor shrinkage are more likely to live longer than nonresponders, regardless of treatment. Its poor surrogacy at trial level, however, may be caused by tumor shrinkage resulting in a negative survival outcome if this is followed by accelerated proliferation of tumor cells.20 Similarly, drug resistance during or after initial response or the effects of subsequent treatments on OS cannot be captured. Furthermore, ORR, assessed by computed tomography scans, does not take into account the tumor microenvironment or tumor mutational burden.21
RECIST assessment,16 and even iRECIST,22 which was developed for immunotherapy, are not perfect in assessing mixed response (heterogeneity of tumor response). Most trials in this study used the standard tumor assessment of RECIST v1.1.16 Despite reports of pseudoprogression23,24 and hyperprogression20 related to immunotherapy, these both are uncommon and are therefore unlikely to explain our findings.24
Our findings do not preclude tumor response from having a role as a prognostic end point (criterion 1). Similar prognostic results have been reported in trials of conventional chemotherapy and targeted therapies.25 Tumor response as a prognostic marker is useful to screen treatment in early-phase studies. With more discoveries of genetic variations, many cancers are subdivided into rare or ultrarare cancers, and treatment may be tumor-agnostic, so RCTs in these tumors will be difficult or impossible to conduct. Tumor response will continue to aid us in deciding whether to continue or change treatment.
CR was neither prognostic of 12-month OS nor a surrogate for OS. This paradox could be explained by the narrow range of CR rate in immunotherapy (minimum 0% and maximum 22%) across all tumor types and line of treatment in our study. CR is ideal but a high bar for any anticancer treatment to achieve and it relies on the burden of the disease at baseline and a strict definition by RECIST, ie, disappearance of all target and nontarget lesions.16 The selected cases of CR require further studies to better understand the underlying explanation such as hypermutated tumors26 and other genomic changes.
The lack of association between duration of response and OS as shown in our study needs to be interpreted carefully. There were only 18 trials that were evaluable for treatment effects on duration of response and only seven in NSCLC where strong correlation with OS was found. In the computation of duration of response, there is a selection bias where only those who have responded are being assessed, and the time taken to response is not taken into account. Future trials are encouraged to report on 6-month DR rates to enable further validation in each tumor type.
Our study calls for further research to define valid surrogate end points for immunotherapy. Other measures of tumor response should be explored, such as restricted mean differences in duration of response27 and categorization of depth of response into quartiles.28,29 Patient-centered end points including symptomatic improvement or prolongation of time to symptom deterioration and biomarkers such as tumor mutational burden or circulating tumor DNA may provide earlier information on burden and clearance of disease prior to scans30 will be important to supplement assessment of treatment benefit. Landmark surrogate end points assessment could be expanded to early-stage cancers (eg, 12-month disease-free survival) for neoadjuvant or adjuvant therapies, where OS benefits are harder to assess because of long-term follow-up requirements.
To our knowledge, this is the first large-scale validation of duration and depth of response as surrogate end points in immunotherapy, at both trial and arm levels. By pooling data from 63 trials and 78 ICI treatment arms, we achieved a sample size of more than 30,000 patients, representing 15 different tumor types and various lines of therapies. Our 12-month OS prediction model further validated the prognostic value of ORR for 12-month OS. Our methods of assessing landmarks of duration of response (at 6 and 12 month) adapted from other landmark surrogate validation, such as 6-month progression-free survival and 12-month OS,9,31,32 were novel and validated here.
We included data (32 of 78 studies) reported in conference abstracts or presentations, which undergo less rigorous peer review, but have the advantage of reporting on relevant contemporary ICI, including negative studies that may not reach full publication. Some of these abstracts have shorter follow-up time (range 4-48 months), potentially overestimating 12-month OS. We extracted landmark end points, duration of response, and OS from Kaplan-Meier curves via Digitizelt software (version 2.0). There may be discrepancies without access to the individual patient data.
In conclusion, tumor response end points, which include ORR, 6-month DR, and CR, are poor surrogate end points for OS in immunotherapy trials and OS remains the most important and clinically meaningful end point. However, ORR and 6-month DR are prognostic for 12-month OS. This supports their use in drug development and screening of agents for phase III studies.
Peey-Sei Kok
Research Funding: AstraZeneca
Travel, Accommodations, Expenses: Pfizer
Other Relationship: Roche, AstraZeneca
Ian Marschner
Consulting or Advisory Role: AbbVie
Research Funding: Janssen
Michael Friedlander
Honoraria: AstraZeneca, MSD, Lilly, Takeda, Novartis, GlaxoSmithKline
Consulting or Advisory Role: AstraZeneca, MSD, AbbVie, Lilly, Takeda, Novartis, GlaxoSmithKline
Speakers' Bureau: AstraZeneca, ACT Genomics
Research Funding: BeiGene, AstraZeneca, Novartis
Travel, Accommodations, Expenses: AstraZeneca
Chee Khoon Lee
Honoraria: AstraZeneca, Pfizer, Amgen, Takeda, Yuhan, Boehringer Ingelheim
Consulting or Advisory Role: Novartis, Boehringer Ingelheim, Takeda, AstraZeneca, Yuhan, Amgen
Research Funding: AstraZeneca, Roche, Merck KGaA
No other potential conflicts of interest were reported.
AUTHOR CONTRIBUTIONS
Conception and design: Peey-Sei Kok, Michael Friedlander, Chee Khoon Lee
Collection and assembly of data: Peey-Sei Kok, Won-Hee Yoon
Data analysis and interpretation: Peey-Sei Kok, Sally Lord, Ian Marschner, Chee Khoon Lee
Manuscript writing: All authors
Final approval of manuscript: All authors
Accountable for all aspects of the work: All authors
AUTHORS' DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST
The following represents disclosure information provided by the authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO’s conflict of interest policy, please refer to www.asco.org/rwc or ascopubs.org/po/author-center.
Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments).
Peey-Sei Kok
Research Funding: AstraZeneca
Travel, Accommodations, Expenses: Pfizer
Other Relationship: Roche, AstraZeneca
Ian Marschner
Consulting or Advisory Role: AbbVie
Research Funding: Janssen
Michael Friedlander
Honoraria: AstraZeneca, MSD, Lilly, Takeda, Novartis, GlaxoSmithKline
Consulting or Advisory Role: AstraZeneca, MSD, AbbVie, Lilly, Takeda, Novartis, GlaxoSmithKline
Speakers' Bureau: AstraZeneca, ACT Genomics
Research Funding: BeiGene, AstraZeneca, Novartis
Travel, Accommodations, Expenses: AstraZeneca
Chee Khoon Lee
Honoraria: AstraZeneca, Pfizer, Amgen, Takeda, Yuhan, Boehringer Ingelheim
Consulting or Advisory Role: Novartis, Boehringer Ingelheim, Takeda, AstraZeneca, Yuhan, Amgen
Research Funding: AstraZeneca, Roche, Merck KGaA
No other potential conflicts of interest were reported.
REFERENCES
- 1.Zhang AD, Puthumana J, Downing NS, et al. Assessment of clinical trials supporting US Food and Drug Administration approval of novel therapeutic agents, 1995-2017. JAMA Netw Open. 2020;3:e203284. doi: 10.1001/jamanetworkopen.2020.3284. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.US Food and Drug Administration . Clinical Trial Endpoints for the Approval of Cancer Drugs and Biologics. Guidance for Industry. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-trial-endpoints-approval-cancer-drugs-and-biologics 2018 [Google Scholar]
- 3.European Medicines Agency . Support for Early Access. https://www.ema.europa.eu/en/human-regulatory/overview/support-early-access [Google Scholar]
- 4.Gill J, Prasad V.A reality check of the accelerated approval of immune-checkpoint inhibitors Nat Rev Clin Oncol 16656–6582019 [DOI] [PubMed] [Google Scholar]
- 5.US Food and Drug Administration . Demonstrating Substantial Evidence of Effectiveness for Human Drug and Biological Products Guidance for Industry. https://www.fda.gov/media/133660/download [Google Scholar]
- 6.European Medicines Agency . Conditional Marketing Authorisation. https://www.ema.europa.eu/en/human-regulatory/marketing-authorisation/conditional-marketing-authorisation [Google Scholar]
- 7.Petrelli F, Ghidini M, Costanzo A, et al. Surrogate endpoints in immunotherapy trials for solid tumors. Ann Transl Med. 2019;7:154. doi: 10.21037/atm.2019.03.20. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Ritchie G, Gasper H, Man J, et al. Is objective response rate (ORR) a valid primary endpoint in phase 2 trials (Ph2t) of immune checkpoint inhibitors (ICI) for advanced solid cancers? Ann Oncol. 2017;28:v411. [Google Scholar]
- 9.Mushti SL, Mulkey F, Sridhara R.Evaluation of overall response rate and progression-free survival as potential surrogate endpoints for overall survival in immunotherapy trials Clin Cancer Res 242268–22752018 [DOI] [PubMed] [Google Scholar]
- 10.Sharpe AH.Introduction to checkpoint inhibitors and cancer immunotherapy Immunol Rev 2765–82017 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Robert C, Ribas A, Hamid O, et al. Durable complete response after discontinuation of pembrolizumab in patients with metastatic melanoma J Clin Oncol 361668–16742018 [DOI] [PubMed] [Google Scholar]
- 12.Garon EB, Hellmann MD, Rizvi NA, et al. Five-year overall survival for patients with advanced non‒small-cell lung cancer treated with pembrolizumab: Results from the phase I KEYNOTE-001 study J Clin Oncol 372518–25272019 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Li J, He Q, Yu X, et al. Complete response associated with immune checkpoint inhibitors in advanced non-small-cell lung cancer: A meta-analysis of nine randomized controlled trials Cancer Manag Res 111623–16292019 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.McDermott DF, Drake CG, Sznol M, et al. Survival, durable response, and long-term safety in patients with previously treated advanced renal cell carcinoma receiving nivolumab J Clin Oncol 332013–20202015 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Iacovelli R, Ciccarese C, Schutz FA, et al. Complete response to immune checkpoint inhibitors-based therapy in advanced renal cell carcinoma patients. A meta-analysis of randomized clinical trials Urol Oncol 38798.e17–798.e242020 [DOI] [PubMed] [Google Scholar]
- 16.Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1) Eur J Cancer 45228–2472009 [DOI] [PubMed] [Google Scholar]
- 17.Wolchok JD, Hoos A, O'Day S, et al. Guidelines for the evaluation of immune therapy activity in solid tumors: Immune-related response criteria Clin Cancer Res 157412–74202009 [DOI] [PubMed] [Google Scholar]
- 18.Byrne M, Nowak AK.Modified RECIST criteria for assessment of response in malignant pleural mesothelioma Ann Oncol 15257–2602004 [DOI] [PubMed] [Google Scholar]
- 19.Prentice RL.Surrogate endpoints in clinical trials: Definition and operational criteria Stat Med 8431–4401989 [DOI] [PubMed] [Google Scholar]
- 20.Frelaut M, Le Tourneau C, Borcoman E. Hyperprogression under immunotherapy. Int J Mol Sci. 2019;20:2674. doi: 10.3390/ijms20112674. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Marabelle A, Fakih M, Lopez J, et al. Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: Prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study Lancet Oncol 211353–13652020 [DOI] [PubMed] [Google Scholar]
- 22.Seymour L, Bogaerts J, Perrone A, et al. iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics Lancet Oncol 18e143–e1522017 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Kurra V, Sullivan RJ, Gainor JF, et al. Pseudoprogression in cancer immunotherapy: Rates, time course and patient outcomes. J Clin Oncol. 2016;34 suppl; abstr 6580. [Google Scholar]
- 24.Katz SI, Hammer M, Bagley SJ, et al. Radiologic pseudoprogression during anti–PD-1 therapy for advanced non–small cell lung cancer J Thorac Oncol 13978–9862018 [DOI] [PubMed] [Google Scholar]
- 25.Haslam A, Hey SP, Gill J, et al. A systematic review of trial-level meta-analyses measuring the strength of association between surrogate end-points and overall survival in oncology Eur J Cancer 106196–2112019 [DOI] [PubMed] [Google Scholar]
- 26.Schumacher TN, Schreiber RD.Neoantigens in cancer immunotherapy Science 34869–742015 [DOI] [PubMed] [Google Scholar]
- 27.Huang B, Tian L, Talukder E, et al. Evaluating treatment effect based on duration of response for a comparative oncology study JAMA Oncol 4874–8762018 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.McCoach CE, Blumenthal GM, Zhang L, et al. Exploratory analysis of the association of depth of response and survival in patients with metastatic non-small-cell lung cancer treated with a targeted therapy or immunotherapy Ann Oncol 282707–27142017 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Osgood C, Mulkey F, Mishra-Kalyani PS, et al. FDA analysis of depth of response (DpR) and survival across 10 randomized controlled trials in patients with previously untreated unresectable or metastatic melanoma (UMM) by therapy type. J Clin Oncol. 2019;37 suppl; abstr 9508. [Google Scholar]
- 30.Hrebien S, Citi V, Garcia-Murillas I, et al. Early ctDNA dynamics as a surrogate for progression-free survival in advanced breast cancer in the BEECH trial Ann Oncol 30945–9522019 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Ritchie G, Gasper H, Man J, et al. Defining the most appropriate primary end point in phase 2 trials of immune checkpoint inhibitors for advanced solid cancers: A systematic review and meta-analysis JAMA Oncol 4522–5282018 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Kaufman HL, Schwartz LH, William WN, et al. Evaluation of classical clinical endpoints as surrogates for overall survival in patients treated with immune checkpoint blockers: A systematic review and meta-analysis J Cancer Res Clin Oncol 1442245–22612018 [DOI] [PMC free article] [PubMed] [Google Scholar]