Current challenges for assessing the long-term clinical benefit of cancer immunotherapy: a multi-stakeholder perspective

Effect of immune checkpoint inhibitors at different treatment time periods on prognosis of patients with extensive-stage small-cell lung cancer

BACKGROUND

The application of immune checkpoint inhibitors (ICIs) in treating patients with extensive-stage small-cell lung cancer (ES-SCLC) has brought us new hope, but the real-world outcome is relatively lacking. Our aim was to investigate the clinical use, efficacy, and survival benefit of ICIs in ES-SCLC from real-world data analysis.

METHODS

A retrospective analysis of ES-SCLC patients was conducted between 2012 and 2022. Progression-free survival (PFS) and overall survival (OS) were assessed between groups to evaluate the value of ICIs at different lines of treatment. PFS1 was defined as the duration from initial therapy to disease progression or death. PFS2 was defined as the duration from the first disease progression to the second disease progression or death.

RESULTS

One hundred and eighty patients with ES-SCLC were included. We performed landmark analysis, which showed that compared to the second-line and subsequent-lines ICIs-combined therapy group (2SL-ICIs) and non-ICIs group, the first-line ICIs-combined therapy group (1L-ICIs) prolonged OS and PFS1. There was a trend toward prolonged OS in the 2SL-ICIs group than in the non-ICIs group, but the significance threshold was not met (median OS 11.94 months vs. 11.10 months, P = 0.14). A longer PFS2 was present in the 2SL-ICIs group than in the non-ICIs group (median PFS2 4.13 months vs. 2.60 months, P < 0.001).

CONCLUSION

First-line ICIs plus chemotherapy should be applied in clinical practice. If patients did not use ICIs plus chemotherapy in first-line therapy, the use of ICIs in the second line or subsequent lines of treatment could prolong PFS2.

Treatment Effect Waning in Immuno-oncology Health Technology Assessments: A Review of Assumptions and Supporting Evidence with Proposals to Guide Modelling

Treatment effect waning (TEW) refers to the attenuation of treatment effects over time. Assumptions of a sustained immuno-oncologic treatment effect have been a source of contention in health technology assessment (HTA). We review how TEW has been addressed in HTA and in the wider scientific literature. We analysed company submissions to English language HTA agencies and summarised methods and assumptions used. We subsequently reviewed TEW-related work in the ISPOR Scientific Presentations Database and conducted a targeted literature review (TLR) for evidence of the maintenance of immuno-oncology (IO) treatment effects post-treatment discontinuation. We found no standardised approach adopted by companies in submissions to HTA agencies, with immediate TEW most used in scenario analyses. Independently fitted survival models do however suggest TEW may often be implicitly modelled. Materials in the ISPOR scientific database suggest gradual TEW is more plausible than immediate TEW. The TLR uncovered evidence of durable survival in patients treated with IOs but no evidence that directly addresses the presence or absence of TEW. Our HTA review shows the need for a consistent and appropriate implementation of TEW in oncology appraisals. However, the TLR highlights the absence of direct evidence on TEW in literature, as TEW is defined in terms of relative treatment effects-not absolute survival. We propose a sequence of steps for analysts to use when assessing whether a TEW scenario is necessary and appropriate to present in appraisals of IOs.

Cost-effectiveness analysis of atezolizumab plus bevacizumab compared with sorafenib as first-line treatment in advanced hepatocellular carcinoma in Singapore

OBJECTIVES

This study aims to explore the cost-effectiveness of atezolizumab plus bevacizumab against sorafenib for first-line treatment of locally advanced or metastatic hepatocellular carcinoma (HCC) in Singapore.

METHODS

A partitioned survival model was developed from a healthcare system perspective, with a 10-year lifetime horizon. Clinical inputs and utilities were obtained from the IMbrave150 trial. Healthcare resource use costs were obtained from published local sources; drug costs reflected the most recent public hospital selling prices. Outcomes included life years, quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs). Deterministic and probabilistic sensitivity analyses were performed to assess the model's robustness.

RESULTS

Atezolizumab plus bevacizumab offered an additional 1.42 life years and 1.09 QALYs, with an additional cost of S$111,847; the ICER was S$102,988/QALY. The World Health Organization considers interventions with ICERs <1 gross domestic product (GDP)/capita to be highly cost-effective. At a willingness-to-pay (WTP) threshold of S$114,165/QALY (Singapore's 2022 GDP/capita), atezolizumab plus bevacizumab is cost-effective compared with sorafenib. The ICER was most sensitive to variations in utilities, but all parameter variations had no significant impact on the model outcomes.

CONCLUSION

At a WTP threshold of Singapore's GDP/capita, atezolizumab plus bevacizumab is cost-effective compared with sorafenib.

Avelumab first-line maintenance in advanced urothelial carcinoma: Complete screening for prognostic and predictive factors using machine learning in the JAVELIN Bladder 100 phase 3 trial

BACKGROUND

Avelumab first-line (1 L) maintenance is a standard of care for advanced urothelial carcinoma (aUC) based on the JAVELIN Bladder 100 phase 3 trial, which showed that avelumab 1 L maintenance + best supportive care (BSC) significantly prolonged overall survival (OS) and progression-free survival (PFS) vs BSC alone in patients who were progression free after receiving 1 L platinum-containing chemotherapy. Here, we comprehensively screened JAVELIN Bladder 100 trial datasets to identify prognostic factors that define subpopulations of patients with longer or shorter OS irrespective of treatment, and predictive factors that select patients who could obtain a greater OS benefit from avelumab 1 L maintenance treatment.

METHODS

We performed machine learning analyses to screen a large set of baseline covariates, including patient demographics, disease characteristics, laboratory values, molecular biomarkers, and patient-reported outcomes. Covariates were identified from previously reported analyses and established prognostic and predictive markers. Variables selected from random survival forest models were processed further in univariate Cox models with treatment interaction and visually inspected using correlation analysis and Kaplan-Meier curves. Results were summarized in a multivariable Cox model.

RESULTS

Prognostic baseline covariates associated with OS included in the final model were assignment to avelumab 1 L maintenance treatment, Eastern Cooperative Oncology Group performance status, site of metastasis, sum of longest target lesion diameters, levels of C-reactive protein and alkaline phosphatase in blood, lymphocyte proportion in intratumoral stroma, tumor mutational burden, and tumor CD8+ T-cell infiltration. Potential predictive factors included site of metastasis, tumor mutation burden, and tumor CD8+ T-cell infiltration. An analysis in patients with PD-L1+ tumors had similar findings to those in the overall population.

CONCLUSIONS

Machine learning analyses of data from the JAVELIN Bladder 100 trial identified potential prognostic and predictive factors for avelumab 1 L maintenance treatment in patients with aUC, which warrant further evaluation in other clinical datasets.

Metastatic Kidney Cancer: Does the Location of the Metastases Matter? Moving towards Personalized Therapy for Metastatic Renal Cell Carcinoma

The management of renal cell carcinoma (RCC) has been revolutionized over the past two decades with several practice-changing treatments. Treatment for RCC often requires a multimodal approach: Local treatment, such as surgery or ablation, is typically recommended for patients with localized tumors, while stage IV cancers often require both local and systemic therapy. The treatment of advanced RCC heavily relies on immunotherapy and targeted therapy, which are highly contingent upon histological subtypes. Despite years of research on biomarkers for RCC, the standard of care is to choose systemic therapy based on the risk profile according to the International Metastatic RCC Database Consortium and Memorial Sloan Kettering Cancer Centre models. However, many questions still need to be answered. Should we consider metastatic sites when deciding on treatment options for metastatic RCC? How do we choose between dual immunotherapy and combinations of immunotherapy and tyrosine kinase inhibitors? This review article aims to answer these unresolved questions surrounding the concept of personalized medicine.

Updated cost-effectiveness analysis of adebrelimab plus chemotherapy for extensive-stage small cell lung cancer in China

OBJECTIVE

The CAPSTONE-1 trial demonstrated that adebrelimab-based immunotherapy yielded a favourable survival benefit compared with chemotherapy for patients with extensive-stage small cell lung cancer (ES-SCLC). This study aims to evaluate the cost-effectiveness of this immunotherapy in the treatment of ES-SCLC from a healthcare system perspective in China.

DESIGN

The TreeAge Pro software was used to establish a three-state partitioned survival model. Survival data came from the CAPSTONE-1 trial (NCT03711305), and only direct medical costs were included. Utility values were obtained from the published literature. Sensitivity analysis was performed to explore the robustness of the model. The cost-effectiveness of immunotherapy was investigated through scenario and exploratory analyses in various settings.

OUTCOME MEASURES

Total costs, incremental costs, life years, quality-adjusted life-years (QALYs), incremental QALYs and incremental cost-effectiveness ratio (ICER).

RESULTS

The basic analysis revealed that the adebrelimab group achieved a total of 1.1 QALYs at a cost of US$65 385, while the placebo group attained 0.78 QALYs at a cost of US$12 741. ICER was US$163 893/QALY. Sensitivity analysis confirmed that the model was robust. Results from scenario and exploratory analyses indicated that the combination of adebrelimab and chemotherapy did not demonstrate cost-effectiveness in any scenario.

CONCLUSIONS

From the perspective of the Chinese healthcare system, adebrelimab in combination with chemotherapy for the treatment of ES-SCLC was not economical compared with chemotherapy.

Extrapolation of Survival Data Using a Bayesian Approach: A Case Study Leveraging External Data from Cilta-Cel Therapy in Multiple Myeloma

INTRODUCTION

Extrapolating long-term overall survival (OS) from shorter-term clinical trial data is key to health technology assessment in oncology. However, extrapolation using conventional methods is often subject to uncertainty. Using ciltacabtagene autoleucel (cilta-cel), a chimeric antigen receptor T-cell therapy for multiple myeloma, we used a flexible Bayesian approach to demonstrate use of external longer-term data to reduce the uncertainty in long-term extrapolation.

METHODS

The pivotal CARTITUDE-1 trial (NCT03548207) provided the primary efficacy data for cilta-cel, including a 12-month median follow-up snapshot of OS. Longer-term (48-month median follow-up) survival data from the phase I LEGEND-2 study (NCT03090659) were also available. Twelve-month CARTITUDE-1 OS data were extrapolated in two ways: (1) conventional survival models with standard parametric distributions (uninformed), and (2) Bayesian survival models whose shape prior was informed from 48-month LEGEND-2 data. For validation, extrapolations from 12-month CARTITUDE-1 data were compared with observed 28-month CARTITUDE-1 data.

RESULTS

Extrapolations of the 12-month CARTITUDE-1 data using conventional uninformed parametric models were highly variable. Using informative priors from the 48-month LEGEND-2 dataset, the ranges of projected OS at different timepoints were consistently narrower. Area differences between the extrapolation curves and the 28-month CARTITUDE-1 data were generally lower in informed Bayesian models, except for the uninformed log-normal model, which had the lowest difference.

CONCLUSIONS

Informed Bayesian survival models reduced variation of long-term projections and provided similar projections as the uninformed log-normal model. Bayesian models generated a narrower and more plausible range of OS projections from 12-month data that aligned with observed 28-month data.

TRIAL REGISTRATION

CARTITUDE-1 ClinicalTrials.gov identifier, NCT03548207. LEGEND-2 ClinicalTrials.gov identifier, NCT03090659, registered retrospectively on 27 March 2017, and ChiCTR-ONH-17012285.

The tumor immune microenvironment in pancreatic cancer and its potential in the identification of immunotherapy biomarkers

INTRODUCTION

Pancreatic cancer (PC) has an extremely poor prognosis, even with surgical resection and triplet chemotherapy treatment. Cancer immunotherapy has been recently approved for tumor-agnostic treatment with genome analysis, including in PC. However, it has limited efficacy.

AREAS COVERED

In addition to the low tumor mutation burden, one of the difficulties of immunotherapy in PC is the presence of abundant stromal cells in its microenvironment. Among stromal cells, cancer-associated fibroblasts (CAFs) play a major role in immunotherapy resistance, and CAF-targeted therapies are currently under development, including those in combination with immunotherapies. Meanwhile, microbiomes and tumor-derived exosomes (TDEs) have been shown to alter the behavior of distant receptor cells in PC. This review discusses the role of CAFs, microbiomes, and TDEs in PC tumor immunity.

EXPERT OPINION

Elucidating the mechanisms by which CAFs, microbiomes, and TDEs are involved in the tumorigenesis of PC will be helpful for developing novel immunotherapeutic strategies and identifying companion biomarkers for immunotherapy. Spatial single-cell analysis of the tumor microenvironment will be useful for identifying biomarkers of PC immunity. Furthermore, given the complexity of immune mechanisms, artificial intelligence models will be beneficial for predicting the efficacy of immunotherapy.

Recent Advances in Molecular Mechanisms of Cancer Immunotherapy

Cancer is among the current leading causes of death worldwide, despite the novel advances that have been made toward its treatment, it is still considered a major public health concern. Considering both the serious impact of cancer on public health and the significant side effects and complications of conventional therapeutic options, the current strategies towards targeted cancer therapy must be enhanced to avoid undesired toxicity. Cancer immunotherapy has become preferable among researchers in recent years compared to conventional therapeutic options, such as chemotherapy, surgery, and radiotherapy. The understanding of how to control immune checkpoints, develop therapeutic cancer vaccines, genetically modify immune cells as well as enhance the activation of antitumor immune response led to the development of novel cancer treatments. In this review, we address recent advances in cancer immunotherapy molecular mechanisms. Different immunotherapeutic approaches are critically discussed, focusing on the challenges, potential risks, and prospects involving their use.

Predicting survival of NSCLC patients treated with immune checkpoint inhibitors: Impact and timing of immune-related adverse events and prior tyrosine kinase inhibitor therapy

Introduction

Immune checkpoint inhibitors (ICIs) produce a broad spectrum of immune-related adverse events (irAEs) affecting various organ systems. While ICIs are established as a therapeutic option in non-small cell lung cancer (NSCLC) treatment, most patients receiving ICI relapse. Additionally, the role of ICIs on survival in patients receiving prior targeted tyrosine kinase inhibitor (TKI) therapy has not been well-defined.

Objective

To investigate the impact of irAEs, the relative time of occurrence, and prior TKI therapy to predict clinical outcomes in NSCLC patients treated with ICIs.

Methods

A single center retrospective cohort study identified 354 adult patients with NSCLC receiving ICI therapy between 2014 and 2018. Survival analysis utilized overall survival (OS) and real-world progression free survival (rwPFS) outcomes. Model performance matrices for predicting 1-year OS and 6-month rwPFS using linear regression baseline, optimal, and machine learning modeling approaches.

Results

Patients experiencing an irAE were found to have a significantly longer OS and rwPFS compared to patients who did not (median OS 25.1 vs. 11.1 months; hazard ratio [HR] 0.51, confidence interval [CI] 0.39- 0.68, P-value <0.001, median rwPFS 5.7 months vs. 2.3; HR 0.52, CI 0.41- 0.66, P-value <0.001, respectively). Patients who received TKI therapy before initiation of ICI experienced significantly shorter OS than patients without prior TKI therapy (median OS 7.6 months vs. 18.5 months; P-value < 0.01). After adjusting for other variables, irAEs and prior TKI therapy significantly impacted OS and rwPFS. Lastly, the performances of models implementing logistic regression and machine learning approaches were comparable in predicting 1-year OS and 6-month rwPFS.

Conclusion

The occurrence of irAEs, the timing of the events, and prior TKI therapy were significant predictors of survival in NSCLC patients on ICI therapy. Therefore, our study supports future prospective studies to investigate the impact of irAEs, and sequence of therapy on the survival of NSCLC patients taking ICIs.

Economic evaluation of toripalimab combined with chemotherapy in the treatment of non-small cell lung cancer

Background and objective

The CHOICE-01 trial showed that toripalimab plus chemotherapy achieved satisfactory outcomes compared with chemotherapy in patients with advanced non-small cell lung cancer (NSCLC) who were negative for driver genes, but the economics of this regimen is unclear. Therefore, this study aimed to evaluate the cost-effectiveness of toripalimab in combination with chemotherapy in advanced NSCLC with negative driver genes from the perspective of the Chinese healthcare system.

Materials and methods

A three-state partitioned survival model was developed to simulate the costs and outcomes associated with adding toripalimab to first-line chemotherapy. The clinical data in the model came from the CHOICE-01 trial, only direct medical costs were included, and utility values were referred to the literature. Four models were applied to explore the differences in the results of fitting and extrapolating K-M curves from different models, and cost-effectiveness subgroup analysis was performed. The incremental cost-effectiveness ratio (ICER) was used as the main outcome measure. Sensitivity analysis was performed to assess the impact of parameter uncertainty on the model.

Results

The baseline analysis showed that toripalimab coupled with chemotherapy cost $21,052 more than chemotherapy ($43,197 vs. $22,145) and also gained 0.71 QALYs more (1.75 QALYs vs. 1.03 QALYs), with an ICER of $29,478/QALYs. At the current willingness-to-pay threshold ($35,108/QALY), the extra cost was well worth it. The results of fitting and extrapolating the survival curves using other models were consistent with the results of the standard parametric model. Subgroup analysis demonstrated that the addition of toripalimab to chemotherapy was economical. Sensitivity analysis showed that the utility values of PD and PFS stages had the greatest impact on the model.

Conclusion

From the viewpoint of the Chinese healthcare system, toripalimab combined with chemotherapy in the treatment of advanced NSCLC with negative driver genes was likely to be cost-effective compared with chemotherapy.

Economic evaluation of first-line sugemalimab plus chemotherapy for metastatic non-small cell lung cancer in China

Objective

To evaluate the economics of sugemalimab plus chemotherapy in the first-line treatment of metastatic non-small cell lung cancer, and to provide a reference for the formulation of relevant medical insurance policies and rational drug use.

Methods

From the perspective of the Chinese health system, a three-state partitioned survival model was constructed based on data from a phase III randomized clinical trial (GEMSTONE 302) to evaluate the cost-utility of sugemalimab plus chemotherapy compared with chemotherapy in first-line treatment of metastatic non-small cell lung cancer. Model results were expressed as total cost, life years, quality-adjusted life years, and incremental cost-effectiveness ratio. The robustness of the underlying analysis results was verified using one-way sensitivity analysis and probabilistic sensitivity analysis.

Results

The results of the base-case analysis showed that sugemalimab plus chemotherapy yielded 1.63 QALYs at a total cost of 130,667.70 USD, chemotherapy yielded 1.04 QALYs at a total cost of 64,001.02 USD, and the ICER was 113,155.52 USD/QALY, which was well above the current willingness-to-pay threshold in China (3 times 2021 per capita GDP) (36,203.88 USD).

Conclusion

This study suggests that sugemalimab in combination with a chemotherapy regimen is more effective but not economical for patients with metastatic non-small cell lung cancer receiving first-line therapy in China and that a reasonable reduction in drug prices could improve the probability of it being economical.

Exploring uncertainty and use of real-world data in the National Institute for Health and Care Excellence single technology appraisals of targeted cancer therapy

OBJECTIVES

Dealing with uncertainty is one of the critical topics in health technology assessment. The greater decision uncertainty in appraisals, the less clear the clinical- and cost-effectiveness of the health technology. Although the development of targeted cancer therapies (TCTs) has improved patient health care, additional complexity has been introduced in drug appraisals due to targeting more specific populations. Real-world data (RWD) are expected to provide helpful information to fill the evidence gaps in appraisals. This study compared appraisals of TCTs with those of non-targeted cancer therapies (non-TCTs) regarding sources of uncertainty and reviewed how RWD have been used to supplement the information in these appraisals.

METHODS

This study reviews single technology appraisals (STAs) of oncology medicines performed by the National Institute for Health and Care Excellence (NICE) over 11 years up to December 2021. Three key sources of uncertainty were identified for comparison (generalisability of clinical trials, availability of direct treatment comparison, maturity of survival data in clinical trials). To measure the intensity of use of RWD in appraisals, three components were identified (overall survival, volume of treatment, and choice of comparators).

RESULTS

TCTs received more recommendations for provision through the Cancer Drugs Fund (27.7, 23.6% for non-TCT), whereas similar proportions were recommended for routine commissioning. With respect to sources of uncertainty, the external validity of clinical trials was greater in TCT appraisals (p = 0.026), whereas mature survival data were available in fewer TCT appraisals (p = 0.027). Both groups showed similar patterns of use of RWD. There was no clear evidence that RWD have been used more intensively in appraisals of TCT.

CONCLUSIONS

Some differences in uncertainty were found between TCT and non-TCT appraisals. The appraisal of TCT is generally challenging, but these challenges are neither new nor distinctive. The same sources of uncertainty were often found in the non-TCT appraisals. The uncertainty when appraising TCT stems from insufficient data rather than the characteristics of the drugs. Although RWD might be expected to play a more active role in appraisals of TCT, the use of RWD has generally been limited.

Transition of the PD‑1 occupancy of nivolumab on T cells after discontinuation and response of nivolumab re‑challenge

Although nivolumab is administered every two or four weeks, high programmed cell death-1 (PD-1) binding of nivolumab on T cells lasting for several months has been reported. A relationship between the PD-1 occupancy rate on T-cells and the efficacy of nivolumab is not yet fully understood. The present study used flow cytometric analyses to determine the time-dependence of PD-1 occupancy in five patients who discontinued nivolumab. The relationship between PD-1 occupancy at relapse and the efficacy of re-challenge was also studied. Occupancies after discontinuation were measured at a total of 32 points. The data indicated that it took 32.4 and 48.9 weeks to decrease occupancy by 50 and 70%, respectively. Subsequently, two patients had recurrence and were re-challenged with nivolumab. At that time, one patient had 70.8% occupancy while the other had 6.6%. Treatment was effective only for the patient with lower occupancy. Overall, the present study suggests that re-challenge with nivolumab may be efficacious in patients with low occupancy at recurrence.

A Review of Overall Survival Extrapolations of Immune-Checkpoint Inhibitors Used in Health Technology Assessments by the French Health Authorities

OBJECTIVES

Extrapolation is often required to inform cost-effectiveness (CE) evaluations of immune-checkpoint inhibitors (ICIs) since survival data from pivotal clinical trials are seldom complete. The objectives of this study were to evaluate the accuracy of estimates of long-term overall survival (OS) predicted in French CE assessment reports of ICIs, and to identify models presenting the best fit to the observed long-term survival data.

METHODS

A systematic review of French assessment reports of ICIs in the metastatic setting since inception until May 2020 was performed. A targeted literature review was conducted to collect associated extended follow-up of randomized controlled trials (RCTs) used in the CE assessment reports. Difference between projected and observed OS was calculated. A range of standard parametric and spline-based models were applied to the extended follow-up data from the RCT to determine the best-fitting survival models.

RESULTS

Of the 121 CE assessment reports published, 11 reports met the inclusion criteria. OS was underestimated in 73 percent of the CE assessment reports. The mean relative difference between each source was -13 percent (median: -15 percent; IQR: -0.4 to 26 percent). Models providing the best fit were those that could reflect nonmonotonic hazards.

CONCLUSIONS

Based on the available data at the time of submission, longer-term survival of ICIs was not fully captured by the extrapolation models used in CE assessments. Standard and flexible parametric models which can capture nonmonotonic hazard functions provided the best fit to the extended follow-up data. However, these models may still have performed poorly if fitted to survival data available at the time of submission to the French National Authority for Health.

Defining unique clinical hallmarks for immune checkpoint inhibitor-based therapies

Introduction

Immuno-oncology therapies, including immune checkpoint inhibitors (ICIs), have transformed cancer care and have brought into question whether classic oncology efficacy assessments adequately describe the distinctive responses observed with these agents. With more ICI-based therapies being approved across multiple tumor types, it is essential to define unique clinical hallmarks of these agents and their associated assessments to better reflect the therapeutic impact for both patients and physicians. Long-term survival and objective responses, such as depth and durability of responses, treatment-free survival, efficacy in brain metastases, improved health-related quality of life, and unique safety profiles, are among the hallmarks that have emerged for ICI therapies. An established clinical hallmark is a sustained long-term survival, as evidenced by a delayed separation of Kaplan-Meier survival curves, and a plateau at ~3 years. Combination ICI therapies provide the opportunity to raise this plateau, thereby affording durable survival benefits to more patients. Deepening of responses over time is a unique clinical ICI hallmark, with patients responding long term and with more durable complete responses. Depth of response has demonstrated prognostic value for long-term survival in some cancers, and several ICI studies have shown sustained responses even after discontinuing ICI therapy, offering the potential for treatment-free intervals. Although clinical evidence supporting efficacy in brain metastases is limited, favorable ICI intracranial responses have been seen that are largely concordant with extracranial responses. While patient outcomes can be significantly improved with ICIs, they are associated with unique immune-mediated adverse reactions (IMARs), including delayed ICI toxicities, and may require multidisciplinary management for optimal care. Interestingly, patients discontinuing ICIs for IMARs may maintain responses similar to patients who did not discontinue for an IMAR, whether they restarted ICI therapy or not.

Conclusion

Herein, we comprehensively review and refine the clinical hallmarks uniquely associated with ICI therapies, which not only will rejuvenate our assessment of ICI therapeutic outcomes but also will lead to a greater appreciation of the effectiveness of ICI therapies.

How accurate are the longer-term projections of overall survival for cancer immunotherapy for standard versus more flexible parametric extrapolation methods?

AIMS

To assess the accuracy of standard parametric survival models, spline models, and mixture cure models (MCMs) fitted to overall survival (OS) data available at the time of submission in the NICE HTA process compared with data subsequently made available.

METHODS

Standard parametric distributions, spline models, and MCMs were fitted to OS data presented in single technology appraisals (TAs) for immune-checkpoint inhibitors (ICIs) in cancer. For each TA, the estimated survival from the fitted models was compared with Kaplan-Meier (KM) data that were made available following the HTA submission using differences between point estimates and restricted area under the curve (AUC) at both the midpoint and the end of additional follow-up. Differences in interval AUC values (calculated for each 6-month period) were also assessed.

RESULTS

Standard parametric survival models and spline models were more likely to underestimate longer-term survival, irrespective of the measure used to assess model accuracy. MCMs were more likely to overestimate survival; however, this was improved in some cases by applying an additional hazard of mortality for "statistically cured" patients.

LIMITATIONS

The accuracy of the models was assessed based on much shorter OS data than the period for which extrapolation is needed, which may impact conclusions regarding the most accurate models. The most recent TAs for ICIs have not been captured.

CONCLUSIONS

There are no definitive findings that unquestionably support the use of one specific extrapolation technique. Rather, each has the potential to provide accurate or inaccurate extrapolation to longer-term data in certain circumstances, but the added flexibility of more complex models can be justified for treatments, like ICIs, that have extended survival for patients across disease areas. The use of mortality adjustments for "statistically cured" patients allows decision-makers to explore more conservative scenarios in the face of high decision uncertainty.

Cost-effectiveness analysis of nivolumab plus ipilimumab plus two cycles of platinum-doublet chemotherapy versus platinum-doublet chemotherapy alone for first-line treatment of stage IV or recurrent non-small cell lung cancer in the United States

AIM

This economic analysis evaluated the cost-effectiveness of nivolumab (NIVO) plus ipilimumab (IPI) plus two cycles of platinum-doublet chemotherapy (PDC) compared with four cycles of PDC as first-line treatment for patients with advanced NSCLC in the United States (US).

METHODS

A partitioned survival model was constructed with three mutually exclusive health states: progression free, progressed disease, and death. The analysis was conducted from a US healthcare payer perspective, using a time horizon of 25 years. Costs and outcomes were discounted at 3% annually. Survival outcomes from CheckMate 9LA were extrapolated with longer follow-up data from CheckMate 227 Part 1 (NIVO + IPI) and validated against data from other relevant clinical trials and real-world registries. Health-related quality of life utility values were derived from EQ-5D-3L data collected in CheckMate 9LA. US-specific costs (2020 dollars) were used for disease management; drug acquisition, administration, and monitoring; end-of-life care; adverse events; and subsequent treatments. Model outcomes included life years (LYs) gained, quality-adjusted LYs (QALYs) gained, and incremental cost-effectiveness ratio (ICER) for NIVO + IPI + PDC versus PDC. Sensitivity and scenario analyses were conducted.

RESULTS

NIVO + IPI + PDC was associated with higher projected health benefits than PDC, including gains in LYs (3.71 vs 1.89) and QALYs (2.86 vs 1.37), and higher costs ($317,581 vs $119,909). The ICER was $132,960/QALY gained. NIVO + IPI + PDC had a 78-100% probability of being cost-effective at a willingness-to-pay threshold of $150,000-$250,000/QALY. Sensitivity and scenario analyses indicated that the results were robust to changes in key parameters.

LIMITATIONS

The inherent limitation in extrapolating clinical trial data was mitigated using data from the more mature CheckMate 227 Part 1 trial and validating the outcomes against data from other relevant trials and real-world registries.

CONCLUSION

NIVO + IPI + PDC (two cycles) provides a new first-line treatment option for patients with advanced NSCLC that is cost-effective within a range considered acceptable in the US.

Heterogeneity in Survival with Immune Checkpoint Inhibitors and Its Implications for Survival Extrapolations: A Case Study in Advanced Melanoma

Background

Survival heterogeneity and limited trial follow-up present challenges for estimating lifetime benefits of oncology therapies. This study used CheckMate 067 (NCT01844505) extended follow-up data to assess the predictive accuracy of standard parametric and flexible models in estimating the long-term overall survival benefit of nivolumab plus ipilimumab (an immune checkpoint inhibitor combination) in advanced melanoma.

Methods

Six sets of survival models (standard parametric, piecewise, cubic spline, mixture cure, parametric mixture, and landmark response models) were independently fitted to overall survival data for treatments in CheckMate 067 (nivolumab plus ipilimumab, nivolumab, and ipilimumab) using successive data cuts (28, 40, 52, and 60 mo). Standard parametric models allow survival extrapolation in the absence of a complex hazard. Piecewise and cubic spline models allow additional flexibility in fitting the hazard function. Mixture cure, parametric mixture, and landmark response models provide flexibility by explicitly incorporating survival heterogeneity. Sixty-month follow-up data, external ipilimumab data, and clinical expert opinion were used to evaluate model estimation accuracy. Lifetime survival projections were compared using a 5% discount rate.

Results

Standard parametric, piecewise, and cubic spline models underestimated overall survival at 60 mo for the 28-mo data cut. Compared with other models, mixture cure, parametric mixture, and landmark response models provided more accurate long-term overall survival estimates versus external data, higher mean survival benefit over 20 y for the 28-mo data cut, and more consistent 20-y mean overall survival estimates across data cuts.

Conclusion

This case study demonstrates that survival models explicitly incorporating survival heterogeneity showed greater accuracy for early data cuts than standard parametric models did, consistent with similar immune checkpoint inhibitor survival validation studies in advanced melanoma. Research is required to assess generalizability to other tumors and disease stages.

Highlights

Immuno-oncology trends: preclinical models, biomarkers, and clinical development

The landscape in immuno-oncology (I-O) has undergone profound changes since its early beginnings up through the rapid advances happening today. The current drug development pipeline consists of thousands of potential I-O therapies and therapy combinations, many of which are being evaluated in clinical trials. The efficient and successful development of these assets requires the investment in and utilization of appropriate tools and technologies that can facilitate the rapid transitions from preclinical evaluation through clinical development. These tools include (i) appropriate preclinical models, (ii) biomarkers of pharmacodynamic, predictive and monitoring utility, and (iii) evolving clinical trial designs that allow rapid and efficient evaluation during the development process. This article provides an overview of how novel discoveries and insights into each of these three areas have the potential to further address the clinical management needs for patients with cancer.

The Importance of Disease-Free Survival as a Clinical Trial Endpoint: A Qualitative Study Among Canadian Survivors of Lung Cancer

BACKGROUND

In lung cancer trials, overall survival is a well-validated and widely used endpoint; yet, in the context of adjuvant or curative intent treatments, disease-free survival (DFS) may be a better indicator of transformative patient outcomes. Although use of DFS is growing, patient perceptions of its relevance have not been established.

OBJECTIVE

We aimed to understand the importance of DFS as a trial endpoint, from the perspective of survivors of lung cancer.

METHODS

Web-based qualitative interviews were conducted with Canadian survivors of stage Ib-IIIa lung cancer. Participants described their experiences of cancer diagnosis and treatment, including their treatment goals and priorities. Participants then provided their perspectives on DFS and overall survival, and how well each aligned with their treatment priorities. Thematic analysis was used to explore patterns in responses.

RESULTS

Among the 18 participants (mean age, 64 years), 83% were female, most (89%) had received surgery, and 56% received chemotherapy. Most participants viewed DFS as an intrinsically meaningful treatment outcome, for reasons such as alignment with treatment goals, and the perception that DFS would help maintain a high quality of life. One individual was interested in DFS only as a potential surrogate for overall survival. Participants desired access to new treatments that improve DFS and emphasized this within the context of promoting patient agency in treatment decision making.

CONCLUSIONS

These findings suggest DFS is a meaningful endpoint from the perspective of survivors of lung cancer; and may help inform decisions regarding regulatory approval and reimbursement of new treatments based on DFS data.

The Role of Mathematical Models in Immuno-Oncology: Challenges and Future Perspectives

Immuno-oncology (IO) focuses on the ability of the immune system to detect and eliminate cancer cells. Since the approval of the first immune checkpoint inhibitor, immunotherapies have become a major player in oncology treatment and, in 2021, represented the highest number of approved drugs in the field. In spite of this, there is still a fraction of patients that do not respond to these therapies and develop resistance mechanisms. In this sense, mathematical models offer an opportunity to identify predictive biomarkers, optimal dosing schedules and rational combinations to maximize clinical response. This work aims to outline the main therapeutic targets in IO and to provide a description of the different mathematical approaches (top-down, middle-out, and bottom-up) integrating the cancer immunity cycle with immunotherapeutic agents in clinical scenarios. Among the different strategies, middle-out models, which combine both theoretical and evidence-based description of tumor growth and immunological cell-type dynamics, represent an optimal framework to evaluate new IO strategies.