Survival Analysis in Patients with Metastatic Merkel Cell Carcinoma Treated with Avelumab

Assessing the Performance of Alternative Methods for Estimating Long-Term Survival Benefit of Immuno-oncology Therapies

OBJECTIVES

This study aimed to determine the accuracy and consistency of established methods of extrapolating mean survival for immuno-oncology (IO) therapies, the extent of any systematic biases in estimating long-term clinical benefit, what influences the magnitude of any bias, and the potential implications for health technology assessment.

METHODS

A targeted literature search was conducted to identify published long-term follow-up from clinical trials of immune-checkpoint inhibitors. Earlier published results were identified and Kaplan-Meier estimates for short- and long-term follow-up were digitized and converted to pseudo-individual patient data using an established algorithm. Six standard parametric, 5 flexible parametric, and 2 mixture-cure models (MCMs) were used to extrapolate long-term survival. Mean and restricted mean survival time (RMST) were estimated and compared between short- and long-term follow-up.

RESULTS

Predicted RMST from extrapolation of early data underestimated observed RMST in long-term follow-up for 184 of 271 extrapolations. All models except the MCMs frequently underestimated observed RMST. Mean survival estimates increased with longer follow-up in 196 of 270 extrapolations. The increase exceeded 20% in 122 extrapolations. Log-logistic and log-normal models showed the smallest change with additional follow-up. MCM performance varied substantially with functional form.

CONCLUSIONS

Standard and flexible parametric models frequently underestimate mean survival for IO treatments. Log-logistic and log-normal models may be the most pragmatic and parsimonious solutions for estimating IO mean survival from immature data. Flexible parametric models may be preferred when the data used in health technology assessment are more mature. MCMs fitted to immature data produce unreliable results and are not recommended.

Real-world outcomes among patients with metastatic colorectal cancer treated first line with a bevacizumab biosimilar (bevacizumab-awwb)

Background

Bevacizumab-awwb (MVASI®) was the first U.S. Food and Drug Administration-approved biosimilar to Avastin® (reference product [RP]) for the treatment of several different types of cancers, including metastatic colorectal cancer (mCRC), an indication approved based on extrapolation.

Objectives

Evaluate treatment outcomes in mCRC patients who received first-line (1L) bevacizumab-awwb at treatment initiation or as continuing bevacizumab therapy (switched from RP).

Design

A retrospective chart review study.

Methods

Adult patients who had a confirmed diagnosis of mCRC (initial presentation of CRC on or after 01 January 2018) and initiated 1L bevacizumab-awwb between 19 July 2019 and 30 April 2020 were identified from the ConcertAI Oncology Dataset. A chart review was conducted to evaluate patient baseline clinical characteristics and effectiveness and tolerability outcomes during the follow-up. Study measures were reported stratified by prior use of RP: (1) naïve patients and (2) switchers (patients who switched to bevacizumab-awwb from RP without advancing the line of therapy).

Results

At the end of study period, naïve patients (n = 129) had a median 1L progression-free survival (PFS) of 8.6 months [95% confidence interval (CI), 7.6-9.9] and a 12-month overall survival (OS) probability of 71.4% (95% CI, 61.0-79.5%). Switchers (n = 105) had a median 1L PFS of 14.1 months (95% CI, 12.1-15.8) and a 12-month OS probability of 87.6% (95% CI, 79.1-92.8%). During treatment with bevacizumab-awwb, 20 events of interest (EOIs) were reported in 18 naïve patients (14.0%) and 4 EOIs reported in 4 switchers (3.8%), of which the most commonly reported events were thromboembolic and hemorrhagic events. Most EOIs resulted in emergency department visit and/or treatment hold/discontinuation/switch. None of the EOIs resulted in death.

Conclusion

In this real-world cohort of mCRC patients who were treated 1L with a bevacizumab biosimilar (bevacizumab-awwb), the clinical effectiveness and tolerability data were as expected and consistent with previously published findings from real-world studies of bevacizumab RP in mCRC patients.

Extrapolating empirical long-term survival data: the impact of updated follow-up data and parametric extrapolation methods on survival estimates in multiple myeloma

BACKGROUND

In economic evaluations, survival is often extrapolated to smooth out the Kaplan-Meier estimate and because the available data (e.g., from randomized controlled trials) are often right censored. Validation of the accuracy of extrapolated results can depend on the length of follow-up and the assumptions made about the survival hazard. Here, we analyze the accuracy of different extrapolation techniques while varying the data cut-off to estimate long-term survival in newly diagnosed multiple myeloma (MM) patients.

METHODS

Empirical data were available from a randomized controlled trial and a registry for MM patients treated with melphalan + prednisone, thalidomide, and bortezomib- based regimens. Standard parametric and spline models were fitted while artificially reducing follow-up by introducing database locks. The maximum follow-up for these locks varied from 3 to 13 years. Extrapolated (conditional) restricted mean survival time (RMST) was compared to the Kaplan-Meier RMST and models were selected according to statistical tests, and visual fit.

RESULTS

For all treatments, the RMST error decreased when follow-up and the absolute number of events increased, and censoring decreased. The decline in RMST error was highest when maximum follow-up exceeded six years. However, even when censoring is low there can still be considerable deviations in the extrapolated RMST conditional on survival until extrapolation when compared to the KM-estimate.

CONCLUSIONS

We demonstrate that both standard parametric and spline models could be worthy candidates when extrapolating survival for the populations examined. Nevertheless, researchers and decision makers should be wary of uncertainty in results even when censoring has decreased, and the number of events has increased.

Drug-Induced Liver Injury due to Biologics and Immune Check Point Inhibitors

Biological agents have in the last two decades become very important therapeutic agents, particularly for the treatment of various autoimmune disorders. The most widely used biologics are the tumor necrosis factor-α (TNF-α) receptor antagonists: infliximab, adalimumab, and etanercept. Other commonly used biological agents are interleukin (IL)-1 receptor antagonist (Anakinra), interleukin (IL)-6 receptor antagonist (tocilizumab), and CD20 surface antigen antagonist (rituximab). The current review will however focus on TNF-α receptor antagonists.

Hepatotoxicity Associated with Immune Checkpoint Inhibitors in Clinical Practice: A Study Leveraging Data from the US Food and Drug Administration's Adverse Event Reporting System

PURPOSE

Immune checkpoint inhibitors (ICIs) are a promising option for the treatment of patients with various cancers. Emerging case reports have raised awareness on hepatotoxicity, a potentially fatal adverse event (AE) that may be associated with the use of ICIs. This study assessed the potential association between ICIs and hepatotoxicity through the mining of data from the US Food and Drug Administration's AE Reporting System (FAERS).

METHODS

A total of 9,217,181 AEs reported in the period from quarter 1 of 2004 to quarter 3 of 2021 were assessed. Information components (ICs) and reporting odds ratios (RORs) were used to evaluate the association between the use of ICIs and hepatotoxicity.

FINDINGS

A total of 52,463 AE reports listed ICIs, used alone or in combination, as a suspected drug. Of these, 1481 cases were related to both ICIs and hepatotoxicity. The use of ICIs was significantly associated with hepatotoxicity compared to all other drugs, making it a safety signal (IC = 1.43 [95% CI, 1.36-1.51]; ROR = 2.78 [95% CI, 2.64-2.93]). With monotherapy, all ICIs, except tremelimumab, were associated with liver damage. The most commonly prescribed combination therapy was nivolumab + ipilimumab (321 cases) with a significant signal detected. Notably, ICI use was significantly associated with hepatic failure (IC = 1.24 [95% CI, 1.06-1.42]; ROR = 2.40 [95% CI, 2.13-2.72]). The risk for ICI-associated hepatotoxicity (including hepatic failure) was greater with ICI combination therapy than with ICI monotherapy. All subgroups by sex and age also showed significant associations between ICI use and hepatotoxicity.

IMPLICATIONS

A significant association was detected between ICI use and hepatotoxicity. The risk for hepatotoxicity (including hepatic failure) was greater with ICI combination therapy compared with ICI monotherapy. (Clin Ther. 2023;45:XXX-XXX) © 2023 Elsevier Inc.

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

Immune checkpoint inhibitor-related hepatotoxicity: A review

The application of immune checkpoint inhibitors (ICI) in advanced cancer has been a major development in the last decade. The indications for ICIs are constantly expanding into new territory across different cancers, disease stages and lines of therapy. With this increased use, adverse events including immune checkpoint inhibitor-related hepatotoxicity (ICH) have emerged as an important clinical problem. This along with the introduction of ICI as first- and second-line treatments for advanced hepatocellular carcinoma makes ICH very relevant to gastroenterologists and hepatologists. The incidence of ICH varies between 1%-20% depending on the number, type and dose of ICI received. Investigation and management generally involve excluding differential diagnoses and following a stepwise escalation of withholding or ceasing ICI, corticosteroid treatment and adding other immunosuppressive agents depending on the severity of toxicity. The majority of patients with ICH recover and some may even safely recommence ICI therapy. Guideline recommendations are largely based on evidence derived from retrospective case series which highlights a priority for future research.

Pancreatic neuroendocrine tumors: A review of serum biomarkers, staging, and management

Pancreatic neuroendocrine tumors (pNETs) are a heterogeneous group of tumors with complicated treatment options that depend on pathological grading, clinical staging, and presence of symptoms related to hormonal secretion. With regard to diagnosis, remarkable advances have been made: Chromogranin A is recommended as a general marker for pNETs. But other new biomarker modalities, like circulating tumor cells, multiple transcript analysis, microRNA profile, and cytokines, should be clarified in future investigations before clinical application. Therefore, the currently available serum biomarkers are insufficient for diagnosis, but reasonably acceptable in evaluating the prognosis of and response to treatments during follow-up of pNETs. Surgical resection is still the only curative therapeutic option for localized pNETs. However, a debulking operation has also been proven to be effective for controlling the disease. As for drug therapy, steroids and somatostatin analogues are the first-line therapy for those with positive expression of somatostatin receptor, while everolimus and sunitinib represent important progress for the treatment of patients with advanced pNETs. Great progress has been achieved in the combination of systematic therapy with local control treatments. The optimal timing of local control intervention, planning of sequential therapies, and implementation of multidisciplinary care remain pending.

Evaluation of survival extrapolation in immuno-oncology using multiple pre-planned data cuts: learnings to aid in model selection

BACKGROUND

Due to limited duration of follow up in clinical trials of cancer treatments, estimates of lifetime survival benefits are typically derived using statistical extrapolation methods. To justify the method used, a range of approaches have been proposed including statistical goodness-of-fit tests and comparing estimates against a previous data cut (i.e. interim data collected). In this study, we extend these approaches by presenting a range of extrapolations fitted to four pre-planned data cuts from the JAVELIN Merkel 200 (JM200) trial. By comparing different estimates of survival and goodness-of-fit as JM200 data mature, we undertook an iterative process of fitting and re-fitting survival models to retrospectively identify early indications of likely long-term survival.

METHODS

Standard and spline-based parametric models were fitted to overall survival data from each JM200 data cut. Goodness-of-fit was determined using an assessment of the estimated hazard function, information theory-based methods and objective comparisons of estimation accuracy. Best-fitting extrapolations were compared to establish which one provided the most accurate estimation, and how statistical goodness-of-fit differed.

RESULTS

Spline-based models provided the closest fit to the final JM200 data cut, though all extrapolation methods based on the earliest data cut underestimated the 'true' long-term survival (difference in restricted mean survival time [RMST] at 36 months: - 1.1 to - 0.5 months). Goodness-of-fit scores illustrated that an increasingly flexible model was favored as data matured. Given an early data cut, a more flexible model better aligned with clinical expectations could be reasonably justified using a range of metrics, including RMST and goodness-of-fit scores (which were typically within a 2-point range of the statistically 'best-fitting' model).

CONCLUSIONS

Survival estimates from the spline-based models are more aligned with clinical expectation and provided a better fit to the JM200 data, despite not exhibiting the definitively 'best' statistical goodness-of-fit. Longer-term data are required to further validate extrapolations, though this study illustrates the importance of clinical plausibility when selecting the most appropriate model. In addition, hazard-based plots and goodness-of-fit tests from multiple data cuts present useful approaches to identify when a more flexible model may be advantageous.

TRIAL REGISTRATION

JAVELIN Merkel 200 was registered with ClinicalTrials.gov as NCT02155647 on June 4, 2014.