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

Abstract

Immuno-oncologics (IOs) differ from chemotherapies as they prime the patient’s immune system to attack the tumor, rather than directly destroying cancer cells. The IO mechanism of action leads to durable responses and prolonged survival in some patients. However, providing robust evidence of the long-term benefits of IOs at health technology assessment (HTA) submission presents several challenges for manufacturers. The aim of this article was to identify, analyze, categorize, and further explore the key challenges that regulators, HTA agencies, and payers commonly encounter when assessing the long-term benefits of IO therapies. Insights were obtained from an international, multi-stakeholder steering committee (SC) and expert panels comprising of payers, economists, and clinicians. The selected individuals were tasked with developing a summary of challenges specific to IOs in demonstrating their long-term benefits at HTA submission. The SC and expert panels agreed that standard methods used to assess the long-term benefit of anticancer drugs may have limitations for IO therapies. Three key areas of challenges were identified: (1) lack of a disease model that fully captures the mechanism of action and subsequent patient responses; (2) estimation of longer-term outcomes, including a lack of agreement on ideal methods of survival analyses and extrapolation of survival curves; and (3) data limitations at the time of HTA submission, for which surrogate survival end points and real-world evidence could prove useful. A summary of the key challenges facing manufacturers when submitting evidence at HTA submission was developed, along with further recommendations for manufacturers in what evidence to produce. Despite almost a decade of use, there remain significant challenges around how best to demonstrate the long-term benefit of checkpoint inhibitor-based IOs to HTA agencies, clinicians, and payers. Manufacturers can potentially meet or mitigate these challenges with a focus on strengthening survival analysis methodology. Approaches to doing this include identifying reliable biomarkers, intermediate and surrogate end points, and the use of real-world data to inform and validate long-term survival projections. Wider education across all stakeholders—manufacturers, payers, and clinicians—in considering the long-term survival benefit with IOs is also important.

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

“… 3 , 4 The shape of the Kaplan-Meier curves for immune checkpoint inhibitors is typically characterized by an initial period of nonseparation of the curves when compared with conventional or targeted therapies due to delayed response, a subsequent period of separation as responses occur, and a final period with a plateau driven by a persistent treatment effect among those who respond. 3 , 5 This variation in cancer survival may be indicative of latent prognostic factors or differences in treatment efficacy in observed or latent patient subgroups and result in a hazard function that standard parametric models may be unable to accurately capture.…”

Defining unique clinical hallmarks for immune checkpoint inhibitor-based therapies

“… 5 This contrasts with chemotherapy, where early clinical responses are often seen but are short-lived. 38 The first long-term ICI outcomes were reported for ipilimumab in advanced melanoma; despite low objective response rates (ORR), responses were durable, as shown by a plateau in the OS curve at ~20% around 3 years, and the plateau was sustained at a 10-year follow-up. 5 This brings the promise of a potential ‘functional cure’ in some patients with ongoing, long-term responses, even after treatment discontinuation.…”

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

“…There is growing evidence that more sophisticated, flexible survival models may be better able to capture delayed treatment responses and long-term survival observed with these drugs (18). Modeling the “tail” of Kaplan–Meier (KM) curves is particularly challenging (19;20), even though this is critical for the assessment of ICIs, where a plateau may occur at unknown times and levels after initiation of therapy (6). The publication of extended follow-up data from RCTs is an opportunity to assess retrospectively the accuracy of OS predictions in CE assessments and to explore differences in best-fitting models between the CE assessments and medium- or long-term data from RCTs of ICIs.…”