The Predicted Impact of Ipilimumab Usage on Survival in Previously Treated Advanced or Metastatic Melanoma in the UK

Current Views about the Inflammatory Damage Triggered by Bacterial Superantigens and Experimental Attempts to Neutralize Superantigen-Mediated Toxic Effects with Natural and Biological Products

Superantigens, i.e., staphylococcal enterotoxins and toxic shock syndrome toxin-1, interact with T cells in a different manner in comparison to conventional antigens. In fact, they activate a larger contingent of T lymphocytes, binding outside the peptide-binding groove of the major histocompatibility complex class II. Involvement of many T cells by superantigens leads to a massive release of pro-inflammatory cytokines, such as interleukin (IL)-1, IL-2, IL-6, tumor necrosis factor-alpha and interferon-gamma. Such a storm of mediators has been shown to account for tissue damage, multiorgan failure and shock. Besides conventional drugs and biotherapeutics, experiments with natural and biological products have been undertaken to attenuate the toxic effects exerted by superantigens. In this review, emphasis will be placed on polyphenols, probiotics, beta-glucans and antimicrobial peptides. In fact, these substances share a common functional denominator, since they skew the immune response toward an anti-inflammatory profile, thus mitigating the cytokine wave evoked by superantigens. However, clinical applications of these products are still scarce, and more trials are needed to validate their usefulness in humans.

Transgenic viral expression of PH-20, IL-12, and sPD1-Fc enhances immune cell infiltration and anti-tumor efficacy of an oncolytic virus

Oncolytic viruses are of significant clinical interest due to their ability to directly infect and kill tumors and enhance the anti-tumor immune response. Previously, we developed KLS-3010, a novel oncolytic virus derived from the International Health Department-White (IHD-W) strain vaccinia virus, which has robust tumoricidal effects. In the present study, we generated a recombinant oncolytic virus, KLS-3020, by inserting three transgenes (hyaluronidase [PH-20], interleukin-12 [IL-12], and soluble programmed cell death 1 fused to the Fc domain [sPD1-Fc]) into KLS-3010 and investigated its anti-tumor efficacy and ability to induce anti-tumor immune responses in CT26.WT and B16F10 mouse tumor models. A single injection of KLS-3020 significantly decreased tumor growth. The roles of the transgenes were investigated using viruses expressing each single transgene alone and KLS-3020. PH-20 promoted virus spread and tumor immune cell infiltration, IL-12 activated and reprogrammed T cells to inflammatory phenotypes, and sPD1-Fc increased intra-tumoral populations of activated T cells. The tumor-specific systemic immune response and the abscopal tumor control elicited by KLS-3020 were demonstrated in the CT26.WT tumor model. The insertion of transgenes into KLS-3020 increased its anti-tumor efficacy, supporting further clinical investigation of KLS-3020 as a novel oncolytic immunotherapy.

A Systematic Review of Methods to Incorporate External Evidence into Trial-Based Survival Extrapolations for Health Technology Assessment

BACKGROUND

External evidence is commonly used to inform survival modeling for health technology assessment (HTA). While there are a range of methodological approaches that have been proposed, it is unclear which methods could be used and how they compare.

PURPOSE

This review aims to identify, describe, and categorize established methods to incorporate external evidence into survival extrapolation for HTA.

DATA SOURCES

Embase, MEDLINE, EconLit, and Web of Science databases were searched to identify published methodological studies, supplemented by hand searching and citation tracking.

STUDY SELECTION

Eligible studies were required to present a novel extrapolation approach incorporating external evidence (i.e., data or information) within survival model estimation.

DATA EXTRACTION

Studies were classified according to how the external evidence was integrated as a part of model fitting. Information was extracted concerning the model-fitting process, key requirements, assumptions, software, application contexts, and presentation of comparisons with, or validation against, other methods.

DATA SYNTHESIS

Across 18 methods identified from 22 studies, themes included use of informative prior(s) (n = 5), piecewise (n = 7), and general population adjustment (n = 9), plus a variety of "other" (n = 8) approaches. Most methods were applied in cancer populations (n = 13). No studies compared or validated their method against another method that also incorporated external evidence.

LIMITATIONS

As only studies with a specific methodological objective were included, methods proposed as part of another study type (e.g., an economic evaluation) were excluded from this review.

CONCLUSIONS

Several methods were identified in this review, with common themes based on typical data sources and analytical approaches. Of note, no evidence was found comparing the identified methods to one another, and so an assessment of different methods would be a useful area for further research.HighlightsThis review aims to identify methods that have been used to incorporate external evidence into survival extrapolations, focusing on those that may be used to inform health technology assessment.We found a range of different approaches, including piecewise methods, Bayesian methods using informative priors, and general population adjustment methods, as well as a variety of "other" approaches.No studies attempted to compare the performance of alternative methods for incorporating external evidence with respect to the accuracy of survival predictions. Further research investigating this would be valuable.

Blended Survival Curves: A New Approach to Extrapolation for Time-to-Event Outcomes from Clinical Trials in Health Technology Assessment

BACKGROUND

Survival extrapolation is essential in cost-effectiveness analysis to quantify the lifetime survival benefit associated with a new intervention, due to the restricted duration of randomized controlled trials (RCTs). Current approaches of extrapolation often assume that the treatment effect observed in the trial can continue indefinitely, which is unrealistic and may have a huge impact on decisions for resource allocation.

OBJECTIVE

We introduce a novel methodology as a possible solution to alleviate the problem of survival extrapolation with heavily censored data from clinical trials.

METHOD

The main idea is to mix a flexible model (e.g., Cox semiparametric) to fit as well as possible the observed data and a parametric model encoding assumptions on the expected behavior of underlying long-term survival. The two are "blended" into a single survival curve that is identical with the Cox model over the range of observed times and gradually approaching the parametric model over the extrapolation period based on a weight function. The weight function regulates the way two survival curves are blended, determining how the internal and external sources contribute to the estimated survival over time.

RESULTS

A 4-y follow-up RCT of rituximab in combination with fludarabine and cyclophosphamide versus fludarabine and cyclophosphamide alone for the first-line treatment of chronic lymphocytic leukemia is used to illustrate the method.

CONCLUSION

Long-term extrapolation from immature trial data may lead to significantly different estimates with various modelling assumptions. The blending approach provides sufficient flexibility, allowing a wide range of plausible scenarios to be considered as well as the inclusion of external information, based, for example, on hard data or expert opinion. Both internal and external validity can be carefully examined.

HIGHLIGHTS

Interim analyses of trials with limited follow-up are often subject to high degrees of administrative censoring, which may result in implausible long-term extrapolations using standard approaches.In this article, we present an innovative methodology based on "blending" survival curves to relax the traditional proportional hazard assumption and simultaneously incorporate external information to guide the extrapolation.The blended method provides a simple and powerful framework to allow a careful consideration of a wide range of plausible scenarios, accounting for model fit to the short-term data as well as the plausibility of long-term extrapolations.

Tumour treating fields therapy for glioblastoma: current advances and future directions

Glioblastoma multiforme (GBM) is the most common primary brain tumour in adults and continues to portend poor survival, despite multimodal treatment using surgery and chemoradiotherapy. The addition of tumour-treating fields (TTFields)-an approach in which alternating electrical fields exert biophysical force on charged and polarisable molecules known as dipoles-to standard therapy, has been shown to extend survival for patients with newly diagnosed GBM, recurrent GBM and mesothelioma, leading to the clinical approval of this approach by the FDA. TTFields represent a non-invasive anticancer modality consisting of low-intensity (1-3 V/cm), intermediate-frequency (100-300 kHz), alternating electric fields delivered via cutaneous transducer arrays configured to provide optimal tumour-site coverage. Although TTFields were initially demonstrated to inhibit cancer cell proliferation by interfering with mitotic apparatus, it is becoming increasingly clear that TTFields show a broad mechanism of action by disrupting a multitude of biological processes, including DNA repair, cell permeability and immunological responses, to elicit therapeutic effects. This review describes advances in our current understanding of the mechanisms by which TTFields mediate anticancer effects. Additionally, we summarise the landscape of TTFields clinical trials across various cancers and consider how emerging preclinical data might inform future clinical applications for TTFields.

HSP90 Inhibition Enhances Cancer Immunotherapy by Modulating the Surface Expression of Multiple Immune Checkpoint Proteins

Cancer immunotherapies, including immune checkpoint blockade, have the potential to significantly impact treatments for diverse tumor types. At present, response failures and immune-related adverse events remain significant issues, which could be addressed using optimized combination therapies. Through a cell-based chemical screen of ∼200,000 compounds, we identified that HSP90 inhibitors robustly decrease PD-L1 surface expression, through a mechanism that appears to involve the regulation of master transcriptional regulators (i.e., STAT-3 and c-Myc). Interestingly, HSP90 inhibitors were found to also modulate the surface expression of additional checkpoint proteins (i.e., PD-L2). In the MC-38 syngeneic mouse tumor model, HSP90 inhibition was found to dramatically reduce PD-L1 surface expression on isolated live tumor cells and, consistent with recent findings, was found to increase the number of activated CD8+ T cells within the tumor microenvironment. These findings provide further rationale to explore HSP90 inhibitors as part of combination immunotherapies for the treatment of cancer.

PD-L1 Checkpoint Inhibition Narrows the Antigen-Specific T Cell Receptor Repertoire in Chronic Lymphocytic Choriomeningitis Virus Infection

Checkpoint inhibitors are effective in restoring exhausted CD8⁺ T cell responses in persistent viral infections or tumors. Several compounds are in clinical use for different malignancies, but trials in patients with chronic viral infections have also been conducted. In a mouse model of persistent lymphocytic choriomeningitis virus (LCMV) infection, it was shown that checkpoint inhibitor treatment increased T cell proliferation and functionality, but its influence on the antigen-specific T cell receptor (TCR) repertoire is unknown. NP396-specific CD8⁺ T cells dominate during acute LCMV infection and are predominantly exhausted during chronic infection. Next-generation sequencing of NP396-specific TCRs showed that exhaustion corresponds with a significantly reduced NP396-specific TCR repertoire diversity: Shannon indices of 4 in immunized mice to 2.6 in persistently infected mice. Anti-PD-L1 treatment during persistent LCMV infection restored NP396-specific T cell responses and reduced viral titers. Nevertheless, anti-PD-L1-treated mice showed an even more narrowed TCR repertoire, with reduced TCR diversity compared to that of persistently infected control mice (Shannon indices of 2.1 and 2.6, respectively). Interestingly, anti-PD-L1 treatment-induced narrowing of the TCR repertoire negatively correlates with functional and physical restoration of the antigen-specific T cell response. Further, we found that private, hyperexpanded TCR clonotypes dominated the T cell response after anti-PD-L1 treatment. Although being private, these top clonotypes from anti-PD-L1-treated mice revealed a more closely related CDR3 motif than those of top clonotypes from persistently infected control mice. In conclusion, although targeting the PD-1/PD-L1 pathway reinvigorates exhausted CD8⁺ T cells, it fails to restore T cell repertoire diversity.IMPORTANCE Checkpoint inhibitors are effective immunotherapeutics to restore cancer- and virus-induced exhausted CD8⁺ T cells, by enhancing the quality and survival of immune responses. Although checkpoint inhibitors are already used as therapy against various cancers, not much is known about their multifaceted impact on the exhausted CD8⁺ T cell receptor (TCR) repertoire. This report describes for the first time the evolvement of an exhausted antigen-specific CD8⁺ TCR repertoire under checkpoint inhibitor treatment. By using a well-established virus model, we were able to show major shifts toward oligoclonality of the CD8⁺ TCR repertoire response against a massively exhausted lymphocytic choriomeningitis virus (LCMV) epitope. While supporting viral control in the LCMV model, oligoclonality and more private of TCR repertoires may impact future pathogenic challenges and may promote viral escape. Our results may explain the ongoing problems of viral escapes, unpredictable autoimmunity, and heterogeneous responses appearing as adverse effects of checkpoint inhibitor treatments.

Relevance of immune cell and tumor microenvironment imaging in the new era of immunotherapy

Tumor-infiltrating immune cells play a key role against cancer. However, malignant cells are able to evade the immune response and establish a very complex balance in which different immune subtypes may drive tumor progression, metastatization and resistance to therapy. New immunotherapeutic approaches aim at restoring the natural balance and increase immune response against cancer by different mechanisms. The complexity of these interactions and the heterogeneity of immune cell subpopulations are a real challenge when trying to develop new immunotherapeutics and evaluate or predict their efficacy in vivo. To this purpose, molecular imaging can offer non-invasive diagnostic tools like radiopharmaceuticals, contrast agents or fluorescent dyes. These agents can be useful for preclinical and clinical purposes and can overcome [18F]FDG limitations in discriminating between true-progression and pseudo-progression. This review provides a comprehensive overview of immune cells involved in microenvironment, available immunotherapies and imaging agents to highlight the importance of new therapeutic biomarkers and their in vivo evaluation to improve the management of cancer patients.

Tumor treating fields and maintenance temozolomide for newly-diagnosed glioblastoma: a cost-effectiveness study

Purpose: The EF-14 trial demonstrated that adding tumor treating fields (TTFields) to maintenance temozolomide (TMZ) significantly extends progression-free survival (PFS) and overall survival (OS) for newly-diagnosed glioblastoma (GBM) patients. This study assessed the cost-effectiveness of TTFields and TMZ for newly-diagnosed GBM from the US healthcare system perspective. Methods and materials: Outcomes for newly-diagnosed GBM patients were estimated over a lifetime horizon using an area under the curve model with three states: stable disease, progressive disease, or death. The survival model integrated the 5-year EF-14 trial results with long-term GBM epidemiology data and US background mortality rates. Adverse event rates were derived from the EF-14 trial data. Utility values to determine quality-adjusted life-years, adverse event costs, and supportive care costs were obtained from published literature. A 3% discount rate was applied to future costs and outcomes. One-way and probabilistic sensitivity analyses were performed to assess result uncertainty due to parameter variability. Results: Treatment with TTFields and TMZ was estimated to result in a mean increase in survival of 1.25 life years (95% credible range [CR] = 0.89-1.67) and 0.96 quality-adjusted life years (QALYs) (95% CR = 0.67-1.30) compared to treatment with TMZ alone. The incremental total cost was $188,637 (95% CR = $145,324-$225,330). The incremental cost-effectiveness ratio (ICER) was $150,452 per life year gained and $197,336 per QALY gained. The model was most sensitive to changes in the cost of TTFields treatment. Conclusions: Adding TTFields to maintenance TMZ resulted in a substantial increase in the estimated mean lifetime survival and quality-adjusted survival for newly-diagnosed GBM patients. Treatment with TTFields can be considered cost-effective within the reported range of willingness-to-pay thresholds in the US.

Can We Accurately Predict Cost Effectiveness Without Access to Overall Survival Data? The Case Study of Nivolumab in Combination with Ipilimumab for the Treatment of Patients with Advanced Melanoma in England

BACKGROUND

Nivolumab with ipilimumab (the Regimen) is the first immuno-oncology combination treatment to demonstrate long-term clinical benefit for advanced melanoma patients. We evaluated the cost effectiveness of the Regimen in this population, with and without the availability of overall survival (OS) data.

METHODS

A partitioned survival model and a Markov state-transition model were developed to estimate the lifetime costs and benefits of the Regimen versus ipilimumab. These models were built with and without the availability of OS data, as only progression-free survival data were available from the head-to-head, phase III trial against ipilimumab at the time of the National Institute for Health and Care Excellence (NICE) submission. Patient utilities and resource use data were sourced from trial data or the literature.

RESULTS

Incremental cost-effectiveness ratios (ICERs) and absolute costs were similar between the models with and without OS data, but the model with OS data generated more than 1 additional quality-adjusted life-year (QALY) across both treatment arms. In both models, based on list prices, the Regimen was the most cost-effective treatment.

CONCLUSIONS

The analyses show that the Regimen is a cost-effective treatment for advanced melanoma patients in England, and methods to overcome the lack of OS can give reasonable estimates of QALYs gained and ICERs.

Estimated lifetime survival benefit of tumor treating fields and temozolomide for newly diagnosed glioblastoma patients

Aim:

To estimate the mean lifetime survival benefit, an essential component of health economic evaluations in oncology, of adding tumor treating fields (TTFields) to maintenance temozolomide (TMZ) for newly diagnosed glioblastoma patients.

Methods:

We integrated EF-14 trial data with glioblastoma epidemiology data. The model provided for an evidence-based approach to estimate lifetime survival for the material number of EF-14 trial patients still alive at 5 years.

Results & conclusion:

Patients treated with TTFields and TMZ had an incremental mean lifetime survival of 1.8 years (TTFields/TMZ: 4.2 vs TMZ alone: 2.4). Patients alive at year 2 after starting TTFields had a 20.7% probability of surviving to year 10. The results presented here provide the required incremental survival benefit necessary for a future assessment of the incremental cost–effectiveness of TTFields.

Modelling the Survival Outcomes of Immuno-Oncology Drugs in Economic Evaluations: A Systematic Approach to Data Analysis and Extrapolation

BACKGROUND

New immuno-oncology (I-O) therapies that harness the immune system to fight cancer call for a re-examination of the traditional parametric techniques used to model survival from clinical trial data. More flexible approaches are needed to capture the characteristic I-O pattern of delayed treatment effects and, for a subset of patients, the plateau of long-term survival.

OBJECTIVES

Using a systematic approach to data management and analysis, the study assessed the applicability of traditional and flexible approaches and, as a test case of flexible methods, investigated the suitability of restricted cubic splines (RCS) to model progression-free survival (PFS) in I-O therapy.

METHODS

The goodness of fit of each survival function was tested on data from the CheckMate 067 trial of monotherapy versus combination therapy (nivolumab/ipilimumab) in metastatic melanoma using visual inspection and statistical tests. Extrapolations were validated using long-term data for ipilimumab.

RESULTS

Modelled PFS estimates using traditional methods did not provide a good fit to the Kaplan-Meier (K-M) curve. RCS estimates fit the K-M curves well, particularly for the plateau phase. RCS with six knots provided the best overall fit, but RCS with one knot performed best at the plateau phase and was preferred on the grounds of parsimony.

CONCLUSIONS

RCS models represent a valuable addition to the range of flexible approaches available to model survival when assessing the effectiveness and cost-effectiveness of I-O therapy. A systematic approach to data analysis is recommended to compare the suitability of different approaches for different diseases and treatment regimens.