Current State of Health Economic Analyses for Low-Grade Glioma Management: A Systematic Review.

BACKGROUND: Health economic analyses help determine the value of a medical intervention by assessing the costs and outcomes associated with it. The objective of this study was to assess the level of evidence in economic evaluations for low-grade glioma (LGG) management. METHODS: Following the PRISMA guidelines, we conducted a systematic review of English articles in Medline, Embase, The Central Registration Depository, EconPapers, and EconLit. The results were screened, and data were extracted by 2 independent reviewers for studies reporting economic evaluations for LGG. The quality of each study was evaluated using the CHEERS (Consolidated Health Economic Evaluation Reporting Standards) checklist, the hierarchy scale developed by Cooper et al. (2005), and the Quality of Health Economic Studies instrument. RESULTS: Three studies met our inclusion criteria. The adjusted incremental cost-effectiveness ratio (ICER) values for the included studies ranged from $3934 to $9936, but each evaluated a different aspect of LGG management. All had a good quality of reporting per the CHEERS checklist. Based on the Cooper et al. hierarchy scale, the quality of data use was lacking most for utilities. The quality of study design was scored as 82, 92, and 100 for each study using the Quality of Health Economic Studies instrument. CONCLUSIONS: Although a limited number of economic evaluations were identified, the studies evaluated here were well designed. The interventions assessed were all considered cost-effective, but pooled analysis was not possible because of heterogeneity in the interventions assessed. Given the importance of value and cost-effectiveness in medical care, more evidence is needed in this area.

Mutation position is an important determinant for predicting cancer neoantigens.

Tumor-specific mutations can generate neoantigens that drive CD8 T cell responses against cancer. Next-generation sequencing and computational methods have been successfully applied to identify mutations and predict neoantigens. However, only a small fraction of predicted neoantigens are immunogenic. Currently, predicted peptide binding affinity for MHC-I is often the major criterion for prioritizing neoantigens, although little progress has been made toward understanding the precise functional relationship between affinity and immunogenicity. We therefore systematically assessed the immunogenicity of peptides containing single amino acid mutations in mouse tumor models and divided them into two classes of immunogenic mutations. The first comprises mutations at a nonanchor residue, for which we find that the predicted absolute binding affinity is predictive of immunogenicity. The second involves mutations at an anchor residue; here, predicted relative affinity (compared with the WT counterpart) is a better predictor. Incorporating these features into an immunogenicity model significantly improves neoantigen ranking. Importantly, these properties of neoantigens are also predictive in human datasets, suggesting that they can be used to prioritize neoantigens for individualized neoantigen-specific immunotherapies.