Germinal immunogenetics as a predictive factor for immunotherapy. (September 2019)

• Record Type: Journal Article
• Authors: Refae, Sadal; Gal, Jocelyn; Brest, Patrick; Milano, Gerard
• Is Part Of: Critical reviews in oncology/hematology. Volume 141(2019)
• Page Start: 146
• Page End: 152
• Publication Date: 2019-09
• Subjects: Immunotherapy -- Check-point inhibitors -- Predictive factors -- Germinal immunogenetics; Oncology -- Periodicals; Hematology -- Periodicals; 616.994
• DOI: 10.1016/j.critrevonc.2019.06.013
• Languages: English
• ISSNs: 1040-8428

Abstract

Graphical abstract: Clinical response to checkpoint inhibitors-based (CPIs) therapies can vary among tumor types and between patients according to several factors. Entering host-related parameters (germinal immunogenetics) into the biomarker panel of CPI should provide a valuable strategy for identifying not only factors predictive of treatment efficacy but also of treatment-related toxicity. A major issue concerns the real functional significance of the reported single-nucleotide polymorphisms (SNPs) linked to CPI-treatment outcome. Highlights: Most predictors for CPI treatment are centered on tumor and its microenvironment. The part of the host among predictors for CPI is largely underestimated. Germinal immunogenetics aim to examine the impact of individual gene polymorphisms on CPI use. Abstract: Clinical response to checkpoint inhibitors-based (CPIs) therapies can vary among tumor types and between patients. This led to a significant amount of pre-clinical and clinical research into biomarker identification. Biomarkers have been found to cover both the tumor itself and the tumor microenvironment. Entering host-related parameters into the equation should provide a valuable strategy for identifying not only factors predictive of treatment efficacy but also of treatment-related toxicity. It is clear that germline variants can offer efficient and easily-assessable indicators (blood DNA) to enlarge the spectrum of predictive markers for CPI-based treatment. A major issue concerns the real functional significance of the reported single-nucleotide polymorphisms (SNPs) linked to CPI-treatment outcome. Powered calculations should lead to an optimal trade-off between sample size and allele frequency. New molecular technologies and new analytical methods should provide opportunities to bridge the knowledge gap between SNP-CPI treatment associations and the functional impact of these SNPs.