ABSTRACT
BACKGROUND: Tumor mutational burden (TMB) measurements aid in identifying patients who are likely to benefit from immunotherapy; however, there is empirical variability across panel assays and factors contributing to this variability have not been comprehensively investigated. Identifying sources of variability can help facilitate comparability across different panel assays, which may aid in broader adoption of panel assays and development of clinical applications. MATERIALS AND METHODS: Twenty-nine tumor samples and 10 human-derived cell lines were processed and distributed to 16 laboratories; each used their own bioinformatics pipelines to calculate TMB and compare to whole exome results. Additionally, theoretical positive percent agreement (PPA) and negative percent agreement (NPA) of TMB were estimated. The impact of filtering pathogenic and germline variants on TMB estimates was assessed. Calibration curves specific to each panel assay were developed to facilitate translation of panel TMB values to whole exome sequencing (WES) TMB values. RESULTS: Panel sizes >667 Kb are necessary to maintain adequate PPA and NPA for calling TMB high versus TMB low across the range of cut-offs used in practice. Failure to filter out pathogenic variants when estimating panel TMB resulted in overestimating TMB relative to WES for all assays. Filtering out potential germline variants at >0% population minor allele frequency resulted in the strongest correlation to WES TMB. Application of a calibration approach derived from The Cancer Genome Atlas data, tailored to each panel assay, reduced the spread of panel TMB values around the WES TMB as reflected in lower root mean squared error (RMSE) for 26/29 (90%) of the clinical samples. CONCLUSIONS: Estimation of TMB varies across different panels, with panel size, gene content, and bioinformatics pipelines contributing to empirical variability. Statistical calibration can achieve more consistent results across panels and allows for comparison of TMB values across various panel assays. To promote reproducibility and comparability across assays, a software tool was developed and made publicly available.
Subject(s)
Mutation , Neoplasms , Biomarkers, Tumor , Humans , Neoplasms/diagnosis , Neoplasms/genetics , Reproducibility of Results , Tumor BurdenABSTRACT
AIMS/HYPOTHESIS: We investigated the risk associated with HLA-B*39 alleles in the context of specific HLA-DR/DQ haplotypes. METHODS: We studied a readily available dataset from the Type 1 Diabetes Genetics Consortium that consists of 2,300 affected sibling pair families genotyped for both HLA alleles and 2,837 single nucleotide polymorphisms across the major histocompatibility complex region. RESULTS: The B*3906 allele significantly enhanced the risk of type 1 diabetes when present on specific HLA-DR/DQ haplotypes (DRB1 0801-DQB1 0402: p = 1.6 × 10(-6), OR 25.4; DRB1 0101-DQB1 0501: p = 4.9 × 10(-5), OR 10.3) but did not enhance the risk of DRB1 0401-DQB1 0302 haplotypes. In addition, the B 3901 allele enhanced risk on the DRB1 1601-DQB1 0502 haplotype (p = 3.7 × 10(-3), OR 7.2). CONCLUSIONS/INTERPRETATION: These associations indicate that the B 39 alleles significantly increase risk when present on specific HLA-DR/DQ haplotypes, and HLA-B typing in concert with specific HLA-DR/DQ genotypes should facilitate genetic prediction of type 1 diabetes, particularly in a research setting.
