ABSTRACT
Identifying pathogenic missense variants in hereditary cancer is critical to the efforts of patient surveillance and risk-reduction strategies. For this purpose, many different gene panels consisting of different number and/or set of genes are available and we are particularly interested in a panel of 26 genes with a varying degree of hereditary cancer risk consisting of ABRAXAS1, ATM, BARD1, BLM, BRCA1, BRCA2, BRIP1, CDH1, CHEK2, EPCAM, MEN1, MLH1, MRE11, MSH2, MSH6, MUTYH, NBN, PALB2, PMS2, PTEN, RAD50, RAD51C, RAD51D, STK11, TP53, and XRCC2. In this study, we have compiled a collection of the missense variations reported in any of these 26 genes. More than a thousand missense variants were collected from ClinVar and the targeted screen of a breast cancer cohort of 355 patients which contributed to this set with 160 novel missense variations. We analyzed the impact of the missense variations on protein stability by five different predictors including both sequence- (SAAF2EC and MUpro) and structure-based (Maestro, mCSM, CUPSAT) predictors. For the structure-based tools, we have utilized the AlphaFold (AF2) protein structures which comprise the first structural analysis of this hereditary cancer proteins. Our results agreed with the recent benchmarks that computed the power of stability predictors in discriminating the pathogenic variants. Overall, we reported a low-to-medium-level performance for the stability predictors in discriminating pathogenic variants, except MUpro which had an AUROC of 0.534 (95% CI [0.499-0.570]). The AUROC values ranged between 0.614-0.719 for the total set and 0.596-0.682 for the set with high AF2 confidence regions. Furthermore, our findings revealed that the confidence score for a given variant in the AF2 structure could alone predict pathogenicity more robustly than any of the tested stability predictors with an AUROC of 0.852. Altogether, this study represents the first structural analysis of the 26 hereditary cancer genes underscoring 1) the thermodynamic stability predicted from AF2 structures as a moderate and 2) the confidence score of AF2 as a strong descriptor for variant pathogenicity.
ABSTRACT
AIM: To investigate the effect of mitochondrial DNA (mtDNA) variants mainly in D-loop on glioma biology. MATERIAL AND METHODS: Sanger sequencing of D-loop (15971?16451 bp) for 52 glioma patients was performed and the variations were statistically analyzed for gender, WHO classification, morphological grade, IDH/TERT status. RESULTS: Total of 122 variations (51 unique) were identified in 52 patients. C16223T, T16189C, T16311C and T16126C variants were frequently detected. The total variation number was statistically non-significant among the analyzed categories. When individual variants were considered, T16311C and T16224C were statistically significant for WHO classification (p=0.033), morphological grade (p=0.036) and gender (p=0.039), respectively. CONCLUSION: Total variation number in D-loop was not found to be related with clinical variables. Our data suggests that individual variants may play a critical role in glioma biology.
