Table1_Evaluation of AlphaFold structure-based protein stability prediction on missense variations in cancer.xlsx

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.

识别遗传性癌症中的致病性错义突变对于患者监测和风险降低策略的实施至关重要。为此，多种包含不同数量和/或基因组合的基因检测面板可供选择，而我们特别关注一个包含26个基因的面板，这些基因具有不同程度的遗传性癌症风险，包括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和XRCC2。在本研究中，我们收集了关于这26个基因中任何基因报告的错义变异。从ClinVar收集了超过一千个错义变异，并从355名乳腺癌患者的靶向筛查中获得了160个新的错义变异，从而构成了这一集合。我们通过包括基于序列（SAAF2EC和MUpro）和结构（Maestro、mCSM、CUPSAT）预测器的五种不同预测器分析了错义变异对蛋白质稳定性的影响。对于基于结构的工具，我们使用了AlphaFold（AF2）蛋白质结构，这是对这一遗传性癌症蛋白质的首次结构分析。我们的结果与最近计算稳定性预测器在区分致病性变异方面的能力的基准测试结果一致。总体而言，我们报告了稳定性预测器在区分致病性变异方面的低至中等水平的表现，除了MUpro，其AUROC值为0.534（95% CI [0.499–0.570]）。AUROC值介于0.614–0.719之间，对于总集合而言，介于0.596–0.682之间，对于具有高AF2置信区域的集合而言。此外，我们的研究结果揭示了AF2结构中给定变异的置信度评分可以独立于任何测试的稳定性预测器，以0.852的AUROC值更稳健地预测致病性。总之，本研究首次对26个遗传性癌症基因进行了结构分析，强调了1）从AF2结构预测的热力学稳定性作为中等程度，以及2）AF2置信度评分作为变异致病性的强大描述符。