Department of Biochemistry, University of Otago, Dunedin, New Zealand.
Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
Cancer Med. 2023 Dec;12(23):21545-21560. doi: 10.1002/cam4.6711. Epub 2023 Nov 17.
Genome-wide measures of genetic disruption such as tumour mutation burden (TMB) and mutation signatures are emerging as useful biomarkers to stratify patients for treatment. Clinicians commonly use cancer gene panels for tumour mutation burden estimation, and whole genome sequencing is the gold standard for mutation signature analysis. However, the accuracy and cost associated with these assays limits their utility at scale.
WGS data from 560 breast cancer patients was used for in silico library simulations to evaluate the accuracy of an FDA approved cancer gene panel as well as restriction enzyme associated DNA sequencing (RADseq) libraries for TMB estimation and mutation signature analysis. We also transfected a mouse mammary cell line with APOBEC enzymes and sequenced resulting clones to evaluate the efficacy of RADseq in an experimental setting.
RADseq had improved accuracy of TMB estimation and derivation of mutation profiles when compared to the FDA approved cancer panel. Using simulated immune checkpoint blockade (ICB) trials, we show that inaccurate TMB estimation leads to a reduction in power for deriving an optimal TMB cutoff to stratify patients for immune checkpoint blockade treatment. Additionally, prioritisation of APOBEC hypermutated tumours in these trials optimises TMB cutoff determination for breast cancer. The utility of RADseq in an experimental setting was also demonstrated, based on characterisation of an APOBEC mutation signature in an APOBEC3A transfected mouse cell line.
In conclusion, our work demonstrates that RADseq has the potential to be used as a cost-effective, accurate solution for TMB estimation and mutation signature analysis by both clinicians and basic researchers.
全基因组遗传破坏指标，如肿瘤突变负荷（TMB）和突变特征，正在成为对患者进行治疗分层的有用生物标志物。临床医生通常使用肿瘤基因面板来估计肿瘤突变负荷，而全基因组测序是突变特征分析的金标准。然而，这些检测的准确性和成本限制了它们在大规模应用中的实用性。
使用 560 名乳腺癌患者的 WGS 数据进行计算机模拟文库，以评估 FDA 批准的癌症基因面板以及与限制酶相关的 DNA 测序（RADseq）文库在 TMB 估计和突变特征分析方面的准确性。我们还转染了一个带有 APOBEC 酶的小鼠乳腺细胞系，并对产生的克隆进行测序，以评估 RADseq 在实验环境中的效果。
与 FDA 批准的癌症面板相比，RADseq 具有更高的 TMB 估计准确性和突变特征推导能力。使用模拟的免疫检查点阻断（ICB）试验，我们表明不准确的 TMB 估计会降低推导最佳 TMB 截止值以分层患者进行免疫检查点阻断治疗的能力。此外，在这些试验中优先考虑 APOBEC 高突变肿瘤可以优化用于乳腺癌的 TMB 截止值确定。基于 APOBEC3A 转染的小鼠细胞系中 APOBEC 突变特征的特征，还证明了 RADseq 在实验环境中的实用性。
总之，我们的工作表明，RADseq 有可能成为一种具有成本效益且准确的 TMB 估计和突变特征分析解决方案，既适用于临床医生，也适用于基础研究人员。
