目的:我们的研究旨在开发和验证中国乳腺癌人群的同源重组缺陷(HRD)评分算法。
方法:通过全基因组测序(WGS)分析了96个内部乳腺癌(BC)样本和6个HRD阳性标准细胞。此外,将来自TCGA数据库的122个BC下采样至〜1XWGS。我们构建了一种名为AcornHRD的算法,用于基于WGS在低覆盖率下计算的HRD得分作为输入数据,以估计基因组上的大规模拷贝数改变(LCNA)事件。50例BCs(15例携带BRCA突变)的临床队列用于评估HRD状态与基于蒽环类药物的新辅助治疗结果之间的相关性。
结果:使用41个内部案例和TCGA数据集,将100kb窗口定义为最佳大小。HRD得分高阈值被确定为使用55个具有BRCA突变的内部BCs达到95%BRCA阳性一致率的HRD得分≥10。此外,AcornHRD的HRD状态同意率为100%,而浅层HRD在标准单元中是60%。BRCA突变与TCGA数据集中由AcornHRD和ShallowHRD评估的高HRD评分显著相关(分别为p=0.008和p=0.003)。然而,AcornHRD显示出比ShallowHRD算法更高的阳性一致率(70%vs60%)。此外,在临床队列中,AcornHRD的BRCA阳性符合率优于ShallowHRD(87%vs13%).重要的是,通过AcornHRD评估的高HRD评分与残留癌症负荷评分0或1(RCB0/1)显著相关.此外,HRD阳性组比HRD阴性组更有可能对蒽环类化疗产生应答(pCR[OR=9.5,95%CI1.11~81.5,p=0.040]和RCB0/1[OR=10.29,95%CI2.02~52.36,p=0.005]).
结论:使用AcornHRD算法评估,我们的分析证明了LCNA基因组特征在乳腺癌HRD检测中的高性能.
OBJECTIVE: Our study aimed to develop and validate a homologous recombination deficiency (HRD) scoring algorithm in the Chinese breast cancer population.
METHODS: Ninety-six in-house breast cancer (BC) samples and 6 HRD-positive standard cells were analyzed by whole-genome sequencing (WGS). Besides, 122 BCs from the TCGA database were down-sampled to ~ 1X WGS. We constructed an algorithm named AcornHRD for HRD score calculated based on WGS at low coverage as input data to estimate large-scale copy number alteration (LCNA) events on the genome. A clinical cohort of 50 BCs (15 cases carrying BRCA mutation) was used to assess the association between HRD status and anthracyclines-based neoadjuvant treatment outcomes.
RESULTS: A 100-kb window was defined as the optimal size using 41 in-house cases and the TCGA dataset. HRD score high threshold was determined as HRD score ≥ 10 using 55 in-house BCs with BRCA mutation to achieve a 95% BRCA-positive agreement rate. Furthermore, the HRD status agreement rate of AcornHRD is 100%, while the ShallowHRD is 60% in standard cells. BRCA mutation was significantly associated with a high HRD score evaluated by AcornHRD and ShallowHRD (p = 0.008 and p = 0.003, respectively) in the TCGA dataset. However, AcornHRD showed a higher positive agreement rate than did the ShallowHRD algorithm (70% vs 60%). In addition, the BRCA-positive agreement rate of AcornHRD was superior to that of ShallowHRD (87% vs 13%) in the clinical cohort. Importantly, the high HRD score assessed by AcornHRD was significantly correlated with a residual cancer burden score of 0 or 1 (RCB0/1). Besides, the HRD-positive group was more likely to respond to anthracycline-based chemotherapy than the HRD-negative group (pCR [OR = 9.5, 95% CI 1.11-81.5, p = 0.040] and RCB0/1 [OR = 10.29, 95% CI 2.02-52.36, p = 0.005]).
CONCLUSIONS: Using the AcornHRD algorithm evaluation, our analysis demonstrated the high performance of the LCNA genomic signature for HRD detection in breast cancers.