PDF(Pubmed)
Abstract：
BACKGROUND: Homologous recombination deficiency (HRD) stands as a clinical indicator for discerning responsive outcomes to platinum-based chemotherapy and poly ADP-ribose polymerase (PARP) inhibitors. One of the conventional approaches to HRD prognostication has generally centered on identifying deleterious mutations within the BRCA1/2 genes, along with quantifying the genomic scars, such as Genomic Instability Score (GIS) estimation with scarHRD. However, the scarHRD method has limitations in scenarios involving tumors bereft of corresponding germline data. Although several RNA-seq-based HRD prediction algorithms have been developed, they mainly support cohort-wise classification, thereby yielding HRD status without furnishing an analogous quantitative metric akin to scarHRD. This study introduces the expHRD method, which operates as a novel transcriptome-based framework tailored to n-of-1-style HRD scoring.
RESULTS: The prediction model has been established using the elastic net regression method in the Cancer Genome Atlas (TCGA) pan-cancer training set. The bootstrap technique derived the HRD geneset for applying the expHRD calculation. The expHRD demonstrated a notable correlation with scarHRD and superior performance in predicting HRD-high samples. We also performed intra- and extra-cohort evaluations for clinical feasibility in the TCGA-OV and the Genomic Data Commons (GDC) ovarian cancer cohort, respectively. The innovative web service designed for ease of use is poised to extend the realms of HRD prediction across diverse malignancies, with ovarian cancer standing as an emblematic example.
CONCLUSIONS: Our novel approach leverages the transcriptome data, enabling the prediction of HRD status with remarkable precision. This innovative method addresses the challenges associated with limited available data, opening new avenues for utilizing transcriptomics to inform clinical decisions.
RESULTS: The prediction model has been established using the elastic net regression method in the Cancer Genome Atlas (TCGA) pan-cancer training set. The bootstrap technique derived the HRD geneset for applying the expHRD calculation. The expHRD demonstrated a notable correlation with scarHRD and superior performance in predicting HRD-high samples. We also performed intra- and extra-cohort evaluations for clinical feasibility in the TCGA-OV and the Genomic Data Commons (GDC) ovarian cancer cohort, respectively. The innovative web service designed for ease of use is poised to extend the realms of HRD prediction across diverse malignancies, with ovarian cancer standing as an emblematic example.
CONCLUSIONS: Our novel approach leverages the transcriptome data, enabling the prediction of HRD status with remarkable precision. This innovative method addresses the challenges associated with limited available data, opening new avenues for utilizing transcriptomics to inform clinical decisions.
摘要：
背景：同源重组缺陷（HRD）是辨别铂类化疗和聚ADP-核糖聚合酶（PARP）抑制剂反应性结果的临床指标。HRD预测的常规方法之一通常集中在识别BRCA1/2基因内的有害突变，随着基因组疤痕的量化，如基因组不稳定评分(GIS)估计与scarHRD。然而,scarHRD方法在缺乏相应种系数据的肿瘤患者中存在局限性.尽管已经开发了几种基于RNA-seq的HRD预测算法，他们主要支持按队列分类，从而产生HRD状态，而不提供类似于scarHRD的类似定量度量。本研究介绍了expHRD方法，它作为一个新颖的基于转录组的框架，为n-of-1风格的HRD评分量身定制。
结果：已使用癌症基因组图谱（TCGA）泛癌症训练集中的弹性网络回归方法建立了预测模型。引导技术导出了用于应用expHRD计算的HRD基因集。expHRD显示出与scarHRD的显着相关性，并且在预测HRD高样本方面具有优越的性能。我们还在TCGA-OV和基因组数据共享（GDC）卵巢癌队列中进行了临床可行性的队列内和队列外评估，分别。为易于使用而设计的创新Web服务已准备好将HRD预测的领域扩展到各种恶性肿瘤中，卵巢癌是一个象征性的例子。
结论：我们的新方法利用了转录组数据，能够以显著的精度预测HRD状态。这种创新的方法解决了与有限的可用数据相关的挑战，开辟了利用转录组学为临床决策提供信息的新途径。
结果：已使用癌症基因组图谱（TCGA）泛癌症训练集中的弹性网络回归方法建立了预测模型。引导技术导出了用于应用expHRD计算的HRD基因集。expHRD显示出与scarHRD的显着相关性，并且在预测HRD高样本方面具有优越的性能。我们还在TCGA-OV和基因组数据共享（GDC）卵巢癌队列中进行了临床可行性的队列内和队列外评估，分别。为易于使用而设计的创新Web服务已准备好将HRD预测的领域扩展到各种恶性肿瘤中，卵巢癌是一个象征性的例子。
结论：我们的新方法利用了转录组数据，能够以显著的精度预测HRD状态。这种创新的方法解决了与有限的可用数据相关的挑战，开辟了利用转录组学为临床决策提供信息的新途径。
