Ritch Elie J, Herberts Cameron, Warner Evan W, Ng Sarah W S, Kwan Edmond M, Bacon Jack V W, Bernales Cecily Q, Schönlau Elena, Fonseca Nicolette M, Giri Veda N, Maurice-Dror Corinne, Vandekerkhove Gillian, Jones Steven J M, Chi Kim N, Wyatt Alexander W
Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada.
Department of Medical Oncology, BC Cancer, Vancouver, BC, Canada.
NPJ Precis Oncol. 2023 Mar 13;7(1):27. doi: 10.1038/s41698-023-00366-z.
Specific classes of DNA damage repair (DDR) defect can drive sensitivity to emerging therapies for metastatic prostate cancer. However, biomarker approaches based on DDR gene sequencing do not accurately predict DDR deficiency or treatment benefit. Somatic alteration signatures may identify DDR deficiency but historically require whole-genome sequencing of tumour tissue. We assembled whole-exome sequencing data for 155 high ctDNA fraction plasma cell-free DNA and matched leukocyte DNA samples from patients with metastatic prostate or bladder cancer. Labels for DDR gene alterations were established using deep targeted sequencing. Per sample mutation and copy number features were used to train XGBoost ensemble models. Naive somatic features and trinucleotide signatures were associated with specific DDR gene alterations but insufficient to resolve each class. Conversely, XGBoost-derived models showed strong performance including an area under the curve of 0.99, 0.99 and 1.00 for identifying BRCA2, CDK12, and mismatch repair deficiency in metastatic prostate cancer. Our machine learning approach re-classified several samples exhibiting genomic features inconsistent with original labels, identified a metastatic bladder cancer sample with a homozygous BRCA2 copy loss, and outperformed an existing exome-based classifier for BRCA2 deficiency. We present DARC Sign (DnA Repair Classification SIGNatures); a public machine learning tool leveraging clinically-practical liquid biopsy specimens for simultaneously identifying multiple types of metastatic prostate cancer DDR deficiencies. We posit that it will be useful for understanding differential responses to DDR-directed therapies in ongoing clinical trials and may ultimately enable prospective identification of prostate cancers with phenotypic evidence of DDR deficiency.
特定类型的DNA损伤修复（DDR）缺陷会导致转移性前列腺癌对新兴疗法产生敏感性。然而，基于DDR基因测序的生物标志物方法并不能准确预测DDR缺陷或治疗获益。体细胞改变特征可能可识别DDR缺陷，但从历史上看，这需要对肿瘤组织进行全基因组测序。我们收集了155例高循环肿瘤DNA（ctDNA）比例的转移性前列腺癌或膀胱癌患者的血浆游离DNA及匹配的白细胞DNA样本的全外显子测序数据。使用深度靶向测序确定DDR基因改变的标签。每个样本的突变和拷贝数特征用于训练XGBoost集成模型。单纯的体细胞特征和三核苷酸特征与特定的DDR基因改变相关，但不足以区分每一类。相反，XGBoost衍生模型表现出强大的性能，在识别转移性前列腺癌中的BRCA2、CDK12和错配修复缺陷方面，曲线下面积分别为0.99、0.99和1.00。我们的机器学习方法对几个表现出与原始标签不一致的基因组特征的样本进行了重新分类，识别出一个具有纯合BRCA2拷贝缺失的转移性膀胱癌样本，并且在BRCA2缺陷方面优于现有的基于外显子组的分类器。我们提出了DARC Sign（DNA修复分类特征）；这是一种公共机器学习工具，利用临床实用的液体活检标本同时识别多种类型的转移性前列腺癌DDR缺陷。我们认为，它将有助于理解正在进行的临床试验中对DDR导向疗法的不同反应，并最终可能实现对具有DDR缺陷表型证据的前列腺癌进行前瞻性识别。
