Predictors of germline DNA damage repair gene mutations (gDDRm) in patients (pts) with metastatic castration-resistant prostate cancer (CRPC).
Predictors of germline DNA damage repair gene mutations (gDDRm) in patients (pts) with metastatic castration-resistant prostate cancer (CRPC).
复制标题
转移性去势抵抗性前列腺癌 (CRPC) 患者 (pts) 种系 DNA 损伤修复基因突变 (gDDRm) 的预测因子。
DOI：
10.1200/jco.2019.37.7_suppl.159
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发表时间：
2019
影响因子：
45.3
通讯作者：
K. Chi
中科院分区：
文献类型：
--
作者：
S. Yip;K. Sunderland;Arshia Beigi;A. Angeles;D. Khalaf;E. Warner;G. Vandekerkhove;Sophie Sun;M. Annala;A. Wyatt;K. Chi
159背景：gDDRm在6-12%的CRPC患者中存在，并与预后、治疗结果和家族性筛查有关。这项研究的目的是确定与携带GDDRm的PT风险较高相关的临床/病理特征。方法：对631例转移性前列腺癌患者进行22个DNA修复基因(包括ATM、BRCA1/2、MSH2/6、FANC/ERCC成员)的种系突变筛查。比较gDDRm+和gDDRm-患者的临床/病理特征(诊断时年龄、CRPC时内脏转移(METS)、Gleason评分、诊断时导管内/筛状组织学(I/C)、前列腺癌、乳腺癌、卵巢癌或胰腺癌家族史(FHx)、雄激素剥夺治疗开始至CRPC的时间)。通过有目的地选择协变量，建立了gDDRm状态的多变量Logistic模型，其中除了统计学意义外，还使用了临床判断。通过将每个风险因素(Rf)乘以其估计的β系数，建立了加权评分系统。每个PT的总分由每个加权RF的总和计算。结果：GDDRm+38例(6.0%，BRCA2=28例，ATM=4例，BRCA1=2例，PALB2=2例，MSH2=1例，ERCC3=1例)。模型包括29例具有完整临床/病理信息的gDDRm+和128例gDDRm-病例。多变量模型显示内脏蛋氨酸、FHx和I/C与GDDRm+有统计学意义的相关性；65岁的≤被包括在模型中，因为它被认为是临床相关的(表)。根据模型结果，每个RF的权重为：内脏蛋氨酸(2)、FHx(1)、I/C(2)、年龄≤65(1)。以总分≥1为界值，该模型的灵敏度、特异度、阳性预测值和阴性预测值分别为97%、31%、24%和98%。结论：在这个队列中，内脏蛋氨酸、FHx和I/C独立地与存在GDDRm的较高风险相关。预测模型可以帮助选择PTS进行GDDRm筛查。需要进行模型验证。[表：见正文]
159 Background: gDDRm are present in 6-12% of pts with CRPC and have implications for prognosis, treatment outcomes and familial screening. This study's aim was to identify clinical/pathologic characteristics associated with a higher risk of a pt harboring a gDDRm. Methods: 631 consecutive pts with metastatic prostate cancer were screened for germline mutations in 22 DNA repair genes (including ATM, BRCA1/2, MSH2/6, FANC/ERCC members). Clinical/pathologic characteristics (age at diagnosis, visceral metastases (mets) at time of CRPC, Gleason score, intraductal/cribriform histology (I/C) at diagnosis, family history (FHx) of prostate, breast, ovarian, or pancreatic cancer, time from androgen deprivation therapy initiation to CRPC) were compared between gDDRm+ and gDDRm- cases. A multivariate logistic model of gDDRm status was constructed using purposeful selection of covariates where clinical judgement was employed in addition to statistical significance. A weighted scoring system was created by multiplying each risk factor (RF) by its estimated β coefficient. A pt’s total score was calculated by the sum of each weighted RF. Results: gDDRm+ were identified in 38/631 pts (6.0%, BRCA2 = 28, ATM = 4, BRCA1 = 2, PALB2 = 2, MSH2 = 1, ERCC3 = 1). 29 gDDRm+ and 128 gDDRm- cases with complete clinical/pathologic information were included in the model. Multivariate modeling revealed that visceral mets, FHx and I/C were statistically significantly associated with gDDRm+; age ≤ 65 was included in the model because it was considered clinically relevant (Table). Based on the model results, the weights of each RF are: visceral mets (2), FHx (1), I/C (2), age ≤ 65 (1). Using a total score cut-off ≥ 1, the model achieves a sensitivity, specificity, positive predictive value, and negative predictive value of 97%, 31%, 24%, and 98%, respectively. Conclusions: In this cohort, visceral mets, FHx and I/C were independently associated with a higher risk of harboring a gDDRm. The predictive model may aid in selecting pts for gDDRm screening. Model validation is required. [Table: see text]