Suppr超能文献

确定转移性前列腺癌患者中的同源重组评分阈值以预测PARP抑制剂的疗效。

Establishing the homologous recombination score threshold in metastatic prostate cancer patients to predict the efficacy of PARP inhibitors.

作者信息

Zhao Diwei, Wang Anqi, Li Yuanwei, Cai Xinyang, Zhao Junliang, Zhang Tianyou, Zhao Yi, Dong Yu, Zhou Fangjian, Li Yonghong, Wang Jun

机构信息

State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.

Department of Urology, Sun Yat-sen University Cancer Center, Guangzhou, China.

出版信息

J Natl Cancer Cent. 2024 May 25;4(3):280-287. doi: 10.1016/j.jncc.2024.05.005. eCollection 2024 Sep.

Abstract

BACKGROUND

The homologous recombination deficiency (HRD) score serves as a promising biomarker to identify patients who are eligible for treatment with PARP inhibitors (PARPi). Previous studies have suggested a 3-biomarker Genomic Instability Score (GIS) threshold of ≥ 42 as a valid biomarker to predict response to PARPi in patients with ovarian cancer and breast cancer. However, the GIS threshold for prostate cancer (PCa) is still lacking. Here, we conducted an exploratory analysis to investigate an appropriate HRD score threshold and to evaluate its ability to predict response to PARPi in PCa patients.

METHODS

A total of 181 patients with metastatic castration-resistant PCa were included in this study. Tumor tissue specimens were collected for targeted next-generation sequencing for homologous recombination repair (HRR) genes and copy number variation (CNV) analysis. The HRD score was calculated based on over 50,000 single-nucleotide polymorphisms (SNP) distributed across the human genome, incorporating three SNP-based assays: loss of heterozygosity, telomeric allelic imbalance, and large-scale state transition. The HRD score threshold was set at the last 5th percentile of the HRD scores in our cohort of known HRR-deficient tumors. The relationship between the HRD score and the efficacy in 16 patients of our cohort who received PARPi treatment were retrospectively analyzed.

RESULTS

Genomic testing was succeeded in 162 patients. In our cohort, 61 patients (37.7%) had HRR mutations (HRRm). mutations occurred in 15 patients (9.3%). The median HRD score was 4 (ranged from 0 to 57) in the total cohort, which is much lower than that in breast and ovarian cancers. Patients who harbored HRRm and or mutations had higher HRD scores. CNV occured more frequently in patients with HRRm. The last 5th percentile of HRD scores was 43 in the HRR-mutant cohort and consequently HRD high was defined as HRD scores 43. In the 16 patients who received PARPi in our cohort, 4 patients with a high HRD score achieved an objective response rate (ORR) of 100% while 12 patients with a low HRD score achieved an ORR of 8.3%. Progression-free survival (PFS) in HRD high patients was longer compared to HRD low patients, regardless of HRRm.

CONCLUSIONS

A HRD score threshold of 43 was established and preliminarily validated to predict the efficacy of PARPi in this study. Future studies are needed to further verify this threshold.

摘要

背景

同源重组缺陷(HRD)评分是一种很有前景的生物标志物,可用于识别适合接受聚二磷酸腺苷核糖聚合酶抑制剂(PARPi)治疗的患者。先前的研究表明,基因组不稳定评分(GIS)≥42的三生物标志物阈值是预测卵巢癌和乳腺癌患者对PARPi反应的有效生物标志物。然而,前列腺癌(PCa)的GIS阈值仍未明确。在此,我们进行了一项探索性分析,以研究合适的HRD评分阈值,并评估其预测PCa患者对PARPi反应的能力。

方法

本研究共纳入181例转移性去势抵抗性PCa患者。收集肿瘤组织标本进行同源重组修复(HRR)基因的靶向二代测序和拷贝数变异(CNV)分析。HRD评分基于分布在人类基因组中的超过50,000个单核苷酸多态性(SNP)计算得出,纳入了三种基于SNP的检测方法:杂合性缺失、端粒等位基因失衡和大规模状态转换。HRD评分阈值设定为我们已知HRR缺陷肿瘤队列中HRD评分的最后第5百分位数。回顾性分析了我们队列中16例接受PARPi治疗患者的HRD评分与疗效之间的关系。

结果

162例患者成功进行了基因检测。在我们的队列中,61例患者(37.7%)存在HRR突变(HRRm)。15例患者(9.3%)发生了 突变。整个队列的HRD评分中位数为4(范围为0至57),远低于乳腺癌和卵巢癌。携带HRRm和/或 突变的患者HRD评分更高。CNV在HRRm患者中更频繁发生。HRR突变队列中HRD评分的最后第5百分位数为43,因此HRD高被定义为HRD评分≥43。在我们队列中接受PARPi治疗的16例患者中,4例HRD评分高的患者客观缓解率(ORR)为100%,而12例HRD评分低的患者ORR为8.3%。无论HRRm如何,HRD高的患者无进展生存期(PFS)均长于HRD低的患者。

结论

本研究确定并初步验证了HRD评分阈值43可预测PARPi的疗效。未来需要进一步研究以验证该阈值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3037/11401495/56445e8e478c/gr1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验