Use of Circulating Tumor DNA for the Clinical Management of Metastatic Castration-Resistant Prostate Cancer: A Multicenter, Real-World Study

Authors: Baijun Dong PhD1, Liancheng Fan MD1, Bin Yang MD, PhD2, Wei Chen MD3, Yonghong Li MD4, Kaijie Wu MD, PhD5, Fengbo Zhang MD6, Haiying Dong MD, PhD7, Huihua Cheng MBBS8, Jiahua Pan MD1, Yinjie Zhu MD1, Chenfei Chi MD1, Liang Dong MD1, Jianjun Sha MD1, Lei Li MD, PhD5, Xudong Yao MD2, and Wei Xue MD1
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  • 1 Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai;
  • | 2 Department of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai;
  • | 3 Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou;
  • | 4 Department of Urology, Sun Yat-Sen University Cancer Center, Guangzhou;
  • | 5 Department of Urology, The First Affiliated Hospital of Xi’an Jiao Tong University, Xi’an;
  • | 6 Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing;
  • | 7 Department of Urology, Zhejiang Provincial People’s Hospital, Hangzhou;
  • | 8 900th Hospital of Joint Logistic Support Force, Fuzhou.

Background: This study aimed to describe the aberrations of DNA damage repair genes and other important driving genes in Chinese patients with metastatic castration-resistant prostate cancer (mCRPC) using circulating tumor (ctDNA) sequencing and to evaluate the associations between the clinical outcomes of multiple therapies and key genomic alterations in mCRPC, especially DNA damage repair genes. Patients and Methods: A total of 292 Chinese patients with mCRPC enrolled from 8 centers. Multigene targeted sequencing was performed on 306 ctDNA samples and 23 matched tumor biopsies. The frequency of genomic alterations were compared with the Stand Up to Cancer–Prostate Cancer Foundation (SU2C-PCF) cohort. The Kaplan-Meier method was used to evaluate progression-free survival (PFS) following standard systemic treatments for mCRPC. Cox regression analyses were performed to determine prognostic factors associated with PFS resulting from treatments for mCRPC. Results: In total, 33 of 36 (91.7%) mutations were found consistently between ctDNA and paired biopsy samples. The most common recurrent genomic alterations were found in AR (34.6%), TP53 (19.5%), CDK12 (15.4%), BRCA2 (13%), and RB1 (5.8%). The frequency of CDK12 alterations (15.4%) in our cohort was significantly higher than that in Western populations (5%–7%). AR amplification and TP53 and/or RB1 alterations were associated with resistance to abiraterone or docetaxel. Patients with a CDK12 defect showed rapid disease progression after abiraterone treatment. However, the clinical outcome after docetaxel treatment was similar between patients with and without CDK12 defects. In multivariate Cox regression analysis, a CDK12 defect was significantly associated with inferior PFS after abiraterone treatment. Patients with a BRCA2 defect showed marked response to both PARP inhibitors and platinum-based chemotherapy. Conclusions: Our study explored the genomic landscape of Chinese patients with mCRPC at different treatment stages using minimally invasive methods and evaluated the clinical implications of the driver genomic alterations on patients’ response to the most widely used therapies for mCRPC. We observed a significantly higher alteration frequency of CDK12 in our cohort compared with the SU2C-PCF cohort.

Background

Metastatic castration-resistant prostate cancer (mCRPC) remains incurable, despite incremental improvements provided by multiple agents, including new-generation hormone therapies, taxane chemotherapy, and PARP inhibitors.16 Genomic profiling of metastatic biopsies of mCRPC has revealed multiple molecular alterations, including alterations of DNA damage repair (DDR) genes, the androgen receptor (AR) gene, and the tumor suppressor genes TP53 and RB1.7 Previous studies preliminarily showed the prognostic relevance of these genes with multiple treatments in patients with mCRPC.811 Despite this important advance in patient management, the understanding of mCRPC genetic epidemiology is mainly based on data from Western populations, with limited data available from other ethnicities. The large differences between Chinese and Western patients, especially in terms of their genomics, should not be ignored.12,13 Whether these genetic alterations, especially those involving DDR genes, can predict the clinical outcomes of Chinese patients with mCRPC following specific treatments remains to be fully elucidated.

Routine sampling of bone and other metastatic tissues in patients with mCRPC is impractical. Therefore, the use of circulating tumor DNA (ctDNA) as a sample source for genomic analysis has gained momentum. ctDNA analysis is compatible with multiple sampling at different time points and can accurately capture tumor heterogeneity in a minimally invasive manner. Previous studies have shown substantial interpatient and intrapatient genomic heterogeneity in mCRPC.7,14 In addition, the genomic profile of mCRPC is dynamic and evolves with systemic treatments, explaining its diverse molecular signatures at different treatment stages.15,16

Consequently, we hypothesized that ctDNA analysis could detect genomic alterations of mCRPC at different treatment stages and that these results might support its clinical utility in the standard-of-care setting. In the present study, we enrolled 292 patients with mCRPC from 8 centers in a real-world study and applied deep targeted ctDNA sequencing for an exploratory analysis of clinical implications.

Patients and Methods

Patients

This study was approved by the Committee for Ethics of Renji Hospital (approval number: [2016]115K), and informed consent was obtained from each of the 292 enrolled patients with mCRPC treated at Renji Hospital of Shanghai Jiaotong University School of Medicine, Shanghai Tenth People’s Hospital, The First Affiliated Hospital of Wenzhou Medical University, Sun Yat-sen University Cancer Center, The First Affiliated Hospital of Xi’an Jiaotong University, Beijing Friendship Hospital, Fuzhou Central Hospital of Nanjing Military Command, and Zhejiang Provincial People’s Hospital between December 2017 and December 2019. The study included 306 ctDNA samples and 23 matched biopsied tumor tissue samples (20 from the prostate, 2 from the liver, 1 from bone) collected at the same time. Among the 292 patients with mCRPC, 14 had 2 serial monitoring ctDNA samples and 23 had concurrent biopsied tumor tissue samples and ctDNA samples (supplemental eTable 1, available with this article at JNCCN.org). Clinical data were collected by the lead investigators at each site. Progression-free survival (PFS) was defined as the time from the date of treatment initiation to confirmed prostate-specific antigen (PSA) increase, clinical or radiographic progression, or death.

Target Capture and Sequencing

The targeted next-generation sequencing test of all samples was performed at GloriousMed Clinical Laboratory Co., Ltd. For blood samples, plasma was isolated by centrifugation at 1,600 × g for 10 minutes and then at 16,000 × g for 10 minutes. Cell-free DNA was extracted from 3 to 5 mL of plasma using a QIAamp Circulating Nucleic Acid Kit (Qiagen) according to the manufacturer’s instructions. Tumor formalin-fixed paraffin-embedded (FFPE) DNA was extracted from 5 to 10 sections (5 mm thick) using a QIAamp DNA FFPE Tissue Kit (Qiagen), and genomic DNA was extracted from white blood cells using a Blood Genomic DNA Mini Kit (Cwbiotech). Two separate custom-designed DNA enrichment panels were used: The NimbleGen SeqCap EZ choice probe pool (Roche) was used to capture the coding regions of 620 or 642 genes, and the xGen Lockdown Probe Pool (Integrated DNA Technologies, Inc.) was used to capture the coding regions of 50 or 66 genes. To confirm concordance between the different gene panels used in the present study, 12 ctDNA samples were duplicated and sequenced from the 66-gene panel and the 620/642-gene panel. For analysis, we focused on the common 50 genes shown in supplemental eTable 2. For each sample, 20 to 100 ng of cell-free DNA, 200 to 500 ng of FFPE DNA, or 500 ng of genomic DNA were then used for library preparation and quantification, guided by KAPA Hyper Prep protocols (Kapa Biosystems, Inc.). Pools of 4 to 6 libraries were used to hybridize to the capture panel for 16 hours at 47°C for NimbleGen panel and at 65°C for xGen panel. Washing, recovery, and amplification were performed sequentially according the standard procedures of the NimbleGen SeqCap EZ and xGen panels. The libraries were then purified using AMPure XP (Beckman) and quantified using a Qubit dsDNA HS Assay Kit (ThermoFisher). The final libraries were sequenced on the Illumina Nextseq500 (75 bp paired-end reads) or the Novoseq6000 (150 bp paired-end reads) instruments (Illumina).

Quality Control and Variant Calling

Sequencing adapters were trimmed from the raw data using Trimmomatic.17 The reads after adapter trimming were then aligned with the human reference genome using Burrows-Wheeler Alignment tool.18 Duplicated reads were removed using Picard (http://broadinstitute.github.io/picard/). Mapped reads were also realigned to the genome using the Genome Analysis Toolkit (GATK).19 Somatic and germline mutations were called using Mutect2 and the GATK Haplotype Caller19 with a paired workflow, respectively. Variants were then annotated using ANNOVAR20 and an in-house developed code. An in-house script was used to verify the human identity concordance of paired samples. Somatic copy number alterations were also detected using the GATK.19

Germline Variant Filtering

Germline variants called by the GATK on WBC samples were first filtered using a threshold of minimum coverage of 50× and an allele frequency of >30%. Variants not on coding regions and synonymous mutations annotated with ANNOVAR20 were filtered out. Furthermore, variants with >0.1% population minor allele frequency annotated in the ExAC database (http://exac.broadinstitute.org/) were considered less functional and were ignored in the downstream analysis. Germline mutations considered deleterious (nonsense/stop-gains, frameshift insertions and deletions, and ± 1,2 splice-site variants, or those reported as pathogenic or likely pathogenic in the ClinVar database; https://www.ncbi.nlm.nih.gov/clinvar/) were included for analysis.

Somatic Variant Filtering

Somatic mutations from ctDNA samples were filtered using the following rules: (1) 10 allele reads support, (2) 1% allele frequency, (3) supporting reads should be <4 in the WBC control, (4) mutation frequency should be 5 times higher than in the WBC control, (5) mutations should not occur more than twice in the panel of normals, and (6) no significant strand bias (GATK parameter FisherStrand annotation >60 for single-nucleotide polymorphisms and GATK parameter FisherStrand annotation >200 for insertions/deletions). Similar filtering rules were applied for somatic mutations from FFPE samples except for the allele frequency, which was required to be >5%, and the mutation frequency, which was required to be 8 times higher than that in the WBC control. Functional filtering removed variants located in noncoding regions, and synonymous mutations were removed for downstream analysis. A log2 ratio of >0.6 was considered a copy gain event for AR. A log2 ratio of <−0.7 was considered a copy loss.

Biallelic Inactivation Definition

Biallelic inactivation was defined as either homozygous deletion, ≥2 deleterious somatic mutations, 1 deleterious germline mutation with concurrent heterozygous loss of the wild-type allele, or 1 somatic mutation with loss of heterozygosity as computed using the FACETS algorithm.21 Only samples with a ctDNA fraction ≥0.2 were used to infer biallelic inactivation.22

ctDNA Fraction Estimation

The mutant allele fraction (MAF) was first calculated using the somatic mutation profile from the sequencing results, followed by a correction model.8 The ctDNA% was defined as 2/(1/MAF + 1) in diploid chromosomes as MAF, and ctDNA% was related as MAF = (ctDNA × 1)/[(1 − ctDNA) × 2 + ctDNA × 1].

Statistical Analysis

All statistical analysis was conducted using R version 3.7 (R Foundation for Statistical Computing). The Fisher exact test was used to test the significance of differences for the somatic alterations between different groups. The Kaplan-Meier method was used to estimate the PFS of different treatments for patients, and differences between groups were analyzed using the log-rank test in the survival package (version 2.44-1.1). Univariate and multivariate Cox regression analyses were used to calculate their respective hazard ratios (HRs) and 95% confidence intervals. Only factors significant in univariate analyses were included in the subsequent multivariate analyses. A test result was considered as statistically significant for P<.05.

Results

Patient Characteristics

From the 8 institutions, 292 patients were recruited for analysis between December 2017 and December 2019 (supplemental eTable 3). Their baseline characteristics are summarized in Table 1. Median age before ctDNA sequencing was 69 years (interquartile range, 65–76 years). The patients were categorized according to treatments received before blood collection. Among the 306 ctDNA samples, the patient numbers (proportion) of group A (treatment-naïve), group B (post first-line treatment of mCRPC), and group C (post second-line or later-line treatment of mCRPC) were 93 (30.4%), 132 (43.1%), and 81 (26.5%), respectively. Patients were also categorized according to treatment after blood collection. Baseline sample numbers from those receiving treatment (after baseline ctDNA collection) were as follows: abiraterone (n=58), docetaxel (n=66), platinum-based chemotherapy post docetaxel (n=19), olaparib (n=27), and other treatments (n=136) (Figure 1).

Table 1.

Summary of Clinical Characteristics of 292 Patients With mCRPC (306 Samples)

Table 1.
Figure 1.
Figure 1.

Flow diagram depicting the patients included in this cohort and patient groups according to treatments received before and after blood collection.

Abbreviation: mCRPC, metastatic castration-resistant prostate cancer.

Citation: Journal of the National Comprehensive Cancer Network 2021; 10.6004/jnccn.2020.7663

Mutational Concordance Between Tumor Tissue and ctDNA

Twenty-three patients with ctDNA samples also had paired tumor biopsy tissues available (20 from the prostate, 2 from the liver, 1 from the bone, collected at the same time). Among these 23 patients, 5 had no detectable somatic mutations in either the ctDNA or the tissue sample. For the remaining 18 patients, 54 and 36 somatic mutations were identified in ctDNA samples and the corresponding tumor tissue samples, respectively. In total, 33 of 36 (91.7%) mutations were found consistently between ctDNA and tissue samples (Figure 2A, supplemental eTable 4). All CDK12 mutations identified in tissue samples were found in ctDNA. Among the 24 mutations detected in 1 sample type but not in the other (ie, discordant mutations), 3 were present in tumor tissues but absent in ctDNA (FOXA1 S436fs, RB1 P29fs, PALB2 L708X) and 21 were unique to ctDNA, including mutations in AR, BRCA2, ATM, PTEN, and TP53 (Figure 2B).

Figure 2.
Figure 2.

Concordance of mutation calls between ctDNA samples and paired tumor tissues in 23 patients. (A) Somatic mutation count of ctDNA and tumor tissues. (B) Variant allele frequencies for selected driven mutations between matched liquid cell tumor DNA and solid tumor tissue.

Abbreviations: ctDNA, circulating tumor DNA; L, liquid cell tumor DNA; S, solid tumor tissue.

Citation: Journal of the National Comprehensive Cancer Network 2021; 10.6004/jnccn.2020.7663

As the sequencing platform evolved, different targeted gene sequencing panels were used in the present study. To confirm the concordance between different gene panels, 12 ctDNA samples were duplicated and subjected to tests with the 66-gene and 620/642-gene panels. All somatic mutations detected using the 620/642-gene panel were confirmed using the 66-gene panel, with remarkably consistent allele fractions (supplemental eFigure 1A). Copy number calls in 4 driver genes (AR, BRCA2, CDK12, and TP53) of mCRPC were also concordant between the 2 DNA capture panels (supplemental eFigure 1B).

Genomic Landscape of Chinese Patients With mCRPC

Somatic and deleterious germline alterations were identified in 201 of 292 patients (for the 14 patients with serial samples, data from the first sample were used) (supplemental eFigure 2A, B and eTable 5). In total, 43.2% of patients carried alterations of homologous recombination repair (HRR) genes and 6.85% harbored alterations in mismatch repair genes. Among the top 3 altered HRR genes, CDK12 (15.4%) alterations were exclusively somatic events, and alterations in BRCA2 (13%) and ATM (7.5%) were either in the germline or somatic (supplemental eFigure 2B, C, D). Among the patients with a ctDNA fraction >20%, biallelic inactivation occurred in 62.5% (10/16), 73.9% (17/23), and 44.4% (4/9) of BRCA2, CDK12, and ATM, respectively. Compared with other DDR genes (including BRCA1, BRCA2, ATM, MLH1, and MSH2), the proportion of CDK12 mutations (15.4%) was significantly higher than reported previously in the Stand Up to Cancer–Prostate Cancer Foundation (SU2C-PCF) cohort (supplemental eFigure 3).

According to the number of prior therapies before sampling, the 306 samples from the 292 patients were divided into 3 groups: treatment-naïve (group A; n=93), post first-line treatment (group B; n=132), and post second-line or later-line treatment (group C; n=81). The somatic alteration patterns in these subgroups are shown in Figure 3A. Alterations in AR increased progressively from group A (21.5%) to group B (37.1%) to group C (46.9%) (P<.001). Likewise, the frequency of TP53 alterations increased significantly in group C (24.7%) compared with group A (12.9%) (P=.035) (Figure 3B). The somatic profile of 14 patients with 2 serial ctDNA samples is shown in supplemental eFigure 4.

Figure 3.
Figure 3.

Somatic genomic alterations across different treatment stage groups for mCRPC. (A) Somatic alterations in patients stratified into 3 groups based on prior number of mCRPC systemic treatments: group A (treatment-naïve; n=93), group B (post first-line treatment with abiraterone or docetaxel; n=132), and group C (post second-line or later-line treatment; n=81). (B) Comparison of frequency of somatic alteration in selected genes from the 3 groups based on prior number of treatments.

Abbreviations: indel, insertions/deletions; mCRPC, metastatic castration-resistant prostate cancer.

*P<.05; **P<.01; ***P<.001.

Citation: Journal of the National Comprehensive Cancer Network 2021; 10.6004/jnccn.2020.7663

Somatic Alterations of CDK12, TP53, and/or RB1 Associated With Outcomes of Abiraterone

Among patients with ctDNA collected before abiraterone treatment (n=58), the PSA response (>50% decline) in 12 weeks was observed in 27 patients (46.55%) (Figure 4A, supplemental eTable 6). Only 1 (14.29%) of 7 patients with CDK12 defects achieved a PSA response. None of the patients with TP53 or RB1 defects (n=8) achieved a PSA response. No significant difference in PFS was observed between patients with and without BRCA2 or ATM defects in the abiraterone-treated group (supplemental eFigure 5A, B). CDK12 defects were associated with shorter PFS after abiraterone treatment (1.6 vs 10.4 months; P=.001) (Figure 4B). Patients with TP53 or RB1 defects had a significantly shorter PFS than those without these defects after abiraterone treatment (2.0 vs 11.0 months; P<.001) (supplemental eFigure 5C). Median PFS for patients receiving abiraterone treatment was 3.0 months for those with AR gain (n=5) compared with 10.4 months in those without AR gain (P=.007) (supplemental eFigure 5D). No significant difference in median PFS was observed between patients with and without AR mutations (supplemental eFigure 5E).

Figure 4.
Figure 4.

Association between genomic alterations and clinical outcomes of abiraterone or docetaxel. (A) Waterfall plot for percent PSA change in response to abiraterone in 12 weeks. (B) Kaplan-Meier curves for PFS in patients with and without a CDK12 defect in the abiraterone treatment group. (C) Waterfall plot for percent PSA change in response to docetaxel in 12 weeks. (D) Kaplan-Meier curves for PFS in patients with and without a CDK12 defect in the docetaxel treatment group. The asterisk (*) above each bar indicates that it was truncated.

Abbreviations: NA, not available; PSA, prostate-specific antigen; PFS, progression-free survival.

Citation: Journal of the National Comprehensive Cancer Network 2021; 10.6004/jnccn.2020.7663

In univariate analysis, 6 variables were significantly associated with PFS after abiraterone treatment (Table 2). In multivariable analysis, after adjusting for clinical factors, mutation classification, and therapeutic information (Table 2), CDK12 defects (HR, 19.587; 95% CI, 3.788–101.278; P<.001), TP53 or RB1 defects (HR, 4.727; 95% CI, 1.554–14.383; P=.006), and visceral metastasis (HR, 10.827; 95% CI, 2.386–49.119; P=.002) remained significant.

Table 2.

Univariate and Multivariate Analyses of Various Prognostic Parameters in PFS Using Abiraterone and Docetaxel

Table 2.

Somatic Alterations of TP53 and/or RB1 Associated With Outcomes of Docetaxel

Among patients with ctDNA collected before docetaxel-only treatment (n=66), a PSA response in 12 weeks was observed in 29 patients (43.94%) (Figure 4C, supplemental eTable 7). A total of 4 (40%) of 10 patients with CDK12 defects achieved a PSA response, and 2 (20%) of 10 patients with TP53 or RB1 defects achieved a PSA response. No significant difference in median PFS was observed between patients with and without CDK12, BRCA2, or ATM defects (Figure 4D, supplemental eFigure 6A, B). Patients with TP53 or RB1 defects had a significantly shorter PFS than those without TP53 or RB1 defects after docetaxel treatment (4.8 vs 8.0 months; P=.019; supplemental eFigure 6C). Median PFS for patients receiving docetaxel treatment was 5.0 months for those with AR gain compared with 8.0 months in those without AR gain (P=.012) (supplemental eFigure 6D).

In univariate analysis, 4 variables were significantly associated with PFS after docetaxel treatment (Table 2). In multivariable analysis, after adjusting for clinical factors, mutation classification, and therapeutic information (Table 2), TP53 or RB1 defects (HR, 2.805; 95% CI, 1.130–6.965; P=.026), PSA level (>100 vs ≤100 ng/mL; HR, 2.731; 95% CI, 1.418–5.262; P=.003), and visceral metastasis (HR, 11.517; 95% CI, 2.348–56.503; P=.003) showed statistical significance.

DDR Genes May Help Predict Efficacy of Platinum-Based Chemotherapy and PARP Inhibitors

Nineteen patients were sequenced before platinum-based chemotherapy, and 3 of these discontinued treatment after 1 cycle of cisplatin or carboplatin because of serious adverse effects. Of these 16 patients, 8 showed deleterious alterations in DDR genes (supplemental eTable 8). PSA changes at 12 weeks for each patient during platinum-based chemotherapy are displayed in supplemental eFigure 7A. Of the 8 patients with a DDR defect, 7 (87.5%) had a PSA decline (including 1 patient with a CDK12 defect) and 6 (75%) had a PSA decline >50%. Conversely, in the 8 patients who had no PSA decline, only 1 harbored an ERCC3 alteration and the remaining 7 had no detectable alteration of DDR genes.

Median PFS after platinum-based chemotherapy in patients with a DDR gene defect was significantly longer than for those without a DDR gene defect (12.0 vs 2.0 months; P=.002; supplemental eFigure 8A). Patients with BRCA2 defects had a median PFS of 12 months (95% CI, 11.0–not available), compared with 13 months in patients with other DDR defects and 2.0 months in those with no DDR defect (P=.008; supplemental eFigure 8B).

A total of 27 patients in the entire cohort received olaparib, including 5 who were lost to follow-up; 10 revealed no defect in DDR genes, whereas 12 of 22 patients had DDR gene defects (supplemental eTable 9), and PSA responses were observed in 5 (41.7%) of these 12 patients. All 4 patients with BRCA2 defects had a PSA level decline, 3 (75%) of whom achieved a PSA response, and 1 (25%) of the 4 patients with CDK12 defects achieved a PSA response (supplemental eFigure 7B). There was no significant difference in median PFS between patients with and without DDR gene defects (4.0 vs 1.8 months; P=.19; supplemental eFigure 8C). The median PFS of patients receiving olaparib was 10.7 months for those with BRCA2 defects (n=4) compared with 2.9 months for those with other DDR gene defects (including ATM and CDK12 defects; n=8) and 1.8 months for those without detectable DDR gene defects (n=10; P=.1; supplemental eFigure 8D).

Discussion

This was a real-world multi-institutional study that explored the genomic landscape of Chinese patients with mCRPC at different treatment stages and evaluated the relevance of ctDNA targeted sequencing with treatments for mCRPC. First, we found that CDK12 alterations (15.4%) in our Chinese CRPC cohort were significantly more frequent than in Caucasian patients. In addition, we found that CDK12 defect had a predictive role in mCRPC’s response to multiple treatments, including abiraterone and docetaxel chemotherapy, which may help guide treatment selection in mCRPC. Third, we found that DDR genes, especially BRCA2, detected by ctDNA targeted sequencing may help predict the efficacy of platinum-based chemotherapy and PARP inhibitors.

Recently, a novel molecular subtype of advanced prostate cancer harboring CDK12 mutations was reported in patients with mCRPC.7,2327 In a whole-exome sequencing study of 150 metastatic biopsies, the SU2C-PCF International Consortium identified DDR gene inactivation in 23% of patients, with BRCA2 defects being the most common (12%).7 Previous studies in Western populations reported that the frequency of CDK12 mutations was 5% to 7%.7,8,14 In our cohort of Chinese men with mCRPC, the most frequently altered gene among the DDR genes was CDK12 (15.4%), which was almost double that observed in Western populations with mCRPC.7,8,14 By contrast, the alteration frequencies of BRCA1, BRCA2, and ATM were more in line with the SU2C-PCF International Consortium data.7 CDK12 functions in DNA transcription and RNA splicing, regulates DDR genes involved in HRR, and has been suggested to increase susceptibility to PARP inhibitors.2831 More recently, studies have shown that CDK12-mutated prostate cancer had different molecular characteristics23 with aggressive clinical behaviors.25,32,33 Biallelic inactivation of CDK12 in prostate cancer is associated with elevated neoantigen burden and increased tumor T-cell infiltration/clonal expansion, resulting in sensitivity to immune checkpoint inhibitors.24 However, the clinical outcomes of CDK12-mutated prostate cancer using standard systemic therapies remain controversial.25,32,33 Our data support that CDK12 had a predictive role in mCRPC’s response to multiple treatments, including abiraterone and docetaxel chemotherapy. CDK12 defects were associated with worse efficacy after abiraterone treatment, whereas the clinical outcome after docetaxel treatment was similar between patients with and without CDK12 defects. Further studies will assess how CDK12 defects are involved in the resistance or sensitivity to multiple therapies in mCRPC.

Emerging evidence suggests that DDR-associated prostate cancer has an impressive response to platinum-based chemotherapy.3436 In this study, we observed that 7 patients experienced a PSA response, and 6 of them (85.7%) had deleterious alterations in DDR genes (3 with somatic BRCA2 defects). Conversely, of the 8 patients who had no PSA decline, 7 had no detectable alteration of DDR genes. We also found that the median PFS after platinum-based chemotherapy in patients with DDR defects was significantly longer than in those without a DDR defect.

Recently, the FDA approved PARP inhibitors to treat HRR gene–mutated mCRPC. Our results suggest that not all DDR gene defects are equally predictive of a PARP inhibitor response. Patients with BRCA2 defects experienced superior outcomes compared with those with other DDR defects, which is consistent with previous reports.37,38 This finding suggests that when considering PARP inhibitor treatment in patients with mCRPC, it is necessary to carefully examine the established functional association of changes in DDR genes, especially in genes other than BRCA2.

AR amplification was reported to indicate worse efficacy of the AR signaling pathway inhibition (ARPI) in previous studies.8,9 Our results are consistent with the conclusions made in those studies. Previous analyses have also suggested that mutational loss of TP53 and RB1 may predict poor survival in patients with mCRPC treated using ARPI.8,9 Loss of TP53 and RB1 is frequently observed in lethal prostate cancer and has been correlated with lineage plasticity and the formation of neuroendocrine differentiation in prostate cancer,39,40 which explains primary resistance to ARPI. However, the association between TP53 or RB1 defects and the efficacy of docetaxel had not been examined previously. Our results showed that patients with mCRPC with TP53 and/or RB1 defects were associated with rapid resistance to both abiraterone and docetaxel.

Our study has several limitations. Although the high concordance between tumor tissue and ctDNA has been shown and liquid biopsies may capture tumor heterogeneity, they may fail to detect ctDNA in patients with a low disease burden and may have included low-frequency mutations from clonal hematopoiesis. In addition, the panels used in the study only capture exon regions, and therefore some meaningful intron mutations could be missing. Moreover, because of the small sample size in each treatment subgroup, clinical outcomes of the biallelic versus monoallelic mutations of key genes in patients with prostate cancer were not compared. Thus, the correlation between mutational status and treatment response should be interpreted with caution. The predictive value of specific genes and clinical benefit of the ctDNA test must be confirmed by additional prospective studies.

Conclusions

Our study explored the genomic landscape of Chinese patients with mCRPC at different treatment stages using minimally invasive methods and evaluated the clinical implications of the driver genomic alterations on the patients’ response to the most widely used therapies for mCRPC. We observed a significantly higher alteration frequency of CDK12 in our cohort compared with the SU2C-PCF cohort.7

Acknowledgments

We thank Tingting Zhao, Xuan Zou, Yining Yang, and Fangqin Wang, who gave strong support to the present study.

References

  • 1.

    de Bono JS, Logothetis CJ, Molina A, et al. . Abiraterone and increased survival in metastatic prostate cancer. N Engl J Med 2011;364:19952005.

    • Search Google Scholar
    • Export Citation
  • 2.

    Tannock IF, de Wit R, Berry WR, et al. . Docetaxel plus prednisone or mitoxantrone plus prednisone for advanced prostate cancer. N Engl J Med 2004;351:15021512.

    • Search Google Scholar
    • Export Citation
  • 3.

    Scher HI, Fizazi K, Saad F, et al. . Increased survival with enzalutamide in prostate cancer after chemotherapy. N Engl J Med 2012;367:11871197.

    • Search Google Scholar
    • Export Citation
  • 4.

    de Bono JS, Oudard S, Ozguroglu M, et al. . Prednisone plus cabazitaxel or mitoxantrone for metastatic castration-resistant prostate cancer progressing after docetaxel treatment: a randomised open-label trial. Lancet 2010;376:11471154.

    • Search Google Scholar
    • Export Citation
  • 5.

    de Bono J, Mateo J, Fizazi K, et al. . Olaparib for metastatic castration-resistant prostate cancer. N Engl J Med 2020;382:20912102.

  • 6.

    Abida W, Campbell D, Patnaik A, et al. . Genomic characteristics associated with clinical activity of rucaparib in patients (pts) with BRCA1 or BRCA2 (BRCA)-mutated metastatic castration-resistant prostate cancer (mCRPC) [abstract]. J Clin Oncol 2020;38(Suppl):Abstract 178.

    • Search Google Scholar
    • Export Citation
  • 7.

    Robinson D, Van Allen EM, Wu YM, et al. . Integrative clinical genomics of advanced prostate cancer. Cell 2015;161:12151228.

  • 8.

    Annala M, Vandekerkhove G, Khalaf D, et al. . Circulating tumor DNA genomics correlate with resistance to abiraterone and enzalutamide in prostate cancer. Cancer Discov 2018;8:444457.

    • Search Google Scholar
    • Export Citation
  • 9.

    Chen WS, Aggarwal R, Zhang L, et al. . Genomic drivers of poor prognosis and enzalutamide resistance in metastatic castration-resistant prostate cancer. Eur Urol 2019;76:562571.

    • Search Google Scholar
    • Export Citation
  • 10.

    Annala M, Struss WJ, Warner EW, et al. . Treatment outcomes and tumor loss of heterozygosity in germline DNA repair-deficient prostate cancer. Eur Urol 2017;72:3442.

    • Search Google Scholar
    • Export Citation
  • 11.

    Castro E, Romero-Laorden N, Del Pozo A, et al. . PROREPAIR-B: a prospective cohort study of the impact of germline DNA repair mutations on the outcomes of patients with metastatic castration-resistant prostate cancer. J Clin Oncol 2019;37:490503.

    • Search Google Scholar
    • Export Citation
  • 12.

    Wei Y, Wu J, Gu W, et al. . Germline DNA repair gene mutation landscape in Chinese prostate cancer patients. Eur Urol 2019;76:280283.

  • 13.

    Li J, Xu C, Lee HJ, et al. . A genomic and epigenomic atlas of prostate cancer in Asian populations. Nature 2020;580:9399.

  • 14.

    Sowalsky AG, Ye H, Bhasin M, et al. . Neoadjuvant-intensive androgen deprivation therapy selects for prostate tumor foci with diverse subclonal oncogenic alterations. Cancer Res 2018;78:47164730.

    • Search Google Scholar
    • Export Citation
  • 15.

    Aggarwal R, Huang J, Alumkal JJ, et al. . Clinical and genomic characterization of treatment-emergent small-cell neuroendocrine prostate cancer: a multi-institutional prospective study. J Clin Oncol 2018;36:24922503.

    • Search Google Scholar
    • Export Citation
  • 16.

    Arora K, Barbieri CE. Molecular subtypes of prostate cancer. Curr Oncol Rep 2018;20:58.

  • 17.

    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:21142120.

  • 18.

    Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:17541760.

  • 19.

    McKenna A, Hanna M, Banks E, et al. . The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010;20:12971303.

    • Search Google Scholar
    • Export Citation
  • 20.

    Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010;38:e164.

    • Search Google Scholar
    • Export Citation
  • 21.

    Shen R, Seshan VE. FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. Nucleic Acids Res 2016;44:e131.

    • Search Google Scholar
    • Export Citation
  • 22.

    Mayrhofer M, De Laere B, Whitington T, et al. . Cell-free DNA profiling of metastatic prostate cancer reveals microsatellite instability, structural rearrangements and clonal hematopoiesis. Genome Med 2018;10:85.

    • Search Google Scholar
    • Export Citation
  • 23.

    Quigley DA, Dang HX, Zhao SG, et al. . Genomic hallmarks and structural variation in metastatic prostate cancer. Cell 2018;174:758–769;.

  • 24.

    Wu YM, Cieślik M, Lonigro RJ, et al. . Inactivation of CDK12 delineates a distinct immunogenic class of advanced prostate cancer. Cell 2018;173:17701782.e14.

    • Search Google Scholar
    • Export Citation
  • 25.

    Reimers MA, Yip SM, Zhang L, et al. . Clinical outcomes in cyclin-dependent kinase 12 mutant advanced prostate cancer. Eur Urol 2020;77:333341.

    • Search Google Scholar
    • Export Citation
  • 26.

    Chou J, Quigley DA, Robinson TM, et al. . Transcription-associated cyclin-dependent kinases as targets and biomarkers for cancer therapy. Cancer Discov 2020;10:351370.

    • Search Google Scholar
    • Export Citation
  • 27.

    van Dessel LF, van Riet J, Smits M, et al. . The genomic landscape of metastatic castration-resistant prostate cancers reveals multiple distinct genotypes with potential clinical impact. Nat Commun 2019;10:5251.

    • Search Google Scholar
    • Export Citation
  • 28.

    Chilà R, Guffanti F, Damia G. Role and therapeutic potential of CDK12 in human cancers. Cancer Treat Rev 2016;50:8388.

  • 29.

    Dubbury SJ, Boutz PL, Sharp PA. CDK12 regulates DNA repair genes by suppressing intronic polyadenylation. Nature 2018;564:141145.

  • 30.

    Blazek D, Kohoutek J, Bartholomeeusen K, et al. . The cyclin K/Cdk12 complex maintains genomic stability via regulation of expression of DNA damage response genes. Genes Dev 2011;25:21582172.

    • Search Google Scholar
    • Export Citation
  • 31.

    Bajrami I, Frankum JR, Konde A, et al. . Genome-wide profiling of genetic synthetic lethality identifies CDK12 as a novel determinant of PARP1/2 inhibitor sensitivity. Cancer Res 2014;74:287297.

    • Search Google Scholar
    • Export Citation
  • 32.

    Nguyen B, Mota JM, Nandakumar S, et al. . Pan-cancer analysis of CDK12 alterations identifies a subset of prostate cancers with distinct genomic and clinical characteristics. Eur Urol 2020;78:671679.

    • Search Google Scholar
    • Export Citation
  • 33.

    Antonarakis ES, Isaacsson Velho P, Fu W, et al. . CDK12-altered prostate cancer: clinical features and therapeutic outcomes to standard systemic therapies, poly (ADP-Ribose) polymerase inhibitors, and PD-1 inhibitors. JCO Precis Oncol 2020;4:370381.

    • Search Google Scholar
    • Export Citation
  • 34.

    Cheng HH, Pritchard CC, Boyd T, et al. . Biallelic inactivation of BRCA2 in platinum-sensitive metastatic castration-resistant prostate cancer. Eur Urol 2016;69:992995.

    • Search Google Scholar
    • Export Citation
  • 35.

    Pomerantz MM, Spisák S, Jia L, et al. . The association between germline BRCA2 variants and sensitivity to platinum-based chemotherapy among men with metastatic prostate cancer. Cancer 2017;123:35323539.

    • Search Google Scholar
    • Export Citation
  • 36.

    Zafeiriou Z, Bianchini D, Chandler R, et al. . Genomic analysis of three metastatic prostate cancer patients with exceptional responses to carboplatin indicating different types of DNA repair deficiency. Eur Urol 2019;75:184192.

    • Search Google Scholar
    • Export Citation
  • 37.

    Marshall CH, Sokolova AO, McNatty AL, et al. . Differential response to olaparib treatment among men with metastatic castration-resistant prostate cancer harboring BRCA1 or BRCA2 versus ATM mutations. Eur Urol 2019;76:452458.

    • Search Google Scholar
    • Export Citation
  • 38.

    Lu E, Thomas GV, Chen Y, et al. . DNA repair gene alterations and PARP inhibitor response in patients with metastatic castration-resistant prostate cancer. J Natl Compr Canc Netw 2018;16:933937.

    • Search Google Scholar
    • Export Citation
  • 39.

    Beltran H, Prandi D, Mosquera JM, et al. . Divergent clonal evolution of castration-resistant neuroendocrine prostate cancer. Nat Med 2016;22:298305.

    • Search Google Scholar
    • Export Citation
  • 40.

    Ku SY, Rosario S, Wang Y, et al. . Rb1 and Trp53 cooperate to suppress prostate cancer lineage plasticity, metastasis, and antiandrogen resistance. Science 2017;355:7883.

    • Search Google Scholar
    • Export Citation

Submitted May 12, 2020; accepted for publication September 28, 2020.

Disclosures: The authors have disclosed that they have not received any financial consideration from any person or organization to support the preparation, analysis, results, or discussion of this article.

Funding: This study was supported by funds to the Department of Urology, Ren Ji Hospital, from National Natural Science Foundation of China (81772742, 81672850), Youth Program of National Natural Science Foundation of China (82002710), Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (20191906), and Shanghai Sailing Program (20YF1425300).

Correspondence: Wei Xue, MD, Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. Email: xuewei@renji.com; and Xudong Yao, MD, Department of Urology, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China. Email: yaoxudong1967@163.com

Supplementary Materials

  • View in gallery

    Flow diagram depicting the patients included in this cohort and patient groups according to treatments received before and after blood collection.

    Abbreviation: mCRPC, metastatic castration-resistant prostate cancer.

  • View in gallery

    Concordance of mutation calls between ctDNA samples and paired tumor tissues in 23 patients. (A) Somatic mutation count of ctDNA and tumor tissues. (B) Variant allele frequencies for selected driven mutations between matched liquid cell tumor DNA and solid tumor tissue.

    Abbreviations: ctDNA, circulating tumor DNA; L, liquid cell tumor DNA; S, solid tumor tissue.

  • View in gallery

    Somatic genomic alterations across different treatment stage groups for mCRPC. (A) Somatic alterations in patients stratified into 3 groups based on prior number of mCRPC systemic treatments: group A (treatment-naïve; n=93), group B (post first-line treatment with abiraterone or docetaxel; n=132), and group C (post second-line or later-line treatment; n=81). (B) Comparison of frequency of somatic alteration in selected genes from the 3 groups based on prior number of treatments.

    Abbreviations: indel, insertions/deletions; mCRPC, metastatic castration-resistant prostate cancer.

    *P<.05; **P<.01; ***P<.001.

  • View in gallery

    Association between genomic alterations and clinical outcomes of abiraterone or docetaxel. (A) Waterfall plot for percent PSA change in response to abiraterone in 12 weeks. (B) Kaplan-Meier curves for PFS in patients with and without a CDK12 defect in the abiraterone treatment group. (C) Waterfall plot for percent PSA change in response to docetaxel in 12 weeks. (D) Kaplan-Meier curves for PFS in patients with and without a CDK12 defect in the docetaxel treatment group. The asterisk (*) above each bar indicates that it was truncated.

    Abbreviations: NA, not available; PSA, prostate-specific antigen; PFS, progression-free survival.

  • 1.

    de Bono JS, Logothetis CJ, Molina A, et al. . Abiraterone and increased survival in metastatic prostate cancer. N Engl J Med 2011;364:19952005.

    • Search Google Scholar
    • Export Citation
  • 2.

    Tannock IF, de Wit R, Berry WR, et al. . Docetaxel plus prednisone or mitoxantrone plus prednisone for advanced prostate cancer. N Engl J Med 2004;351:15021512.

    • Search Google Scholar
    • Export Citation
  • 3.

    Scher HI, Fizazi K, Saad F, et al. . Increased survival with enzalutamide in prostate cancer after chemotherapy. N Engl J Med 2012;367:11871197.

    • Search Google Scholar
    • Export Citation
  • 4.

    de Bono JS, Oudard S, Ozguroglu M, et al. . Prednisone plus cabazitaxel or mitoxantrone for metastatic castration-resistant prostate cancer progressing after docetaxel treatment: a randomised open-label trial. Lancet 2010;376:11471154.

    • Search Google Scholar
    • Export Citation
  • 5.

    de Bono J, Mateo J, Fizazi K, et al. . Olaparib for metastatic castration-resistant prostate cancer. N Engl J Med 2020;382:20912102.

  • 6.

    Abida W, Campbell D, Patnaik A, et al. . Genomic characteristics associated with clinical activity of rucaparib in patients (pts) with BRCA1 or BRCA2 (BRCA)-mutated metastatic castration-resistant prostate cancer (mCRPC) [abstract]. J Clin Oncol 2020;38(Suppl):Abstract 178.

    • Search Google Scholar
    • Export Citation
  • 7.

    Robinson D, Van Allen EM, Wu YM, et al. . Integrative clinical genomics of advanced prostate cancer. Cell 2015;161:12151228.

  • 8.

    Annala M, Vandekerkhove G, Khalaf D, et al. . Circulating tumor DNA genomics correlate with resistance to abiraterone and enzalutamide in prostate cancer. Cancer Discov 2018;8:444457.

    • Search Google Scholar
    • Export Citation
  • 9.

    Chen WS, Aggarwal R, Zhang L, et al. . Genomic drivers of poor prognosis and enzalutamide resistance in metastatic castration-resistant prostate cancer. Eur Urol 2019;76:562571.

    • Search Google Scholar
    • Export Citation
  • 10.

    Annala M, Struss WJ, Warner EW, et al. . Treatment outcomes and tumor loss of heterozygosity in germline DNA repair-deficient prostate cancer. Eur Urol 2017;72:3442.

    • Search Google Scholar
    • Export Citation
  • 11.

    Castro E, Romero-Laorden N, Del Pozo A, et al. . PROREPAIR-B: a prospective cohort study of the impact of germline DNA repair mutations on the outcomes of patients with metastatic castration-resistant prostate cancer. J Clin Oncol 2019;37:490503.

    • Search Google Scholar
    • Export Citation
  • 12.

    Wei Y, Wu J, Gu W, et al. . Germline DNA repair gene mutation landscape in Chinese prostate cancer patients. Eur Urol 2019;76:280283.

  • 13.

    Li J, Xu C, Lee HJ, et al. . A genomic and epigenomic atlas of prostate cancer in Asian populations. Nature 2020;580:9399.

  • 14.

    Sowalsky AG, Ye H, Bhasin M, et al. . Neoadjuvant-intensive androgen deprivation therapy selects for prostate tumor foci with diverse subclonal oncogenic alterations. Cancer Res 2018;78:47164730.

    • Search Google Scholar
    • Export Citation
  • 15.

    Aggarwal R, Huang J, Alumkal JJ, et al. . Clinical and genomic characterization of treatment-emergent small-cell neuroendocrine prostate cancer: a multi-institutional prospective study. J Clin Oncol 2018;36:24922503.

    • Search Google Scholar
    • Export Citation
  • 16.

    Arora K, Barbieri CE. Molecular subtypes of prostate cancer. Curr Oncol Rep 2018;20:58.

  • 17.

    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:21142120.

  • 18.

    Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25:17541760.

  • 19.

    McKenna A, Hanna M, Banks E, et al. . The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 2010;20:12971303.

    • Search Google Scholar
    • Export Citation
  • 20.

    Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010;38:e164.

    • Search Google Scholar
    • Export Citation
  • 21.

    Shen R, Seshan VE. FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing. Nucleic Acids Res 2016;44:e131.

    • Search Google Scholar
    • Export Citation
  • 22.

    Mayrhofer M, De Laere B, Whitington T, et al. . Cell-free DNA profiling of metastatic prostate cancer reveals microsatellite instability, structural rearrangements and clonal hematopoiesis. Genome Med 2018;10:85.

    • Search Google Scholar
    • Export Citation
  • 23.

    Quigley DA, Dang HX, Zhao SG, et al. . Genomic hallmarks and structural variation in metastatic prostate cancer. Cell 2018;174:758–769;.

  • 24.

    Wu YM, Cieślik M, Lonigro RJ, et al. . Inactivation of CDK12 delineates a distinct immunogenic class of advanced prostate cancer. Cell 2018;173:17701782.e14.

    • Search Google Scholar
    • Export Citation
  • 25.

    Reimers MA, Yip SM, Zhang L, et al. . Clinical outcomes in cyclin-dependent kinase 12 mutant advanced prostate cancer. Eur Urol 2020;77:333341.

    • Search Google Scholar
    • Export Citation
  • 26.

    Chou J, Quigley DA, Robinson TM, et al. . Transcription-associated cyclin-dependent kinases as targets and biomarkers for cancer therapy. Cancer Discov 2020;10:351370.

    • Search Google Scholar
    • Export Citation
  • 27.

    van Dessel LF, van Riet J, Smits M, et al. . The genomic landscape of metastatic castration-resistant prostate cancers reveals multiple distinct genotypes with potential clinical impact. Nat Commun 2019;10:5251.

    • Search Google Scholar
    • Export Citation
  • 28.

    Chilà R, Guffanti F, Damia G. Role and therapeutic potential of CDK12 in human cancers. Cancer Treat Rev 2016;50:8388.

  • 29.

    Dubbury SJ, Boutz PL, Sharp PA. CDK12 regulates DNA repair genes by suppressing intronic polyadenylation. Nature 2018;564:141145.

  • 30.

    Blazek D, Kohoutek J, Bartholomeeusen K, et al. . The cyclin K/Cdk12 complex maintains genomic stability via regulation of expression of DNA damage response genes. Genes Dev 2011;25:21582172.

    • Search Google Scholar
    • Export Citation
  • 31.

    Bajrami I, Frankum JR, Konde A, et al. . Genome-wide profiling of genetic synthetic lethality identifies CDK12 as a novel determinant of PARP1/2 inhibitor sensitivity. Cancer Res 2014;74:287297.

    • Search Google Scholar
    • Export Citation
  • 32.

    Nguyen B, Mota JM, Nandakumar S, et al. . Pan-cancer analysis of CDK12 alterations identifies a subset of prostate cancers with distinct genomic and clinical characteristics. Eur Urol 2020;78:671679.

    • Search Google Scholar
    • Export Citation
  • 33.

    Antonarakis ES, Isaacsson Velho P, Fu W, et al. . CDK12-altered prostate cancer: clinical features and therapeutic outcomes to standard systemic therapies, poly (ADP-Ribose) polymerase inhibitors, and PD-1 inhibitors. JCO Precis Oncol 2020;4:370381.

    • Search Google Scholar
    • Export Citation
  • 34.

    Cheng HH, Pritchard CC, Boyd T, et al. . Biallelic inactivation of BRCA2 in platinum-sensitive metastatic castration-resistant prostate cancer. Eur Urol 2016;69:992995.

    • Search Google Scholar
    • Export Citation
  • 35.

    Pomerantz MM, Spisák S, Jia L, et al. . The association between germline BRCA2 variants and sensitivity to platinum-based chemotherapy among men with metastatic prostate cancer. Cancer 2017;123:35323539.

    • Search Google Scholar
    • Export Citation
  • 36.

    Zafeiriou Z, Bianchini D, Chandler R, et al. . Genomic analysis of three metastatic prostate cancer patients with exceptional responses to carboplatin indicating different types of DNA repair deficiency. Eur Urol 2019;75:184192.

    • Search Google Scholar
    • Export Citation
  • 37.

    Marshall CH, Sokolova AO, McNatty AL, et al. . Differential response to olaparib treatment among men with metastatic castration-resistant prostate cancer harboring BRCA1 or BRCA2 versus ATM mutations. Eur Urol 2019;76:452458.

    • Search Google Scholar
    • Export Citation
  • 38.

    Lu E, Thomas GV, Chen Y, et al. . DNA repair gene alterations and PARP inhibitor response in patients with metastatic castration-resistant prostate cancer. J Natl Compr Canc Netw 2018;16:933937.

    • Search Google Scholar
    • Export Citation
  • 39.

    Beltran H, Prandi D, Mosquera JM, et al. . Divergent clonal evolution of castration-resistant neuroendocrine prostate cancer. Nat Med 2016;22:298305.

    • Search Google Scholar
    • Export Citation
  • 40.

    Ku SY, Rosario S, Wang Y, et al. . Rb1 and Trp53 cooperate to suppress prostate cancer lineage plasticity, metastasis, and antiandrogen resistance. Science 2017;355:7883.

    • Search Google Scholar
    • Export Citation
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