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. 1 –6 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. 8 –11 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 Caller 19 with a paired workflow, respectively. Variants were then annotated using ANNOVAR 20 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 ANNOVAR 20 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).
Summary of Clinical Characteristics of 292 Patients With mCRPC (306 Samples)
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 19, 8; 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).
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 19, 8; 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.
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 19, 8; 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).
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 19, 8; 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.
Univariate and Multivariate Analyses of Various Prognostic Parameters in PFS Using Abiraterone and Docetaxel
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. 7,23 –27 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. 28 –31 More recently, studies have shown that CDK12-mutated prostate cancer had different molecular characteristics 23 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. 34 –36 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.
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