A systematic review of the prevalence of DNA damage response gene mutations in prostate cancer
- Authors:
- Published online on: July 16, 2019 https://doi.org/10.3892/ijo.2019.4842
- Pages: 597-616
-
Copyright: © Lang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Prostate cancer (PC) is a major global health burden, with a worldwide incidence of 1.1 million in 2012 (1). While typical active treatments may involve surgery, chemotherapy, brachytherapy and/or androgen-deprivation therapy, these often fail to be curative, with many patients developing metastatic disease or therapeutic resistance. Aggressive prostate disease, such as metastatic castration-resistant PC (mCRPC), is usually lethal.
The DNA damage response (DDR) is an essential pathway that ensures the survival of both normal and malignant prostate cells and includes many important genes, such as breast cancer susceptibility gene (BRCA)1/2, ataxia telangiectasia mutated (ATM) and partner and localizer of BRCA2 (PALB2) (2). Efficient and specific repair of DNA damage maintains the genomic integrity of the cell and ensures its ability to persist and proliferate. Mutations in DDR genes contribute to destabilizing PC cells, often making them more susceptible to cell death (3). Poly (ADP)-ribose polymerase (PARP) inhibitors represent an emerging therapeutic approach to target the DDR pathway in malignant cells (3). In cancer cells that already harbor multiple other genetic mutations (e.g. BRCA1/2 mutations), PARP inhibition can render the cell unable to repair DNA damage, leading to cell death (3).
In PC, mutations in genes involved in the DDR pathway are relatively common, particularly in the advanced stages of the disease (4). Several ongoing international PC clinical trials are exploring PARP inhibitors that target the DDR pathway, and the contribution of DDR gene mutations to improving therapeutic outcomes in the context of PARP inhibitor therapy is becoming more evident (Swift et al, unpublished data).
Treatment guidelines and consensus statements from international clinical organizations have recently been published to reflect the importance of DDR mutation screening for the management of PC, although the precise genes or gene panels are not always in accordance. The National Comprehensive Cancer Network (NCCN) 2018 guidelines for PC state that a strong family history consists of: Brother, father or multiple family members diagnosed with prostate cancer by at least 60 years of age; and known germline DNA repair gene abnormalities, especially BRCA2 mutation. These guidelines advise clinicians as follows: 'Consider testing for mutations in these genes (germline and somatic): BRCA1, BRCA2, ATM, PALB2, FANCA; refer to genetic counseling if positive. At present, this information may be useful for genetic counseling, early use of platinum chemotherapy, or eligibility for clinical trials (e.g. PARP inhibitors)' (5). The Philadelphia Prostate Cancer Consensus Conference 2017 recommended that all patients with familial and metastatic PC (mPC) consider genetic testing [encompassing ATM, BRCA1, BRCA2, nibrin (NBN) and DNA mismatch repair genes] (6): 'There was strong consensus to factor BRCA2 mutations into [prostate cancer] screening discussions. BRCA2 achieved moderate consensus for factoring into early stage management discussion, with stronger consensus in high-risk/advanced and metastatic setting[s]' (6). The St Gallen Advanced Prostate Cancer Consensus Conference 2017 reported 'that BRCA1, BRCA2 and ATM mutations should be reported [for mCRPC] because that knowledge will likely influence management decisions' (7). It is important that national/regional healthcare providers and decision makers be kept informed of the burden of DDR mutations in PC.
The authors of the present study undertook a systematic review of data to identify and summarize the prevalence of DDR mutations in the unselected (general) population of patients with PC (including mPC, mCRPC and CRPC). Selected subgroup data for familial PC were also presented, since this population is currently considered a key focus for genetic testing guidelines. Studies that reported on other selected subgroups (including young-onset PC, lethal PC, ductal PC, patients receiving pre-specified treatment regimens, and Ashkenazi Jewish and African-American populations) were identified but were not the focus of the review. Methodological factors and limitations of the included studies that may have led to variation in the prevalence rates reported are noted and discussed.
Materials and methods
Inclusion criteria
This systematic review was carried out in accordance with the methodologies recommended by the Cochrane Collaboration (8) and the Centre for Reviews and Dissemination (9). The review adhered to a pre-defined protocol, which stipulated the methodology provided below. Studies that reported the prevalence of mutated DDR genes in men with PC, castration-resistant PC (CRPC), mPC or mCRPC were included. Detailed inclusion criteria (including DDR definition) are provided in Data S1, Appendix S1.
Search methods
In order to identify relevant studies, a range of electronic databases (n=11) were searched from their inception to December 2017, including Medline (Ovid), Embase (Ovid), CINAHL (EBSCO) and the Cochrane Database of Systematic Reviews (Wiley). Searches used a combination of text and database thesaurus terms. No restrictions on language or publication status were applied. Searches of conference abstracts and reference lists of articles were conducted. Full details of the search methods employed, the databases searched, and the Embase search strategy are provided in Data S1, Appendix S2. Titles and abstracts were independently screened by two reviewers. Full paper copies were independently examined in detail by two reviewers to determine whether or not they met the inclusion criteria. Data were extracted from the included studies using a specifically designed and piloted data extraction sheet developed using Microsoft Excel 2016 (Microsoft Corporation). Details of the study methods, population characteristics, risk of bias, and outcome data were extracted from each study by one reviewer and checked for accuracy against the original publication by a second reviewer. Criteria used to assess the risk of bias were taken from the Joanna Briggs Institute Critical Appraisal Checklist for studies reporting prevalence data (10). Any disagreements or discrepancies in study selection or data extraction were resolved by consensus or through consultation with a third reviewer.
Data synthesis
A narrative summary of all included studies was produced. The results prioritized the following countries: Australia, Canada, France, Germany, Israel, Italy, Japan, Korea, Russia, Spain, the UK and the USA. Multinational studies were summarized separately. Analysis of prevalence was primarily conducted for the unselected PC population and familial PC subgroups only; data for additional subgroups identified during screening (including young-onset PC, ductal PC, lethal PC, African-American with PC, Ashkenazi Jewish with PC or patients receiving pre-specified treatment regimens) were also extracted. Studies were excluded from the analysis of prevalence if they involved: i) Specific mutation(s) in a given gene rather than all mutations for a given gene (unless the study was familial); ii) <50 participants (unless the study was familial), to focus on well powered studies (11,12); or iii) did not clearly report whether a mutation was germline or somatic. Such studies are summarized in Data S1, Appendix S3, together with reported prevalence values. The median prevalence and range were reported for each gene of interest or combination of genes (DDR). If multiple definitions were available for a given gene mutation, then the broadest definition was included where possible. The median prevalence rates were compared between germline and somatic mutations or between prostate subgroups if the dataset was greater than 500 combined participants, to focus on data with a large sample size.
Results
Search methods and inclusion assessment
In total, 11,648 titles were retrieved from the database searches. A total of 14 articles were identified from hand and citation searching. Four articles were identified from conference abstracts. Fig. 1 summarizes the flow of studies through the search and screening process. A total of 265 articles were excluded after the full records were read (Fig. 1 and Data S1, Appendix S3). There were 123 records identified for inclusion in the review, though 20 records were not from countries of interest and were not considered further (Data S1, Appendix S3). For the prioritized countries, 103 records from 80 studies (some of which had multiple records or publications) were identified for inclusion in the present review. A total of 59 records focused on unselected patient populations, and 13 records focused on familial PC patients.
Several records reporting on additional selected subgroups were identified, including 11 for Ashkenazi Jewish patients, 10 for patients receiving pre-specified treatment regimens, five for young-onset (≤55 or 65 years) PC, five for lethal PC, two for ductal PC and one for African-American patients with PC. The results for these subgroups are presented in Data S1, Appendices S3 and S4, together with reported prevalence values.
Unselected populations
A total of 47 studies (59 records) were identified for the unselected population. Out of the 47 studies, the majority (22 studies) were conducted exclusively in the USA (Table I). A total of five studies were conducted in the UK, three in Canada and two in Japan. Denmark, Germany, Israel and Spain each provided a single study. A total of nine studies recruited patients from multiple locations; the country of recruitment was not reported for two studies (both abstracts).
In total, 37 of the 47 primary studies reported results for patients with unselected PC, five of which focused on primary or localized PC (the authors' descriptions are reported where the definitions of disease terms were not made more explicit). Four studies focused on mPC and six studies focused on mCRPC.
The numbers of patients in the studies varied considerably, ranging from 8 to 39,014. A total of 10 studies included <50 patients in their cohort, 23 studies included between 50 and 500 patients, and 14 studies included >500 patients. The majority of studies failed to report recruitment or enrollment dates (32 studies). Of the remaining studies, 11 commenced recruitment between 1990 and 1998, and only four recruited patients after the year 2000.
Detailed patient baseline details are presented in Data S1, Appendix S5. Generally, studies provided limited baseline details and inclusion criteria.
Familial prostate cancer
A total of 11 primary studies (and two related publications) focused on patients with familial PC (Table II). As with the studies targeting unselected populations, the majority included patients from the USA (six studies, including one multinational study) and the UK (two studies). Two studies were identified for Germany (including the single multinational study), and one study each included patients from Australia and Japan. All 11 studies exclusively reported results for patients with unselected PC. For the selected familial PC subgroup, specific DDR mutations are described in Data S1, Appendix S4.
DNA damage response gene definition and reported methodology
The DDR genes reported and analyzed are presented in Table III. In total, 40 studies (50%) and 25 studies (31%) analyzed germline and somatic mutations, respectively. Six studies (7.5%) considered both germline and somatic mutations, and nine studies (11%) did not specify the nature of the mutation.
The source of DNA and the methods of genetic analysis varied considerably among studies (Data S1, Appendix S6). The major sources of DNA were blood (39 studies, 48.1%) and tumor tissue (28 studies, 34.6%). Six samples were sourced from cell-free DNA (7.4%), five from paraffin-embedded tumor tissue (6.2%), and four from buccal swabs/saliva (4.9%). One study sourced DNA from a single-cell suspension of a primary tumor (1.2%). Nine studies did not report the source of the DNA analyzed (11.1%).
The two most frequently used methods of mutational analysis were PCR (29 studies, 35.8%) and next-generation sequencing (19 studies, 23.5%). Seven studies performed whole-exome sequencing (8.6%), three used Sanger sequencing (3.7%), three used capture sequencing (3.7%) and two used fluorescence in situ hybridization (FISH; 2.5%). A total of 10 studies used methods other than those specified within this report (12.3%), whereas six studies (7.4%) did not report which methods were used.
DDR genes could be separated into three general categories: Those that focused on specific mutations in a specific gene (21 studies, 25.9%); those that focused on all, or undefined, mutations in a specific gene (22 studies, 27.1%); and those that focused on undefined mutations in multiple DDR genes (38 studies, 46.9%). Definitions for individual gene mutations or DDR combinations varied considerably from study to study.
Risk of bias of included studies
None of the studies were free from the risk of bias based on the Joanna Briggs Institute Critical Appraisal Checklist; 88% (of the 80 included studies) had at least one domain judged to be high risk (Data S1, Appendix S7 and Fig. S1). The majority of studies had a sample size of >50 participants (79% of studies), an analysis with sufficient coverage/clearly described mutations (84%), and appropriate statistical analysis with a clear prevalence calculation (74%). Overall, however, the quality of reporting was poor and did not allow for judgment of how well participants were recruited or how data were acquired; consequently, 76% of studies were judged to have an unclear risk of bias. Likewise, the criteria for the diagnosis of PC (e.g. the Gleason score) were unreported in the majority of studies (74%), and it was unclear in most studies (91%) whether a pathologist had performed the diagnosis. Confounding factors were not taken into consideration for prevalence reporting in the majority of studies (71%). In addition, the majority of studies (73%) did not describe one or more of the participant baseline characteristics for this review.
Results for prevalence in the unselected population
All studies excluded at the analysis stage are summarized in Data S1, Appendix S3, together with prevalence calculations. To establish if there were patterns of prevalence between somatic and germline mutations, between different genes, and between different PC types, all median prevalence values for all gene datasets were compared (Fig. 2 and Data S1, Appendix S8). Patients identified as having primary PC were grouped under unselected PC; thus, unselected PC was a mixed group in terms of overall diagnosis.
The most evidence was available for DDR gene panels [14 datasets; 11 studies (13-23)], ATM [17 datasets; 13 studies (13,17,18,20-22,24-29)], BRCA1 [19 datasets; 14 studies (17,18, 20-22,24,25,28,30-35)] and BRCA2 [27 datasets; 19 studies (17,18,20-26,28,30,31,33-38)]. Other genes had between four and 11 datasets. For individual genes, the most common mutations were in ATM [median, 1.5-6% across prostate groups (general PC, mPC and mCRPC); full reported range, 0-12%], BRCA2 (median, 1.1-5.2% across prostate groups; full reported range, 0-11.8%), and PALB2 (median, 0-4% across prostate groups; full reported range, 0-4%). The median prevalence of all MutL homolog 1 (MLH1), BRCA1 and Fanconi anemia complementation group A (FANCA) mutations was <4%, and the median prevalence of all checkpoint kinase 2 (CHEK2), NBN, RAD51 paralog C (RAD51C), ataxia telangiectasia and Rad3-related protein (ATR) and MRE11 homolog A, double-strand break repair nuclease (MRE11A) mutations was <2%.
In unselected PC populations and datasets including >500 patients, the frequency of somatic mutations was higher than that of germline mutations in ATM (3.9% vs. 1.5%, respectively), ATR (0.6% vs. 0%), BRCA1 (1.1% vs. 0.6%), BRCA2 (4.9% vs. 1.1%), MLH1 (0.6% vs. 0%), NBN (1.2% vs. 0.3%), PALB2 (1.3% vs. 0.5%) and RAD51C (1.5% vs. 0.5%). By contrast, germline mutations were more common than somatic mutations in CHEK2 (1.8% vs. 1.2%, respectively) and MRE11A (0.2% vs. 0%). In mCRPC, somatic mutations were more common than germline mutations for BRCA1 (2.8% vs. 0.8%, respectively), whereas somatic and germline mutations were equally common for BRCA2 (5% for each group). The prevalence rates for other prostate subgroups were based on <500 patients and were not summarized.
Germline mutations were more common in patients with mPC (based on datasets that included >500 patients) than in patients with general PC for ATM (1.7% vs. 1.5%, respectively), ATR (0.3% vs. 0%), BRCA1 (0.9% vs. 0.6%) and BRCA2 (5.2% vs. 1.1%), but not for CHEK2 (1.5% vs. 1.8%), NBN (0.2% vs. 0.3%), PALB2 (0.5% for both groups) or RAD51c (0.2% vs. 0.5%). Germline mutations were more common in mCRPC than in general PC for BRCA1 (0.8% vs. 0.6%) and BRCA2 (5.0% vs. 1.1%). Fewer than 500 patients were available for other prostate subgroups, and prevalence in those groups was therefore not summarized.
Somatic mutations were more common in the mCRPC population (based on datasets including >500 patients) than in the unselected PC population for BRCA1 (2.8% vs. 1.1%), whereas similar rates of somatic mutations were seen in the two populations for BRCA2 (5.0% vs. 4.9%).
The prevalence for DDR genes as a combined term was much higher than for the individual genes. This was expected, as the definition was based on the presence of multiple gene mutations (Table III and Data S1, Appendix S8). In general PC, the prevalence of somatic DDR gene mutations was in the range of 4.9-22%, while germline DDR mutation rates ranged between 17.2 and 19%. In mPC, the prevalence of somatic DDR mutations ranged between 10 and 16.4%, while germline mutation rates ranged between 11.4 and 11.8%. In mCRPC, the prevalence of germline DDR mutations was in the range of 7.5-9.1%; no somatic mutations were reported. In general PC, based on datasets including >500 patients, germline mutations had a higher prevalence than somatic mutations (18.6% vs. 10.7%, respectively); other somatic subgroups included <500 patients, and so prevalence rates were not analyzed. Germline DDR mutation rates were higher in patients with general PC (18.6%) than in those with mPC (11.6%) or mCRPC (8.3%).
Two multinational studies (4,30) and one US study (17) reported the prevalence of mutations using definitions that combined germline and somatic variants (Table IV). These studies used different sequencing methods to investigate mutation rates in mCRPC (4,30) and general PC (17). Decker et al (30) reported the prevalence for patients who had both somatic and germline mutations, whereas Robinson et al (4) and Abida et al (17) reported the prevalence for patients who had either a somatic or a germline mutation (or both). Thus, the rates in the study of Robinson et al (4) for BRCA1 (2.67%) and BRCA2 (12.67%) are approximately double those in the study of Decker et al (30) (0.66 and 6%, respectively). The combined prevalence of germline and/or somatic DDR gene mutations was 22.67% in mCRPC (4) and 27% in general PC (17).
 | Table IVSummary of DDR gene mutation prevalence in combined definitions for germline and/or somatic tissues. |
Results for prevalence in the familial subgroup
The prevalence of BRCA2 and DDR mutations in familial PC is summarized in Fig. 3 and Data S1, Appendix S4. The median prevalence of germline BRCA2 mutations in the familial PC subgroup was 3.7% (range, 1.3-7.9%), based on six studies (n=945). While these studies reported variable definitions for familial PC, they generally required more than two family members with PC. Only one-half of the studies reported baseline characteristics (Data S1, Appendix S5); therefore, further analysis of the influence of baseline details was not possible. The median prevalence of germline BRCA2 mutations (3.7%) was notably higher than that in the unselected population (1.1%).
The median prevalence of germline DDR mutations in familial PC was 29.3% (range, 7.3-91.67%), based on three studies (n=327). It is noteworthy that one other study (39) provided two definitions for 'DNA damage response or androgen-signaling gene variants' (Data S1, Appendix S6); one definition referred to any affected gene (91.7%), whereas the other referred to two or more affected genes (41.7%). The inclusion of 'androgen-signaling gene variants' in the definition of DDR was inconsistent with the definitions of DDR in the other studies, and therefore this study was excluded from a sensitivity analysis, leading to a median prevalence of 12.1% (range, 7.3-16.9%).
Discussion
To the best of our knowledge, this is the first review that has used rigorous systematic review methods (8,9) to report a comprehensive summary of recently published data on the prevalence of DDR genes in PC (including in mPC, mCRPC and selected subgroups).
The most common mutations (measured by median prevalence) in all unselected populations were ATM, BRCA2 and PALB2. The highest median reported rates for germline mutations in BRCA2 were found in mPC and mCRPC. The highest median reported rates for somatic mutations in mCRPC were found for ATM and BRCA2.
Overall, the median prevalence for DDR germline mutations was 18.6% in general PC (range, 17.2-19%; n=1,712), 11.6% in mPC (range, 11.4-11.8%; n=1,261), and 8.3% in mCRPC (range, 7.5-9.1%; n=738). The median prevalence for DDR somatic mutations was 10.7% in general PC (range, 4.9-22%; n=1,537) and 13.2% in mPC (range, 10-16.4%; n=105).
The prevalence for DDR germline and/or somatic mutations was 27% in general PC (17) and 22.67% in mCRPC (4). The higher rate of 27% (17) for germline and/or somatic mutations in general PC may reflect the mixed population of the sample, which was predominantly metastatic rather than localized; it may also reflect differences in the genes included in the definition of 'DDR.' A recent study by Armenia et al (40) (after the search dates of this review) reported a combined germline and somatic mutation rate of 27% for DDR genes in mCRPC. This rate is similar to those reported here. Differences in the precise rates for mCRPC reported by Armenia et al (40) and Robinson et al (4) could be due to variations in the genes included in the definition of 'DDR' or the use of different sequencing methods (next-generation sequencing vs. whole-exome sequencing). The median rate of germline BRCA2 mutations in patients with familial PC history was 3.7%, higher than that in the equivalent unselected population (1.1%). Studies report that DDR pathways are good candidates for PC predisposition and that, although BRCA2 is the most frequently mutated gene, a wider range of DDR genes are likely to predispose to PC (41,42).
These results should be interpreted with caution given the limited reporting of baseline characteristics and the heterogeneity of sequencing methods and mutational definitions. In particular, the patients included in the 'unselected PC' population varied widely. For example, unselected PC cases in Abida et al (17) represented a mixed population of cancer types (local and metastatic) dominated by metastatic samples (77%), whereas patients from the Cancer Genome Atlas (18) were described as primary since they were derived from prostatectomies, but in fact included many high-grade cancers that were likely to be metastatic.
No other systematic reviews on the prevalence of DNA repair gene mutations and PC were identified. Two recent papers by Isaacsson Velho et al (43) and Quigley et al (44) identified similar mutational rates to those reported in this review. Isaacsson Velho et al (43) found a germline mutational rate of BRCA2 in mPC of 6%, compared with the median prevalence of 5.2% reported here. Quigley et al (44) found germline and/or somatic BRCA2 rates in mCRPC of 10%, compared with the rate of 12.7% (4) reported here. It is likely that the differences in rates may be explained by differences in sequencing methodology, precise definitions of mutations, and/or differences in populations. Another recent paper (45) used The Cancer Genome Atlas (18) tissues and those reported by Armenia et al (40) to investigate the influence of Gleason score and tumor stage. The overall prevalence of somatic DDR gene mutations in localized tumors was 8%, which is within the range reported here (4.9-22%). Marshall et al (45) identified an increase in DDR mutation prevalence in patients with Gleason grade ≥3 and clinical stage ≥cT3 disease.
The findings of the present review provide evidence in support of the testing of DDR germline mutations in advanced disease, in line with NCCN and Philadelphia Prostate Cancer Consensus recommendations, and the St Gallen Advanced Prostate Cancer Consensus Conference 2017 (5-7). Some germline DDR mutations may present an increased healthcare burden, since family members may also be at risk of PC. Depending on which DDR gene mutations are present, family members may also have an increased risk of breast, ovarian, pancreatic and colorectal cancer, melanoma and other cancer types (5). Given that somatic mutations were found to have a higher or similar prevalence compared with germline mutations in patients with mCRPC, somatic mutations may provide useful genetic information to guide participation in clinical trials or additional mutation testing. These findings support the testing of all patients with metastatic disease and not just those with familial disease. BRCA2 was identified as having the highest mutation rate of any individual DDR gene, supporting the use BRCA2 screening as recommended across all consensus conferences and NCCN (5-7).
Of note, some of the included studies provided limited baseline details. Where reported, there was evident heterogeneity in terms of the period of data collection, data sources, previous treatments, sequencing/screening methods and the risk of bias. Prevalence data based on low patient/study numbers combined with limited information about and/or variation in study characteristics prevented a thorough interrogation of the results, and the reasons for variation in prevalence could not always be determined. The repository data in many of the studies included in this review were unaccompanied by baseline patient details (13), and the source of the samples was often mentioned only incidentally within the results. This hampered data extraction and increased the risk of double counting (using the same data source twice). For example, among the three sets of tissues analyzed in the 2016 report of Decker et al (30), one was the same tumors used in the 2015 study of Robinson et al (4). Despite this apparent overlap, the data presented in the two papers do not double count for any result presented. To avoid double counting, only original data were extracted from each paper. Study sizes were limited, and only eight studies included >500 participants (20,21,25,31,32,34,36,46).
Definitions for individual gene mutations or DDR combinations varied considerably from study to study. Sources of tissue varied (being either fresh or paraffin-embedded), as did methods of mutational analysis. Different DNA sequencing/screening methods used for the detection of DDR mutation may explain the variations observed in the prevalences reported; this was demonstrated by the two linked publications of Williams et al (47) and Gao et al (48). Both publications used the same 23 patients with PC, but reported using different techniques to identify BRCA1 mutations. The first (48) detected loss of heterozygosity, using PCR, at the BRCA1 locus in 22% of samples, whereas the second paper (47), using FISH, found that the loss was not in the BRCA1 gene but was distal, and therefore reported a 0% prevalence. Small gene panels and circulating DNA analysis may have lower coverage of large tumor suppressor genes such as ATM, BRCA1 and BRCA2 compared to whole genome or whole exome sequencing.
Differences in the definitions of what is considered a mutation can also arise, such as the use of monoallelic versus biallelic approaches to categorizing DDR mutations compared with non-mutations. One study (49) reported a comparison between DDR mutations (defined as biallelic mutations in DDR genes) compared with non-mutations (defined as wild-type or monoallelic mutations in DDR genes, which the authors considered non-deleterious). By contrast, all other studies (with one partial exception) reported any mutation in a DDR gene (whether it affected one or both alleles) as a bona fide DDR mutation. The exception involved a study (13) that provided two definitions for DDR, alternately based on one or both alleles being defective.
The use of different reference genome builds as comparator sequences introduces uncertainty. It is standard practice to use such builds as normal comparator sequences in order to identify mutations, but these reference sequences may change over time (with improved methodologies), which may influence what is defined as a genuine mutation. The use of different sequencing depths introduces uncertainty for between-study comparisons. For example, studies that perform next-generation sequencing to an extended depth [e.g. x265 (38)] may achieve more accurate sequencing (lower error rates) than studies that sequence to a relatively minimal depth [e.g. x100 (27)]. The use of different frequency cut-off thresholds for the inclusion of mutations introduces uncertainty for between-study comparisons. For example, studies that apply a relatively high frequency threshold when analyzing exonic mutations (e.g. excluding mutations with frequencies >2% in the observed population) (37) will include a larger pool of mutations than studies that apply a more restrictive frequency cutoff (e.g. excluding mutations with a population allele frequency ≥0.5%) (50).
There is a risk of sampling bias with somatic mutations, given that PC is a very heterogeneous disease (51). This heterogeneity means that within a patient tumor there are multiple sub-tumors and normal tissues with different genotypes (different mutational profiles) or clones (52,53). For example, PC can have areas of moderately differentiated tumor and undifferentiated tumor, and each clone will have a different prognosis. Tumor heterogeneity impacts biomarker studies, since the sample taken for analysis may not be the same as the sample given to the pathologist, and false associations between biomarker and histological stage can arise. Any method other than micro-dissected tissue confirmed by a pathologist for tumor grade is at risk of sampling error.
In the present study, no prevalence data were available for CRPC populations with >50 participants. There was insufficient evidence to judge whether patterns of prevalence were influenced by the country of recruitment. While there was some evidence for all the DDR genes on which the study focused, evidence was particularly limited for ATR, FANCA, MLH1 and MRE11A, as mutations in these genes were reported in five or fewer studies in total.
Future research should focus on other subgroups (including African-Americans, and young-onset, ductal and lethal prostate cancer) that may have DDR mutation prevalences that are different from that of the wider PC population. In addition, future work should consider the contribution of founder mutations in unselected populations and a consideration of mutations that are prevalent in other countries. A more focused review would be required to examine in detail whether mutations were pathogenic or were variants of uncertain significance.
In future research, authors should better clarify patient baseline characteristics, the diagnostic methods employed, and whether the somatic samples used for analysis were diagnosed directly or inferred from other pathology samples. Assessment of data according to cancer stage and grade rather than broad terms such as 'PC' may also be more useful. Definitions of what types of genetic changes constitute 'mutations' (e.g. single-copy deletion compared with homozygous deletion) need to be standardized, and pathogenic classification should be routinely incorporated into the reporting of mutations. Finally, reviews of biomarker studies need to consider the aforementioned scientific limitations of these studies, as well as study design limitations. Future guidelines/consensus are required for greater standardization of sequencing methods used for genetic association studies.
Given the generally poor quality and poor reporting of studies showing prevalence data, there is a need for future epidemiological studies that use recognized methodologies and reporting tools. Authors should be encouraged to adhere to the following reporting guidelines for prevalence studies: The Simon et al (54) guidelines for the use of archived specimens in the evaluation of prognostic and predictive biomarkers; Biospecimen Reporting for Improved Study Quality (55); Strengthening the Reporting of Genetic Association Studies, an extension of the STROBE statement (56); and the Joanna Briggs Institute Critical Appraisal Checklist for Studies Reporting Prevalence Data (to reduce the risk of bias) (10).
A number of recent updates (5-7) in clinical practice guidelines have followed the arrival of new types of genetic tests and ongoing clinical trials with drugs that specifically target germline and/or somatic DDR mutations in mCRPC. In the unselected PC population, the median prevalence of germline and/or somatic DDR mutations was 27%. In the mPC populations, the median rate of DDR mutations was 11.6% for germline mutations and 13.2% for somatic mutations. In mCRPC populations, the prevalence rate was 22.67% for germline and/or somatic mutations. In patients with a familial history of PC, the median rate of germline DDR mutations was 12.1% and the median rate of germline BRCA2 mutations was 3.7%.
The present review highlighted variations in definitions and methodology among studies investigating biomarkers, including differences in tumor sampling, tumor heterogeneity, sequencing depth and mutational frequency thresholds, and comparability of mutational definitions (genetic terminology). Further large, well-reported prevalence studies in PC are needed to provide international prevalence data on the burden of DDR dysfunction in patients with PC.
Funding
The present study was sponsored by Pfizer, Inc.
Availability of data and materials
All data analyzed during this study are included in this published article.
Authors' contributions
SHL, SLS, JK, RGWQ contributed to the conception and design, acquisition of data, analysis and interpretation of data. HW contributed to the acquisition of data, analysis and interpretation of data. KM designed and performed the search strategies. All authors contributed to the writing of the manuscript and its final approval.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
SHL, SLS, HW, KM and JK are employees of Kleijnen Systematic Reviews Ltd., who were paid consultants to Pfizer, Inc. in connection with the development of this manuscript; they have no other competing interests. RGWQ is an employee of Pfizer, Inc..
Supplementary Materials
Acknowledgments
Editorial and medical writing support was provided by Dr Ying Jean, Dr Gautam Bijur, Ms. Mary Kacillas and Mr. Joshua Safran, and was funded by Pfizer, Inc.
Abbreviations:
|
ATM |
ataxia telangiectasia mutated |
|
ATR |
ataxia telangiectasia and Rad3-related protein |
|
BRCA |
breast cancer susceptibility gene |
|
BRISQ |
Biospecimen Reporting for Improved Study Quality |
|
CHEK2 |
checkpoint kinase 2 |
|
CRPC |
castration-resistant prostate cancer |
|
DDR |
DNA damage response |
|
ESMO |
European Society for Medical Oncology |
|
FANCA |
Fanconi anemia complementation group A |
|
FISH |
fluorescence in situ hybridization |
|
mCRPC |
metastatic castration-resistant prostate cancer |
|
MLH1 |
MutL homolog 1 |
|
mPC |
metastatic prostate cancer |
|
MRE11A |
MRE11 homolog A, double-strand break repair nuclease |
|
NBN |
nibrin |
|
NCCN |
National Comprehensive Cancer Network |
|
NR |
not reported |
|
PALB2 |
partner and localizer of BRCA2 |
|
PARP |
poly (ADP)-ribose polymerase |
|
PC |
prostate cancer |
|
PRISMA |
Preferred Reporting Items for Systematic Reviews and Meta-analyses |
|
RAD51C |
RAD51 paralog C |
|
STREGA |
Strengthening the Reporting of Genetic Association Studies |
|
SUO |
Society of Urologic Oncology |
References
|
Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin D, Forman D and Bray F: GLOBOCAN 2012 v10, Cancer Incidence and Mortality Worldwide IARC CancerBase No. 11. International Agency for Research on Cancer; Lyon: 2013, http://globocan.iarc.fr. Accessed September 3 , 2018. | |
|
Castro E, Mateo J, Olmos D and de Bono JS: Targeting DNA repair: The role of PARP inhibition in the treatment of castration-resistant prostate cancer. Cancer J. 22:353–356. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Lord CJ and Ashworth A: PARP inhibitors: Synthetic lethality in the clinic. Science. 355:1152–1158. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Robinson D, Van Allen EM, Wu YM, Schultz N, Lonigro RJ, Mosquera JM, Montgomery B, Taplin ME, Pritchard CC, Attard G, et al: Integrative clinical genomics of advanced prostate cancer. Cell. 161:1215–1228. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
National Comprehensive Cancer Network Inc.: NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines): Prostate cancer (Version 4. 2018 8/15/18). NCCN; Fort Washington, PA: 2018, https://www.nccn.org/professionals/physician_gls/pdf/prostate.pdf. Accessed September 3 , 2018. | |
|
Giri VN, Knudsen KE, Kelly WK, Abida W, Andriole GL, Bangma CH, Bekelman JE, Benson MC, Blanco A, Burnett A, et al: Role of genetic testing for inherited prostate cancer risk: Philadelphia Prostate Cancer Consensus Conference 2017. J Clin Oncol. 36:414–424. 2018. View Article : Google Scholar : | |
|
Gillessen S, Attard G, Beer TM, Beltran H, Bossi A, Bristow R, Carver B, Castellano D, Chung BH, Clarke N, et al: Management of patients with advanced prostate cancer: The report of the Advanced Prostate Cancer Consensus Conference APCCC 2017. Eur Urol. 73:178–211. 2018. View Article : Google Scholar | |
|
Higgins JPT and Green S: Cochrane handbook for systematic reviews of interventions [Internet] The Cochrane Collaboration, Version 5.1.0. updated March 2011. 2011, http://handbook.cochrane.org/. Accessed November 22 , 2017. | |
|
Centre for Reviews and Dissemination Systematic Reviews: CRD's guidance for undertaking reviews in health care. University of York; York: 2017, http://www.york.ac.uk/inst/crd/SysRev/!SSL!/WebHelp/SysRev3.htm. Accessed November 22 , 2017. | |
|
Munn Z, Moola S, Lisy K, Riitano D and Tufanaru C: Methodological guidance for systematic reviews of observational epidemiological studies reporting prevalence and cumulative incidence data. Int J Evid-Based Healthc. 13:147–153. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Ioannidis JPA: Why most published research findings are false. PLoS Med. 2:e1242005. View Article : Google Scholar : PubMed/NCBI | |
|
Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J, Robinson ES and Munafò MR: Power failure: Why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. 14:365–376. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Timms KM, Cuzick J, Neff C, Reid J, Solimeno C, Sangale Z, Pruss D, Gutin A, Lanchbury JS and Stone S: The molecular landscape of genome instability in prostate cancer. Proceedings of the European Society for Medical Oncology Congress; Copenhagen. 2016, http://discovery.northernlight.com/document.php?datasource=PHE&docid=PE20161109050002300&context=WK%40northernlight.com"xrefwindow" href="https://doi.org/10.1093/annonc/mdw363.63" id="d7e1504" name="d7e1504" shape="rect">https://doi.org/10.1093/annonc/mdw363.63. Accessed December 7 , 2017. | |
|
Jefferies M, Cox A, Clarke A and Kynaston H: Targeted next-generation sequencing analysis of primary prostate cancer identifies potential therapeutic targets. J Urol. 197(Suppl. 4): e594–e595. 2017. View Article : Google Scholar | |
|
Gourdin TS and Lilly MB: Genomic profiling of metastatic prostate cancer through analysis of circulating tumor DNA (ctDNA). J Clin Oncol. 34(Suppl 2): 1742016. View Article : Google Scholar | |
|
Pritchard CC, Morrissey C, Kumar A, Zhang X, Smith C, Coleman I, Salipante SJ, Milbank J, Yu M, Grady WM, et al: Complex MSH2 and MSH6 mutations in hypermutated microsatellite unstable advanced prostate cancer. Nat Commun. 5:49882014. View Article : Google Scholar : PubMed/NCBI | |
|
Abida W, Armenia J, Gopalan A, Brennan R, Walsh M, Barron D, Danila D, Rathkopf D, Morris M, Slovin S, et al: Prospective genomic profiling of prostate cancer across disease states reveals germline and somatic alterations that may affect clinical decision making. JCO Precis Oncol. 2017:1–16. 2017. | |
|
Abeshouse A, Ahn J, Akbani R, Ally A, Amin S, Andry CD, Annala M, Aprikian A, Armenia J, Arora A, et al: Cancer Genome Atlas Research Network: The molecular taxonomy of primary prostate cancer. Cell. 163:1011–1025. 2015. View Article : Google Scholar | |
|
Nicolosi PLW, Michalski ST, Freschi B, O'Leary E, Quintana R, Wilson I, Powers MP and Sartor O: Need for re-evaluation of current guidelines based on results from germline genetic testing in prostate cancer. J Clin Oncol. 35(Suppl 15): 50092017. View Article : Google Scholar | |
|
Nelson P, Mateo J, Beltran H, De Sarkar N, Elemento O, Rubin MA, Vinson JN, Filipenko J, Robinson DR, Chinnaiyan AM, et al: Inherited mutations in DNA repair genes in men with metastatic castration-resistant prostate cancer. J Clin Oncol. 34(Suppl 15): 50092016. View Article : Google Scholar | |
|
Pritchard CC, Mateo J, Walsh MF, De Sarkar N, Abida W, Beltran H, Garofalo A, Gulati R, Carreira S, Eeles R, et al: Inherited DNA-repair gene mutations in men with metastatic prostate cancer. N Engl J Med. 375:443–453. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Romero Laorden N, Lozano Mejorada R, Piulats Rodriguez J, Vallespin E, Montesa A, Lorente Estelles D, Villaguzman J, Grau G, Rodriquez-Vida A, Ibanez K, et al: 826P - Prevalence and baseline clinico-pathological associations of germline deleterious mutations in DNA repair genes (gmDDR) in a metastatic castration resistant prostate cancer (mCRPC) prospective spanish cohort (PROREPAIR-B study). Presented at European Society for Medical Oncology (ESMO) 2017 Congress; 1-12 Sep 2017; Madrid: Spain. https://cslide.ctimeetingtech.com/library/esmo/browse/search/2yZL#2Bb5p0SK Accessed February 28 , 2018. | |
|
Struss WJ, Annala M, Warner EW, Beja K, Vandekerkhove G, Wong A, Gleave M, Chi KN and Wyatt AW: Germline DNA repair mutations in metastatic castration-resistant prostate cancer: Therapy response and applicability of circulating tumor DNA. J Clin Oncol. 35(Suppl 6): 1402017. View Article : Google Scholar | |
|
Lu C, Xie M, Wendl MC, Wang J, McLellan MD, Leiserson MDM Huang KL, Wyczalkowski MA, Jayasinghe R, Banerjee T, et al: Patterns and functional implications of rare germline variants across 12 cancer types. Nat Commun. 6:100862015. View Article : Google Scholar : PubMed/NCBI | |
|
Na R, Zheng SL, Han M, Yu H, Jiang D, Shah S, Ewing CM, Zhang L, Novakovic K, Petkewicz J, et al: Germline mutations in ATM and BRCA1/2 distinguish risk for lethal and indolent prostate cancer and are associated with early age at death. Eur Urol. 71:740–747. 2017. View Article : Google Scholar : | |
|
Grasso CS, Wu YM, Robinson DR, Cao X, Dhanasekaran SM, Khan AP, Quist MJ, Jing X, Lonigro RJ, Brenner JC, et al: The mutational landscape of lethal castration-resistant prostate cancer. Nature. 487:239–243. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Manson-Bahr D, Ball R, Gundem G, Sethia K, Mills R, Rochester M, Goody V, Anderson E, O'Meara S, Flather M, et al: Mutation detection in formalin-fixed prostate cancer biopsies taken at the time of diagnosis using next-generation DNA sequencing. J Clin Pathol. 68:212–217. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Evans JR, Zhao SG, Chang SL, Tomlins SA, Erho N, Sboner A, Schiewer MJ, Spratt DE, Kothari V, Klein EA, et al: Patient-level DNA damage and repair pathway profiles and prognosis after prosta-tectomy for high-risk prostate cancer. JAMA Oncol. 2:471–480. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Patel VL, Busch E, D'Amico AV and Rebbeck TR: BRCA2 mutations in prostate cancer assort into cluster regions. Int J Radiat Oncol. 96:E569–E570. 2016. View Article : Google Scholar | |
|
Decker B, Karyadi DM, Davis BW, Karlins E, Tillmans LS, Stanford JL, Thibodeau SN and Ostrander EA: Biallelic BRCA2 mutations shape the somatic mutational landscape of aggressive prostate tumors. Am J Hum Genet. 98:818–829. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Castro E, Goh CL, Olmos D, Leongamornlert D, Saunders E, Tymrakiewicz M, Mahmud N, Dadaev T, Govindasami K, Guy M, et al: Correlation of germ-line BRCA2 mutations with aggressive prostate cancer and outcome. J Clin Oncol. 29(Suppl 15): 15172011. View Article : Google Scholar | |
|
Leongamornlert D, Mahmud N, Tymrakiewicz M, Saunders E, Dadaev T, Castro E, Goh C, Govindasami K, Guy M, O'Brien L, et al: UKGPCS Collaborators: Germline BRCA1 mutations increase prostate cancer risk. Br J Cancer. 106:1697–1701. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Sonpavde G, Nagy RJ, Sartor AO, Pond GR, Gourdin TS, Nandagopal L, Ledet EM, Agarwal N, Carroll E, Naik G, et al: Circulating tumor (ct)-DNA alterations in metastatic castration-resistant prostate cancer (mCRPC): Association with outcomes and evolution with therapy. J Clin Oncol. 35(Suppl 6): 1492017. View Article : Google Scholar | |
|
Lara P, McPherson J, Heyer W, Hartmaier R, DeVere White R, Ching J, Ali S and Dall'Era M: 827P - Comprehensive char-acterization of BRCA1 and BRCA2 alterations in circulating tumor DNA and tumor tissue in men with prostate cancer: Implications for clinical care. Presented at European Society for Medical Oncology (ESMO) 2017 Congress; 1-12 Sep 2017; Madrid: Spain. 2017, https://cslide.ctimeetingtech.com/library/esmo/browse/search/2ape#2Bb5p0Vn. Accessed February 28 , 2018. | |
|
Myers CE, Feldman R, Abbott BL, Reddy SK and Castro M: Frequency of BRCA mutations and co-occurring alterations in prostate cancer. J Clin Oncol. 34(Suppl 2): 2892016. View Article : Google Scholar | |
|
Akbari MR, Wallis CJD, Toi A, Trachtenberg J, Sun P, Narod SA and Nam RK: The impact of a BRCA2 mutation on mortality from screen-detected prostate cancer. Br J Cancer. 111:1238–1240. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Maier C, Herkommer K, Luedeke M, Rinckleb A, Schrader M and Vogel W: Subgroups of familial and aggressive prostate cancer with considerable frequencies of BRCA2 mutations. Prostate. 74:1444–1451. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Fontugne J, Ramazanoglu S, Knudsen K, De Bono J, Feng F, Sboner A and Rubin M: DNA damage response genes in prostate cancer: Development of a novel targeted sequencing platform. Lab Invest. 95:220A–221A. 2015. | |
|
Nicolas E, Arora S, Zhou Y, Serebriiskii IG, Andrake MD, Handorf ED, Bodian DL, Vockley JG, Dunbrack RL, Ross EA, et al: Systematic evaluation of underlying defects in DNA repair as an approach to case-only assessment of familial prostate cancer. Oncotarget. 6:39614–39633. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Armenia J, Wankowicz SAM, Liu D, Gao J, Kundra R, Reznik E, Chatila WK, Chakravarty D, Han GC, Coleman I, et al: PCF/SU2C International Prostate Cancer Dream Team: The long tail of oncogenic drivers in prostate cancer. Nat Genet. 50:645–651. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Leongamornlert D, Saunders E, Dadaev T, Tymrakiewicz M, Goh C, Jugurnauth-Little S, Kozarewa I, Fenwick K, Assiotis I, Barrowdale D, et al: UKGPCS Collaborators: Frequent germline deleterious mutations in DNA repair genes in familial prostate cancer cases are associated with advanced disease. Br J Cancer. 110:1663–1672. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Hayano T, Matsui H, Nakaoka H, Ohtake N, Hosomichi K, Suzuki K and Inoue I: Germline variants of prostate cancer in Japanese families. PLoS One. 11:e01642332016. View Article : Google Scholar : PubMed/NCBI | |
|
Isaacsson Velho P, Silberstein JL, Markowski MC, Luo J, Lotan TL, Isaacs WB and Antonarakis ES: Intraductal/ductal histology and lymphovascular invasion are associated with germline DNA-repair gene mutations in prostate cancer. Prostate. 78:401–407. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Quigley DA, Dang HX, Zhao SG, Lloyd P, Aggarwal R, Alumkal JJ, Foye A, Kothari V, Perry MD, Bailey AM, et al: Genomic hallmarks and structural variation in metastatic prostate cancer. Cell. 174:758–769.e9. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Marshall CH, Fu W, Wang H, Baras AS, Lotan TL and Antonarakis ES: Prevalence of DNA repair gene mutations in localized prostate cancer according to clinical and pathologic features: Association of Gleason score and tumor stage. Prostate Cancer Prostatic Dis. 22:59–65. 2019. View Article : Google Scholar : | |
|
Petrovics G, Ravindranath L, Chen Y, Ying K, Ali A, Young D, McLeod D, Sesterhenn I, Rosner I, Dahut W, et al: Higher frequency of germline BRCA1 and BRCA2 mutations in African American prostate cancer. J Urol. 195(Suppl. 4): e5482016. View Article : Google Scholar | |
|
Williams BJ, Jones E, Zhu XL, Steele MR, Stephenson RA, Rohr LR and Brothman AR: Evidence for a tumor suppressor gene distal to BRCA1 in prostate cancer. J Urol. 155:720–725. 1996. View Article : Google Scholar : PubMed/NCBI | |
|
Gao X, Zacharek A, Salkowski A, Grignon DJ, Sakr W, Porter AT and Honn KV: Loss of heterozygosity of the BRCA1 and other loci on chromosome 17q in human prostate cancer. Cancer Res. 55:1002–1005. 1995.PubMed/NCBI | |
|
Hussain M, Daignault-Newton S, Twardowski PW, Albany C, Stein MN, Kunju LP, Siddiqui J, Wu YM, Robinson D, Lonigro RJ, et al: Targeting androgen receptor and DNA repair in metastatic castration-resistant prostate cancer: Results from NCI 9012. J Clin Oncol. 36:991–999. 2018. View Article : Google Scholar : | |
|
Annala M, Struss WJ, Warner EW, Beja K, Vandekerkhove G, Wong A, Khalaf D, Seppälä IL, So A, Lo G, et al: Treatment outcomes and tumor loss of heterozygosity in germline DNA repair-deficient prostate cancer. Eur Urol. 72:34–42. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Cyll K, Ersvær E, Vlatkovic L, Pradhan M, Kildal W, Avranden Kjær M, Kleppe A, Hveem TS, Carlsen B, Gill S, et al: Tumour heterogeneity poses a significant challenge to cancer biomarker research. Br J Cancer. 117:367–375. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Bedard PL, Hansen AR, Ratain MJ and Siu LL: Tumour heterogeneity in the clinic. Nature. 501:355–364. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Stanta G and Bonin S: A practical approach to tumor heterogeneity in clinical research and diagnostics. Pathobiology. 85:7–17. 2018. View Article : Google Scholar | |
|
Simon RM, Paik S and Hayes DF: Use of archived specimens in evaluation of prognostic and predictive biomarkers. J Natl Cancer Inst. 101:1446–1452. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Moore HM, Kelly AB, Jewell SD, McShane LM, Clark DP, Greenspan R, Hayes DF, Hainaut P, Kim P, Mansfield E, et al: Biospecimen reporting for improved study quality (BRISQ). J Proteome Res. 10:3429–3438. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Little J, Higgins JP, Ioannidis JP, Moher D, Gagnon F, von Elm E, Khoury MJ, Cohen B, Davey-Smith G, Grimshaw J, et al: STrengthening the REporting of Genetic Association Studies: STrengthening the REporting of Genetic Association Studies (STREGA): An extension of the STROBE statement. PLoS Med. 6:e222009. View Article : Google Scholar | |
|
Nam RK, Zhang WW, Jewett MA, Trachtenberg J, Klotz LH, Emami M, Sugar L, Sweet J, Toi A and Narod SA: The use of genetic markers to determine risk for prostate cancer at prostate biopsy. Clin Cancer Res. 11:8391–8397. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Näslund-Koch C, Nordestgaard BG and Bojesen SE: Increased risk for other cancers in addition to breast cancer for CHEK2*1100delC heterozygotes estimated from the Copenhagen General Population Study. J Clin Oncol. 34:1208–1216. 2016. View Article : Google Scholar | |
|
Maier C, Wiest I, Luedeke M, Surowy H, Rinckleb A, Herkommer K, Kuefer R and Vogel W: BRCA2 mutation analysis in familial and early onset prostate cancer. Med Genetik. 22:97–98. 2010. | |
|
Vazina A, Baniel J, Yaacobi Y, Shtriker A, Engelstein D, Leibovitz I, Zehavi M, Sidi AA, Ramon Y, Tischler T, et al: The rate of the founder Jewish mutations in BRCA1 and BRCA2 in prostate cancer patients in Israel. Br J Cancer. 83:463–466. 2000. View Article : Google Scholar : PubMed/NCBI | |
|
Tanaka Y, Zaman MS, Majid S, Liu J, Kawakami K, Shiina H, Tokizane T, Dahiya AV, Sen S and Nakajima K: Polymorphisms of MLH1 in benign prostatic hyperplasia and sporadic prostate cancer. Biochem Biophys Res Commun. 383:440–444. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Uchida T, Wang C, Sato T, Gao J, Takashima R, Irie A, Ohori M and Koshiba K: BRCA1 gene mutation and loss of heterozygosity on chromosome 17q21 in primary prostate cancer. Int J Cancer. 84:19–23. 1999. View Article : Google Scholar : PubMed/NCBI | |
|
Cendón Flórez Y, Nombela Blanco P, Medina A, Romero Laorden N, Puente J, López Casas P, Gutierrez Pecharromán A, Sanchez-Escribano R, Magraner L, Gallardo Diaz E, et al: 1660P - ATM role in prostate cancer (PrCa) progression and survival. Presented at European Society for Medical Oncology (ESMO) 2017 Congress; 1-12 Sep 2017; Madrid: Spain. https://cslide.ctimeetingtech.com/library/esmo/browse/search/2yZz. Accessed February 28 , 2018. | |
|
Centro Nacional de Investigaciones Oncologicas CARLOS III: Prospective multicentre cohort study PROREPAIR-B (mCRPC) NCT03075735. WHO International Clinical Trials Registry Platform (ICTRP) [Internet] Geneva: World Health Organization (WHO); 2017, https://clinicaltrials.gov/show/NCT03075735. Accessed December 12 , 2017. | |
|
Angèle S, Falconer A, Edwards SM, Dörk T, Bremer M, Moullan N, Chapot B, Muir K, Houlston R, Norman AR, et al: ATM polymorphisms as risk factors for prostate cancer development. Br J Cancer. 91:783–787. 2004. View Article : Google Scholar : PubMed/NCBI | |
|
Dawson NA, Heath EI, Feldman R, Reddy SK, Spetzler D, Poste GH and Raghavan D: Use of panomic assessment to reveal DNA repair alterations and to predict potential therapeutic response to taxaneplatinum combination therapy in prostate cancer. J Clin Oncol. 34(Suppl 15): pp. 50402016, View Article : Google Scholar | |
|
Xia S, Kohli M, Du M, Dittmar RL, Lee A, Nandy D, Yuan T, Guo Y, Wang Y, Tschannen MR, et al: Plasma genetic and genomic abnormalities predict treatment response and clinical outcome in advanced prostate cancer. Oncotarget. 6:16411–16421. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Abida W, Curtis KR, Taylor BS, Arcila ME, Brennan R, Danila DC, Rathkopf DE, Morris MJ, Slovin SF, Solit DB, et al: Genomic characterization of primary and metastatic prostate cancer (PC) using a targeted next-generation sequencing assay. J Clin Oncol. 33(Suppl 15): 50622015. View Article : Google Scholar | |
|
Abida W, Walsh MF, Armenia J, Vijai J, Gopalan A, Brennan R, Curtis K, Arcila M, Danila D, Arnold A, et al: Next generation sequencing of prostate cancer reveals germline and somatic alterations detected at diagnosis and at metastasis that may impact clinical decision making. Cancer Res. 76:LB-0702016. | |
|
Abida W, Brennan R, Armenia J, Curtis KR, Gopalan A, Arcila ME, Danila DC, Rathkopf DE, Morris MJ, Slovin SF, et al: Genomic characterization of primary and metastatic prostate cancer (PC) using a targeted next-generation sequencing assay. J Clin Oncol. 34(Suppl 2): 2542016. View Article : Google Scholar | |
|
Cheng ML, Abida W, Rathkopf DE, Arcila ME, Barron D, Autio KA, Zehir A, Danila DC, Morris MJ, Gopalan A, et al: Next-generation sequencing (NGS) of tissue and cell free DNA (cfDNA) to identify somatic and germline alterations in advanced prostate cancer. J Clin Oncol. 35(Suppl 15): 15102017. View Article : Google Scholar | |
|
Feldman RA, Dan Basu G, Xiu J, Arguello D, Millis SZ, Bender R, Gatalica Z, Paul L and Myers CE: Molecular profiling of advanced refractory prostate cancer. J Clin Oncol. 32(Suppl 4): 1072014. View Article : Google Scholar | |
|
Palapattu GS, Cani AK, Huang J, Hovelson DH, Lu D, Margolis D, Natarajan S, Mehra R, Montgomery JS, Morgan TM, et al: Progression of low-to high-grade prostate cancer: Molecular profiling of tissue obtained by serial targeted biopsy. J Clin Oncol. 33(Suppl 15): 50172015. View Article : Google Scholar | |
|
Browning RE IV, Li H, Shinohara ET, Cai Q, Chen H, Courtney R, Cao C, Zheng W and Lu B: ATM polymorphism IVS62+60G>A is not associated with disease aggressiveness in prostate cancer. Urology. 67:1320–1323. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Patel V, Busch E, D'Amico A and Rebbeck T: BRCA2 mutations in prostate cancer assort into cluster regions. J Radiat Oncol. 6:229–230. 2017. | |
|
Wu X, Dong X, Liu W and Chen J: Characterization of CHEK2 mutations in prostate cancer. Hum Mutat. 27:742–747. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Dall'Era M, Glass A, Lara P, Hartmaier R, DeVere White R and McPherson J: Frequency of DNA repair gene mutations in localized and metastatic prostate cancer. J Clin Oncol. 35(Suppl 6): 102017. View Article : Google Scholar | |
|
Glass A, Lara P, Hartmaier R, DeVere White R, McPherson J and Dall'Era M: Frequency of DNA repair gene mutations in localized and metastatic prostate cancer. J Urol. 197(Suppl 4): e56–e57. 2017. View Article : Google Scholar | |
|
Beltran H, Eng K, Mosquera JM, Sigaras A, Romanel A, Rennert H, Kossai M, Pauli C, Faltas B, Fontugne J, et al: Whole-exome sequencing of metastatic cancer and biomarkers of treatment response. JAMA Oncol. 1:466–474. 2015. View Article : Google Scholar : PubMed/NCBI | |
|
Beltran H, Sboner A, Mosquera JM, Rickman D, Eng K, Prandi D, Kossai M, Faltas B, Pauli C, Fontugne J, et al: Precision medicine program for whole-exome sequencing (WES) provides new insight on platinum sensitivity in advanced prostate cancer (PCa). J Clin Oncol. 33(Suppl 7): 1582015. View Article : Google Scholar | |
|
Beltran H, Yelensky R, Frampton GM, Park K, Downing SR, MacDonald TY, Jarosz M, Lipson D, Tagawa ST, Nanus DM, et al: Targeted next-generation sequencing of advanced prostate cancer identifies potential therapeutic targets and disease heterogeneity. Eur Urol. 63:920–926. 2013. View Article : Google Scholar : | |
|
Gambhira R, Ledet EM, Dotiwala A, Mandal D and Sartor AO: Copy number variations in AR-associated and DNA repair genes from plasma cell-free DNA of metastatic CRPC patients. J Clin Oncol. 34(Suppl 2): 2812016. View Article : Google Scholar | |
|
Daniel S, Gornstein E, Frampton GM, Sun J, Morley S, Heilmann A, Reddy P, Chung J, Suh J, Ramkissoon S, et al: BRCA1/2 reversion mutations in prostate cancer identified from clinical tissue and liquid biopsy samples. J Clin Oncol. 35(Suppl 15): 50242017. View Article : Google Scholar | |
|
Robbins CM, Tembe WA, Baker A, Sinari S, Moses TY, Beckstrom-Sternberg S, Beckstrom-Sternberg J, Barrett M, Long J, Chinnaiyan A, et al: Copy number and targeted mutational analysis reveals novel somatic events in metastatic prostate tumors. Genome Res. 21:47–55. 2011. View Article : Google Scholar : | |
|
Hebbring SJ, Fredriksson H, White KA, Maier C, Ewing C, McDonnell SK, Jacobsen SJ, Cerhan J, Schaid DJ, Ikonen T, et al: Role of the Nijmegen breakage syndrome 1 gene in familial and sporadic prostate cancer. Cancer Epidemiol Biomarkers Prev. 15:935–938. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Baca SC, Prandi D, Lawrence MS, Mosquera JM, Romanel A, Drier Y, Park K, Kitabayashi N, MacDonald TY, Ghandi M, et al: Punctuated evolution of prostate cancer genomes. Cell. 153:666–677. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Liu Z, Zhou M, Lepor H, Zoino R, Rajoria G and Klimek S: Mutational analysis of prostate cancer using next generation cancer hotspot panel. Lab Invest. 96:246A–247A. 2016. | |
|
Cheng Y, Thorne H, Bolton D, Clouston D, Willems A, Li J, Niedermeyer E, Fox S and Mitchell G: Altered significance of D'Amico risk assessment in BRCA2 positive vs negative patients from high risk breast cancer families. J Urol. 185(Suppl. 4): e622011. View Article : Google Scholar | |
|
Gayther SA, de Foy KA, Harrington P, Pharoah P, Dunsmuir WD, Edwards SM, Gillett C, Ardern-Jones A, Dearnaley DP, Easton DF, et al: The Cancer Research Campaign/British Prostate Group United Kingdom Familial Prostate Cancer Study Collaborators: The frequency of germ-line mutations in the breast cancer predisposition genes BRCA1 and BRCA2 in familial prostate cancer. Cancer Res. 60:4513–4518. 2000.PubMed/NCBI | |
|
Zuhlke KA, Johnson AM, Okoth LA, Stoffel EM, Robbins CM, Tembe WA, Salinas CA, Zheng SL, Xu J, Carpten JD, et al: Identification of a novel NBN truncating mutation in a family with hereditary prostate cancer. Fam Cancer. 11:595–600. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
LaDuca H, Espenschied C, Dolinsky JS, Smith LP, Fulk K, Pronold M, Horton C, Couch FJ and Davis BT: Hereditary cancer panel results identify gaps in knowledge of cancer risks and limitations in current guidelines. In: Presented at the American Society of Human Genetics Annual Meeting; 17 - 21 Oct , 2017; Orlando: United States. 2017 | |
|
Ledet EM, Ernst EM, Schiff J, Lin S, Lewis BE and Sartor AO: Germline variants and family history in caucasian and African-American prostate cancer. J Clin Oncol. 35(Suppl 15): pp. e165482017, View Article : Google Scholar | |
|
Lin S, Ledet EM, Schiff J, Ernst EM, Garvey CE, Lewis BE and Sartor O: Inherited pathologic mutations and family history in patients with prostate cancer. J Clin Oncol. 35(Suppl 6): pp. 1852017, View Article : Google Scholar | |
|
Marshall M, Tully D, Susswein L, Theobald K, Murphy P and Klein R: Panel testing in men with prostate cancer meeting NCCN genetic testing criteria (AB2017-59). J Natl Compr Canc Netw. 15:e15–e16. 2017. | |
|
Tischkowitz MD, Yilmaz A, Chen LQ, Karyadi DM, Novak D, Kirchhoff T, Hamel N, Tavtigian SV, Kolb S, Bismar TA, et al: Identification and characterization of novel SNPs in CHEK2 in Ashkenazi Jewish men with prostate cancer. Cancer Lett. 270:173–180. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Hamel N, Kotar K and Foulkes WD: Founder mutations in BRCA1/2 are not frequent in Canadian Ashkenazi Jewish men with prostate cancer. BMC Med Genet. 4:72003. View Article : Google Scholar : PubMed/NCBI | |
|
Damaraju S, Murray D, Dufour J, Carandang D, Myrehaug S, Fallone G, Field C, Greiner R, Hanson J, Cass CE, et al: Association of DNA repair and steroid metabolism gene polymorphisms with clinical late toxicity in patients treated with conformal radiotherapy for prostate cancer. Clin Cancer Res. 12:2545–2554. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Meyer A, Wilhelm B, Dörk T, Bremer M, Baumann R, Karstens JH and Machtens S: ATM missense variant P1054R predisposes to prostate cancer. Radiother Oncol. 83:283–288. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Nientiedt C, Heller M, Endris V, Volckmar AL, Zschäbitz S, Tapia-Laliena MA, Duensing A, Jäger D, Schirmacher P, Sültmann H, et al: Mutations in BRCA2 and taxane resistance in prostate cancer. Sci Rep. 7:45742017. View Article : Google Scholar : PubMed/NCBI | |
|
Hubert A, Peretz T, Manor O, Kaduri L, Wienberg N, Lerer I, Sagi M and Abeliovich D: The Jewish Ashkenazi founder mutations in the BRCA1/BRCA2 genes are not found at an increased frequency in Ashkenazi patients with prostate cancer. Am J Hum Genet. 65:921–924. 1999. View Article : Google Scholar : PubMed/NCBI | |
|
Giusti RM, Rutter JL, Duray PH, Freedman LS, Konichezky M, Fisher-Fischbein J, Greene MH, Maslansky B, Fischbein A, Gruber SB, et al: A twofold increase in BRCA mutation related prostate cancer among Ashkenazi Israelis is not associated with distinctive histopathology. J Med Genet. 40:787–792. 2003. View Article : Google Scholar : PubMed/NCBI | |
|
Kote-Jarai Z, Leongamornlert D, Saunders E, Tymrakiewicz M, Castro E, Mahmud N, Guy M, Edwards S, O'Brien L, Sawyer E, et al: UKGPCS Collaborators: BRCA2 is a moderate penetrance gene contributing to young-onset prostate cancer: Implications for genetic testing in prostate cancer patients. Br J Cancer. 105:1230–1234. 2011. View Article : Google Scholar : PubMed/NCBI | |
|
Edwards SM, Kote-Jarai Z, Meitz J, Hamoudi R, Hope Q, Osin P, Jackson R, Southgate C, Singh R, Falconer A, et al: Cancer Research UK/Bristish Prostate Group UK Familial Prostate Cancer Study Collaborators; British Association of Urological Surgeons Section of Oncology: Two percent of men with early-onset prostate cancer harbor germline mutations in the BRCA2 gene. Am J Hum Genet. 72:1–12. 2003. View Article : Google Scholar | |
|
Agalliu I, Karlins E, Kwon EM, Iwasaki LM, Diamond A, Ostrander EA and Stanford JL: Rare germline mutations in the BRCA2 gene are associated with early-onset prostate cancer. Br J Cancer. 97:826–831. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Gallagher DJ, Cronin AM, Milowsky MI, Morris MJ, Bhatia J, Scardino PT, Eastham JA, Offit K and Robson ME: Germline BRCA mutation does not prevent response to taxane-based therapy for the treatment of castration-resistant prostate cancer. BJU Int. 109:713–719. 2012. View Article : Google Scholar | |
|
Agalliu I, Gern R, Leanza S and Burk RD: Associations of high-grade prostate cancer with BRCA1 and BRCA2 founder mutations. Clin Cancer Res. 15:1112–1120. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Kirchhoff T, Kauff ND, Mitra N, Nafa K, Huang H, Palmer C, Gulati T, Wadsworth E, Donat S, Robson ME, et al: BRCA mutations and risk of prostate cancer in Ashkenazi Jews. Clin Cancer Res. 10:2918–2921. 2004. View Article : Google Scholar : PubMed/NCBI | |
|
Lehrer S, Fodor F, Stock RG, Stone NN, Eng C, Song HK and McGovern M: Absence of 185delAG mutation of the BRCA1 gene and 6174delT mutation of the BRCA2 gene in Ashkenazi Jewish men with prostate cancer. Br J Cancer. 78:771–773. 1998. View Article : Google Scholar : PubMed/NCBI | |
|
Cesaretti JA, Stock RG, Atencio DP, Peters SA, Peters CA, Burri RJ, Stone NN and Rosenstein BS: A genetically determined dose-volume histogram predicts for rectal bleeding among patients treated with prostate brachytherapy. Int J Radiat Oncol Biol Phys. 68:1410–1416. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Zhu Y, Marshall D, Garrett-Mayer E, Zhou D and Jenrette J: Association between urinary morbidity/erectile dysfunction induced by radiotherapy and SNPs of radiosensitivity-relevant genes in prostate cancer patients. Clin Chem. 56:A902010. | |
|
Pomerantz MM, Spisák S, Jia L, Cronin AM, Csabai I, Ledet E, Sartor AO, Rainville I, O'Connor EP, Herbert ZT, et al: The association between germline BRCA2 variants and sensitivity to platinum-based chemotherapy among men with metastatic prostate cancer. Cancer. 123:3532–3539. 2017. View Article : Google Scholar : PubMed/NCBI | |
|
Hart SN, Ellingson MS, Schahl K, Vedell PT, Carlson RE, Sinnwell JP, Barman P, Sicotte H, Eckel-Passow JE, Wang L, et al: Determining the frequency of pathogenic germline variants from exome sequencing in patients with castrate-resistant prostate cancer. BMJ Open. 6:e0103322016. View Article : Google Scholar : PubMed/NCBI | |
|
Antonarakis ES, Lu C, Luber B, Liang C, Wang H, Chen Y, Silberstein JL, Piana D, Lai Z, Chen Y, et al: Germline DNA-repair gene mutations and outcomes in men with metastatic castration-resistant prostate cancer receiving first-line abiraterone and enzalutamide. Eur Urol. 74:218–225. 2018. View Article : Google Scholar : PubMed/NCBI | |
|
Nastiuk KL, Mansukhani M, Terry MB, Kularatne P, Rubin MA, Melamed J, Gammon MD, Ittmann M and Krolewski JJ: Common mutations in BRCA1 and BRCA2 do not contribute to early prostate cancer in Jewish men. Prostate. 40:172–177. 1999. View Article : Google Scholar : PubMed/NCBI | |
|
Schweizer MT, Cheng HH, Tretiakova MS, Vakar-Lopez F, Klemfuss N, Konnick EQ, Mostaghel EA, Nelson PS, Yu EY, Montgomery B, et al: Mismatch repair deficiency may be common in ductal adenocarcinoma of the prostate. Oncotarget. 7:82504–82510. 2016. View Article : Google Scholar : PubMed/NCBI | |
|
Stephens PJ, Gay LM, Ali SM, Hoffman-Censits JH, Elvin JA, Vergilio JA, Suh J, Mian B, Fisher HAG, Nazeer T, et al: Comprehensive genomic profiling of neuroendocrine carcinoma of the prostate. J Clin Oncol. 34(Suppl 15): 1872016. View Article : Google Scholar |
