Abstract
Background
Recurrent mutations in the Speckle-Type POZ Protein (SPOP) gene occur in up to 15% of prostate cancers. However, the frequency and features of cancers with these mutations across different populations is unknown.
Objective
To investigate SPOP mutations across diverse cohorts and validate a series of assays employing high-resolution melting (HRM) analysis and Sanger sequencing for mutational analysis of formalin-fixed paraffin-embedded material.
Design, Setting, and Participants
720 prostate cancer samples from six international cohorts spanning Caucasian, African American, and Asian patients, including both prostate-specific antigen-screened and unscreened populations, were screened for their SPOP mutation status. Status of SPOP was correlated to molecular features (ERG rearrangement, PTEN deletion, and CHD1 deletion) as well as clinical and pathologic features.
Results and Limitations
Overall frequency of SPOP mutations was 8.1% (4.6% to 14.4%), SPOP mutation was inversely associated with ERG rearrangement (P < .01), and SPOP mutant (SPOPmut) cancers had higher rates of CHD1 deletions (P < .01). There were no significant differences in biochemical recurrence in SPOPmut cancers. Limitations of this study include missing mutational data due to sample quality and lack of power to identify a difference in clinical outcomes.
Conclusion
SPOP is mutated in 4.6% to 14.4% of patients with prostate cancer across different ethnic and demographic backgrounds. There was no significant association between SPOP mutations with ethnicity, clinical, or pathologic parameters. Mutual exclusivity of SPOP mutation with ERG rearrangement as well as a high association with CHD1 deletion reinforces SPOP mutation as defining a distinct molecular subclass of prostate cancer.
Introduction
Prostate cancer is a significant health burden, with 241,740 new diagnoses and 28,170 deaths in the United States in 2012 [1]. The inability to distinguish indolent from aggressive disease is a challenge [2]. Identification of driver lesions for specific subsets of prostate cancer could ultimately lead to the development of biomarkers to improve prognostic ability and risk stratification.
Recent advances have uncovered multiple recurrent alterations in prostate cancer. The TMPRSS2-ERG fusion has been observed in nearly 50% of prostate cancers [3,4].
We recently reported somatic mutations in the Speckle-Type POZ Protein (SPOP) gene in 6% to 15% of prostate cancers [5]. SPOP mutations define a distinct subclass of prostate cancer: SPOP mutations and ETS rearrangements are mutually exclusive; SPOP mutant (SPOPmut) prostate tumors generally lack lesions in the phosphatidylinositide 3-kinase (PI3K) pathway, and they are also independent of mutations in the tumor suppressor gene TP53 [5–7].
Although the recurrent nature of SPOP mutations is clear, less is known about the frequency of SPOP mutations across different ethnicities and screening practices, the associations with clinical and pathologic characteristics, and the effects on patient outcomes. This study represents the largest multi-institutional study to date investigating these associations with SPOP mutations across different cohorts.
In addition, detection of SPOP mutations in older well-annotated archival prostate cancer samples represents a technical challenge. Formalin fixation of tissue followed by paraffin embedding (FFPE) is widely used; however, analysis of nucleic acid from FFPE material is difficult due to cross-linking between nucleic acid and proteins [8]. The tissue heterogeneity in prostate cancer samples can dilute the signal of tumor-associated mutations with benign wild-type contamination and make the detection of point mutations difficult.
In an effort to overcome these challenges, we developed a series of assays employing high-resolution melting (HRM) analysis, which relies on alterations in the melting curve of mutated nucleic acids, and Sanger sequencing [9–11]. We optimized the HRM assay as a high sensitivity pre-screening tool, followed by Sanger sequencing for specific confirmation of mutations. Finally, we employed a next-generation sequencing approach on a small subset of samples to determine if massively parallel sequencing could rescue samples considered assay failures using the HRM-Sanger methods.
Materials and Methods
Patient Populations
Table 1 lists the clinical, pathologic, and survival data according to each cohort. In total, prostate cancer samples from 996 patients [radical prostatectomy (RP), transurethral resection of the prostate (TURP), or metastatic biopsies] were examined. Of the 996 patients, 720 samples had DNA of sufficient quality and were screened for their SPOP status. Cohorts included patients from Memorial Sloan-Kettering Cancer Center (MSKCC), cohort from Kyungpook National University School of Medicine, Korea (Korean), African American cohort from New York-Presbyterian Hospital (AA), Weill Cornell Medical College (WCMC) cohorts, and University Hospital of Zurich (USZ), as well as the Swedish watchful waiting cohort (SWWC). A detailed description about each individual cohort can be found in the Supplementary Materials and Methods. Samples were categorized into SPOP wild type (SPOPwt) and SPOPmut and analyzed for correlation between SPOP mutation and pathologic-clinical data. Pathologists with expertise in genitourinary pathology reviewed the archival material at each participating institution. Institutional Review Board approval was obtained at all participating sites.
Table 1.
Demographics by Cohort.
| N | Median Age | Median PSA | Pathologic Gleason Grade | Pathologic Stage | Clinical Outcomes | |||||||
| 6 | 7 | 8 to 10 | pT2 | pT3 | pT4 | PSM | +BCR | DOD | ||||
| African American | 105 | 61 | 5.6 | 19 (18.1%) | 79 (75.2%) | 5 (6.7%) | 85 (81.0%) | 20 (19.0%) | 13 (12.4%) | 12 (11.4%) | NA | |
| Korean | 127 | 67 | 10.4 | 14 (12.6%) | 70 (63.1%) | 27 (24.3%) | 45 (35.4%) | 82 (64.6%) | 66 (52%) | 22 (17.6%) | NA | |
| Swedish | 141 | 74 | NA | 25 (17.7%) | 61 (43.3%) | 55 (39.0%) | NA | NA | NA | NA | NA | 93 (66%) |
| Zurich | 421 | 66 | 11 | 54 (14.8%) | 186 (51.0%) | 125 (34.3%) | 181 (60.5%) | 106 (35.5%) | 12 (4.0%) | NA | 62 (32.8%) | 24 (9.1%) |
| MSKCC | 218 | 58.2 | 6.6 | 51 (26.7) | 107 (56.0%) | 33 (17.3) | 109 (56.2%) | 65 (33.5%) | 20 (10.3) | 53 (26.5) | 39 (30.7) | 4 (1.9%) |
| WCMC | 125 | 63 | 5.0 | 12 (9.7%) | 93 (75.0%) | 19 (15.3%) | 62 (69.7%) | 27 (30.3%) | 15 (20.0%) | 11 (15.3%) | NA | |
PSM indicates positive surgical margin; +BCR, positive biochemical recurrence; DOD, died of disease.
Patient-Related Variables
All clinical and pathologic data were prospectively collected by each individual center, including information on patient age, preoperative prostate-specific antigen (PSA) level, Gleason score, pathologic stage, surgical margin status, and biochemical recurrence (BCR). Detailed description of staging and BCR as well as PSA characterization can be found in the Supplementary Materials and Methods. For a subset of 118 unselected prostate cancers from the WCMC cohort, 15 morphologic features of prostate cancer were assessed (Qiagen, Hilden, Germany). A detailed description of the morphologic features as well as the process of review is described in the Supplementary Materials and Methods.
DNA Extraction
DNA for the AA and Korean cohorts and SWWC was extracted from tissue cores using a Qiagen BioRobot Universal System. Detailed description of the protocol can be found in the Supplementary Materials and Methods. Detailed description of DNA extraction for the cohorts from WCMC, MSKCC, as well as Zurich have been previously described [5,12,13].
Targeted Enrichment for SPOP Exons 6 and 7 and HRM Analysis
The mutational screening assay involves an initial pre-polymerase chain reaction (PCR) amplification step to enrich the target region followed by an HRM screen and Sanger sequencing. PCR assay setup, as well as cycling conditions, HRM assay, and analysis are described in the Supplementary Materials and Methods.
Sanger Sequencing
PCR products were purified using Qiagen PCR purification kit and eluted as per the manufacturers' instructions and sent for Sanger sequencing a minimum of three times to confirm the mutation status. (For a complete list of primers, see Table W1.)
Fluorescence In Situ Hybridization and Immunohistochemistry
Fluorescence In Situ Hybridization (FISH) methods to detect TMPRSS2-ETS gene fusions have been previously described [3,14]. We used ERG break-apart FISH assays to confirm gene rearrangement on the DNA level [15]. To assess the status of PTEN, we used a locus-specific probe and a reference probe, as previously described [16], and a similar approach was used to detect CHD1 deletions. The sequences of all FISH probes are listed in Table W1. ERG expression was assessed by immunohistochemistry, as previously described [17].
DNA Capture, Library Preparations, and MiSeq Runs
A customized TrueSeq amplicon kit was designed by using Illumina DesignStudio, and the library preparation was performed as suggested by the manufacturer. More information about the design and the library preparation can be found in the Supplementary Materials and Methods.
Alignment and Analysis
Reads were aligned independently to the human genome reference sequence (GRC37/hg19) using a Smith-Waterman mode BWA [18] to maximize the number of reads aligned to the genome. An inhouse tool was then used to determine the genotype of the locations of SPOP.
Statistical Analysis
Results were expressed as a contingency table to show association between SPOP status with clinical and pathologic variables along with available molecular data (Table 3). Differences were considered statistically significant at P < .05. All statistical analyses were performed with Stata SE, version 11.0 (StataCorp, College Station, TX).
Table 3.
Demographics and Molecular Changes by SPOP Mutation.
| WT | +SPOP | P Value | |
| N (%) | 662/720 (91.9%) | 58/720 (8.1%) | |
| Median age | 65 (34–89) | 64 (46–84) | 0.49 |
| Median PSA | 7.1 | 7.4 | 0.65 |
| Gleason grade | 0.26 | ||
| 6 | 97 (15.9%) | 4 (7.3%) | |
| 3 + 4 | 230 (37.7%) | 20 (36.4%) | |
| 4 + 3 | 132 (21.6%) | 16 (29.1%) | |
| 8 to 10 | 151 (24.8%) | 15 (27.3%) | |
| Pathologic stage | 0.58 | ||
| pT2 | 298 (62.6%) | 29 (70.7%) | |
| pT3 | 166 (34.9%) | 12 (29.3%) | |
| pT4 | 12 (2.5%) | 0 | |
| Median follow-up time, months (range) | 73 (1.9–182) | 100.5 (13–160) | 0.67 |
| Positive BCR | 70 (19.9%) | 7 (25.9%) | 0.45 |
| Median time to BCR, months (range) | 18 (1.2–139) | 15 (11–55.4) | 0.29 |
| Status | 0.69 | ||
| NED | 158 (50.8%) | 10 (47.6%) | |
| AWD | 65 (20.9%) | 6 (28.6%) | |
| DOD | 77 (24.8%) | 4 (19.0%) | |
| DOC | 11 (3.5%) | 1 (4.8%) | |
| ERG | <0.01 | ||
| WT | 337 (59.4%) | 46 (95.8%) | |
| Positive | 230 (40.6%) | 2 (4.2%) | |
| PTEN | 0.31 | ||
| WT | 350 (74.1%) | 31 (81.6%) | |
| Deletion | 122 (25.9%) | 7 (18.4%) | |
| CHD1 | <0.01 | ||
| WT | 147 (85.0%) | 8 (42.1%) | |
| Deletion | 26 (15.0%) | 11 (57.9%) |
BCR indicates biochemical recurrence; AWD, alive with disease; NED, no evidence of disease; DOD, died of disease; DOC, died of other causes; WT, wild type.
Results
Performance of HRM Assay
In this study, we screened 996 samples from six independent cohorts: WCMC, AA, Korean, MSKCC, Zurich, and SWWC for their SPOP status. Of 996 samples, we were able to successfully evaluate SPOP status in 720 samples (72.3%), which were classified into 662 SPOPwt and 58 SPOPmut samples. A flowchart of sample testing is shown in Figure 1. To determine the sensitivity of HRM and reliability for annotating wild-type samples, 100 samples with wild-type HRM results (no shift) were subsequently Sanger sequenced. All 100 samples were SPOPwt. A dilution series of plasmid DNA showed that less than 5% of SPOPmut DNA was sufficient for a notable shift in the melting curve (Figure W1).
Figure 1.
Workflow of specimen screening process. The workflow of the sample screening process for 421, 127, 141, and 61 samples from the Zurich, Korean, SWWC, and AA cohorts is shown. A number of at least three 0.6-mm cores were obtained for DNA extraction. The DNA concentrations of 226 (Zurich), 9 (SWWC), and 1 (AA) samples were less than 20 to 50 ng/µl and therefore excluded from the study. Of the remaining samples, 20 (Zurich), 38 (Korean), 32 (SWWC), and 2 (AA) failed the PCR-based assay. By the HRM assay, 222 (Zurich), 80 (Korean), 93 (SWWC), and 84 (AA) samples were classified as SPOPwt. Twenty-nine (Zurich), 9 (Korean), 7 (SWWC), and 4 (AA) samples that had a melting curve shift were sent off for Sanger sequencing for further characterization. A mutation in either exon 6 or exon 7 of the SPOP gene was found in 19 (Zurich), 6 (Korean), 6 (SWWC), and 4 (AA) samples. No alteration was found for the remaining 10 (Zurich), 3 (Korean), and 1 (SWWC) samples and therefore excluded from the study. Numbers shown in the tables indicate the number of samples per processing step and their percentage.
A subset of 22 samples was flagged positive by HRM but could not be validated by Sanger sequencing due to assay failure; deep sequencing of those samples was attempted on the MiSeq platform; however, overall read quality was poor and we therefore excluded those samples from the study. With an overall coverage of 98.7% of the SPOP exome and an average number of 591,242 mapped reads for 10 control samples, 6 FFPE material and 4 fresh frozen, the efficiency of the capture assay and the sequencing was demonstrated.
SPOP Mutation across Cohorts
SPOP mutations were detected in 58/720 evaluable cases (8.1%) targeting all previously detected mutations in prostate cancer. Consistent with previous results, the most frequently mutated residue was F133 (50%) followed by Y87 (15%), W131 and F102 (9%), F125 (3%), and K129, F104, and K135 (2%). All mutations were missense and clustered in the substrate binding MATH domain (Figure 2). Mutations at F104 and K135 have not been previously described.
Figure 2.
Schematic overview of the SPOP gene and localization of mutations. The two protein domains, meprin and TRAF homology (MATH) and broad complex, tramtrack and bric-a-brac (BTB), and the specific amino acid positions harboring the alterations and its relative recurrence are shown. The proportion of the colors in bars indicates the abundance of the mutation in each of the six cohorts, WCMC (orange), MSKCC (light blue), Zurich (purple), Swedish (green), Korean (red), and AA (dark blue). The overall frequency of mutated SPOP in the cohort is shown next to the color coding system.
Association of SPOP Mutation with Clinicopathologic Characteristics and Outcomes
The clinicopathologic characteristics of the 996 patients are summarized in Table 1. Overall, the rate of SPOP mutation was 8.1% (4.6%–14.4%, see Table 2). The AA cohort had the lowest rate of SPOP mutations (4.6%), and the highest rate was in the WCMC cohort (14.4%), but there were no significant differences between cohorts (P = .14). Patients with SPOP mutations had no significant differences in Gleason grade and rates of BCR (P = .26 and P = .18, respectively; Table 3). In a separate sub-analysis, those who had treatment for their prostate cancer by RP (N = 911) were compared to those who received only palliative treatment (i.e., TURP; N = 197), and no significant differences were noted in frequency of SPOP mutations (8.75 vs 6.1%, P = .40). Patients with mutant SPOP had a median time to BCR of 15 months compared to 18 months for SPOPwt (P = .30). There were no significant differences noted in age, preoperative PSA, lymph node positivity, or pathologic stage according to SPOP mutation status. There were also no noted differences in survival according to SPOP status (P = .78). There were no significant associations noted with SPOP mutations on univariable or multi-variable Cox regression models for relapse-free survival, cancer-specific survival, and overall survival (P = .57, .62, .75, respectively).
Table 2.
Molecular Alterations by Cohort.
| No. of Evaluable Samples | SPOP | ERG | PTEN | CHD1 | |||||
| Wild Type | Mutant | Wild Type | Positive | Wild Type | Deletion | Wild Type | Deletion | ||
| African American | 88 | 84 (95.4%) | 4 (4.6%) | 66 (75.0%) | 22 (25.0%) | 78 (91.8%) | 7 (8.2%) | NA | NA |
| Korean | 87 | 81 (93.1%) | 6 (6.9%) | 55 (70.5%) | 23 (29.5%) | 53 (82.8%) | 11 (17.2%) | NA | NA |
| Swedish | 99 | 93 (93.9%) | 6 (6.1%) | 79 (79.8%) | 20 (20.2%) | NA | NA | 52 (82.5%) | 11 (17.5%) |
| Zurich | 241 | 222 (92.1%) | 19 (7.9%) | 80 (45.5%) | 96 (54.5%) | 93 (51.7%) | 87 (48.3%) | NA | NA |
| MSKCC | 80 | 75 (93.7%) | 5 (6.3%) | 38 (47.5%) | 42 (52.5%) | 75 (93.8%) | 5 (6.3%) | 67 (87.0%) | 10 (13.0%) |
| WCMC | 125 | 107 (85.6%) | 18 (14.4%) | 65 (61.9%) | 37 (35.2%) | 82 (81.2%) | 19 (18.8%) | 36 (70.6%) | 15 (29.4%) |
Association of SPOP Mutations with Morphologic Features of Prostate Cancer
No morphologic features were associated with positive SPOPmut status. Cribriform morphology was present in 64% of SPOPmut compared to 18% in nonmutants (P = .08). Foamy gland morphology showed a significant negative association with SPOPmut, with 18% of SPOPmut cases exhibiting this feature compared to 72% of control cases (P = .03). However, when this P value was adjusted to account for multiple comparisons (Bonferroni method), the statistical significance was lost.
Association of SPOP Mutations with Other Molecular Alterations
Across all six cohorts, the frequency of ERG rearrangement was 38.3% (range, 20.2%–54.5%), PTEN deletions 25.3% (6.3%48.3%), and CHD1 deletions 19.3% (13.0%–29.4%). We found only two SPOPmut cases with an ERG rearrangement (P < .01), consistent with previously reported mutual exclusivity [5]. There were no significant associations between SPOP mutations and PTEN deletions (P = .31) despite previously reported inverse association in clinically localized disease [5]. Patients with SPOP mutations demonstrated significantly higher rates of CHD1 deletion (57.9% vs 15.0%, respectively, P < .01).
Discussion
This study represents the largest multi-institutional collaboration to evaluate for SPOP mutations across different populations. In the current study, the rate of SPOP mutations was similar across different ethnicities and cohorts, at a rate of 8.1% of prostate cancers, ranging from 4.6% in the AA cohort to 14% in the WCMC cohort. In addition, differences in SPOP frequency might be influenced by intratumor heterogeneity, prostate tumor density, tumor multifocality and sample handling variations among different institutions. Method of detection and sample quality may also play a role; next-generation sequencing and high-quality tissue specimens will likely maximize sensitivity. Finally, subclonality of certain point mutations will likely impact detection rates. SPOP mutations have been shown to be highly clonal [19], limiting this concern for these specific mutations, but the heterozygous nature of SPOP mutations does impact sensitivity.
Previous studies have suggested that SPOP mutations identify a distinct molecular subclass of prostate cancer compared to ETS family rearrangements [5,20]. This study confirms those findings; of 58 SPOP-mutated cases, only two showed an ERG rearrangement in the same patient. Co-occurrence of SPOP mutation and ERG rearrangement may be due to sampling adjacent but molecularly distinct tumor foci in the same specimen, as previously described [5], or as a result of intratumoral heterogeneity. We also cannot exclude the possibility that SPOP mutations and ETS rearrangement do occur together in the same tumor cells at exceptionally low frequency.
SPOP mutations are also associated with CHD1 deletions [5,19]. In this study, 57.9% of the SPOPmut harbored CHD1 deletions. These findings suggest that SPOP mutations may be driver lesions in a distinct molecular subclass of prostate cancer. Across all cohorts, we saw no significant association between PTEN deletion and SPOP mutation. We had previously reported an inverse association between SPOP mutation and PTEN lesions in clinically localized prostate cancer but in a PSA-screened population of primarily Caucasian men [5]. Previously, we observed no association between SPOP mutation and PTEN deletions in metastatic cancer, highlighting that the relationship between these two abnormalities is highly dependent on the context of the specific patient cohort.
We detected no signification difference in rates or time to BCR in relation to SPOP status. However, despite the considerable sample size, the study was underpowered to evaluate oncologic outcomes in relation to SPOP status: Assuming a 15% rate of BCR rate after 5 years of definitive surgical management according to recent large studies [21–23], and assuming an overall SPOP mutation rate of approximately 10% [5], to achieve at least 80% power to detect a significant difference in BCR, the sample size would need to be several thousand patients with long-term follow-up. With the establishment of assays defined here, we can continue multi-institutional collaborative efforts to identify associations of SPOP mutation with clinical outcomes to connect molecular classification with risk stratification.
We have developed an HRM-based screening assay, which allows us to screen samples from archival FFPE material in a high-throughput and cost-efficient manner. This assay demonstrates high sensitivity, reliability, and efficiency. Less than 5% mutated tumor DNA has to be present to lead to a positive result. However, it is important to note that no mutation calls were made solely based on HRM; all potential mutations were verified by Sanger sequencing. Sanger sequencing has a sensitivity of roughly 20% [24], making the HRM assay considerably more sensitive but difficult to validate. Samples showing a shift on HRM but no mutation by sequencing were classified as assay failures and excluded to rule out false-negative calls. One concern about archival material is the potential poor quality of the DNA. Most likely poor DNA quality was the reason for the assay failure rate (28.3% of all samples). Assay failure could not be rescued by next-generation sequencing, reinforcing that the sample characteristics were likely more important than the assay characteristics.
Our study is the first to examine morphologic features of SPOPmut prostate cancer. Although the clinical significance of SPOPmut status is yet to be established, we sought to assess if SPOPmut prostate cancer could be recognized histologically. When subsets of SPOPmut and SPOPwt cases with similar Gleason score and pathologic stage were compared, no morphologic features were associated with positive SPOPmut status.
In conclusion, we have shown that SPOP is mutated in 4.6% to 14.4% of prostate cancers in six cohorts with different ethnic and demographic backgrounds. There was no significant difference in SPOP mutation frequency among cohorts. Mutual exclusivity of mutated SPOP and ERG rearrangement as well as high correlation of SPOP mutation with deletion of CHD1 across different cohorts and ethnicities reinforces SPOP mutation as defining a distinct subclass of prostate cancer. Further studies, potentially with large numbers of patients, will be needed to determine the impact of SPOP mutation on clinical outcome.
Supplementary Materials and Methods
Patient Population and Patient-Related Variable
Tumor staging and grading were standardized to the 2002 American Joint Committee on Cancer recommendations. After RP, patients were followed until death for disease recurrence. BCR was defined as a serum PSA level > 0.2 ng/ml on more than two consecutive occasions for the WCMC and AA cohorts. BCR in the Zurich cohort was defined as any increase in postoperative PSA > 0.1 ng/ml after achieving a PSA nadir < 0.1 ng/ml, while the Korean cohort defined BCR as any postoperative PSA > 0.2 ng/ml after achieving a PSA nadir < 0.1 ng/ml. Follow-up data on BCR was present in 537 patients (54.0%), and survival data were present on 226 patients (22.6%).
Swedish Watchful Waiting Cohort
The SWWC, as described previously [25], consisted of non-PSA-screened men with localized prostate cancer diagnosed by TURP and managed with watchful waiting. A subset of this population (141 patients) had FFPE specimens that were satisfactory for evaluation, of which 99 had DNA of sufficient quality and were included in this study. SPOP status was determined by performing HRM assay followed by Sanger sequencing. ERG rearrangement, PTEN deletions, and CHD1 deletions were detected by FISH.
Korean Cohort
The Korean cohort consists of 127 consecutive patients with prostate cancer treated by robotic-assisted laparoscopic prostatectomy (RALP) at Kyungpook National University School of Medicine. The majority of these patients were found to have prostate cancer because of obstructive lower urinary tract symptoms; PSA is not routinely used as a screening tool in South Korea. Of the 127 patients, 87 had DNA of sufficient quality for evaluation. Slides of FFPE tissue from RALP were reviewed by study pathologists to confirm diagnosis and the pathologic characteristics, including Gleason grade, surgical margin, and pathologic stage. SPOP status was screened by performing HRM assay followed by Sanger sequencing. ERG status was annotated by immunohistochemistry and FISH. PTEN deletions were also identified by FISH.
USZ Cohort
FFPE prostate tissues from 421 consecutive men who underwent either RP (336), TURP (56), or metastasis (29) collection were obtained at the University Hospital of Zurich between 1993 and 2007 [12]. Slides of FFPE tissue were reviewed by study pathologists to confirm diagnosis and the pathologic characteristics, including Gleason grade, surgical margin, and pathologic stage. Of the 421 specimens, 251 were of sufficient quality to be analyzed. SPOP status was screened by performing HRM assay followed by Sanger sequencing. ERG rearrangements, CHD1 deletions, and PTEN deletions were determined by performing FISH.
MSKCC Cohort
A total of 218 fresh frozen tumor samples were obtained from patients treated by RP at MSKCC, as previously described [13]. Eighty patients had DNA that could be reliably analyzed for this study. There were no significant differences in Gleason grade, stage, or clinical outcomes between the 80 cases from MSKCC that had evaluable DNA for SPOP and the 138 cases that did not have evaluable DNA. The percentage of tumor tissue versus normal tissue was at least 70% to be extracted [13]. SPOP status was identified by Sanger sequencing. ERG rearrangements were inferred using outlier mRNA expression, and PTEN and CHD1 deletions were determined from array comparative genomic hybridization (CGH), as previously described [13].
WCMC Cohort
Tumors for the WCMC cohort were collected from 125 patients undergoing RALP from 2007 to 2010 by one single surgeon at a single institution. Cores in areas with high tumor density have been taken with an overall average of at least 60% tumor material. All African American patients were excluded from the WCMC cohort and grouped into a separate cohort. SPOP status was collected from previously reported whole exome data, RNA-Seq data, as well as Sanger sequencing [5]. We excluded four SPOPmut cases that showed inconsistent results between detection methods. ERG status was determined using FISH and immunohistochemistry (IHC); CHD1 deletions and PTEN deletions were identified using FISH and CGH as previously described [5].
AA Cohort
Archival RP from 105 consecutive self-identified African American men were obtained at WCMC between 2001 and 2009, of which 88 had evaluable DNA. SPOP status was screened by performing HRM assay followed by Sanger sequencing. ERG expression was determined by IHC, and identification of ERG rearrangement and PTEN deletion was done by FISH. Molecular and clinical details of this cohort have been reported elsewhere [26].
Determining if SPOPmut Prostate Cancer Is Associated with a Morphologic Phenotype
A subset of 118 unselected prostate cancers from the WCMC was assessed. This group included 11 SPOPmut cases. Blinded to mutation status, two reviewers at WCMC assessed all prostatectomy slides from these 11 SPOPmut cancers and 11 SPOPwt cases with similar Gleason score and pathologic stage. Each prostatectomy specimen was assessed for the presence or absence of 15 morphologic features of prostate cancer: intraductal spread, cribriform morphology, blue-tinged mucin, crystalloids, high nucleocytoplasmic ratio, macro-nucleoli, foamy gland features, collagenous micronodules, small cell/neuroendocrine differentiation, perineural invasion, extraprostatic extension, signet ring-like cell features, ductal morphology, glomerulations, and comedonecrosis. Statistical analysis was performed to look for significant associations between morphology and SPOPmut status.
DNA Extraction
Citrosolve (400 µl) was added to each well of a 96-well plate. The plate was then placed on a shaking pad, followed by incubation on a heat plate for 10 minutes, placed back on the shaking pad, and repeated for a total of three times. After the three cycles, the citrosolve was removed and the samples received two 400 µl of ethanol washes. After a 1-hour drying step, 300 µl of AL tissue lysis buffer was added in addition to 20 µl of proteinase K solution for an overnight incubation. Two additional sets of 20 µl of proteinase K were added if cores were still visible, and each time incubated at 55°C for 3 hours with intervals of shaking. After digestion, 300 µl of lysis buffer and 600 µl of ethanol were added to the lysate and the samples were transferred to a filter column. AW1 and AW2 wash buffers with two doses of 96% ethanol were added to purify the samples. DNA was then eluted in two steps in 50 µl of AE buffer by centrifugation. DNA was quantified using the Nanodrop spectrophotometer. Samples with a DNA concentration of 20 to 50 ng/µl or above were included in the study. DNA purity was determined by measuring UV absorbance [15]. DNA extraction from the WCMC cohort [5] and MSKCC cohort [15] has been previously described. DNA from patients of the hospital of Zurich was extracted by using the blood and tissue kit (Qiagen) as suggested by the manufacturer.
Targeted Enrichment for SPOP Exons 6 and 7
The pre-PCR reactions were performed in 30-µl volumes. For the exon 6 assay, 1 µl of patient DNA (30–80 ng), 2 µl of working primer solution (forward + reverse, 3 µM concentration), 2.4 µl of 50 mM MgCl2,4 µl of 10 mM deoxyribonucleotide (dNTP) solution (Invitrogen, Carlsbad, CA), 2 µl of 1.5 mM High-Fidelity PCR Buffer (Thermo Scientific, Waltham, MA), and 17.8 µl of sterile water were used. For the exon 7 assay, the process was the same as for exon 6 except for the addition of 11.8 µl of sterile water and 6 µl of 5x combinatorial enhancer solution (CES) enhancer solution [27].
Pre-PCR was performed on Eppendorf Mastercycler Pro. Optimized cycling conditions used for both assays are given as follows: initial activation step at 98°C for 1 minute, followed by 30 cycles of 95°C for 5 seconds, 60°C for 10 seconds, and 72°C for 30 seconds. After a final step of 72°C for 30 seconds, samples were held at 4°C. (For complete list of primers, see Table W1.)
HRM Analysis
Pre-PCR products were diluted 1:100 with sterile water. Samples were run in triplicate on the LightCycler 480 II (Roche Diagnostics, Basel, Switzerland). A total reaction volume of 10 µl was used, which consisted of 1 µl of diluted pre-PCR products and 3 µMforward and reverse HRM primers, 5 µl of High Resolution Melting Master Mix (from Roche), 1 µl (exon 6 assay) or 1.4 µl (exon 7 assay) of 25 mM MgCl2 plus 2 µl (exon 6 assay) or 1.6 µl (exon 7 assay) of PCR-grade water.
HRM conditions used were given as follows: An activation step of 10 minutes at 95°C is followed by 30 cycles of 10 seconds at 55°C and 72°C for 30 seconds. Before the HRM, a heteroduplex forming step that involves heating the PCR products to 95°C for 1 minute and a rapid cooling to 45°C for 1 minute is performed. HRM was performed from 72°C to 95°C at a temperature gradient of 1°C per second, acquiring 30 data points per °C. (For complete list of primers, see Table W1). This assay targets two exons of the SPOP gene containing all previously detected mutations in prostate cancer (amino acids 80 to 106 and amino acids 120 to 140).
The melting curves were normalized at the pre-melt (100% fluorescence) and post-melt (0% fluorescence) stages using gene scanning software (Roche) [28]. Further assay details and cycling conditions can be found in the Supplementary Materials and Methods. Seven controls were run in each assay along with 96 samples on a 384-well plate. Normalization of the melting curves was done using these controls. The controls used were five known heterozygous mutations F133V, F133C, K129E, F102C, and Y87C and two known wild-type controls (one for exon 6 and one for exon 7). The average size of 50 to 150 bp of the targeted fragment is notable, as the use of archival material, such as DNA from FFPE material, can show a high degree of degradation. Every sample that showed a notable shift in the melting curve was sent off for Sanger sequencing to confirm and further specify its alteration.
To determine the assay sensitivity, plasmid DNA of mutated SPOP was mixed with plasmid DNA of wild-type SPOP in different ratios. This allows us to investigate the sensitivity in the most accurate manner and leads to the highest possible sensitivity under best conditions.
DNA Capture, Library Preparations, and MiSeq Runs
By using the Illumina DesignStudio application (http://www.illumina.com/applications/designstudio.ilmn), we designed a customized TrueSeq amplicon kit to cover all exons of SPOP (∼3.5 kb). Forty-six amplicons with an average size of 174nt (±6) were designed. All samples were run on one lane of MiSeq that generated 13 Mb paired end reads (2 x 250 bp) for a total of ∼6.5 Gb.
The library preparation was performed as suggested by the manufacturer (http://supportres.illumina.com/documents/myillumina/b718c350-b3b2-4234-b71a-0b832f14cda3/truseq_custom_amplicon_libraryprep_ug_15027983_b.pdf). We considered samples passing quality control (QC) if they had more than 250 ng in 5 µl, quantified by Qubit, and a minimal fragment size of 45 bp, determined by Bioanalyzer.
Acknowledgments
We are grateful to patients and families who contributed to this study, their doctors, and all the contributing centers, which made it possible. We thank R. Kim and R. Leung for their critical contributions to the Weill Cornell Prostate Cancer Tumor Bank, S. Dettwiler for assistance with the USZ cohort, and A. Romanel for his contribution to the computational analysis of the sequencing data.
Abbreviations
- AA
African American cohort from New York-Presbyterian Hospital
- BCR
biochemical recurrence
- FFPE
formalin fixation of tissue followed by paraffin embedding
- HRM
high-resolution melting
- MSKCC
Memorial Sloan-Kettering Cancer Center
- PSA
prostate-specific antigen
- SWWC
Swedish watchful waiting cohort
- SPOPwt
SPOP wild type
- SPOPmut
SPOP mutant
- TURP
transurethral resection of the prostate
- USZ
University Hospital of Zurich
- WCMC
Weill Cornell Medical College
Footnotes
This project was supported by the National Cancer Institute (1RO1CA125612 and 5UO1CA11275) as well as the Prostate Cancer Foundation. P.J.W. is supported by a SystemsX.ch grant (PhosphoNet-PPM). C.E.B. is supported by a Prostate Cancer Foundation Young Investigator Award and a Urology Care Foundation Research Scholar Award. A patent has been issued to Weill Medical College of Cornell University on SPOP mutations in prostate cancer; C.E.B. and M.A.R. are listed as co-inventors.
This article refers to supplementary materials, which are designated by Table W1 and Figure W1 and are available online at www.neoplasia.com.
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