- Email: [email protected]
Prognostic value of the SPOP mutant genomic subclass in prostate cancer
ARTICLE IN PRESS
Urologic Oncology: Seminars and Original Investigations 000 (2020) 1−5
Laboratory-Prostate cancer
Prognostic value of the SPOP mutant genomic subclass in prostate cancer Jonathan Shoag, M.D.a, Deli Liu, Ph.D.a,b, Xiaoyue Ma, M.S.c, Clara Oromendia, M.S.c, Paul Christos, Ph.D.c, Karla Ballman, Ph.D.c, Cynthia Angulo, B.A.a, Peter Y Cai, M.D.a, Christopher Gaffney, M.D.a, Eric Klein, M.D.d, Jeffrey Karnes, M.D.e, Robert B. Den, M.D.f, Yang Liu, Ph.D.g, Elai Davicioni, Ph.D.g, Christopher E. Barbieri, M.D., Ph.D.a,h,* a
Department of Urology, New York Presbyterian Hospital, Joan and Sanford I. Weill Medical College of Cornell University, New York, NY b Institute for Computational Biomedicine, Joan and Sanford I. Weill Medical College of Cornell University, New York, NY c Department of Healthcare Policy and Research, Joan and Sanford I. Weill Medical College of Cornell University, New York, NY d Department of Urology, Glickman Urology and Kidney Institute, Cleveland Clinic, Cleveland, OH e Department of Urology, Mayo Clinic, Rochester, MN f Department of Radiation Oncology, Bodine Center for Cancer Treatment, Thomas Jefferson University Hospital, Philadelphia, PA g GenomeDx Inc., Vancouver, BC h Sandra and Edward Meyer Cancer Center, Joan and Sanford I. Weill Medical College of Cornell University, New York, NY Received 7 August 2019; received in revised form 7 February 2020; accepted 9 February 2020
Abstract Background: Speckle-type POZ protein (SPOP) mutation defines one of the dominant prostate cancer genomic subtypes, yet the impact of this mutation on clinical prognosis is unknown. Methods: We defined SPOP mutation status either by DNA sequencing or by transcriptional signature in a pooled retrospective multiinstitutional cohort, the Decipher retrospective cohort, the Decipher Genomics Resource Information Database prospective cohort, and The Cancer Genome Atlas. Kaplan-Meier survival analysis and multivariable Cox models were used to assess the independent impact of SPOP mutation on survival, biochemical recurrence and time to metastasis. The Decipher retrospective cohort was also used to assess the impact of the addition of SPOP mutation status to a model predicting adverse pathology at prostatectomy which was then validated in the Decipher prospective cohort. Results: A fixed-effect model incorporating results from multivariable Cox regression including 5,811 subjects demonstrated that SPOP mutation was associated with a lower rate of adverse pathology at radical prostatectomy (odds ratios 0.57, 95% confidence interval 0.34−0.93), independent of preoperative prostate-specific antigen, age, and pathologic Gleason score. SPOP was not associated with biochemical recurrence, metastasis-free survival, or cancer-specific survival independent of pathologic information. The addition of SPOP status to prognostic models reclassified a large proportion of patients with the mutation (55%) into a favorable risk group when used to predict adverse pathology. Conclusion: While the clinical utility of delineating any single molecular alteration in prostate cancer remains unclear, these results illustrates the importance of genomic subtypes in prostate cancer behavior and potential role in prognostic tools. Ó 2020 Elsevier Inc. All rights reserved.
Keywords: Prostate cancer; SPOP; Genomic subtype; Prognosis
1. Introduction Funding: JS was supported by the The Frederick J. and Theresa Wallace Foundation of the New York Community Trust. *Corresponding author. Tel.: 646-962-6295. E-mail address: [email protected] (C.E. Barbieri). https://doi.org/10.1016/j.urolonc.2020.02.011 1078-1439/Ó 2020 Elsevier Inc. All rights reserved.
Research over the past two decades has divided prostate cancer into distinct subclasses identifiable by underlying genomic alterations: those defined by erythroblast transformation-
ARTICLE IN PRESS 2
J. Shoag et al. / Urologic Oncology: Seminars and Original Investigations 00 (2020) 1−5
specific (ETS) and ETS-related gene (ERG) rearrangements (»50%), Speckle-type POZ protein (SPOP) mutation (»10%), as well as other less common classes. Assessing the clinical significance of these subclasses has been a challenge, with most of the effort focused on the ERG subclass showing mixed results as to the relevance of subclass to clinical behavior [1]. The large comparator group against which to assess prognostic impact, and clear distinction based on genomic, transcriptomic, and methylation profiles, from other classes [2], allows SPOP mutant cancers to serve as a litmus test for defining the relationship between clinical findings and genomic subclass. We previously reported the development of a computational tool to identify the SPOP mutant subclass from transcriptional data, and reported univariate associations of SPOP mutation with higher preoperative prostate-specific antigen (PSA) level and several clinical outcomes [3]. Here, we use diverse data sources to characterize the independent impact of SPOP mutation in the context of known prognostic risk factors with the goal of understanding how clinical behavior relates to genomic subclass.
2. Materials and methods To delineate cancers with SPOP mutation, a transcriptional signature was derived from The Cancer Genome Atlas (TCGA) on the basis of a support vector machine model, with validation finding the classifier had 89% sensitivity and 95% specificity for SPOP mutation prediction in transcriptional data [3]. Datasets from a multi-institutional cohort we previously published [4], TCGA, and the Decipher Genomics Resource Information Database (GRID) prospective cohort contained information on adverse pathology only. Briefly, the multi-institutional cohort of 281 subjects was comprised of a subset of cancers which had been profiled that also contained information on preoperative PSA, and
demographic and pathologic parameters. Specifically, we used the African American cohort from New York-Presbyterian Hospital (n = 65), the Kyungpook National University School of Medicine cohort (n = 87), Memorial Sloan Kettering Cancer Center (n = 73) cohort, and Weill Cornell Medical College cohort (n = 56). The Decipher retrospective cohort [5] additionally contained information on biochemical recurrence, the development of metastases, and prostate cancer-specific mortality. Our primary outcome was the presence of adverse pathologic features, defined by lymph node positivity, extracapsular extension, seminal vesicle invasion, or positive surgical margins. Secondary outcomes included prostate cancer-specific survival, biochemical recurrence, and time from diagnosis to development of metastasis. Listwise deletion was performed to handle missing data. The chi-square test was used to compare proportions, and 2-sample t test/Wilcoxon rank-sum test were used for continuous parameters. KaplanMeier survival analysis was used to assess prostate cancerspecific survival time, biochemical recurrence, and time to metastasis from time of diagnosis. The association between SPOP mutation and survival was evaluated by multivariable Cox models. Multivariable logistic regression was performed to evaluate the independent effect of SPOP mutation status on the presence of adverse pathologic features with metaanalysis performed using a fixed-effect model. Estimates from the Decipher retrospective cohort were used to predict adverse pathology, with the optimal cutoff chosen at 90% sensitivity. The prediction model was then tested using the Decipher prospective cohort. Analyses were performed in R version 3.4.4 and SAS Version 9.4.
3. Results Baseline characteristics of the study populations are shown in Supplemental Table 1. As seen in Fig. 1, SPOP
Fig. 1. SPOP mutation is associated with favorable pathology at prostatectomy. Multivariable model adjusted for age, pathologic Gleason score, PSA, and SPOP mutational status. Odds ratios for adverse pathology comparing SPOP mutant vs. non-SPOP mutant cancers are shown.
ARTICLE IN PRESS J. Shoag et al. / Urologic Oncology: Seminars and Original Investigations 00 (2020) 1−5
mutant cancers, as defined either by sequencing or transcriptional profiling, were associated with favorable pathologic stage in the Decipher Retrospective cohort, OR 0.64 (0.41−0.98, P = 0.035), Decipher Prospective cohort, OR 0.68 (0.50−0.93, P = 0.014), and the multi-institutional cohort, OR 0.23 (0.06−0.69, P = 0.011) independent of patient age, pathologic Gleason score, and preoperative PSA. There was a trend towards SPOP mutation being associated with favorable pathologic stage in the TCGA cohort which was not statistically significant, OR 0.54 (0.27−1.07, P = 0.076). Combining data from all cohorts, including a total of 5,811 patients using a fixed-effect model, demonstrated that SPOP mutation was associated with diminished odds of adverse pathology, OR 0.57 (95% confidence interval [CI] 0.34−0.93), P = 0.026. These models incorporated prostatectomy Gleason score, as biopsy Gleason score, which is more relevant for adverse pathology prediction, was not available. The use of prostatectomy Gleason score could be problematic if SPOP mutation is associated with differential concordance of biopsy and
3
prostatectomy Gleason scores. We therefore assessed the impact of substituting biopsy and prostatectomy Gleason score in our multivariable model in a small cohort of patients for whom genomic sequencing and both Gleason scores were available (Taylor et al, n = 131) [6]. We found that the magnitude of effect did not differ between biopsy and prostatectomy Gleason scores when incorporated into the multivariable model, OR 0.71 vs 0.70, Supplemental Table 2, although there were a small number of SPOP mutant cases (n = 17). Utilizing the Decipher retrospective cohort, which contains information on long-term oncologic outcomes, adjusted Cox proportional hazards multivariable regression model demonstrated that patients with SPOP mutations had numerically lower (though not statistically different) rates of biochemical recurrence, metastases and prostate cancer specific mortality, with hazard ratios of 0.90 (95% CI 0.69−1.16), 0.72 (95% CI 0.51−1.02) and 0.71 (95% CI 0.44−1.15), respectively, Fig. 2. When additionally controlling for prostatectomy pathologic features, SPOP mutant status was not associated with any of these end points.
Fig. 2. Kaplan Meier curves stratified by pathologic stage demonstrating the relationship between SPOP mutation and biochemical recurrence, metastasis and cancer specific mortality in the Decipher retrospective cohort.
ARTICLE IN PRESS 4
J. Shoag et al. / Urologic Oncology: Seminars and Original Investigations 00 (2020) 1−5
We assessed whether knowledge of SPOP mutation could alter adverse pathologic stage prediction. A model incorporating PSA, age, and Gleason score was compared to one incorporating these variables as well as SPOP mutation in the Decipher retrospective cohort. The addition of SPOP mutation to this model did not increase its overall predictive capacity, area under the curve (AUC) 0.600 and 0.602. As SPOP mutation affects a relatively small subset of subjects (10%), we hypothesized that in this subset, knowledge of SPOP mutational status may have a larger impact on pathologic outcome prediction. There was no cut-point that would yield 90% specificity for adverse pathology prediction in this model. We therefore studied a cut-point with 90% sensitivity for adverse pathology. The addition of SPOP mutation to the model reclassified a total of 103 (7.25%) patients in this derivation cohort (Supplemental Table 3). The small percentage of patients reclassified likely reflects the small percentage of prostate cancer patients who have SPOP mutations. Carrying the coefficients from this model forward to an independent validation dataset (Decipher prospective, N = 3,632) maintained 88.2% sensitivity and 14.9% specificity for the model without SPOP mutation status, and 91.25% sensitivity and 12.6% specificity for that with SPOP mutation status. The addition of SPOP mutation to this model reclassified a total of 369 (10.2%) subjects. In this validation cohort we examined the reclassification among only those predicted to have an SPOP mutation (N = 263), Fig. 3. Our initial model predicted adverse pathology in 237 patients and favorable pathology in 26 patients. Of those patients with true adverse pathology, 19 (9.2%) were incorrectly predicted to have favorable pathology and 187 (90.8%) were correctly predicted as having adverse pathology. Of patients with favorable pathology, 7 (12.3%) were correctly predicted as having favorable pathology while 50 (87.7%) were incorrectly predicted as having adverse pathology. After incorporating SPOP mutation status into our model, 131 of the initial 237 patients predicted to have
adverse pathology (55.3%) were reclassified into the favorable pathology group. Of those patients with favorable pathology, 44 (77.2%) were now correctly predicted and 13 (22.8%) were incorrectly predicted as having adverse pathology. This effect is expected given that SPOP mutation is associated with favorable pathology. However, the sensitivity and specificity of this model in this subgroup (45.1% and 77.2%, respectively) was inadequate for clinical application. 4. Discussion We find that SPOP mutation is independently associated with favorable pathology at prostatectomy, and that addition of SPOP mutation to existing prognostic models reclassifies a large proportion of patients with this mutation into a predicted favorable pathologic risk group. Although SPOP mutant cancers showed significantly improved pathologic outcomes, the impact on clinical outcomes (biochemical recurrence, metastasis, and prostate cancer-specific mortality) was negligible when final pathology was considered. The combination of PSA screening, changes in primary treatment, advances in treatment of advanced disease [7], and improving technology such as magnetic resonance imaging fusion biopsy which has improved our ability to detect clinically significant prostate cancer [8,9], have likely made it difficult to improve on our predictive models based on a single variable. For decades clinicians have utilized clinical nomograms (e.g., Kattan nomogram, Partin tables) [10,11] in an effort to predict outcomes with an ultimate goal of providing the best counseling and treatment options for patients. As the understanding of prostate cancer genomics has advanced, there has been development of gene-based assays, such as Decipher and Oncotype Dx, that have been validated to predict aggressive prostate cancer and development of metastasis after prostatectomy and mortality after biopsy [12−15]. Genomic characterization of prostate cancer has helped to define disease heterogeneity, improve our understanding of biology, and ultimately
Fig. 3. Effect of addition of SPOP mutation on predicted adverse pathology in prognostic models. Decipher retrospective data used as training dataset, Decipher prospective as validation.
ARTICLE IN PRESS J. Shoag et al. / Urologic Oncology: Seminars and Original Investigations 00 (2020) 1−5
may guide our treatment algorithms and development of new therapies [16]. This study is not without limitations. Several of the datasets used infer SPOP mutation status from transcriptional data rather than genomic sequencing. The Decipher cohort was deliberately enriched for subjects with adverse pathologic features, potentially limiting the generalizability of our findings. Further, inter- and intratumoral heterogeneity are common in prostate cancer, potentially limiting measures of effect size [1]. 5. Conclusion SPOP mutation, independent of other clinical factors, predicts favorable pathologic stage at prostatectomy illustrating the importance of genomic subtypes in prostate cancer behavior. However, our study also demonstrates nominal clinical utility in incorporating SPOP in a prognostic tool, especially in the setting of final prostatectomy pathology. Further studies should be done on the potential prognostic value of molecular subclass for men who undergo nonsurgical treatments such as radiation or focal therapy. Author contributions Study concept and design: JS, DL, XY, CO, CA, PC, YL, ED. Acquisition of data: JS, DL, XY, CO, CA, PC, YL, ED. Analysis and interpretation of data: JS, XM, CO, PC, KB, CA, PC, EK, JK, RD, YL, ED, CB. Drafting and critical revision of manuscript: JS, PC, KB, CA, PC, EK, JK, RD, YL, ED, CB. Statistical analysis: XM, CO, PC, KB. Administrative, technical, and material support: PC, KB, EK, JK, RD, YL, ED, CB. Conflict of interest YL and ED are employees of GenomeDx. Acknowledgment The authors would like to thank Bruce Trock for his contribution to the Decipher data. Supplementary materials Supplementary material associated with this article can be found in the online version at https://doi.org/10.1016/j. urolonc.2020.02.011.
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References [1] Shoag J, Barbieri C. Clinical variability and molecular heterogeneity in prostate cancer. Asian J Androl 2016. https://doi.org/10.4103/ 1008-682x.178852. [2] Abeshouse A, Ahn J, Akbani R, et al. The molecular taxonomy of primary prostate cancer. Cell 2015. https://doi.org/10.1016/j. cell.2015.10.025. [3] Liu D, Takhar M, Alshalalfa M, et al. Impact of the SPOP mutant subtype on the interpretation of clinical parameters in prostate cancer. JCO Precis Oncol 2018. https://doi.org/10.1200/ po.18.00036. [4] Blattner M, Lee DJ, O’Reilly C, et al. SPOP mutations in prostate cancer across demographically diverse patient cohorts. Neoplasia 2014. https://doi.org/10.1593/neo.131704. [5] Ross AE, Johnson MH, Yousefi K, et al. Tissue-based genomics augments post-prostatectomy risk stratification in a natural history cohort of intermediate- and high-risk men. Eur Urol 2016. https://doi.org/ 10.1016/j.eururo.2015.05.042. [6] Taylor BS, Schultz N, Hieronymus H, et al. Integrative genomic profiling of human prostate cancer. Cancer Cell 2010. https://doi.org/ 10.1016/j.ccr.2010.05.026. [7] Etzioni R, Gulati R, Tsodikov A, et al. The prostate cancer conundrum revisited: treatment changes and prostate cancer mortality declines. Cancer 2012. https://doi.org/10.1002/cncr.27594. [8] Kasivisvanathan V, Rannikko AS, Borghi M, et al. MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med 2018. https://doi.org/10.1056/NEJMoa1801993. [9] Siddiqui MM, Rais-Bahrami S, Turkbey B, et al. Comparison of MR/ ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer. J Am Med Assoc 2015. https://doi. org/10.1001/jama.2014.17942. [10] Kattan MW, Wheeler TM, Scardino PT. Postoperative nomogram for disease recurrence after radical prostatectomy for prostate cancer. J Clin Oncol 1999. https://doi.org/10.1200/JCO.1999.17.5.1499. [11] Eifler JB, Feng Z, Lin BM, et al. An updated prostate cancer staging nomogram (Partin tables) based on cases from 2006 to 2011. BJU Int 2013. https://doi.org/10.1111/j.1464-410X.2012.11324.x. [12] Erho N, Crisan A, Vergara IA, et al. Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy. PLoS One 2013. https://doi.org/ 10.1371/journal.pone.0066855. [13] Cullen J, Rosner IL, Brand TC, et al. A biopsy-based 17-gene genomic prostate score predicts recurrence after radical prostatectomy and adverse surgical pathology in a racially diverse population of men with clinically low- and intermediate-risk prostate cancer. Eur Urol 2015. https://doi.org/10.1016/j.euuro.2017.09.013. [14] Karnes RJ, Bergstralh EJ, Davicioni E, et al. Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population. J Urol 2013. https://doi.org/10.1016/j. juro.2013.06.017. [15] Nguyen PL, Haddad Z, Ross AE, et al. Ability of a genomic classifier to predict metastasis and prostate cancer-specific mortality after radiation or surgery based on needle biopsy specimens. Eur Urol 2017. https://doi.org/10.1016/j.eururo.2017.05.009. [16] Arora K, Barbieri CE. Molecular subtypes of prostate cancer. Curr Oncol Rep 2018. https://doi.org/10.1007/s11912-018-0707-9.
Urologic Oncology: Seminars and Original Investigations 000 (2020) 1−5
Laboratory-Prostate cancer
Prognostic value of the SPOP mutant genomic subclass in prostate cancer Jonathan Shoag, M.D.a, Deli Liu, Ph.D.a,b, Xiaoyue Ma, M.S.c, Clara Oromendia, M.S.c, Paul Christos, Ph.D.c, Karla Ballman, Ph.D.c, Cynthia Angulo, B.A.a, Peter Y Cai, M.D.a, Christopher Gaffney, M.D.a, Eric Klein, M.D.d, Jeffrey Karnes, M.D.e, Robert B. Den, M.D.f, Yang Liu, Ph.D.g, Elai Davicioni, Ph.D.g, Christopher E. Barbieri, M.D., Ph.D.a,h,* a
Department of Urology, New York Presbyterian Hospital, Joan and Sanford I. Weill Medical College of Cornell University, New York, NY b Institute for Computational Biomedicine, Joan and Sanford I. Weill Medical College of Cornell University, New York, NY c Department of Healthcare Policy and Research, Joan and Sanford I. Weill Medical College of Cornell University, New York, NY d Department of Urology, Glickman Urology and Kidney Institute, Cleveland Clinic, Cleveland, OH e Department of Urology, Mayo Clinic, Rochester, MN f Department of Radiation Oncology, Bodine Center for Cancer Treatment, Thomas Jefferson University Hospital, Philadelphia, PA g GenomeDx Inc., Vancouver, BC h Sandra and Edward Meyer Cancer Center, Joan and Sanford I. Weill Medical College of Cornell University, New York, NY Received 7 August 2019; received in revised form 7 February 2020; accepted 9 February 2020
Abstract Background: Speckle-type POZ protein (SPOP) mutation defines one of the dominant prostate cancer genomic subtypes, yet the impact of this mutation on clinical prognosis is unknown. Methods: We defined SPOP mutation status either by DNA sequencing or by transcriptional signature in a pooled retrospective multiinstitutional cohort, the Decipher retrospective cohort, the Decipher Genomics Resource Information Database prospective cohort, and The Cancer Genome Atlas. Kaplan-Meier survival analysis and multivariable Cox models were used to assess the independent impact of SPOP mutation on survival, biochemical recurrence and time to metastasis. The Decipher retrospective cohort was also used to assess the impact of the addition of SPOP mutation status to a model predicting adverse pathology at prostatectomy which was then validated in the Decipher prospective cohort. Results: A fixed-effect model incorporating results from multivariable Cox regression including 5,811 subjects demonstrated that SPOP mutation was associated with a lower rate of adverse pathology at radical prostatectomy (odds ratios 0.57, 95% confidence interval 0.34−0.93), independent of preoperative prostate-specific antigen, age, and pathologic Gleason score. SPOP was not associated with biochemical recurrence, metastasis-free survival, or cancer-specific survival independent of pathologic information. The addition of SPOP status to prognostic models reclassified a large proportion of patients with the mutation (55%) into a favorable risk group when used to predict adverse pathology. Conclusion: While the clinical utility of delineating any single molecular alteration in prostate cancer remains unclear, these results illustrates the importance of genomic subtypes in prostate cancer behavior and potential role in prognostic tools. Ó 2020 Elsevier Inc. All rights reserved.
Keywords: Prostate cancer; SPOP; Genomic subtype; Prognosis
1. Introduction Funding: JS was supported by the The Frederick J. and Theresa Wallace Foundation of the New York Community Trust. *Corresponding author. Tel.: 646-962-6295. E-mail address: [email protected] (C.E. Barbieri). https://doi.org/10.1016/j.urolonc.2020.02.011 1078-1439/Ó 2020 Elsevier Inc. All rights reserved.
Research over the past two decades has divided prostate cancer into distinct subclasses identifiable by underlying genomic alterations: those defined by erythroblast transformation-
ARTICLE IN PRESS 2
J. Shoag et al. / Urologic Oncology: Seminars and Original Investigations 00 (2020) 1−5
specific (ETS) and ETS-related gene (ERG) rearrangements (»50%), Speckle-type POZ protein (SPOP) mutation (»10%), as well as other less common classes. Assessing the clinical significance of these subclasses has been a challenge, with most of the effort focused on the ERG subclass showing mixed results as to the relevance of subclass to clinical behavior [1]. The large comparator group against which to assess prognostic impact, and clear distinction based on genomic, transcriptomic, and methylation profiles, from other classes [2], allows SPOP mutant cancers to serve as a litmus test for defining the relationship between clinical findings and genomic subclass. We previously reported the development of a computational tool to identify the SPOP mutant subclass from transcriptional data, and reported univariate associations of SPOP mutation with higher preoperative prostate-specific antigen (PSA) level and several clinical outcomes [3]. Here, we use diverse data sources to characterize the independent impact of SPOP mutation in the context of known prognostic risk factors with the goal of understanding how clinical behavior relates to genomic subclass.
2. Materials and methods To delineate cancers with SPOP mutation, a transcriptional signature was derived from The Cancer Genome Atlas (TCGA) on the basis of a support vector machine model, with validation finding the classifier had 89% sensitivity and 95% specificity for SPOP mutation prediction in transcriptional data [3]. Datasets from a multi-institutional cohort we previously published [4], TCGA, and the Decipher Genomics Resource Information Database (GRID) prospective cohort contained information on adverse pathology only. Briefly, the multi-institutional cohort of 281 subjects was comprised of a subset of cancers which had been profiled that also contained information on preoperative PSA, and
demographic and pathologic parameters. Specifically, we used the African American cohort from New York-Presbyterian Hospital (n = 65), the Kyungpook National University School of Medicine cohort (n = 87), Memorial Sloan Kettering Cancer Center (n = 73) cohort, and Weill Cornell Medical College cohort (n = 56). The Decipher retrospective cohort [5] additionally contained information on biochemical recurrence, the development of metastases, and prostate cancer-specific mortality. Our primary outcome was the presence of adverse pathologic features, defined by lymph node positivity, extracapsular extension, seminal vesicle invasion, or positive surgical margins. Secondary outcomes included prostate cancer-specific survival, biochemical recurrence, and time from diagnosis to development of metastasis. Listwise deletion was performed to handle missing data. The chi-square test was used to compare proportions, and 2-sample t test/Wilcoxon rank-sum test were used for continuous parameters. KaplanMeier survival analysis was used to assess prostate cancerspecific survival time, biochemical recurrence, and time to metastasis from time of diagnosis. The association between SPOP mutation and survival was evaluated by multivariable Cox models. Multivariable logistic regression was performed to evaluate the independent effect of SPOP mutation status on the presence of adverse pathologic features with metaanalysis performed using a fixed-effect model. Estimates from the Decipher retrospective cohort were used to predict adverse pathology, with the optimal cutoff chosen at 90% sensitivity. The prediction model was then tested using the Decipher prospective cohort. Analyses were performed in R version 3.4.4 and SAS Version 9.4.
3. Results Baseline characteristics of the study populations are shown in Supplemental Table 1. As seen in Fig. 1, SPOP
Fig. 1. SPOP mutation is associated with favorable pathology at prostatectomy. Multivariable model adjusted for age, pathologic Gleason score, PSA, and SPOP mutational status. Odds ratios for adverse pathology comparing SPOP mutant vs. non-SPOP mutant cancers are shown.
ARTICLE IN PRESS J. Shoag et al. / Urologic Oncology: Seminars and Original Investigations 00 (2020) 1−5
mutant cancers, as defined either by sequencing or transcriptional profiling, were associated with favorable pathologic stage in the Decipher Retrospective cohort, OR 0.64 (0.41−0.98, P = 0.035), Decipher Prospective cohort, OR 0.68 (0.50−0.93, P = 0.014), and the multi-institutional cohort, OR 0.23 (0.06−0.69, P = 0.011) independent of patient age, pathologic Gleason score, and preoperative PSA. There was a trend towards SPOP mutation being associated with favorable pathologic stage in the TCGA cohort which was not statistically significant, OR 0.54 (0.27−1.07, P = 0.076). Combining data from all cohorts, including a total of 5,811 patients using a fixed-effect model, demonstrated that SPOP mutation was associated with diminished odds of adverse pathology, OR 0.57 (95% confidence interval [CI] 0.34−0.93), P = 0.026. These models incorporated prostatectomy Gleason score, as biopsy Gleason score, which is more relevant for adverse pathology prediction, was not available. The use of prostatectomy Gleason score could be problematic if SPOP mutation is associated with differential concordance of biopsy and
3
prostatectomy Gleason scores. We therefore assessed the impact of substituting biopsy and prostatectomy Gleason score in our multivariable model in a small cohort of patients for whom genomic sequencing and both Gleason scores were available (Taylor et al, n = 131) [6]. We found that the magnitude of effect did not differ between biopsy and prostatectomy Gleason scores when incorporated into the multivariable model, OR 0.71 vs 0.70, Supplemental Table 2, although there were a small number of SPOP mutant cases (n = 17). Utilizing the Decipher retrospective cohort, which contains information on long-term oncologic outcomes, adjusted Cox proportional hazards multivariable regression model demonstrated that patients with SPOP mutations had numerically lower (though not statistically different) rates of biochemical recurrence, metastases and prostate cancer specific mortality, with hazard ratios of 0.90 (95% CI 0.69−1.16), 0.72 (95% CI 0.51−1.02) and 0.71 (95% CI 0.44−1.15), respectively, Fig. 2. When additionally controlling for prostatectomy pathologic features, SPOP mutant status was not associated with any of these end points.
Fig. 2. Kaplan Meier curves stratified by pathologic stage demonstrating the relationship between SPOP mutation and biochemical recurrence, metastasis and cancer specific mortality in the Decipher retrospective cohort.
ARTICLE IN PRESS 4
J. Shoag et al. / Urologic Oncology: Seminars and Original Investigations 00 (2020) 1−5
We assessed whether knowledge of SPOP mutation could alter adverse pathologic stage prediction. A model incorporating PSA, age, and Gleason score was compared to one incorporating these variables as well as SPOP mutation in the Decipher retrospective cohort. The addition of SPOP mutation to this model did not increase its overall predictive capacity, area under the curve (AUC) 0.600 and 0.602. As SPOP mutation affects a relatively small subset of subjects (10%), we hypothesized that in this subset, knowledge of SPOP mutational status may have a larger impact on pathologic outcome prediction. There was no cut-point that would yield 90% specificity for adverse pathology prediction in this model. We therefore studied a cut-point with 90% sensitivity for adverse pathology. The addition of SPOP mutation to the model reclassified a total of 103 (7.25%) patients in this derivation cohort (Supplemental Table 3). The small percentage of patients reclassified likely reflects the small percentage of prostate cancer patients who have SPOP mutations. Carrying the coefficients from this model forward to an independent validation dataset (Decipher prospective, N = 3,632) maintained 88.2% sensitivity and 14.9% specificity for the model without SPOP mutation status, and 91.25% sensitivity and 12.6% specificity for that with SPOP mutation status. The addition of SPOP mutation to this model reclassified a total of 369 (10.2%) subjects. In this validation cohort we examined the reclassification among only those predicted to have an SPOP mutation (N = 263), Fig. 3. Our initial model predicted adverse pathology in 237 patients and favorable pathology in 26 patients. Of those patients with true adverse pathology, 19 (9.2%) were incorrectly predicted to have favorable pathology and 187 (90.8%) were correctly predicted as having adverse pathology. Of patients with favorable pathology, 7 (12.3%) were correctly predicted as having favorable pathology while 50 (87.7%) were incorrectly predicted as having adverse pathology. After incorporating SPOP mutation status into our model, 131 of the initial 237 patients predicted to have
adverse pathology (55.3%) were reclassified into the favorable pathology group. Of those patients with favorable pathology, 44 (77.2%) were now correctly predicted and 13 (22.8%) were incorrectly predicted as having adverse pathology. This effect is expected given that SPOP mutation is associated with favorable pathology. However, the sensitivity and specificity of this model in this subgroup (45.1% and 77.2%, respectively) was inadequate for clinical application. 4. Discussion We find that SPOP mutation is independently associated with favorable pathology at prostatectomy, and that addition of SPOP mutation to existing prognostic models reclassifies a large proportion of patients with this mutation into a predicted favorable pathologic risk group. Although SPOP mutant cancers showed significantly improved pathologic outcomes, the impact on clinical outcomes (biochemical recurrence, metastasis, and prostate cancer-specific mortality) was negligible when final pathology was considered. The combination of PSA screening, changes in primary treatment, advances in treatment of advanced disease [7], and improving technology such as magnetic resonance imaging fusion biopsy which has improved our ability to detect clinically significant prostate cancer [8,9], have likely made it difficult to improve on our predictive models based on a single variable. For decades clinicians have utilized clinical nomograms (e.g., Kattan nomogram, Partin tables) [10,11] in an effort to predict outcomes with an ultimate goal of providing the best counseling and treatment options for patients. As the understanding of prostate cancer genomics has advanced, there has been development of gene-based assays, such as Decipher and Oncotype Dx, that have been validated to predict aggressive prostate cancer and development of metastasis after prostatectomy and mortality after biopsy [12−15]. Genomic characterization of prostate cancer has helped to define disease heterogeneity, improve our understanding of biology, and ultimately
Fig. 3. Effect of addition of SPOP mutation on predicted adverse pathology in prognostic models. Decipher retrospective data used as training dataset, Decipher prospective as validation.
ARTICLE IN PRESS J. Shoag et al. / Urologic Oncology: Seminars and Original Investigations 00 (2020) 1−5
may guide our treatment algorithms and development of new therapies [16]. This study is not without limitations. Several of the datasets used infer SPOP mutation status from transcriptional data rather than genomic sequencing. The Decipher cohort was deliberately enriched for subjects with adverse pathologic features, potentially limiting the generalizability of our findings. Further, inter- and intratumoral heterogeneity are common in prostate cancer, potentially limiting measures of effect size [1]. 5. Conclusion SPOP mutation, independent of other clinical factors, predicts favorable pathologic stage at prostatectomy illustrating the importance of genomic subtypes in prostate cancer behavior. However, our study also demonstrates nominal clinical utility in incorporating SPOP in a prognostic tool, especially in the setting of final prostatectomy pathology. Further studies should be done on the potential prognostic value of molecular subclass for men who undergo nonsurgical treatments such as radiation or focal therapy. Author contributions Study concept and design: JS, DL, XY, CO, CA, PC, YL, ED. Acquisition of data: JS, DL, XY, CO, CA, PC, YL, ED. Analysis and interpretation of data: JS, XM, CO, PC, KB, CA, PC, EK, JK, RD, YL, ED, CB. Drafting and critical revision of manuscript: JS, PC, KB, CA, PC, EK, JK, RD, YL, ED, CB. Statistical analysis: XM, CO, PC, KB. Administrative, technical, and material support: PC, KB, EK, JK, RD, YL, ED, CB. Conflict of interest YL and ED are employees of GenomeDx. Acknowledgment The authors would like to thank Bruce Trock for his contribution to the Decipher data. Supplementary materials Supplementary material associated with this article can be found in the online version at https://doi.org/10.1016/j. urolonc.2020.02.011.
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