Shuang G Zhao1, S Laura Chang1, Nicholas Erho2, Menggang Yu3, Jonathan Lehrer2, Mohammed Alshalalfa2, Corey Speers1, Matthew R Cooperberg4, Won Kim5, Charles J Ryan5, Robert B Den6, Stephen J Freedland7, Edwin Posadas8, Howard Sandler9, Eric A Klein10, Peter Black11, Roland Seiler11, Scott A Tomlins12,13, Arul M Chinnaiyan12,13,14,15, Robert B Jenkins16, Elai Davicioni2, Ashley E Ross17, Edward M Schaeffer18, Paul L Nguyen19, Peter R Carroll4, R Jeffrey Karnes16, Daniel E Spratt1, Felix Y Feng1,4,5,13,20. 1. Department of Radiation Oncology, University of Michigan, Ann Arbor 2. GenomeDx Biosciences Inc, Vancouver, British Columbia, Canada 3. Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison 4. Department of Urology, Helen Diller Comprehensive Cancer Center, University of California, San Francisco 5. Department of Medicine, Helen Diller Comprehensive Cancer Center, University of California, San Francisco 6. Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, Pennsylvania 7. Department of Urology, Cedars-Sinai Medical Center, Los Angeles, California 8. Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California 9. Department of Radiation Oncology, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California 10. Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, Ohio 11. Department of Urology, University of British Columbia, Vancouver, British Columbia, Canada 12. Department of Pathology, University of Michigan, Ann Arbor 13. Michigan Center for Translational Pathology, University of Michigan, Ann Arbor 14. Department of Urology, University of Michigan, Ann Arbor 15. Howard Hughes Medical Institute, University of Michigan, Ann Arbor 16. Department of Urology, Mayo Clinic, Rochester, Minnesota 17. James Buchanan Brady Urological Institute, Johns Hopkins Medical Institutions, Baltimore, Maryland 18. Department of Urology, Northwestern University, Chicago, Illinois 19. Department of Radiation Oncology, Dana-Farber/Brigham and Women’s Cancer Center, Harvard Medical School, Boston, Massachusetts 20. Department of Radiation Oncology, Helen Diller Comprehensive Cancer Center, University of California, San Francisco
Abstract
Importance: There is a clear need for a molecular subtyping approach in prostate cancer to identify clinically distinct subgroups that benefit from specific therapies. Objectives: To identify prostate cancer subtypes based on luminal and basal lineage and to determine associations with clinical outcomes and response to treatment. Design, Setting, and Participants: The PAM50 classifier was used to subtype 1567 retrospectively collected (median follow-up, 10 years) and 2215 prospectively collected prostate cancer samples into luminal- and basal-like subtypes. Main Outcomes and Measures: Metastasis, biochemical recurrence, overall survival, prostate cancer–specific survival, associations with biological pathways, and clinicopathologic variables were the main outcomes. Results: Among the 3782 samples, the PAM50 classifier consistently segregated prostate cancer into 3 subtypes in both the retrospective and prospective cohorts: luminal A (retrospective, 538 [34.3%]; prospective, 737 [33.3%]), luminal B (retrospective, 447 [28.5%]; prospective, 723 [32.6%]), and basal (retrospective, 582 [37.1%]; prospective, 755 [34.1%]). Known luminal lineage markers, such as NKX3.1 and KRT18, were enriched in luminal-like cancers, and the basal lineage CD49f signature was enriched in basal-like cancers, demonstrating the connection between these subtypes and established prostate cancer biology. In the retrospective cohort, luminal B prostate cancers exhibited the poorest clinical prognoses on both univariable and multivariable analyses accounting for standard clinicopathologic prognostic factors (10-year biochemical recurrence-free survival [bRFS], 29%; distant metastasis-free survival [DMFS], 53%; prostate cancer-specific survival [PCSS], 78%; overall survival [OS], 69%), followed by basal prostate cancers (10-year bRFS, 39%; DMFS, 73%; PCSS, 86%; OS, 80%) and luminal A prostate cancers (10-year bRFS, 41%; DMFS, 73%; PCSS, 89%; OS, 82%). Although both luminal-like subtypes were associated with increased androgen receptor expression and signaling, only luminal B prostate cancers were significantly associated with postoperative response to androgen deprivation therapy (ADT) in a subset analysis in our retrospective cohorts (n = 315) matching patients based on clinicopathologic variables (luminal B 10-year metastasis: treated, 33% vs untreated, 55%; nonluminal B 10-year metastasis: treated, 37% vs untreated, 21%; P = .006 for interaction). Conclusions and Relevance: Luminal- and basal-like prostate cancers demonstrate divergent clinical behavior, and patients with luminal B tumors respond better to postoperative ADT than do patients with non–luminal B tumors. These findings contribute novel insight into prostate cancer biology, providing a potential clinical tool to personalize ADT treatment for prostate cancer by predicting which men may benefit from ADT after surgery.
Importance: There is a clear need for a molecular subtyping approach in prostate cancer to identify clinically distinct subgroups that benefit from specific therapies. Objectives: To identify prostate cancer subtypes based on luminal and basal lineage and to determine associations with clinical outcomes and response to treatment. Design, Setting, and Participants: The PAM50 classifier was used to subtype 1567 retrospectively collected (median follow-up, 10 years) and 2215 prospectively collected prostate cancer samples into luminal- and basal-like subtypes. Main Outcomes and Measures: Metastasis, biochemical recurrence, overall survival, prostate cancer–specific survival, associations with biological pathways, and clinicopathologic variables were the main outcomes. Results: Among the 3782 samples, the PAM50 classifier consistently segregated prostate cancer into 3 subtypes in both the retrospective and prospective cohorts: luminal A (retrospective, 538 [34.3%]; prospective, 737 [33.3%]), luminal B (retrospective, 447 [28.5%]; prospective, 723 [32.6%]), and basal (retrospective, 582 [37.1%]; prospective, 755 [34.1%]). Known luminal lineage markers, such as NKX3.1 and KRT18, were enriched in luminal-like cancers, and the basal lineage CD49f signature was enriched in basal-like cancers, demonstrating the connection between these subtypes and established prostate cancer biology. In the retrospective cohort, luminal B prostate cancers exhibited the poorest clinical prognoses on both univariable and multivariable analyses accounting for standard clinicopathologic prognostic factors (10-year biochemical recurrence-free survival [bRFS], 29%; distant metastasis-free survival [DMFS], 53%; prostate cancer-specific survival [PCSS], 78%; overall survival [OS], 69%), followed by basal prostate cancers (10-year bRFS, 39%; DMFS, 73%; PCSS, 86%; OS, 80%) and luminal A prostate cancers (10-year bRFS, 41%; DMFS, 73%; PCSS, 89%; OS, 82%). Although both luminal-like subtypes were associated with increased androgen receptor expression and signaling, only luminal B prostate cancers were significantly associated with postoperative response to androgen deprivation therapy (ADT) in a subset analysis in our retrospective cohorts (n = 315) matching patients based on clinicopathologic variables (luminal B 10-year metastasis: treated, 33% vs untreated, 55%; nonluminal B 10-year metastasis: treated, 37% vs untreated, 21%; P = .006 for interaction). Conclusions and Relevance: Luminal- and basal-like prostate cancers demonstrate divergent clinical behavior, and patients with luminal B tumors respond better to postoperative ADT than do patients with non–luminal B tumors. These findings contribute novel insight into prostate cancer biology, providing a potential clinical tool to personalize ADT treatment for prostate cancer by predicting which men may benefit from ADT after surgery.
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