Aditya Dutta1, Sukanya Panja2, Renu K Virk3, Jaime Yeji Kim4, Roseann Zott5, Serge Cremers6, David M Golombos7, Deli Liu8, Juan Miguel Mosquera9, Elahe A Mostaghel10, Christopher E Barbieri11, Antonina Mitrofanova12, Cory Abate-Shen13. 1. Departments of Medicine and Urology, Institute of Cancer Genetics, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA. 2. Department of Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, Newark, NJ, USA. 3. Department of Pathology and Cell Biology, Columbia University Medical Center, NY, USA. 4. Department of Medicine, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA. 5. The Irving Institute for Clinical and Translational Medicine, Columbia University Medical Center, New York, NY, USA. 6. Departments of Pathology & Cell Biology and Medicine, The Irving Institute for Clinical and Translational Medicine, Columbia University Medical Center, New York, NY, USA. 7. Department of Urology, Weill Cornell Medicine, New York, NY, USA. 8. Department of Urology, HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medical College, New York, New York, USA. 9. Department of Pathology and Laboratory Medicine, Englander Institute for Precision Medicine, Weill Cornell Medicine and New York-Presbyterian Hospital, New York, NY, USA. 10. Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. 11. Department of Urology, Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA. 12. Department of Health Informatics, Rutgers School of Health Professions, Rutgers Biomedical and Health Sciences, Newark, NJ, USA. Electronic address: antonina.mitrofanova@rutgers.edu. 13. Departments of Urology, Medicine, Pathology & Cell Biology, and Systems Biology, Institute of Cancer Genetics, Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, NY, USA. Electronic address: cabateshen@columbia.edu.
Abstract
BACKGROUND: Although men on active surveillance for prostate cancer (PCa) may benefit from intervention with 5α-reductase inhibitors (5-ARIs), it has not been resolved whether 5-ARIs are effective for delaying disease progression and, if so, whether specific patients are more likely to benefit. OBJECTIVE: To identify molecular features predictive of patient response to 5-ARIs. DESIGN, SETTING, AND PARTICIPANTS: Nkx3.1 mutant mice, a model of early-stage PCa, were treated with the 5-ARI finasteride, and histopathological and molecular analyses were performed. Cross-species computational analyses were used to compare expression profiles for treated mice with those of patients who had received 5-ARIs before prostatectomy. INTERVENTION: Finasteride administered to Nkx3.1 mutant mice. 5-ARI-treated patient specimens obtained retrospectively. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Endpoints in mice included histopathology, immunohistochemistry, and molecular profiling. GraphPad Prism software, R-studio, and Matlab were used for statistical and data analyses. RESULTS AND LIMITATIONS: Finasteride treatment of Nkx3.1 mutant mice resulted in a significant reduction in prostatic intraepithelial neoplasia (PIN), as evident from histopathological and expression profiling analyses. Cross-species computational analysis comparing finasteride-treated mice with two independent 5-ARI-treated patient cohorts showed that reduced NKX3.1 expression is predictive of response to 5-ARI. A limitation of the study is that these retrospective human cohorts have relatively few patients with limited clinical outcome data. Future prospective clinical trials are needed to validate whether stratifying patients on the basis of NKX3.1 expression improves the benefit of 5-ARIs during active surveillance. CONCLUSIONS: This co-clinical study implicates NKX3.1 status as a predictor of response to 5-ARIs, and suggests that molecular features, including NKX3.1 expression, may help to identify PCa patients most likely to benefit from 5-ARIs during active surveillance. PATIENT SUMMARY: The aim of precision cancer prevention is to tailor interventions on the basis of individualized patient characteristics. We propose that patients with low NKX3.1 expression are optimal candidates for intervention with 5α-reductase inhibitors as an adjunct to active surveillance.
BACKGROUND: Although men on active surveillance for prostate cancer (PCa) may benefit from intervention with 5α-reductase inhibitors (5-ARIs), it has not been resolved whether 5-ARIs are effective for delaying disease progression and, if so, whether specific patients are more likely to benefit. OBJECTIVE: To identify molecular features predictive of patient response to 5-ARIs. DESIGN, SETTING, AND PARTICIPANTS: Nkx3.1 mutant mice, a model of early-stage PCa, were treated with the 5-ARIfinasteride, and histopathological and molecular analyses were performed. Cross-species computational analyses were used to compare expression profiles for treated mice with those of patients who had received 5-ARIs before prostatectomy. INTERVENTION: Finasteride administered to Nkx3.1 mutant mice. 5-ARI-treated patient specimens obtained retrospectively. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Endpoints in mice included histopathology, immunohistochemistry, and molecular profiling. GraphPad Prism software, R-studio, and Matlab were used for statistical and data analyses. RESULTS AND LIMITATIONS: Finasteride treatment of Nkx3.1 mutant mice resulted in a significant reduction in prostatic intraepithelial neoplasia (PIN), as evident from histopathological and expression profiling analyses. Cross-species computational analysis comparing finasteride-treated mice with two independent 5-ARI-treated patient cohorts showed that reduced NKX3.1 expression is predictive of response to 5-ARI. A limitation of the study is that these retrospective human cohorts have relatively few patients with limited clinical outcome data. Future prospective clinical trials are needed to validate whether stratifying patients on the basis of NKX3.1 expression improves the benefit of 5-ARIs during active surveillance. CONCLUSIONS: This co-clinical study implicates NKX3.1 status as a predictor of response to 5-ARIs, and suggests that molecular features, including NKX3.1 expression, may help to identify PCa patients most likely to benefit from 5-ARIs during active surveillance. PATIENT SUMMARY: The aim of precision cancer prevention is to tailor interventions on the basis of individualized patient characteristics. We propose that patients with low NKX3.1 expression are optimal candidates for intervention with 5α-reductase inhibitors as an adjunct to active surveillance.
Keywords:
5α-Reductase inhibitors; Active surveillance; Chemoprevention; Dutasteride; Finasteride; NKX3.1; Precision cancer prevention; Prostate cancer
Authors: Neil E Fleshner; M Scott Lucia; Blair Egerdie; Lorne Aaron; Gregg Eure; Indrani Nandy; Libby Black; Roger S Rittmaster Journal: Lancet Date: 2012-01-24 Impact factor: 79.321
Authors: Gerald L Andriole; David G Bostwick; Otis W Brawley; Leonard G Gomella; Michael Marberger; Francesco Montorsi; Curtis A Pettaway; Teuvo L Tammela; Claudio Teloken; Donald J Tindall; Matthew C Somerville; Timothy H Wilson; Ivy L Fowler; Roger S Rittmaster Journal: N Engl J Med Date: 2010-04-01 Impact factor: 91.245
Authors: Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov Journal: Proc Natl Acad Sci U S A Date: 2005-09-30 Impact factor: 11.205
Authors: Minjung J Kim; Robert D Cardiff; Nishita Desai; Whitney A Banach-Petrosky; Ramon Parsons; Michael M Shen; Cory Abate-Shen Journal: Proc Natl Acad Sci U S A Date: 2002-02-19 Impact factor: 11.205
Authors: C Bowen; L Bubendorf; H J Voeller; R Slack; N Willi; G Sauter; T C Gasser; P Koivisto; E E Lack; J Kononen; O P Kallioniemi; E P Gelmann Journal: Cancer Res Date: 2000-11-01 Impact factor: 12.701
Authors: Ian M Thompson; Phyllis J Goodman; Catherine M Tangen; Howard L Parnes; Lori M Minasian; Paul A Godley; M Scott Lucia; Leslie G Ford Journal: N Engl J Med Date: 2013-08-15 Impact factor: 91.245
Authors: Alexandros Papachristodoulou; Aditya Dutta; Antonio Rodriguez-Calero; Sukanya Panja; Elizabeth Margolskee; Renu K Virk; Teresa A Milner; Luis Pina Martina; Jaime Y Kim; Matteo Di Bernardo; Alanna B Williams; Elvis A Maliza; Joseph M Caputo; Christopher Haas; Vinson Wang; Guarionex Joel De Castro; Sven Wenske; Hanina Hibshoosh; James M McKiernan; Michael M Shen; Mark A Rubin; Antonina Mitrofanova; Cory Abate-Shen Journal: Cancer Discov Date: 2021-04-23 Impact factor: 39.397
Authors: Clémentine Le Magnen; Renu K Virk; Aditya Dutta; Jaime Yeji Kim; Sukanya Panja; Zoila A Lopez-Bujanda; Andrea Califano; Charles G Drake; Antonina Mitrofanova; Cory Abate-Shen Journal: Dis Model Mech Date: 2018-11-16 Impact factor: 5.758