Emilie Lalonde1, Rached Alkallas2, Melvin Lee Kiang Chua3, Michael Fraser4, Syed Haider2, Alice Meng4, Junyan Zheng4, Cindy Q Yao2, Valerie Picard5, Michele Orain5, Helène Hovington5, Jure Murgic3, Alejandro Berlin3, Louis Lacombe5, Alain Bergeron5, Yves Fradet5, Bernard Têtu5, Johan Lindberg6, Lars Egevad7, Henrik Grönberg6, Helen Ross-Adams8, Alastair D Lamb9, Silvia Halim8, Mark J Dunning8, David E Neal10, Melania Pintilie11, Theodorus van der Kwast12, Robert G Bristow13, Paul C Boutros14. 1. Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada. 2. Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, ON, Canada. 3. Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, ON, Canada. 4. Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada. 5. Centre de recherche du CHU de Québec-Université Laval, Québec City, QC, Canada. 6. Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden. 7. Department of Oncology and Pathology, Karolinska Institute, Stockholm, Sweden. 8. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK. 9. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Urology, Addenbrooke's Hospital, Cambridge, UK; Academic Urology Group, University of Cambridge, Cambridge, UK. 10. Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Urology, Addenbrooke's Hospital, Cambridge, UK. 11. Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada. 12. Department of Pathology and Laboratory Medicine, Toronto General Hospital/University Health Network, Toronto, ON, Canada. 13. Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, ON, Canada; Princess Margaret Cancer Center, University Health Network, Toronto, ON, Canada. Electronic address: Rob.Bristow@rmp.uhn.on.ca. 14. Informatics and Biocomputing Program, Ontario Institute for Cancer Research, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada. Electronic address: Paul.Boutros@oicr.on.ca.
Abstract
BACKGROUND: Localized prostate cancer is clinically heterogeneous, despite clinical risk groups that represent relative prostate cancer-specific mortality. We previously developed a 100-locus DNA classifier capable of substratifying patients at risk of biochemical relapse within clinical risk groups. OBJECTIVE: The 100-locus genomic classifier was refined to 31 functional loci and tested with standard clinical variables for the ability to predict biochemical recurrence (BCR) and metastasis. DESIGN, SETTING, AND PARTICIPANTS: Four retrospective cohorts of radical prostatectomy specimens from patients with localized disease were pooled, and an additional 102-patient cohort used to measure the 31-locus genomic classifier with the NanoString platform. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The genomic classifier scores were tested for their ability to predict BCR (n=563) and metastasis (n=154), and compared with clinical risk stratification schemes. RESULTS AND LIMITATIONS: The 31-locus genomic classifier performs similarly to the 100-locus classifier. It identifies patients with elevated BCR rates (hazard ratio=2.73, p<0.001) and patients that eventually develop metastasis (hazard ratio=7.79, p<0.001). Combining the genomic classifier with standard clinical variables outperforms clinical models. Finally, the 31-locus genomic classifier was implemented using a NanoString assay. The study is limited to retrospective cohorts. CONCLUSIONS: The 100-locus and 31-locus genomic classifiers reliably identify a cohort of men with localized disease who have an elevated risk of failure. The NanoString assay will be useful for selecting patients for treatment deescalation or escalation in prospective clinical trials based on clinico-genomic scores from pretreatment biopsies. PATIENT SUMMARY: It is challenging to determine whether tumors confined to the prostate are aggressive, leading to significant undertreatment and overtreatment. We validated a test based on prostate tumor DNA that improves estimations of relapse risk, and that can help guide treatment planning.
BACKGROUND: Localized prostate cancer is clinically heterogeneous, despite clinical risk groups that represent relative prostate cancer-specific mortality. We previously developed a 100-locus DNA classifier capable of substratifying patients at risk of biochemical relapse within clinical risk groups. OBJECTIVE: The 100-locus genomic classifier was refined to 31 functional loci and tested with standard clinical variables for the ability to predict biochemical recurrence (BCR) and metastasis. DESIGN, SETTING, AND PARTICIPANTS: Four retrospective cohorts of radical prostatectomy specimens from patients with localized disease were pooled, and an additional 102-patient cohort used to measure the 31-locus genomic classifier with the NanoString platform. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The genomic classifier scores were tested for their ability to predict BCR (n=563) and metastasis (n=154), and compared with clinical risk stratification schemes. RESULTS AND LIMITATIONS: The 31-locus genomic classifier performs similarly to the 100-locus classifier. It identifies patients with elevated BCR rates (hazard ratio=2.73, p<0.001) and patients that eventually develop metastasis (hazard ratio=7.79, p<0.001). Combining the genomic classifier with standard clinical variables outperforms clinical models. Finally, the 31-locus genomic classifier was implemented using a NanoString assay. The study is limited to retrospective cohorts. CONCLUSIONS: The 100-locus and 31-locus genomic classifiers reliably identify a cohort of men with localized disease who have an elevated risk of failure. The NanoString assay will be useful for selecting patients for treatment deescalation or escalation in prospective clinical trials based on clinico-genomic scores from pretreatment biopsies. PATIENT SUMMARY: It is challenging to determine whether tumors confined to the prostate are aggressive, leading to significant undertreatment and overtreatment. We validated a test based on prostate tumor DNA that improves estimations of relapse risk, and that can help guide treatment planning.
Authors: Dorota H Sendorek; Emilie Lalonde; Cindy Q Yao; Veronica Y Sabelnykova; Robert G Bristow; Paul C Boutros Journal: Bioinformatics Date: 2018-03-15 Impact factor: 6.937
Authors: Amanda Khoo; Lydia Y Liu; Julius O Nyalwidhe; O John Semmes; Danny Vesprini; Michelle R Downes; Paul C Boutros; Stanley K Liu; Thomas Kislinger Journal: Nat Rev Urol Date: 2021-08-27 Impact factor: 14.432
Authors: Philip Sutera; Matthew P Deek; Kim Van der Eecken; Alexander W Wyatt; Amar U Kishan; Jason K Molitoris; Matthew J Ferris; M Minhaj Siddiqui; Zaker Rana; Mark V Mishra; Young Kwok; Elai Davicioni; Daniel E Spratt; Piet Ost; Felix Y Feng; Phuoc T Tran Journal: Prostate Date: 2022-08 Impact factor: 4.012
Authors: Howard B Lieberman; Alex J Rai; Richard A Friedman; Kevin M Hopkins; Constantinos G Broustas Journal: Transl Cancer Res Date: 2018-01-14 Impact factor: 1.241
Authors: Lingjian Yang; Darren Roberts; Mandeep Takhar; Nicholas Erho; Becky A S Bibby; Niluja Thiruthaneeswaran; Vinayak Bhandari; Wei-Chen Cheng; Syed Haider; Amy M B McCorry; Darragh McArt; Suneil Jain; Mohammed Alshalalfa; Ashley Ross; Edward Schaffer; Robert B Den; R Jeffrey Karnes; Eric Klein; Peter J Hoskin; Stephen J Freedland; Alastair D Lamb; David E Neal; Francesca M Buffa; Robert G Bristow; Paul C Boutros; Elai Davicioni; Ananya Choudhury; Catharine M L West Journal: EBioMedicine Date: 2018-04-23 Impact factor: 8.143