Fan Mo1, Dong Lin2, Mandeep Takhar3, Varune Rohan Ramnarine1, Xin Dong4, Robert H Bell1, Stanislav V Volik1, Kendric Wang1, Hui Xue4, Yuwei Wang4, Anne Haegert1, Shawn Anderson1, Sonal Brahmbhatt1, Nicholas Erho3, Xinya Wang1, Peter W Gout4, James Morris5, R Jeffrey Karnes6, Robert B Den7, Eric A Klein8, Edward M Schaeffer9, Ashley Ross10, Shancheng Ren11, S Cenk Sahinalp12, Yingrui Li13, Xun Xu13, Jun Wang13, Jian Wang13, Martin E Gleave1, Elai Davicioni3, Yinghao Sun11, Yuzhuo Wang14, Colin C Collins15. 1. Vancouver Prostate Centre & Laboratory for Advanced Genome Analysis, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada. 2. Vancouver Prostate Centre & Laboratory for Advanced Genome Analysis, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada; Department of Experimental Therapeutics, BC Cancer Agency, Vancouver, BC, Canada. 3. Research and Development, GenomeDx Biosciences, Vancouver, BC, Canada. 4. Department of Experimental Therapeutics, BC Cancer Agency, Vancouver, BC, Canada. 5. Department of Radiation Oncology, BC Cancer Agency, Vancouver, BC, Canada. 6. Department of Urology, Mayo Clinic College of Medicine, Rochester, MN, USA. 7. Department of Radiation Oncology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA, USA. 8. Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA. 9. Department of Urology, James Buchanan Brady Urological Institute, Department of Oncology, Johns Hopkins Hospital, Baltimore, MD, USA; Department of Urology, Northwestern University School of Medicine, Chicago, IL, USA. 10. Department of Urology, James Buchanan Brady Urological Institute, Department of Oncology, Johns Hopkins Hospital, Baltimore, MD, USA. 11. Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, China. 12. School of Computing Sciences, Simon Fraser University, Burnaby, BC, Canada; School of Informatics and Computing, Indiana University, Bloomington, IN, USA. 13. BGI-Shenzhen, Shenzhen, China. 14. Vancouver Prostate Centre & Laboratory for Advanced Genome Analysis, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada; Department of Experimental Therapeutics, BC Cancer Agency, Vancouver, BC, Canada. Electronic address: ywang@bccrc.ca. 15. Vancouver Prostate Centre & Laboratory for Advanced Genome Analysis, Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada; School of Computing Sciences, Simon Fraser University, Burnaby, BC, Canada. Electronic address: ccollins@prostatecentre.com.
Abstract
BACKGROUND: Clinical grading systems using clinical features alongside nomograms lack precision in guiding treatment decisions in prostate cancer (PCa). There is a critical need for identification of biomarkers that can more accurately stratify patients with primary PCa. OBJECTIVE: To identify a robust prognostic signature to better distinguish indolent from aggressive prostate cancer (PCa). DESIGN, SETTING, AND PARTICIPANTS: To develop the signature, whole-genome and whole-transcriptome sequencing was conducted on five PCa patient-derived xenograft (PDX) models collected from independent foci of a single primary tumor and exhibiting variable metastatic phenotypes. Multiple independent clinical cohorts including an intermediate-risk cohort were used to validate the biomarkers. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The outcome measurement defining aggressive PCa was metastasis following radical prostatectomy. A generalized linear model with lasso regularization was used to build a 93-gene stroma-derived metastasis signature (SDMS). The SDMS association with metastasis was assessed using a Wilcoxon rank-sum test. Performance was evaluated using the area under the curve (AUC) for the receiver operating characteristic, and Kaplan-Meier curves. Univariable and multivariable regression models were used to compare the SDMS alongside clinicopathological variables and reported signatures. AUC was assessed to determine if SDMS is additive or synergistic to previously reported signatures. RESULTS AND LIMITATIONS: A close association between stromal gene expression and metastatic phenotype was observed. Accordingly, the SDMS was modeled and validated in multiple independent clinical cohorts. Patients with higher SDMS scores were found to have worse prognosis. Furthermore, SDMS was an independent prognostic factor, can stratify risk in intermediate-risk PCa, and can improve the performance of other previously reported signatures. CONCLUSIONS: Profiling of stromal gene expression led to development of an SDMS that was validated as independently prognostic for the metastatic potential of prostate tumors. PATIENT SUMMARY: Our stroma-derived metastasis signature can predict the metastatic potential of early stage disease and will strengthen decisions regarding selection of active surveillance versus surgery and/or radiation therapy for prostate cancer patients. Furthermore, profiling of stroma cells should be more consistent than profiling of diverse cellular populations of heterogeneous tumors.
BACKGROUND: Clinical grading systems using clinical features alongside nomograms lack precision in guiding treatment decisions in prostate cancer (PCa). There is a critical need for identification of biomarkers that can more accurately stratify patients with primary PCa. OBJECTIVE: To identify a robust prognostic signature to better distinguish indolent from aggressive prostate cancer (PCa). DESIGN, SETTING, AND PARTICIPANTS: To develop the signature, whole-genome and whole-transcriptome sequencing was conducted on five PCa patient-derived xenograft (PDX) models collected from independent foci of a single primary tumor and exhibiting variable metastatic phenotypes. Multiple independent clinical cohorts including an intermediate-risk cohort were used to validate the biomarkers. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The outcome measurement defining aggressive PCa was metastasis following radical prostatectomy. A generalized linear model with lasso regularization was used to build a 93-gene stroma-derived metastasis signature (SDMS). The SDMS association with metastasis was assessed using a Wilcoxon rank-sum test. Performance was evaluated using the area under the curve (AUC) for the receiver operating characteristic, and Kaplan-Meier curves. Univariable and multivariable regression models were used to compare the SDMS alongside clinicopathological variables and reported signatures. AUC was assessed to determine if SDMS is additive or synergistic to previously reported signatures. RESULTS AND LIMITATIONS: A close association between stromal gene expression and metastatic phenotype was observed. Accordingly, the SDMS was modeled and validated in multiple independent clinical cohorts. Patients with higher SDMS scores were found to have worse prognosis. Furthermore, SDMS was an independent prognostic factor, can stratify risk in intermediate-risk PCa, and can improve the performance of other previously reported signatures. CONCLUSIONS: Profiling of stromal gene expression led to development of an SDMS that was validated as independently prognostic for the metastatic potential of prostate tumors. PATIENT SUMMARY: Our stroma-derived metastasis signature can predict the metastatic potential of early stage disease and will strengthen decisions regarding selection of active surveillance versus surgery and/or radiation therapy for prostate cancerpatients. Furthermore, profiling of stroma cells should be more consistent than profiling of diverse cellular populations of heterogeneous tumors.
Authors: Mitchell G Lawrence; Daisuke Obinata; Shahneen Sandhu; Luke A Selth; Stephen Q Wong; Laura H Porter; Natalie Lister; David Pook; Carmel J Pezaro; David L Goode; Richard J Rebello; Ashlee K Clark; Melissa Papargiris; Jenna Van Gramberg; Adrienne R Hanson; Patricia Banks; Hong Wang; Birunthi Niranjan; Shivakumar Keerthikumar; Shelley Hedwards; Alisee Huglo; Rendong Yang; Christine Henzler; Yingming Li; Fernando Lopez-Campos; Elena Castro; Roxanne Toivanen; Arun Azad; Damien Bolton; Jeremy Goad; Jeremy Grummet; Laurence Harewood; John Kourambas; Nathan Lawrentschuk; Daniel Moon; Declan G Murphy; Shomik Sengupta; Ross Snow; Heather Thorne; Catherine Mitchell; John Pedersen; David Clouston; Sam Norden; Andrew Ryan; Scott M Dehm; Wayne D Tilley; Richard B Pearson; Ross D Hannan; Mark Frydenberg; Luc Furic; Renea A Taylor; Gail P Risbridger Journal: Eur Urol Date: 2018-07-23 Impact factor: 20.096
Authors: Xin Dong; Hui Xue; Fan Mo; Yen-Yi Lin; Dong Lin; Nelson K Y Wong; Yingqiang Sun; Scott Wilkinson; Anson T Ku; Jun Hao; Xinpei Ci; Rebecca Wu; Anne Haegert; Rebecca Silver; Mary-Ellen Taplin; Steven P Balk; Joshi J Alumkal; Adam G Sowalsky; Martin Gleave; Colin Collins; Yuzhuo Wang Journal: Mol Cancer Res Date: 2022-05-04 Impact factor: 6.333
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Authors: Bogdan-Alexandru Luca; Vincent Moulton; Christopher Ellis; Shea P Connell; Daniel S Brewer; Colin S Cooper Journal: Genes (Basel) Date: 2020-07-16 Impact factor: 4.096
Authors: Tobias Lange; Su Jung Oh-Hohenhorst; Simon A Joosse; Klaus Pantel; Oliver Hahn; Tobias Gosau; Sergey A Dyshlovoy; Jasmin Wellbrock; Susanne Feldhaus; Hanna Maar; Renate Gehrcke; Martina Kluth; Ronald Simon; Thorsten Schlomm; Hartwig Huland; Udo Schumacher Journal: Sci Rep Date: 2018-12-03 Impact factor: 4.379
Authors: Haonan Lu; Mubarik Arshad; Andrew Thornton; Giacomo Avesani; Paula Cunnea; Ed Curry; Fahdi Kanavati; Jack Liang; Katherine Nixon; Sophie T Williams; Mona Ali Hassan; David D L Bowtell; Hani Gabra; Christina Fotopoulou; Andrea Rockall; Eric O Aboagye Journal: Nat Commun Date: 2019-02-15 Impact factor: 14.919
Authors: Varune Rohan Ramnarine; Mohammed Alshalalfa; Fan Mo; Noushin Nabavi; Nicholas Erho; Mandeep Takhar; Robert Shukin; Sonal Brahmbhatt; Alexander Gawronski; Maxim Kobelev; Mannan Nouri; Dong Lin; Harrison Tsai; Tamara L Lotan; R Jefferey Karnes; Mark A Rubin; Amina Zoubeidi; Martin E Gleave; Cenk Sahinalp; Alexander W Wyatt; Stanislav V Volik; Himisha Beltran; Elai Davicioni; Yuzhuo Wang; Colin C Collins Journal: Gigascience Date: 2018-06-01 Impact factor: 6.524