Palak G Patel1,2, Thomas Wessel3, Atsunari Kawashima1,2,4, John B A Okello1,2,5, Tamara Jamaspishvili1,2, Karl-Philippe Guérard6, Laura Lee7, Anna Ying-Wah Lee7, Nathan E How1,2, Dan Dion7, Eleonora Scarlata6, Chelsea L Jackson1,2, Suzanne Boursalie1,2, Tanya Sack1,2, Rachel Dunn1,2, Madeleine Moussa8, Karen Mackie/8, Audrey Ellis8, Elizabeth Marra8, Joseph Chin9, Khurram Siddiqui9, Khalil Hetou9, Lee-Anne Pickard10, Vinolia Arthur-Hayward10, Glenn Bauman11,12, Simone Chevalier6, Fadi Brimo13, Paul C Boutros7,14,15, Jacques Lapointe PhD6, John M S Bartlett16, Robert J Gooding2,17, David M Berman1,2. 1. Department of Pathology & Molecular Medicine, Queen's University, Kingston, Ontario, Canada. 2. Division of Cancer Biology & Genetics, Queen's Cancer Research Institute, Queen's University, Kingston, Ontario, Canada. 3. Life Sciences Group, Thermo Fisher Scientific, Waltham, Massachusetts. 4. Department of Urology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan. 5. Cardiac Genome Clinic, Ted Rogers Centre for Heart Research, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, Ontario, Canada. 6. Division of Urology, Department of Surgery, McGill University and the Research Institute of the McGill University Health Centre, Montreal, Québec, Canada. 7. Ontario Institute for Cancer Research (OICR), Toronto, Ontario, Canada. 8. Division of Surgical Pathology, Departmant of Pathology and Laboratory Medicine, London Health Sciences Centre, London, Ontario, Canada. 9. Department of Surgery (Urology), London Health Sciences Centre, London, ON, Canada. 10. Ontario Tumor Bank, Toronto, Ontario, Canada. 11. Division of Radiation Oncology, London Regional Cancer Program, London Health Sciences Centre, London, Ontario, Canada. 12. Department of Physics and Astronomy, University of Western Ontario, London, Ontario, Canada. 13. Department of Pathology, McGill University Health Center and McGill University, Montreal, Québec, Canada. 14. Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada. 15. Departments of Urology and Human Genetics, Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA. 16. Diagnostic Development, Ontario Institute for Cancer Research (OICR), Toronto, Ontario, Canada. 17. Department of Physics, Engineering Physics & Astronomy, Queen's University, Kingston, Ontario, Canada.
Abstract
BACKGROUND: We identify and validate accurate diagnostic biomarkers for prostate cancer through a systematic evaluation of DNA methylation alterations. MATERIALS AND METHODS: We assembled three early prostate cancer cohorts (total patients = 699) from which we collected and processed over 1300 prostatectomy tissue samples for DNA extraction. Using real-time methylation-specific PCR, we measured normalized methylation levels at 15 frequently methylated loci. After partitioning sample sets into independent training and validation cohorts, classifiers were developed using logistic regression, analyzed, and validated. RESULTS: In the training dataset, DNA methylation levels at 7 of 15 genomic loci (glutathione S-transferase Pi 1 [GSTP1], CCDC181, hyaluronan, and proteoglycan link protein 3 [HAPLN3], GSTM2, growth arrest-specific 6 [GAS6], RASSF1, and APC) showed large differences between cancer and benign samples. The best binary classifier was the GAS6/GSTP1/HAPLN3 logistic regression model, with an area under these curves of 0.97, which showed a sensitivity of 94%, and a specificity of 93% after external validation. CONCLUSION: We created and validated a multigene model for the classification of benign and malignant prostate tissue. With false positive and negative rates below 7%, this three-gene biomarker represents a promising basis for more accurate prostate cancer diagnosis.
BACKGROUND: We identify and validate accurate diagnostic biomarkers for prostate cancer through a systematic evaluation of DNA methylation alterations. MATERIALS AND METHODS: We assembled three early prostate cancer cohorts (total patients = 699) from which we collected and processed over 1300 prostatectomy tissue samples for DNA extraction. Using real-time methylation-specific PCR, we measured normalized methylation levels at 15 frequently methylated loci. After partitioning sample sets into independent training and validation cohorts, classifiers were developed using logistic regression, analyzed, and validated. RESULTS: In the training dataset, DNA methylation levels at 7 of 15 genomic loci (glutathione S-transferase Pi 1 [GSTP1], CCDC181, hyaluronan, and proteoglycan link protein 3 [HAPLN3], GSTM2, growth arrest-specific 6 [GAS6], RASSF1, and APC) showed large differences between cancer and benign samples. The best binary classifier was the GAS6/GSTP1/HAPLN3 logistic regression model, with an area under these curves of 0.97, which showed a sensitivity of 94%, and a specificity of 93% after external validation. CONCLUSION: We created and validated a multigene model for the classification of benign and malignant prostate tissue. With false positive and negative rates below 7%, this three-gene biomarker represents a promising basis for more accurate prostate cancer diagnosis.
Authors: Duo Liu; Jingjing Zhu; Dan Zhou; Emily G Nikas; Nikos T Mitanis; Yanfa Sun; Chong Wu; Nicholas Mancuso; Nancy J Cox; Liang Wang; Stephen J Freedland; Christopher A Haiman; Eric R Gamazon; Jason B Nikas; Lang Wu Journal: Int J Cancer Date: 2021-09-25 Impact factor: 7.396
Authors: Anbarasu Kumaraswamy; Katherine R Welker Leng; Thomas C Westbrook; Joel A Yates; Shuang G Zhao; Christopher P Evans; Felix Y Feng; Todd M Morgan; Joshi J Alumkal Journal: Eur Urol Date: 2021-03-27 Impact factor: 24.267
Authors: Leandro Pecchia; Monica Franzese; Rossana Castaldo; Carlo Cavaliere; Andrea Soricelli; Marco Salvatore Journal: J Med Internet Res Date: 2021-04-01 Impact factor: 5.428