TY - JOUR
T1 - A three-gene DNA methylation biomarker accurately classifies early stage prostate cancer
AU - Patel, Palak G.
AU - Wessel, Thomas
AU - Kawashima, Atsunari
AU - Okello, John B.A.
AU - Jamaspishvili, Tamara
AU - Guérard, Karl Philippe
AU - Lee, Laura
AU - Lee, Anna Ying Wah
AU - How, Nathan E.
AU - Dion, Dan
AU - Scarlata, Eleonora
AU - Jackson, Chelsea L.
AU - Boursalie, Suzanne
AU - Sack, Tanya
AU - Dunn, Rachel
AU - Moussa, Madeleine
AU - Mackie, Karen
AU - Ellis, Audrey
AU - Marra, Elizabeth
AU - Chin, Joseph
AU - Siddiqui, Khurram
AU - Hetou, Khalil
AU - Pickard, Lee Anne
AU - Arthur-Hayward, Vinolia
AU - Bauman, Glenn
AU - Chevalier, Simone
AU - Brimo, Fadi
AU - Boutros, Paul C.
AU - Lapointe PhD, Jacques
AU - Bartlett, John M.S.
AU - Gooding, Robert J.
AU - Berman, David M.
N1 - Funding Information:
This work is proudly funded by the Movember Foundation through Prostate Cancer Canada (grant #T2014.01); research grants from Ride for Dad and Southeastern Ontario Academic Medical Association (SEAMO) to DMB, and Terry Fox Foundation Training Program in Transdisciplinary Cancer Research in Partnership with CIHR, Ontario Graduate Scholarship and Queen's Graduate awards to PGP.
Publisher Copyright:
© 2019 Wiley Periodicals, Inc.
PY - 2019/10
Y1 - 2019/10
N2 - 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.
AB - 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.
KW - DNA methylation
KW - biomarker discovery and validation
KW - cancer epigenetics
KW - prostate cancer diagnosis
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U2 - 10.1002/pros.23895
DO - 10.1002/pros.23895
M3 - Article
C2 - 31433512
AN - SCOPUS:85070859430
SN - 0270-4137
VL - 79
SP - 1705
EP - 1714
JO - Prostate
JF - Prostate
IS - 14
ER -