A three-gene DNA methylation biomarker accurately classifies early stage prostate cancer

Palak G. Patel; Thomas Wessel; Atsunari Kawashima; John B.A. Okello; Tamara Jamaspishvili; Karl Philippe Guérard; Laura Lee; Anna Ying Wah Lee; Nathan E. How; Dan Dion; Eleonora Scarlata; Chelsea L. Jackson; Suzanne Boursalie; Tanya Sack; Rachel Dunn; Madeleine Moussa; Karen Mackie; Audrey Ellis; Elizabeth Marra; Joseph Chin; Khurram Siddiqui; Khalil Hetou; Lee Anne Pickard; Vinolia Arthur-Hayward; Glenn Bauman; Simone Chevalier; Fadi Brimo; Paul C. Boutros; Jacques Lapointe PhD; John M.S. Bartlett; Robert J. Gooding; David M. Berman

doi:10.1002/pros.23895

A three-gene DNA methylation biomarker accurately classifies early stage prostate cancer

Palak G. Patel, Thomas Wessel, Atsunari Kawashima, John B.A. Okello, Tamara Jamaspishvili, Karl Philippe Guérard, Laura Lee, Anna Ying Wah Lee, Nathan E. How, Dan Dion, Eleonora Scarlata, Chelsea L. Jackson, Suzanne Boursalie, Tanya Sack, Rachel Dunn, Madeleine Moussa, Karen Mackie, Audrey Ellis, Elizabeth Marra, Joseph ChinKhurram Siddiqui, Khalil Hetou, Lee Anne Pickard, Vinolia Arthur-Hayward, Glenn Bauman, Simone Chevalier, Fadi Brimo, Paul C. Boutros, Jacques Lapointe PhD, John M.S. Bartlett, Robert J. Gooding, David M. Berman^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

22 Citations (Scopus)

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.

Original language	English
Pages (from-to)	1705-1714
Number of pages	10
Journal	Prostate
Volume	79
Issue number	14
DOIs	https://doi.org/10.1002/pros.23895
Publication status	Published - Oct 2019
Externally published	Yes

Keywords

DNA methylation
biomarker discovery and validation
cancer epigenetics
prostate cancer diagnosis

ASJC Scopus subject areas

Oncology
Urology

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1002/pros.23895

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Patel, P. G., Wessel, T., Kawashima, A., Okello, J. B. A., Jamaspishvili, T., Guérard, K. P., Lee, L., Lee, A. Y. W., How, N. E., Dion, D., Scarlata, E., Jackson, C. L., Boursalie, S., Sack, T., Dunn, R., Moussa, M., Mackie, K., Ellis, A., Marra, E., ... Berman, D. M. (2019). A three-gene DNA methylation biomarker accurately classifies early stage prostate cancer. Prostate, 79(14), 1705-1714. https://doi.org/10.1002/pros.23895

Patel, PG, Wessel, T, Kawashima, A, Okello, JBA, Jamaspishvili, T, Guérard, KP, Lee, L, Lee, AYW, How, NE, Dion, D, Scarlata, E, Jackson, CL, Boursalie, S, Sack, T, Dunn, R, Moussa, M, Mackie, K, Ellis, A, Marra, E, Chin, J, Siddiqui, K, Hetou, K, Pickard, LA, Arthur-Hayward, V, Bauman, G, Chevalier, S, Brimo, F, Boutros, PC, Lapointe PhD, J, Bartlett, JMS, Gooding, RJ & Berman, DM 2019, 'A three-gene DNA methylation biomarker accurately classifies early stage prostate cancer', Prostate, vol. 79, no. 14, pp. 1705-1714. https://doi.org/10.1002/pros.23895

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title = "A three-gene DNA methylation biomarker accurately classifies early stage prostate cancer",

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.",

keywords = "DNA methylation, biomarker discovery and validation, cancer epigenetics, prostate cancer diagnosis",

author = "Patel, {Palak G.} and Thomas Wessel and Atsunari Kawashima and Okello, {John B.A.} and Tamara Jamaspishvili and Gu{\'e}rard, {Karl Philippe} and Laura Lee and Lee, {Anna Ying Wah} and How, {Nathan E.} and Dan Dion and Eleonora Scarlata and Jackson, {Chelsea L.} and Suzanne Boursalie and Tanya Sack and Rachel Dunn and Madeleine Moussa and Karen Mackie and Audrey Ellis and Elizabeth Marra and Joseph Chin and Khurram Siddiqui and Khalil Hetou and Pickard, {Lee Anne} and Vinolia Arthur-Hayward and Glenn Bauman and Simone Chevalier and Fadi Brimo and Boutros, {Paul C.} and {Lapointe PhD}, Jacques and Bartlett, {John M.S.} and Gooding, {Robert J.} and Berman, {David M.}",

note = "Publisher Copyright: {\textcopyright} 2019 Wiley Periodicals, Inc.",

year = "2019",

month = oct,

doi = "10.1002/pros.23895",

language = "English",

volume = "79",

pages = "1705--1714",

journal = "Prostate",

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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.

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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DO - 10.1002/pros.23895

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AN - SCOPUS:85070859430

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SP - 1705

EP - 1714

JO - Prostate

JF - Prostate

IS - 14

ER -

A three-gene DNA methylation biomarker accurately classifies early stage prostate cancer

Abstract

Keywords

ASJC Scopus subject areas

UN SDGs

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