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Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression

Although ~40% of screen-detected prostate cancers (PCa) are indolent, advanced-stage PCa is a lethal disease with 5-year survival rates around 29%. Identification of biomarkers for early detection of aggressive disease is a key challenge. Starting with 52 candidate biomarkers, selected from existing...

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Autores principales: Gao, Yuqian, Wang, Yi-Ting, Chen, Yongmei, Wang, Hui, Young, Denise, Shi, Tujin, Song, Yingjie, Schepmoes, Athena A., Kuo, Claire, Fillmore, Thomas L., Qian, Wei-Jun, Smith, Richard D., Srivastava, Sudhir, Kagan, Jacob, Dobi, Albert, Sesterhenn, Isabell A., Rosner, Inger L., Petrovics, Gyorgy, Rodland, Karin D., Srivastava, Shiv, Cullen, Jennifer, Liu, Tao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281161/
https://www.ncbi.nlm.nih.gov/pubmed/32429558
http://dx.doi.org/10.3390/cancers12051268
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author Gao, Yuqian
Wang, Yi-Ting
Chen, Yongmei
Wang, Hui
Young, Denise
Shi, Tujin
Song, Yingjie
Schepmoes, Athena A.
Kuo, Claire
Fillmore, Thomas L.
Qian, Wei-Jun
Smith, Richard D.
Srivastava, Sudhir
Kagan, Jacob
Dobi, Albert
Sesterhenn, Isabell A.
Rosner, Inger L.
Petrovics, Gyorgy
Rodland, Karin D.
Srivastava, Shiv
Cullen, Jennifer
Liu, Tao
author_facet Gao, Yuqian
Wang, Yi-Ting
Chen, Yongmei
Wang, Hui
Young, Denise
Shi, Tujin
Song, Yingjie
Schepmoes, Athena A.
Kuo, Claire
Fillmore, Thomas L.
Qian, Wei-Jun
Smith, Richard D.
Srivastava, Sudhir
Kagan, Jacob
Dobi, Albert
Sesterhenn, Isabell A.
Rosner, Inger L.
Petrovics, Gyorgy
Rodland, Karin D.
Srivastava, Shiv
Cullen, Jennifer
Liu, Tao
author_sort Gao, Yuqian
collection PubMed
description Although ~40% of screen-detected prostate cancers (PCa) are indolent, advanced-stage PCa is a lethal disease with 5-year survival rates around 29%. Identification of biomarkers for early detection of aggressive disease is a key challenge. Starting with 52 candidate biomarkers, selected from existing PCa genomics datasets and known PCa driver genes, we used targeted mass spectrometry to quantify proteins that significantly differed in primary tumors from PCa patients treated with radical prostatectomy (RP) across three study outcomes: (i) metastasis ≥1-year post-RP, (ii) biochemical recurrence ≥1-year post-RP, and (iii) no progression after ≥10 years post-RP. Sixteen proteins that differed significantly in an initial set of 105 samples were evaluated in the entire cohort (n = 338). A five-protein classifier which combined FOLH1, KLK3, TGFB1, SPARC, and CAMKK2 with existing clinical and pathological standard of care variables demonstrated significant improvement in predicting distant metastasis, achieving an area under the receiver-operating characteristic curve of 0.92 (0.86, 0.99, p = 0.001) and a negative predictive value of 92% in the training/testing analysis. This classifier has the potential to stratify patients based on risk of aggressive, metastatic PCa that will require early intervention compared to low risk patients who could be managed through active surveillance.
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spelling pubmed-72811612020-06-15 Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression Gao, Yuqian Wang, Yi-Ting Chen, Yongmei Wang, Hui Young, Denise Shi, Tujin Song, Yingjie Schepmoes, Athena A. Kuo, Claire Fillmore, Thomas L. Qian, Wei-Jun Smith, Richard D. Srivastava, Sudhir Kagan, Jacob Dobi, Albert Sesterhenn, Isabell A. Rosner, Inger L. Petrovics, Gyorgy Rodland, Karin D. Srivastava, Shiv Cullen, Jennifer Liu, Tao Cancers (Basel) Article Although ~40% of screen-detected prostate cancers (PCa) are indolent, advanced-stage PCa is a lethal disease with 5-year survival rates around 29%. Identification of biomarkers for early detection of aggressive disease is a key challenge. Starting with 52 candidate biomarkers, selected from existing PCa genomics datasets and known PCa driver genes, we used targeted mass spectrometry to quantify proteins that significantly differed in primary tumors from PCa patients treated with radical prostatectomy (RP) across three study outcomes: (i) metastasis ≥1-year post-RP, (ii) biochemical recurrence ≥1-year post-RP, and (iii) no progression after ≥10 years post-RP. Sixteen proteins that differed significantly in an initial set of 105 samples were evaluated in the entire cohort (n = 338). A five-protein classifier which combined FOLH1, KLK3, TGFB1, SPARC, and CAMKK2 with existing clinical and pathological standard of care variables demonstrated significant improvement in predicting distant metastasis, achieving an area under the receiver-operating characteristic curve of 0.92 (0.86, 0.99, p = 0.001) and a negative predictive value of 92% in the training/testing analysis. This classifier has the potential to stratify patients based on risk of aggressive, metastatic PCa that will require early intervention compared to low risk patients who could be managed through active surveillance. MDPI 2020-05-17 /pmc/articles/PMC7281161/ /pubmed/32429558 http://dx.doi.org/10.3390/cancers12051268 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Gao, Yuqian
Wang, Yi-Ting
Chen, Yongmei
Wang, Hui
Young, Denise
Shi, Tujin
Song, Yingjie
Schepmoes, Athena A.
Kuo, Claire
Fillmore, Thomas L.
Qian, Wei-Jun
Smith, Richard D.
Srivastava, Sudhir
Kagan, Jacob
Dobi, Albert
Sesterhenn, Isabell A.
Rosner, Inger L.
Petrovics, Gyorgy
Rodland, Karin D.
Srivastava, Shiv
Cullen, Jennifer
Liu, Tao
Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression
title Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression
title_full Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression
title_fullStr Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression
title_full_unstemmed Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression
title_short Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression
title_sort proteomic tissue-based classifier for early prediction of prostate cancer progression
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281161/
https://www.ncbi.nlm.nih.gov/pubmed/32429558
http://dx.doi.org/10.3390/cancers12051268
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