Literature DB >> 24625431

Genomic analysis in active surveillance: predicting high-risk disease using tissue biomarkers.

Michael J Donovan1, Carlos Cordon-Cardo.   

Abstract

PURPOSE OF REVIEW: For patients newly diagnosed with prostate cancer, the most significant question is whether the 'truly malignant' disease has been identified. This review will provide an overview of current prostate cancer genomic and biomarker discovery - validation strategies geared towards identifying aggressive, clinically significant disease at the time of diagnosis. RECENT
FINDINGS: Based on recent findings the prostate cancer aggressive disease phenotype develops as a result of mutations (TP53, PTEN), structural events (TMPRSS2-ETS), epigenetic changes (EZH2, DAB2IP, histone alteration), and transcriptional modifications (SChLAP, PCAT-1). Copy number variability and dysregulation of specific pathways including androgen receptor signaling, PTEN/PAKT and TGF-β continue to play an important role in invasion and metastasis.
SUMMARY: Given the current challenges for applying prostate cancer genomics to clinical management, this review will incorporate some of the current novel genomic approaches and techniques including systems-based precise pathology platforms, and the role of fluid-based assays, notably, exosomes and circulating tumor cells (liquid biopsy), as tools for future diagnostic-treatment algorithms.

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Year:  2014        PMID: 24625431     DOI: 10.1097/MOU.0000000000000051

Source DB:  PubMed          Journal:  Curr Opin Urol        ISSN: 0963-0643            Impact factor:   2.309


  3 in total

1.  Functional outcomes of robot-assisted radical prostatectomy in patients eligible for active surveillance.

Authors:  Marc Zanaty; Khaled Ajib; Kevin Zorn; Assaad El-Hakim
Journal:  World J Urol       Date:  2018-04-21       Impact factor: 4.226

Review 2.  Prognostic DNA methylation markers for prostate cancer.

Authors:  Siri H Strand; Torben F Orntoft; Karina D Sorensen
Journal:  Int J Mol Sci       Date:  2014-09-18       Impact factor: 5.923

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

Authors:  Yuqian Gao; Yi-Ting Wang; Yongmei Chen; Hui Wang; Denise Young; Tujin Shi; Yingjie Song; Athena A Schepmoes; Claire Kuo; Thomas L Fillmore; Wei-Jun Qian; Richard D Smith; Sudhir Srivastava; Jacob Kagan; Albert Dobi; Isabell A Sesterhenn; Inger L Rosner; Gyorgy Petrovics; Karin D Rodland; Shiv Srivastava; Jennifer Cullen; Tao Liu
Journal:  Cancers (Basel)       Date:  2020-05-17       Impact factor: 6.639

  3 in total

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