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Clinical Proteomics for Precision Medicine: The Bladder Cancer Case
Author(s) -
Latosinska Agnieszka,
Frantzi Maria,
Vlahou Antonia,
Merseburger Axel S.,
Mischak Harald
Publication year - 2018
Publication title -
proteomics – clinical applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.948
H-Index - 54
eISSN - 1862-8354
pISSN - 1862-8346
DOI - 10.1002/prca.201700074
Subject(s) - bladder cancer , proteomics , precision medicine , cancer medicine , medicine , cancer , computational biology , biology , pathology , biochemistry , gene
Precision medicine can improve patient management by guiding therapeutic decision based on molecular characteristics. The concept has been extensively addressed through the application of ‐ omics ‐based approaches. Proteomics attract high interest, as proteins reflect a “real‐time” dynamic molecular phenotype. Focusing on proteomics applications for personalized medicine, a literature search was conducted to cover: a) disease prevention, b) monitoring/ prediction of treatment response, c) stratification to guide intervention, and d) identification of drug targets. The review indicates the potential of proteomics for personalized medicine by also highlighting multiple challenges to be addressed prior to actual implementation. In oncology, particularly bladder cancer, application of precision medicine appears especially promising. The high heterogeneity and recurrence rates together with the limited treatment options, suggest that earlier and more efficient intervention, continuous monitoring, and the development of alternative therapies could be accomplished by applying proteomics‐guided personalized approaches. This notion is backed by studies presenting biomarkers that are of value in patient stratification and prognosis, and by recent studies demonstrating the identification of promising therapeutic targets. Herein, we aim to present an approach whereby combining the knowledge on biomarkers and therapeutic targets in bladder cancer could serve as basis towards proteomics‐guided personalized patient management.

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