Using reverse-phase protein arrays as pharmacodynamic assays for functional proteomics, biomarker discovery, and drug development in cancer.
The majority of the targeted therapeutic agents in clinical use target proteins and protein function. Although DNA and RNA analyses have been used extensively to identify novel targets and patients likely to benefit from targeted therapies, these are indirect measures of the levels and functions of most therapeutic targets. More importantly, DNA and RNA analysis is ill-suited for determining the pharmacodynamic effects of target inhibition. Assessing changes in protein levels and function is the most efficient way to evaluate the mechanisms underlying sensitivity and resistance to targeted agents. Understanding these mechanisms is necessary to identify patients likely to benefit from treatment and to develop rational drug combinations to prevent or bypass therapeutic resistance. There is an urgent need for a robust approach to assess protein levels and protein function in model systems and across patient samples. While "shot gun" mass spectrometry can provide in-depth analysis of proteins across a limited number of samples, and emerging approaches such as multiple reaction monitoring have the potential to analyze candidate markers, mass spectrometry has not entered into general use because of the high cost, requirement of extensive analysis and support, and relatively large amount of material needed for analysis. Rather, antibody-based technologies, including immunohistochemistry, radioimmunoassays, enzyme-linked immunosorbent assays (ELISAs), and more recently protein arrays, remain the most common approaches for multiplexed protein analysis. Reverse-phase protein array (RPPA) technology has emerged as a robust, sensitive, cost-effective approach to the analysis of large numbers of samples for quantitative assessment of key members of functional pathways that are affected by tumor-targeting therapeutics. The RPPA platform is a powerful approach for identifying and validating targets, classifying tumor subsets, assessing pharmacodynamics, and identifying prognostic and predictive markers, adaptive responses and rational drug combinations in model systems and patient samples. Its greatest utility has been realized through integration with other analytic platforms such as DNA sequencing, transcriptional profiling, epigenomics, mass spectrometry, and metabolomics. The power of the technology is becoming apparent through its use in pathology laboratories and integration into trial design and implementation.
Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Yiling
Y
Lu
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Shiyun
S
Ling
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Apurva M
AM
Hegde
Department of Thoracic/Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Lauren A
LA
Byers
Department of Biomedical Informatics, The Ohio State University, Columbus, OH.
Kevin
K
Coombes
Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX.
Gordon B
GB
Mills
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX. Electronic address: RAkbani@mdanderson.org.
Rehan
R
Akbani
D000970:Antineoplastic Agents / Q000493:pharmacokinetics*; D014408:Biomarkers, Tumor / Q000032:analysis*; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D033181:Information Dissemination; D040081:Protein Array Analysis / Q000379:methods* / Q000592:standards; D011506:Proteins / Q000032:analysis*; D040901:Proteomics / Q000379:methods; D011786:Quality Control
D000970:Antineoplastic Agents; D014408:Biomarkers, Tumor; D011506:Proteins
Biomarker; Drug development; Pharmacodynamic; Proteomics; RPPA
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