TY - JOUR
T1 - TCPA v3.0
T2 - An integrative platform to explore the pan-cancer analysis of functional proteomic data
AU - Chen, Mei Ju May
AU - Li, Jun
AU - Wang, Yumeng
AU - Akbani, Rehan
AU - Lu, Yiling
AU - Mills, Gordon B.
AU - Liang, Han
N1 - Publisher Copyright:
© 2019 Chen et al.
PY - 2019
Y1 - 2019
N2 - Reverse-phase protein arrays represent a powerful functional proteomics approach to characterizing cell signaling pathways and understanding their effects on cancer development. Using this platform, we have characterized ∼8,000 patient samples of 32 cancer types through The Cancer Genome Atlas and built a widely used, open-access bioinformatic resource, The Cancer Proteome Atlas (TCPA). To maximize the utility of TCPA, we have developed a new module called "TCGA Pan-Cancer Analysis," which provides comprehensive protein-centric analyses that integrate protein expression data and other TCGA data across cancer types. We further demonstrate the value of this module by examining the correlations of RPPA proteins with significantly mutated genes, assessing the predictive power of somatic copy-number alterations, DNA methylation, and mRNA on protein expression, inferring the regulatory effects of miRNAs on protein expression, constructing a co-expression network of proteins and pathways, and identifying clinically relevant protein markers. This upgraded TCPA (v3.0) will provide the cancer research community with a more powerful tool for studying functional proteomics and making translational impacts.
AB - Reverse-phase protein arrays represent a powerful functional proteomics approach to characterizing cell signaling pathways and understanding their effects on cancer development. Using this platform, we have characterized ∼8,000 patient samples of 32 cancer types through The Cancer Genome Atlas and built a widely used, open-access bioinformatic resource, The Cancer Proteome Atlas (TCPA). To maximize the utility of TCPA, we have developed a new module called "TCGA Pan-Cancer Analysis," which provides comprehensive protein-centric analyses that integrate protein expression data and other TCGA data across cancer types. We further demonstrate the value of this module by examining the correlations of RPPA proteins with significantly mutated genes, assessing the predictive power of somatic copy-number alterations, DNA methylation, and mRNA on protein expression, inferring the regulatory effects of miRNAs on protein expression, constructing a co-expression network of proteins and pathways, and identifying clinically relevant protein markers. This upgraded TCPA (v3.0) will provide the cancer research community with a more powerful tool for studying functional proteomics and making translational impacts.
UR - http://www.scopus.com/inward/record.url?scp=85071349984&partnerID=8YFLogxK
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U2 - 10.1074/mcp.RA118.001260
DO - 10.1074/mcp.RA118.001260
M3 - Article
C2 - 31201206
AN - SCOPUS:85071349984
SN - 1535-9476
VL - 18
SP - S15-S25
JO - Molecular and Cellular Proteomics
JF - Molecular and Cellular Proteomics
IS - 8
ER -