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A pan-cancer proteomic perspective on The Cancer Genome Atlas.

Akbani R, Ng PK, Werner HM, Shahmoradgoli M, Zhang F, Ju Z, Liu W, Yang JY, Yoshihara K, Li J, Ling S, Seviour EG, Ram PT, Minna JD, Diao L, Tong P, Heymach JV, Hill SM, Dondelinger F, Städler N, Byers LA, Meric-Bernstam F, Weinstein JN, Broom BM, Verhaak RG, Liang H, Mukherjee S, Lu Y, Mills GB - Nat Commun (2014)

Bottom Line: Therefore, direct study of the functional proteome has the potential to provide a wealth of information that complements and extends genomic, epigenomic and transcriptomic analysis in The Cancer Genome Atlas (TCGA) projects.The resultant proteomic data are integrated with genomic and transcriptomic analyses of the same samples to identify commonalities, differences, emergent pathways and network biology within and across tumour lineages.In addition, tissue-specific signals are reduced computationally to enhance biomarker and target discovery spanning multiple tumour lineages.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Bioinformatics and Computational Biology, 1400 Pressler St., The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA [2].

ABSTRACT
Protein levels and function are poorly predicted by genomic and transcriptomic analysis of patient tumours. Therefore, direct study of the functional proteome has the potential to provide a wealth of information that complements and extends genomic, epigenomic and transcriptomic analysis in The Cancer Genome Atlas (TCGA) projects. Here we use reverse-phase protein arrays to analyse 3,467 patient samples from 11 TCGA 'Pan-Cancer' diseases, using 181 high-quality antibodies that target 128 total proteins and 53 post-translationally modified proteins. The resultant proteomic data are integrated with genomic and transcriptomic analyses of the same samples to identify commonalities, differences, emergent pathways and network biology within and across tumour lineages. In addition, tissue-specific signals are reduced computationally to enhance biomarker and target discovery spanning multiple tumour lineages. This integrative analysis, with an emphasis on pathways and potentially actionable proteins, provides a framework for determining the prognostic, predictive and therapeutic relevance of the functional proteome.

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Analyses of selected potentially actionable proteinsa-b Heatmaps, supervised on the sample axis, depicting protein level of 25 proteins that are (potentially) actionable based on the RBN dataset. Proteins were ordered by unsupervised hierarchical clustering and samples were ordered by (a) cluster and (b) tumor lineage membership and within each ordered by unsupervised hierarchical clustering. Annotation bars include tumor lineage, purity and ploidy (ABSOLUTE algorithm); stromal and immune scores (ESTIMATE algorithm); BRCA (PAM50 classification) and BLCA subtype; 16 significantly mutated genes and two frequently observed amplifications. High-resolution images of the heatmaps can be found online (http://bioinformatics.mdanderson.org/main/TCGA/Pancan11/RPPA).
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Figure 5: Analyses of selected potentially actionable proteinsa-b Heatmaps, supervised on the sample axis, depicting protein level of 25 proteins that are (potentially) actionable based on the RBN dataset. Proteins were ordered by unsupervised hierarchical clustering and samples were ordered by (a) cluster and (b) tumor lineage membership and within each ordered by unsupervised hierarchical clustering. Annotation bars include tumor lineage, purity and ploidy (ABSOLUTE algorithm); stromal and immune scores (ESTIMATE algorithm); BRCA (PAM50 classification) and BLCA subtype; 16 significantly mutated genes and two frequently observed amplifications. High-resolution images of the heatmaps can be found online (http://bioinformatics.mdanderson.org/main/TCGA/Pancan11/RPPA).

Mentions: We analyzed a number of potentially actionable proteins (n=25, Fig. 5a,b), selected based on a literature review (Supplementary Methods) for associations with proteomic and genomic events as well as for potential ability of proteomics to identify patients likely to benefit from targeted therapies. Luminal breast cancers (including AR-positive triple-negative breast cancers which cluster with luminal breast cancers) demonstrated selective elevation of AR, BCL2, FASN and pACC, suggesting these molecules or their associated pathways as potential therapeutic targets. The elevation of HER3 in KIRC may represent a therapeutic opportunity. SRC is activated in all but the hormone-responsive and bladder cancers, offering another potential therapeutic opportunity. EGFR activity, in general, parallels SRC activity, but in GBM is associated with NOTCH1 and HER3 activation, suggesting an interesting opportunity for exploration of combination therapy in (pre)clinical studies. PhosphoSRC, which is a downstream target of EGFR, was highly expressed in a subset of HNSC tumors, suggesting that these may be more sensitive to EGFR targeting strategies. As noted above, HER2 levels are elevated in a subset of UCEC, BLCA, BRCA and colorectal cancers and may represent responsiveness to HER2 targeted therapy. MYC, which may become targetable by emerging therapeutic approaches42, is selectively amplified and expressed in high-grade serous ovarian cancer and may represent an important target in this disease that currently lacks targeted opportunities7.


A pan-cancer proteomic perspective on The Cancer Genome Atlas.

Akbani R, Ng PK, Werner HM, Shahmoradgoli M, Zhang F, Ju Z, Liu W, Yang JY, Yoshihara K, Li J, Ling S, Seviour EG, Ram PT, Minna JD, Diao L, Tong P, Heymach JV, Hill SM, Dondelinger F, Städler N, Byers LA, Meric-Bernstam F, Weinstein JN, Broom BM, Verhaak RG, Liang H, Mukherjee S, Lu Y, Mills GB - Nat Commun (2014)

Analyses of selected potentially actionable proteinsa-b Heatmaps, supervised on the sample axis, depicting protein level of 25 proteins that are (potentially) actionable based on the RBN dataset. Proteins were ordered by unsupervised hierarchical clustering and samples were ordered by (a) cluster and (b) tumor lineage membership and within each ordered by unsupervised hierarchical clustering. Annotation bars include tumor lineage, purity and ploidy (ABSOLUTE algorithm); stromal and immune scores (ESTIMATE algorithm); BRCA (PAM50 classification) and BLCA subtype; 16 significantly mutated genes and two frequently observed amplifications. High-resolution images of the heatmaps can be found online (http://bioinformatics.mdanderson.org/main/TCGA/Pancan11/RPPA).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4109726&req=5

Figure 5: Analyses of selected potentially actionable proteinsa-b Heatmaps, supervised on the sample axis, depicting protein level of 25 proteins that are (potentially) actionable based on the RBN dataset. Proteins were ordered by unsupervised hierarchical clustering and samples were ordered by (a) cluster and (b) tumor lineage membership and within each ordered by unsupervised hierarchical clustering. Annotation bars include tumor lineage, purity and ploidy (ABSOLUTE algorithm); stromal and immune scores (ESTIMATE algorithm); BRCA (PAM50 classification) and BLCA subtype; 16 significantly mutated genes and two frequently observed amplifications. High-resolution images of the heatmaps can be found online (http://bioinformatics.mdanderson.org/main/TCGA/Pancan11/RPPA).
Mentions: We analyzed a number of potentially actionable proteins (n=25, Fig. 5a,b), selected based on a literature review (Supplementary Methods) for associations with proteomic and genomic events as well as for potential ability of proteomics to identify patients likely to benefit from targeted therapies. Luminal breast cancers (including AR-positive triple-negative breast cancers which cluster with luminal breast cancers) demonstrated selective elevation of AR, BCL2, FASN and pACC, suggesting these molecules or their associated pathways as potential therapeutic targets. The elevation of HER3 in KIRC may represent a therapeutic opportunity. SRC is activated in all but the hormone-responsive and bladder cancers, offering another potential therapeutic opportunity. EGFR activity, in general, parallels SRC activity, but in GBM is associated with NOTCH1 and HER3 activation, suggesting an interesting opportunity for exploration of combination therapy in (pre)clinical studies. PhosphoSRC, which is a downstream target of EGFR, was highly expressed in a subset of HNSC tumors, suggesting that these may be more sensitive to EGFR targeting strategies. As noted above, HER2 levels are elevated in a subset of UCEC, BLCA, BRCA and colorectal cancers and may represent responsiveness to HER2 targeted therapy. MYC, which may become targetable by emerging therapeutic approaches42, is selectively amplified and expressed in high-grade serous ovarian cancer and may represent an important target in this disease that currently lacks targeted opportunities7.

Bottom Line: Therefore, direct study of the functional proteome has the potential to provide a wealth of information that complements and extends genomic, epigenomic and transcriptomic analysis in The Cancer Genome Atlas (TCGA) projects.The resultant proteomic data are integrated with genomic and transcriptomic analyses of the same samples to identify commonalities, differences, emergent pathways and network biology within and across tumour lineages.In addition, tissue-specific signals are reduced computationally to enhance biomarker and target discovery spanning multiple tumour lineages.

View Article: PubMed Central - PubMed

Affiliation: 1] Department of Bioinformatics and Computational Biology, 1400 Pressler St., The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA [2].

ABSTRACT
Protein levels and function are poorly predicted by genomic and transcriptomic analysis of patient tumours. Therefore, direct study of the functional proteome has the potential to provide a wealth of information that complements and extends genomic, epigenomic and transcriptomic analysis in The Cancer Genome Atlas (TCGA) projects. Here we use reverse-phase protein arrays to analyse 3,467 patient samples from 11 TCGA 'Pan-Cancer' diseases, using 181 high-quality antibodies that target 128 total proteins and 53 post-translationally modified proteins. The resultant proteomic data are integrated with genomic and transcriptomic analyses of the same samples to identify commonalities, differences, emergent pathways and network biology within and across tumour lineages. In addition, tissue-specific signals are reduced computationally to enhance biomarker and target discovery spanning multiple tumour lineages. This integrative analysis, with an emphasis on pathways and potentially actionable proteins, provides a framework for determining the prognostic, predictive and therapeutic relevance of the functional proteome.

Show MeSH
Related in: MedlinePlus