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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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Pathway analysesPathway analyses of the dataset by RBN clusters, MC clusters and tumor type. For pathway predictor members see Supplementary Table 13.a-b Heatmaps depicting mean pathway scores after unsupervised hierarchical clustering on tumor lineages and protein clusters based on the (a) RBN and (b) MC datasets. The heatmaps were clustered on both axes. As expected, RBN clusters show a strong association with tumor lineages, with very similar patterns between them, whereas MC clusters do not associate with any particular tumor lineage.c-f The heatmaps, supervised on the sample axis, depict the protein levels of the pathway members and of proteins with a high correlation (ρ>0.3/ ρ<−0.3, Spearman’s correlation) to the pathway predictor across RBN clusters (c-d) and tumor lineages (e-f). The EMT pathway (c and e) and the hormone_a pathway (d and f) are shown. Samples are first sorted by either cluster (c-d) or tumor lineage (e-f), then by pathway score (from low to high) within cluster or tumor lineage. Dotplots (lower panel) represent the pathway score for each sample. Each box represents the lower quartile, median and upper quartile, whereas the whiskers represent the most extreme data point within 1.5 × inter-quartile range from the edge of the box. Annotation bars (selected from Fig. 2) are included if statistically associated with the pathway score (P <0.05, Kruskal-Wallis test, n=3,467). Pathway members are marked in red on the left hand side. High-resolution images of the heatmaps can be found online (http://bioinformatics.mdanderson.org/main/TCGA/Pancan11/RPPA).
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Figure 4: Pathway analysesPathway analyses of the dataset by RBN clusters, MC clusters and tumor type. For pathway predictor members see Supplementary Table 13.a-b Heatmaps depicting mean pathway scores after unsupervised hierarchical clustering on tumor lineages and protein clusters based on the (a) RBN and (b) MC datasets. The heatmaps were clustered on both axes. As expected, RBN clusters show a strong association with tumor lineages, with very similar patterns between them, whereas MC clusters do not associate with any particular tumor lineage.c-f The heatmaps, supervised on the sample axis, depict the protein levels of the pathway members and of proteins with a high correlation (ρ>0.3/ ρ<−0.3, Spearman’s correlation) to the pathway predictor across RBN clusters (c-d) and tumor lineages (e-f). The EMT pathway (c and e) and the hormone_a pathway (d and f) are shown. Samples are first sorted by either cluster (c-d) or tumor lineage (e-f), then by pathway score (from low to high) within cluster or tumor lineage. Dotplots (lower panel) represent the pathway score for each sample. Each box represents the lower quartile, median and upper quartile, whereas the whiskers represent the most extreme data point within 1.5 × inter-quartile range from the edge of the box. Annotation bars (selected from Fig. 2) are included if statistically associated with the pathway score (P <0.05, Kruskal-Wallis test, n=3,467). Pathway members are marked in red on the left hand side. High-resolution images of the heatmaps can be found online (http://bioinformatics.mdanderson.org/main/TCGA/Pancan11/RPPA).

Mentions: Cluster_I was primarily driven by phosphoPEA15, YB1, EEF2 and ETS1 proteins (Supplementary Table 9), which were markedly elevated in a subset of colorectal tumors (18%). Cluster_I exhibited enrichment of APC and KRAS mutations, very few HER2 amplifications, but moderately high HER2 protein levels (Fig. 3a, Supplementary Tables 9,12). It also had evidence for suppressed DNA damage response, apoptosis, and mTOR and MAPK pathway levels (Fig. 4b). Cluster_II was divided into two further sub-clusters, one primarily driven by HER2 (IIa) and one by EGFR (IIb) (Supplementary Table 9). Interestingly, a subset of OVCA, UCEC, BLCA and LUAD samples that had HER2 amplification and HER2 protein levels comparable to breast HER2+ samples were located in cluster_IIa, raising intriguing opportunities for (pre)clinical investigation of HER2 targeted therapy and particularly TDM1 therapy as noted above. Cluster_IIa also had activated RTK and cell cycle pathways, but suppressed hormonal signaling pathways (Fig. 4b). Similarly, a subset of HNSC and lung samples that had EGFR levels comparable to a subset of GBM samples (28%) were located in cluster_IIb, warranting exploration of potential benefit from EGFR pathway-targeted drugs29. Tumors in cluster_IIb were enriched in EGFR mutations, contained few PTEN mutations, and had elevated RTK pathway and suppressed mTOR pathway signatures. Clusters III-VII consisted of a mixture of all tissue types. Cluster_V was the most distinctive, exhibiting a strong “reactive” signature5, with elevated MYH11, RICTOR, Caveolin1, and Collagen VI, and an activated EMT signature. Cluster_V also exhibited low cell cycle, Wnt-signaling and DNA damage response pathway signatures. Cluster_V contained the majority of the breast reactive samples along with multiple other tumors with a “reactive” signature consistent with the reactive phenotype being a pan-cancer characteristic. Cluster_III was the antithesis of “reactive” cluster_V and was primarily driven by elevated BRAF, ER-alpha and E-cadherin (Fig. 3b). In contrast to cluster_V, cluster_III had low EMT, apoptosis, and MAPK pathway signatures, but high DNA damage and hormonal pathway signatures. Patients in cluster_III may potentially benefit from (pre)clinical hormone targeting therapies. Cluster_III also had high beta-catenin levels, suggesting activation of the canonical Wnt-signaling pathway. Cluster_IV also had high beta-catenin, as well as activated AKT, MAPK and mTOR pathways, but suppressed DNA damage, apoptosis, EMT and cell cycle pathways. Cluster_IV and cluster_VII were antitheses. The high levels of phosphoAKT and phosphoMAPK in cluster_IV, suggested evaluation of (pre)clinical benefit from kinase-targeted therapies. Cluster_VI showed high EMT, cell cycle, apoptosis, mTOR and MAPK pathway signatures, also suggesting further evaluation of kinase-targeted therapies. Cluster_VI had low beta-catenin, consistent with suppressed Wnt-signaling. Cluster_VII also showed low beta-catenin, with suppressed AKT, MAPK, mTOR and RTK pathways.


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)

Pathway analysesPathway analyses of the dataset by RBN clusters, MC clusters and tumor type. For pathway predictor members see Supplementary Table 13.a-b Heatmaps depicting mean pathway scores after unsupervised hierarchical clustering on tumor lineages and protein clusters based on the (a) RBN and (b) MC datasets. The heatmaps were clustered on both axes. As expected, RBN clusters show a strong association with tumor lineages, with very similar patterns between them, whereas MC clusters do not associate with any particular tumor lineage.c-f The heatmaps, supervised on the sample axis, depict the protein levels of the pathway members and of proteins with a high correlation (ρ>0.3/ ρ<−0.3, Spearman’s correlation) to the pathway predictor across RBN clusters (c-d) and tumor lineages (e-f). The EMT pathway (c and e) and the hormone_a pathway (d and f) are shown. Samples are first sorted by either cluster (c-d) or tumor lineage (e-f), then by pathway score (from low to high) within cluster or tumor lineage. Dotplots (lower panel) represent the pathway score for each sample. Each box represents the lower quartile, median and upper quartile, whereas the whiskers represent the most extreme data point within 1.5 × inter-quartile range from the edge of the box. Annotation bars (selected from Fig. 2) are included if statistically associated with the pathway score (P <0.05, Kruskal-Wallis test, n=3,467). Pathway members are marked in red on the left hand side. High-resolution images of the heatmaps can be found online (http://bioinformatics.mdanderson.org/main/TCGA/Pancan11/RPPA).
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Related In: Results  -  Collection

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Figure 4: Pathway analysesPathway analyses of the dataset by RBN clusters, MC clusters and tumor type. For pathway predictor members see Supplementary Table 13.a-b Heatmaps depicting mean pathway scores after unsupervised hierarchical clustering on tumor lineages and protein clusters based on the (a) RBN and (b) MC datasets. The heatmaps were clustered on both axes. As expected, RBN clusters show a strong association with tumor lineages, with very similar patterns between them, whereas MC clusters do not associate with any particular tumor lineage.c-f The heatmaps, supervised on the sample axis, depict the protein levels of the pathway members and of proteins with a high correlation (ρ>0.3/ ρ<−0.3, Spearman’s correlation) to the pathway predictor across RBN clusters (c-d) and tumor lineages (e-f). The EMT pathway (c and e) and the hormone_a pathway (d and f) are shown. Samples are first sorted by either cluster (c-d) or tumor lineage (e-f), then by pathway score (from low to high) within cluster or tumor lineage. Dotplots (lower panel) represent the pathway score for each sample. Each box represents the lower quartile, median and upper quartile, whereas the whiskers represent the most extreme data point within 1.5 × inter-quartile range from the edge of the box. Annotation bars (selected from Fig. 2) are included if statistically associated with the pathway score (P <0.05, Kruskal-Wallis test, n=3,467). Pathway members are marked in red on the left hand side. High-resolution images of the heatmaps can be found online (http://bioinformatics.mdanderson.org/main/TCGA/Pancan11/RPPA).
Mentions: Cluster_I was primarily driven by phosphoPEA15, YB1, EEF2 and ETS1 proteins (Supplementary Table 9), which were markedly elevated in a subset of colorectal tumors (18%). Cluster_I exhibited enrichment of APC and KRAS mutations, very few HER2 amplifications, but moderately high HER2 protein levels (Fig. 3a, Supplementary Tables 9,12). It also had evidence for suppressed DNA damage response, apoptosis, and mTOR and MAPK pathway levels (Fig. 4b). Cluster_II was divided into two further sub-clusters, one primarily driven by HER2 (IIa) and one by EGFR (IIb) (Supplementary Table 9). Interestingly, a subset of OVCA, UCEC, BLCA and LUAD samples that had HER2 amplification and HER2 protein levels comparable to breast HER2+ samples were located in cluster_IIa, raising intriguing opportunities for (pre)clinical investigation of HER2 targeted therapy and particularly TDM1 therapy as noted above. Cluster_IIa also had activated RTK and cell cycle pathways, but suppressed hormonal signaling pathways (Fig. 4b). Similarly, a subset of HNSC and lung samples that had EGFR levels comparable to a subset of GBM samples (28%) were located in cluster_IIb, warranting exploration of potential benefit from EGFR pathway-targeted drugs29. Tumors in cluster_IIb were enriched in EGFR mutations, contained few PTEN mutations, and had elevated RTK pathway and suppressed mTOR pathway signatures. Clusters III-VII consisted of a mixture of all tissue types. Cluster_V was the most distinctive, exhibiting a strong “reactive” signature5, with elevated MYH11, RICTOR, Caveolin1, and Collagen VI, and an activated EMT signature. Cluster_V also exhibited low cell cycle, Wnt-signaling and DNA damage response pathway signatures. Cluster_V contained the majority of the breast reactive samples along with multiple other tumors with a “reactive” signature consistent with the reactive phenotype being a pan-cancer characteristic. Cluster_III was the antithesis of “reactive” cluster_V and was primarily driven by elevated BRAF, ER-alpha and E-cadherin (Fig. 3b). In contrast to cluster_V, cluster_III had low EMT, apoptosis, and MAPK pathway signatures, but high DNA damage and hormonal pathway signatures. Patients in cluster_III may potentially benefit from (pre)clinical hormone targeting therapies. Cluster_III also had high beta-catenin levels, suggesting activation of the canonical Wnt-signaling pathway. Cluster_IV also had high beta-catenin, as well as activated AKT, MAPK and mTOR pathways, but suppressed DNA damage, apoptosis, EMT and cell cycle pathways. Cluster_IV and cluster_VII were antitheses. The high levels of phosphoAKT and phosphoMAPK in cluster_IV, suggested evaluation of (pre)clinical benefit from kinase-targeted therapies. Cluster_VI showed high EMT, cell cycle, apoptosis, mTOR and MAPK pathway signatures, also suggesting further evaluation of kinase-targeted therapies. Cluster_VI had low beta-catenin, consistent with suppressed Wnt-signaling. Cluster_VII also showed low beta-catenin, with suppressed AKT, MAPK, mTOR and RTK pathways.

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