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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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HER2 RPPA correlations with copy number and mRNAa Histogram of Spearman’s rank correlation (ρ values) for 206 pairs of proteins and matched mRNAs across all tumor types. The black curve represents the background of ρ values using 28,960 random protein-mRNA pairs in the same dataset.b Crosstab identifying HER2-positive tumors by copy number, mRNA expression and protein expression across 11 tumor types. Cutoffs are defined in Methods. BRCA and UCEC are subdivided for clinical relevance regarding HER2 protein levels. Total sample numbers with analyses for all three platforms (CNV, mRNA and protein) are indicated in parentheses. Percentages ≥5% are highlighted (red).c Relationship between HER2 copy number and HER2 protein level by RPPA across all tumor types (n=2,479). The box represents the lower quartile, median and upper quartile, whereas the whiskers represent the most extreme data point within 1.5 × interquartile range from the edge of the box. Each point represents a sample, color-coded by tumor type or subtype. As expected, ERBB2 amplified samples have much higher HER2 protein levels than non-amplified samples.d Relationship between HER2 mRNA and protein expression across all tumor types (n=2,479). Each protein represents a sample, color-coded by tumor type or subtype. Spearman’s correlation between HER2 protein and mRNA is 0.53.
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Figure 1: HER2 RPPA correlations with copy number and mRNAa Histogram of Spearman’s rank correlation (ρ values) for 206 pairs of proteins and matched mRNAs across all tumor types. The black curve represents the background of ρ values using 28,960 random protein-mRNA pairs in the same dataset.b Crosstab identifying HER2-positive tumors by copy number, mRNA expression and protein expression across 11 tumor types. Cutoffs are defined in Methods. BRCA and UCEC are subdivided for clinical relevance regarding HER2 protein levels. Total sample numbers with analyses for all three platforms (CNV, mRNA and protein) are indicated in parentheses. Percentages ≥5% are highlighted (red).c Relationship between HER2 copy number and HER2 protein level by RPPA across all tumor types (n=2,479). The box represents the lower quartile, median and upper quartile, whereas the whiskers represent the most extreme data point within 1.5 × interquartile range from the edge of the box. Each point represents a sample, color-coded by tumor type or subtype. As expected, ERBB2 amplified samples have much higher HER2 protein levels than non-amplified samples.d Relationship between HER2 mRNA and protein expression across all tumor types (n=2,479). Each protein represents a sample, color-coded by tumor type or subtype. Spearman’s correlation between HER2 protein and mRNA is 0.53.

Mentions: Protein data for 3,467 samples across 11 diseases were compared to mRNA, miRNA, copy number, and mutation data for the same samples. A novel approach, called “replicates-based normalization” (RBN, Methods), mitigated batch effects facilitating creation of a single Pan-Cancer protein dataset merging samples across 6 different batches. The RBN output is equivalent to all 3,467 samples being run in a single batch. In contrast to random (trans) protein:mRNA pairs (mean Spearman’s ρ = −0.006), almost half of matched (cis) protein:mRNA pairs in the RBN set demonstrated correlation beyond that expected by chance (mean Spearman’s ρ = 0.3) in both the overall Pan-Cancer dataset (t-test P < 2.2e-16, n=206 matched protein:mRNA pairs) and within particular diseases (Fig. 1a, Supplementary Fig. 1, Supplementary Data 1,2). Approximately 44% of matched (cis) protein:mRNA pairs had a correlation >= 0.3. For micro-RNAs, as expected, (trans) protein:miRNA correlations were much weaker with a mean positive Spearman’s ρ = 0.07, and a mean negative Spearman’s ρ = −0.07 (Supplementary Data 3). On the other hand, (trans) protein:protein correlations, including phosphoproteins, were higher (mean positive Spearman’s ρ = 0.15, mean negative Spearman’s ρ = −0.13, Supplementary Data 4). Detailed protein:protein and phosphoprotein:protein correlations across the total dataset and in particular diseases are available at the TCPA portal11. The results show, not surprisingly, that matched (cis) mRNA:protein correlations were the highest on average (ρ = 0.3), followed by (trans) protein:protein correlations (ρ ≈ ±0.15), whereas (trans) protein:miRNA correlations were lowest on average (ρ = ±0.07).


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)

HER2 RPPA correlations with copy number and mRNAa Histogram of Spearman’s rank correlation (ρ values) for 206 pairs of proteins and matched mRNAs across all tumor types. The black curve represents the background of ρ values using 28,960 random protein-mRNA pairs in the same dataset.b Crosstab identifying HER2-positive tumors by copy number, mRNA expression and protein expression across 11 tumor types. Cutoffs are defined in Methods. BRCA and UCEC are subdivided for clinical relevance regarding HER2 protein levels. Total sample numbers with analyses for all three platforms (CNV, mRNA and protein) are indicated in parentheses. Percentages ≥5% are highlighted (red).c Relationship between HER2 copy number and HER2 protein level by RPPA across all tumor types (n=2,479). The box represents the lower quartile, median and upper quartile, whereas the whiskers represent the most extreme data point within 1.5 × interquartile range from the edge of the box. Each point represents a sample, color-coded by tumor type or subtype. As expected, ERBB2 amplified samples have much higher HER2 protein levels than non-amplified samples.d Relationship between HER2 mRNA and protein expression across all tumor types (n=2,479). Each protein represents a sample, color-coded by tumor type or subtype. Spearman’s correlation between HER2 protein and mRNA is 0.53.
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Figure 1: HER2 RPPA correlations with copy number and mRNAa Histogram of Spearman’s rank correlation (ρ values) for 206 pairs of proteins and matched mRNAs across all tumor types. The black curve represents the background of ρ values using 28,960 random protein-mRNA pairs in the same dataset.b Crosstab identifying HER2-positive tumors by copy number, mRNA expression and protein expression across 11 tumor types. Cutoffs are defined in Methods. BRCA and UCEC are subdivided for clinical relevance regarding HER2 protein levels. Total sample numbers with analyses for all three platforms (CNV, mRNA and protein) are indicated in parentheses. Percentages ≥5% are highlighted (red).c Relationship between HER2 copy number and HER2 protein level by RPPA across all tumor types (n=2,479). The box represents the lower quartile, median and upper quartile, whereas the whiskers represent the most extreme data point within 1.5 × interquartile range from the edge of the box. Each point represents a sample, color-coded by tumor type or subtype. As expected, ERBB2 amplified samples have much higher HER2 protein levels than non-amplified samples.d Relationship between HER2 mRNA and protein expression across all tumor types (n=2,479). Each protein represents a sample, color-coded by tumor type or subtype. Spearman’s correlation between HER2 protein and mRNA is 0.53.
Mentions: Protein data for 3,467 samples across 11 diseases were compared to mRNA, miRNA, copy number, and mutation data for the same samples. A novel approach, called “replicates-based normalization” (RBN, Methods), mitigated batch effects facilitating creation of a single Pan-Cancer protein dataset merging samples across 6 different batches. The RBN output is equivalent to all 3,467 samples being run in a single batch. In contrast to random (trans) protein:mRNA pairs (mean Spearman’s ρ = −0.006), almost half of matched (cis) protein:mRNA pairs in the RBN set demonstrated correlation beyond that expected by chance (mean Spearman’s ρ = 0.3) in both the overall Pan-Cancer dataset (t-test P < 2.2e-16, n=206 matched protein:mRNA pairs) and within particular diseases (Fig. 1a, Supplementary Fig. 1, Supplementary Data 1,2). Approximately 44% of matched (cis) protein:mRNA pairs had a correlation >= 0.3. For micro-RNAs, as expected, (trans) protein:miRNA correlations were much weaker with a mean positive Spearman’s ρ = 0.07, and a mean negative Spearman’s ρ = −0.07 (Supplementary Data 3). On the other hand, (trans) protein:protein correlations, including phosphoproteins, were higher (mean positive Spearman’s ρ = 0.15, mean negative Spearman’s ρ = −0.13, Supplementary Data 4). Detailed protein:protein and phosphoprotein:protein correlations across the total dataset and in particular diseases are available at the TCPA portal11. The results show, not surprisingly, that matched (cis) mRNA:protein correlations were the highest on average (ρ = 0.3), followed by (trans) protein:protein correlations (ρ ≈ ±0.15), whereas (trans) protein:miRNA correlations were lowest on average (ρ = ±0.07).

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