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Extensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma.

Wiśniewski JR, Ostasiewicz P, Duś K, Zielińska DF, Gnad F, Mann M - Mol. Syst. Biol. (2012)

Bottom Line: Functionally similar changes in the proteome were observed comparing rapidly growing and differentiated CaCo-2 cells.In contrast, there was minimal proteomic remodeling between primary cancer and metastases, suggesting that no drastic proteome changes are necessary for the tumor to propagate in a different tissue context.Our proteomic data set furthermore allows mapping quantitative changes of functional protein classes, enabling novel insights into the biology of colon cancer.

View Article: PubMed Central - PubMed

Affiliation: Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Martinsried, Germany. jwisniew@biochem.mpg.de

ABSTRACT
We report a proteomic analysis of microdissected material from formalin-fixed and paraffin-embedded colorectal cancer, quantifying > 7500 proteins between patient matched normal mucosa, primary carcinoma, and nodal metastases. Expression levels of 1808 proteins changed significantly between normal and cancer tissues, a much larger fraction than that reported in transcript-based studies. Tumor cells exhibit extensive alterations in the cell-surface and nuclear proteomes. Functionally similar changes in the proteome were observed comparing rapidly growing and differentiated CaCo-2 cells. In contrast, there was minimal proteomic remodeling between primary cancer and metastases, suggesting that no drastic proteome changes are necessary for the tumor to propagate in a different tissue context. Additionally, we introduce a new way to determine protein copy numbers per cell without protein standards. Copy numbers estimated in enterocytes and cancer cells are in good agreement with CaCo-2 and HeLa cells and with the literature data. Our proteomic data set furthermore allows mapping quantitative changes of functional protein classes, enabling novel insights into the biology of colon cancer.

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Comparison of our data set with previous transcriptomic and proteomic studies. (A) Coverage of our data set of the most frequently observed upregulated or downregulated genes ‘Transcriptomics' (Cardoso et al, 2007) and proteins ‘Proteomics' (Jimenez et al, 2010) in CRC. The numbers of matching genes and proteins are indicated by the ‘green' and ‘blue' areas and numbers within them. The number of proteins changed in the same direction (up or down), but with P>0.05 is given in parentheses. Genes and proteins reported as changed in cancer, but not identified in our study are indicated as gray areas ‘not identified'. (B) Abundances of proteins corresponding to previously found differentially expressed genes ‘Transcriptomics' (Cardoso et al, 2007) and changed protein levels ‘Proteomics' (Jimenez et al, 2010) measured in this study.
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f8: Comparison of our data set with previous transcriptomic and proteomic studies. (A) Coverage of our data set of the most frequently observed upregulated or downregulated genes ‘Transcriptomics' (Cardoso et al, 2007) and proteins ‘Proteomics' (Jimenez et al, 2010) in CRC. The numbers of matching genes and proteins are indicated by the ‘green' and ‘blue' areas and numbers within them. The number of proteins changed in the same direction (up or down), but with P>0.05 is given in parentheses. Genes and proteins reported as changed in cancer, but not identified in our study are indicated as gray areas ‘not identified'. (B) Abundances of proteins corresponding to previously found differentially expressed genes ‘Transcriptomics' (Cardoso et al, 2007) and changed protein levels ‘Proteomics' (Jimenez et al, 2010) measured in this study.

Mentions: Over the past decade a large number of transcriptomic studies aimed at identification of genes that are upregulated or downregulated in CRC have been carried out. Cardoso et al (2007) summarized the results of 30 investigations using array platforms for identification of genes differentially expressed in colorectal carcinomas when compared with normal mucosa. Although in total close to 1000 changes were reported in these studies only 128 genes were identified in at least 3 of them. Strikingly, for the majority of these genes (96) we identified the corresponding proteins in our data set. At the protein level, we found 49 of these to be significantly changed between carcinoma and normal mucosa and in each case the direction of change was the same as that observed by transcriptomics analysis (Supplementary Table 8). This high overlap of the changed proteins found in a single proteomic study with a compiled data set from 30 transcriptomic studies demonstrates the potential and the robustness of proteomics for unveiling gene expression changes in cancer. Of note, the abundances of the 49 proteins whose abundance changed in both our proteomic and transcriptomic data sets are distributed over 4 orders of magnitude of transcript abundance (Figure 8A). This is similar to the abundance distribution of all changed proteins found in this study, suggesting that the sensitivity and dynamic range of proteomic analysis can be comparable to that of microarray-based approaches.


Extensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma.

Wiśniewski JR, Ostasiewicz P, Duś K, Zielińska DF, Gnad F, Mann M - Mol. Syst. Biol. (2012)

Comparison of our data set with previous transcriptomic and proteomic studies. (A) Coverage of our data set of the most frequently observed upregulated or downregulated genes ‘Transcriptomics' (Cardoso et al, 2007) and proteins ‘Proteomics' (Jimenez et al, 2010) in CRC. The numbers of matching genes and proteins are indicated by the ‘green' and ‘blue' areas and numbers within them. The number of proteins changed in the same direction (up or down), but with P>0.05 is given in parentheses. Genes and proteins reported as changed in cancer, but not identified in our study are indicated as gray areas ‘not identified'. (B) Abundances of proteins corresponding to previously found differentially expressed genes ‘Transcriptomics' (Cardoso et al, 2007) and changed protein levels ‘Proteomics' (Jimenez et al, 2010) measured in this study.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3472694&req=5

f8: Comparison of our data set with previous transcriptomic and proteomic studies. (A) Coverage of our data set of the most frequently observed upregulated or downregulated genes ‘Transcriptomics' (Cardoso et al, 2007) and proteins ‘Proteomics' (Jimenez et al, 2010) in CRC. The numbers of matching genes and proteins are indicated by the ‘green' and ‘blue' areas and numbers within them. The number of proteins changed in the same direction (up or down), but with P>0.05 is given in parentheses. Genes and proteins reported as changed in cancer, but not identified in our study are indicated as gray areas ‘not identified'. (B) Abundances of proteins corresponding to previously found differentially expressed genes ‘Transcriptomics' (Cardoso et al, 2007) and changed protein levels ‘Proteomics' (Jimenez et al, 2010) measured in this study.
Mentions: Over the past decade a large number of transcriptomic studies aimed at identification of genes that are upregulated or downregulated in CRC have been carried out. Cardoso et al (2007) summarized the results of 30 investigations using array platforms for identification of genes differentially expressed in colorectal carcinomas when compared with normal mucosa. Although in total close to 1000 changes were reported in these studies only 128 genes were identified in at least 3 of them. Strikingly, for the majority of these genes (96) we identified the corresponding proteins in our data set. At the protein level, we found 49 of these to be significantly changed between carcinoma and normal mucosa and in each case the direction of change was the same as that observed by transcriptomics analysis (Supplementary Table 8). This high overlap of the changed proteins found in a single proteomic study with a compiled data set from 30 transcriptomic studies demonstrates the potential and the robustness of proteomics for unveiling gene expression changes in cancer. Of note, the abundances of the 49 proteins whose abundance changed in both our proteomic and transcriptomic data sets are distributed over 4 orders of magnitude of transcript abundance (Figure 8A). This is similar to the abundance distribution of all changed proteins found in this study, suggesting that the sensitivity and dynamic range of proteomic analysis can be comparable to that of microarray-based approaches.

Bottom Line: Functionally similar changes in the proteome were observed comparing rapidly growing and differentiated CaCo-2 cells.In contrast, there was minimal proteomic remodeling between primary cancer and metastases, suggesting that no drastic proteome changes are necessary for the tumor to propagate in a different tissue context.Our proteomic data set furthermore allows mapping quantitative changes of functional protein classes, enabling novel insights into the biology of colon cancer.

View Article: PubMed Central - PubMed

Affiliation: Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry, Martinsried, Germany. jwisniew@biochem.mpg.de

ABSTRACT
We report a proteomic analysis of microdissected material from formalin-fixed and paraffin-embedded colorectal cancer, quantifying > 7500 proteins between patient matched normal mucosa, primary carcinoma, and nodal metastases. Expression levels of 1808 proteins changed significantly between normal and cancer tissues, a much larger fraction than that reported in transcript-based studies. Tumor cells exhibit extensive alterations in the cell-surface and nuclear proteomes. Functionally similar changes in the proteome were observed comparing rapidly growing and differentiated CaCo-2 cells. In contrast, there was minimal proteomic remodeling between primary cancer and metastases, suggesting that no drastic proteome changes are necessary for the tumor to propagate in a different tissue context. Additionally, we introduce a new way to determine protein copy numbers per cell without protein standards. Copy numbers estimated in enterocytes and cancer cells are in good agreement with CaCo-2 and HeLa cells and with the literature data. Our proteomic data set furthermore allows mapping quantitative changes of functional protein classes, enabling novel insights into the biology of colon cancer.

Show MeSH
Related in: MedlinePlus