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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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Proteomic analysis of FFPE archival samples of colonic mucosa, cancer, and metastasis. (A) Proteomic workflow applied to microdissected samples of colonic mucosa (N), cancer (C), and metastasis (M). (B) Overlap of the proteins identified from colonic mucosa, cancer, and metastasis. (C) Peptide-based identification of proteins. (D) Distribution of protein abundances with selected examples. In red, relative abundance of CEA. Examples of lower abundant proteins identified in this study: Jun, proto-oncogene Jun; Frizzled, a G protein-coupled receptor protein that serves as receptor in the Wnt signaling pathway; and Notch 2. (E) Comparison of abundance distribution of all proteins and proteins that were significantly changed in adenocarcinomas. The protein abundances were calculated on the basis of total peptide intensities of all the quantified proteins.
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f1: Proteomic analysis of FFPE archival samples of colonic mucosa, cancer, and metastasis. (A) Proteomic workflow applied to microdissected samples of colonic mucosa (N), cancer (C), and metastasis (M). (B) Overlap of the proteins identified from colonic mucosa, cancer, and metastasis. (C) Peptide-based identification of proteins. (D) Distribution of protein abundances with selected examples. In red, relative abundance of CEA. Examples of lower abundant proteins identified in this study: Jun, proto-oncogene Jun; Frizzled, a G protein-coupled receptor protein that serves as receptor in the Wnt signaling pathway; and Notch 2. (E) Comparison of abundance distribution of all proteins and proteins that were significantly changed in adenocarcinomas. The protein abundances were calculated on the basis of total peptide intensities of all the quantified proteins.

Mentions: To survey and compare the proteomes of colonic mucosa (normal or N) and colon cancer (cancer or C), we analyzed archival FFPE clinical samples originating from eight patients (Supplementary Table 1; Materials and methods). In addition, for seven of the patients we analyzed the proteomes of nodal metastases (M). Laser capture microdissection was used to obtain enriched populations of enterocytes, primary cancer, and metastasizing cells (Supplementary Figure 1). We made an effort to reduce any contamination by stroma and therefore did not emphasize identification of secreted proteins. From each sample, a volume of about 175 nl of cells were collected and processed using the FFPE-FASP (filter aided sample preparation) procedure (Wisniewski et al, 2011a). Yields were 6.1±1.8 μg total peptide per sample. To maximize depth of proteome coverage, peptides were fractionated by anion exchange chromatography into six fractions before they were analyzed by LC-MS/MS on a linear ion trap Orbitrap mass spectrometer (Wisniewski et al, 2011a; Figure 1A). Fragment spectra were obtained by Higher Energy Collisional Dissociation (HCD) with high mass accuracy (Olsen et al, 2009). The complete data acquisition took 23 days. The obtained mass spectrometric raw data were analyzed in the MaxQuant environment (Cox and Mann, 2008) with the integrated Andromeda searching engine (Cox et al, 2011). Label-free quantitation (LFQ) algorithms in MaxQuant (Luber et al, 2010) allowed quantitative comparison of the individual samples. In our analysis, 72 000 unique peptides corresponding to 8173 proteins were identified at a false discovery rate (FDR) of 1% (Supplementary Tables 2 and 3). Comparison of the N, C, and M proteomes revealed that 99% of the identified proteins were common in all three stages (Figure 1B). Of these, 7576 proteins were identified at least four times in one of the states (N, C, or M) and only these were subjected to further analysis and statistical evaluation (Supplementary Table 4). In all, 90% of these proteins and 94% of the proteins that were found to be significantly changed between the normal and cancer cells were identified by at least two peptides (Figure 1C). The summed mass spectrometric peptide intensities—a proxy for the protein abundances of the identified proteins—span six orders of magnitude, but the levels of 98% of the proteins were within a 10 000-fold expression range (Figure 1D). Comparison of the abundances of all proteins and those identified as significantly changed showed that with descending abundance the frequency of detection of statistically significant protein changes decreases (Figure 1E). For highly abundant proteins, expression levels of half of the proteins changed significantly, whereas for low abundant proteins this proportion was much smaller (Figure 1E). This is likely due to technical factors and indicates that a substantial portion of quantitative differences remains undetected. Assuming that over the entire range of abundance the portion of proteins with altered levels is similar, we expect that about 50% of the proteome is changed between normal and cancer cells.


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)

Proteomic analysis of FFPE archival samples of colonic mucosa, cancer, and metastasis. (A) Proteomic workflow applied to microdissected samples of colonic mucosa (N), cancer (C), and metastasis (M). (B) Overlap of the proteins identified from colonic mucosa, cancer, and metastasis. (C) Peptide-based identification of proteins. (D) Distribution of protein abundances with selected examples. In red, relative abundance of CEA. Examples of lower abundant proteins identified in this study: Jun, proto-oncogene Jun; Frizzled, a G protein-coupled receptor protein that serves as receptor in the Wnt signaling pathway; and Notch 2. (E) Comparison of abundance distribution of all proteins and proteins that were significantly changed in adenocarcinomas. The protein abundances were calculated on the basis of total peptide intensities of all the quantified proteins.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1: Proteomic analysis of FFPE archival samples of colonic mucosa, cancer, and metastasis. (A) Proteomic workflow applied to microdissected samples of colonic mucosa (N), cancer (C), and metastasis (M). (B) Overlap of the proteins identified from colonic mucosa, cancer, and metastasis. (C) Peptide-based identification of proteins. (D) Distribution of protein abundances with selected examples. In red, relative abundance of CEA. Examples of lower abundant proteins identified in this study: Jun, proto-oncogene Jun; Frizzled, a G protein-coupled receptor protein that serves as receptor in the Wnt signaling pathway; and Notch 2. (E) Comparison of abundance distribution of all proteins and proteins that were significantly changed in adenocarcinomas. The protein abundances were calculated on the basis of total peptide intensities of all the quantified proteins.
Mentions: To survey and compare the proteomes of colonic mucosa (normal or N) and colon cancer (cancer or C), we analyzed archival FFPE clinical samples originating from eight patients (Supplementary Table 1; Materials and methods). In addition, for seven of the patients we analyzed the proteomes of nodal metastases (M). Laser capture microdissection was used to obtain enriched populations of enterocytes, primary cancer, and metastasizing cells (Supplementary Figure 1). We made an effort to reduce any contamination by stroma and therefore did not emphasize identification of secreted proteins. From each sample, a volume of about 175 nl of cells were collected and processed using the FFPE-FASP (filter aided sample preparation) procedure (Wisniewski et al, 2011a). Yields were 6.1±1.8 μg total peptide per sample. To maximize depth of proteome coverage, peptides were fractionated by anion exchange chromatography into six fractions before they were analyzed by LC-MS/MS on a linear ion trap Orbitrap mass spectrometer (Wisniewski et al, 2011a; Figure 1A). Fragment spectra were obtained by Higher Energy Collisional Dissociation (HCD) with high mass accuracy (Olsen et al, 2009). The complete data acquisition took 23 days. The obtained mass spectrometric raw data were analyzed in the MaxQuant environment (Cox and Mann, 2008) with the integrated Andromeda searching engine (Cox et al, 2011). Label-free quantitation (LFQ) algorithms in MaxQuant (Luber et al, 2010) allowed quantitative comparison of the individual samples. In our analysis, 72 000 unique peptides corresponding to 8173 proteins were identified at a false discovery rate (FDR) of 1% (Supplementary Tables 2 and 3). Comparison of the N, C, and M proteomes revealed that 99% of the identified proteins were common in all three stages (Figure 1B). Of these, 7576 proteins were identified at least four times in one of the states (N, C, or M) and only these were subjected to further analysis and statistical evaluation (Supplementary Table 4). In all, 90% of these proteins and 94% of the proteins that were found to be significantly changed between the normal and cancer cells were identified by at least two peptides (Figure 1C). The summed mass spectrometric peptide intensities—a proxy for the protein abundances of the identified proteins—span six orders of magnitude, but the levels of 98% of the proteins were within a 10 000-fold expression range (Figure 1D). Comparison of the abundances of all proteins and those identified as significantly changed showed that with descending abundance the frequency of detection of statistically significant protein changes decreases (Figure 1E). For highly abundant proteins, expression levels of half of the proteins changed significantly, whereas for low abundant proteins this proportion was much smaller (Figure 1E). This is likely due to technical factors and indicates that a substantial portion of quantitative differences remains undetected. Assuming that over the entire range of abundance the portion of proteins with altered levels is similar, we expect that about 50% of the proteome is changed between normal and cancer cells.

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