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Proteomic profiling of high risk medulloblastoma reveals functional biology.

Staal JA, Lau LS, Zhang H, Ingram WJ, Hallahan AR, Northcott PA, Pfister SM, Wechsler-Reya RJ, Rusert JM, Taylor MD, Cho YJ, Packer RJ, Brown KJ, Rood BR - Oncotarget (2015)

Bottom Line: We cross-validated the differentially expressed protein candidates using an independent transcriptomic data set and further confirmed them in a separate cohort of medulloblastoma tissue samples to identify the most robust proteogenomic differences.Interestingly, highly expressed proteins associated with MYC-amplified tumors were significantly related to glycolytic metabolic pathways via alternative splicing of pyruvate kinase (PKM) by heterogeneous ribonucleoproteins (HNRNPs).Furthermore, when maintained under hypoxic conditions, these MYC-amplified tumors demonstrated increased viability compared to non-amplified tumors within the same subgroup.

View Article: PubMed Central - PubMed

Affiliation: Center for Cancer and Immunology Research, Children's National Medical Center, Washington DC, USA.

ABSTRACT
Genomic characterization of medulloblastoma has improved molecular risk classification but struggles to define functional biological processes, particularly for the most aggressive subgroups. We present here a novel proteomic approach to this problem using a reference library of stable isotope labeled medulloblastoma-specific proteins as a spike-in standard for accurate quantification of the tumor proteome. Utilizing high-resolution mass spectrometry, we quantified the tumor proteome of group 3 medulloblastoma cells and demonstrate that high-risk MYC amplified tumors can be segregated based on protein expression patterns. We cross-validated the differentially expressed protein candidates using an independent transcriptomic data set and further confirmed them in a separate cohort of medulloblastoma tissue samples to identify the most robust proteogenomic differences. Interestingly, highly expressed proteins associated with MYC-amplified tumors were significantly related to glycolytic metabolic pathways via alternative splicing of pyruvate kinase (PKM) by heterogeneous ribonucleoproteins (HNRNPs). Furthermore, when maintained under hypoxic conditions, these MYC-amplified tumors demonstrated increased viability compared to non-amplified tumors within the same subgroup. Taken together, these findings highlight the power of proteomics as an integrative platform to help prioritize genetic and molecular drivers of cancer biology and behavior.

No MeSH data available.


Related in: MedlinePlus

Effective quantification of human primary tumor cells using a super-SILAC reference standardA. Experimental scheme of quantification analysis using mixed lysates of multiple MB tumor cell lines. Lysates of labeled cells (Lys-8, 13C615N2-Lysine; Arg-10, 13C615N4-Arginine) are mixed with tumor lysate (at a 1:1 ratio) and analyzed by high resolution liquid chromatography-MS/MS. B; Histograms of the ratios between the tumor protein and our super-SILAC reference, and a comparison of ratios (r = Pearson correlation coefficient) between replicates demonstrates the high quantification accuracy of our technique. Superior accuracy is achieved when quantified proteins (proportion indicated by percentage above histogram) lie within four-fold ratio between tumor and super-SILAC reference [10]
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Figure 1: Effective quantification of human primary tumor cells using a super-SILAC reference standardA. Experimental scheme of quantification analysis using mixed lysates of multiple MB tumor cell lines. Lysates of labeled cells (Lys-8, 13C615N2-Lysine; Arg-10, 13C615N4-Arginine) are mixed with tumor lysate (at a 1:1 ratio) and analyzed by high resolution liquid chromatography-MS/MS. B; Histograms of the ratios between the tumor protein and our super-SILAC reference, and a comparison of ratios (r = Pearson correlation coefficient) between replicates demonstrates the high quantification accuracy of our technique. Superior accuracy is achieved when quantified proteins (proportion indicated by percentage above histogram) lie within four-fold ratio between tumor and super-SILAC reference [10]

Mentions: Current antibody based proteomic techniques are useful tools, but only once a small set of target proteins have been identified. Mass spectrometry, by contrast, provides broad proteome coverage, but has historically been plagued by low sensitivity, inaccurate quantitation and technical variability. For this study, we utilized a novel SILAC based mass spectrometry technique to reproducibly quantify proteome differences in human primary tumor cells. The SILAC approach involves the use of a set of stable isotope-labeled labeled peptides that can be spiked, at known amounts, into test samples and used as an internal reference standard for accurate quantification of proteins by mass spectrometry [12]. We developed a unique SILAC reference standard comprised of three labeled MB tumor cell lines (DAOY, D556, D283) and a labeled low passage primary MB tumor cell culture (R026; group 3 non-metastatic tumor) (Figure 1A). This mixture is termed super-SILAC as it is a superset of SILAC cell lines and has previously been shown to achieve superior quantification accuracy compared to single SILAC-labeled cell line standards [10]. Creating the reference from a pool of labeled protein lysates from multiple MB primary and established cell lines broadens proteome coverage to increase the number of quantifiable proteins in the experimental MB samples.


Proteomic profiling of high risk medulloblastoma reveals functional biology.

Staal JA, Lau LS, Zhang H, Ingram WJ, Hallahan AR, Northcott PA, Pfister SM, Wechsler-Reya RJ, Rusert JM, Taylor MD, Cho YJ, Packer RJ, Brown KJ, Rood BR - Oncotarget (2015)

Effective quantification of human primary tumor cells using a super-SILAC reference standardA. Experimental scheme of quantification analysis using mixed lysates of multiple MB tumor cell lines. Lysates of labeled cells (Lys-8, 13C615N2-Lysine; Arg-10, 13C615N4-Arginine) are mixed with tumor lysate (at a 1:1 ratio) and analyzed by high resolution liquid chromatography-MS/MS. B; Histograms of the ratios between the tumor protein and our super-SILAC reference, and a comparison of ratios (r = Pearson correlation coefficient) between replicates demonstrates the high quantification accuracy of our technique. Superior accuracy is achieved when quantified proteins (proportion indicated by percentage above histogram) lie within four-fold ratio between tumor and super-SILAC reference [10]
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4546489&req=5

Figure 1: Effective quantification of human primary tumor cells using a super-SILAC reference standardA. Experimental scheme of quantification analysis using mixed lysates of multiple MB tumor cell lines. Lysates of labeled cells (Lys-8, 13C615N2-Lysine; Arg-10, 13C615N4-Arginine) are mixed with tumor lysate (at a 1:1 ratio) and analyzed by high resolution liquid chromatography-MS/MS. B; Histograms of the ratios between the tumor protein and our super-SILAC reference, and a comparison of ratios (r = Pearson correlation coefficient) between replicates demonstrates the high quantification accuracy of our technique. Superior accuracy is achieved when quantified proteins (proportion indicated by percentage above histogram) lie within four-fold ratio between tumor and super-SILAC reference [10]
Mentions: Current antibody based proteomic techniques are useful tools, but only once a small set of target proteins have been identified. Mass spectrometry, by contrast, provides broad proteome coverage, but has historically been plagued by low sensitivity, inaccurate quantitation and technical variability. For this study, we utilized a novel SILAC based mass spectrometry technique to reproducibly quantify proteome differences in human primary tumor cells. The SILAC approach involves the use of a set of stable isotope-labeled labeled peptides that can be spiked, at known amounts, into test samples and used as an internal reference standard for accurate quantification of proteins by mass spectrometry [12]. We developed a unique SILAC reference standard comprised of three labeled MB tumor cell lines (DAOY, D556, D283) and a labeled low passage primary MB tumor cell culture (R026; group 3 non-metastatic tumor) (Figure 1A). This mixture is termed super-SILAC as it is a superset of SILAC cell lines and has previously been shown to achieve superior quantification accuracy compared to single SILAC-labeled cell line standards [10]. Creating the reference from a pool of labeled protein lysates from multiple MB primary and established cell lines broadens proteome coverage to increase the number of quantifiable proteins in the experimental MB samples.

Bottom Line: We cross-validated the differentially expressed protein candidates using an independent transcriptomic data set and further confirmed them in a separate cohort of medulloblastoma tissue samples to identify the most robust proteogenomic differences.Interestingly, highly expressed proteins associated with MYC-amplified tumors were significantly related to glycolytic metabolic pathways via alternative splicing of pyruvate kinase (PKM) by heterogeneous ribonucleoproteins (HNRNPs).Furthermore, when maintained under hypoxic conditions, these MYC-amplified tumors demonstrated increased viability compared to non-amplified tumors within the same subgroup.

View Article: PubMed Central - PubMed

Affiliation: Center for Cancer and Immunology Research, Children's National Medical Center, Washington DC, USA.

ABSTRACT
Genomic characterization of medulloblastoma has improved molecular risk classification but struggles to define functional biological processes, particularly for the most aggressive subgroups. We present here a novel proteomic approach to this problem using a reference library of stable isotope labeled medulloblastoma-specific proteins as a spike-in standard for accurate quantification of the tumor proteome. Utilizing high-resolution mass spectrometry, we quantified the tumor proteome of group 3 medulloblastoma cells and demonstrate that high-risk MYC amplified tumors can be segregated based on protein expression patterns. We cross-validated the differentially expressed protein candidates using an independent transcriptomic data set and further confirmed them in a separate cohort of medulloblastoma tissue samples to identify the most robust proteogenomic differences. Interestingly, highly expressed proteins associated with MYC-amplified tumors were significantly related to glycolytic metabolic pathways via alternative splicing of pyruvate kinase (PKM) by heterogeneous ribonucleoproteins (HNRNPs). Furthermore, when maintained under hypoxic conditions, these MYC-amplified tumors demonstrated increased viability compared to non-amplified tumors within the same subgroup. Taken together, these findings highlight the power of proteomics as an integrative platform to help prioritize genetic and molecular drivers of cancer biology and behavior.

No MeSH data available.


Related in: MedlinePlus