Integrated Bottom-Up and Top-Down Proteomics of Patient-Derived Breast Tumor Xenografts
2015; Elsevier BV; Volume: 15; Issue: 1 Linguagem: Inglês
10.1074/mcp.m114.047480
ISSN1535-9484
AutoresIoanna Ntai, Richard D. LeDuc, Ryan T. Fellers, Petra Erdmann-Gilmore, Sherri R. Davies, Jeanne M. Rumsey, Bryan P. Early, Paul M. Thomas, Shunqiang Li, Philip D. Compton, Matthew J. Ellis, Kelly V. Ruggles, David Fenyö, Emily S. Boja, Henry Rodriguez, R. Reid Townsend, Neil L. Kelleher,
Tópico(s)Cancer Genomics and Diagnostics
ResumoBottom-up proteomics relies on the use of proteases and is the method of choice for identifying thousands of protein groups in complex samples. Top-down proteomics has been shown to be robust for direct analysis of small proteins and offers a solution to the "peptide-to-protein" inference problem inherent with bottom-up approaches. Here, we describe the first large-scale integration of genomic, bottom-up and top-down proteomic data for the comparative analysis of patient-derived mouse xenograft models of basal and luminal B human breast cancer, WHIM2 and WHIM16, respectively. Using these well-characterized xenograft models established by the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium, we compared and contrasted the performance of bottom-up and top-down proteomics to detect cancer-specific aberrations at the peptide and proteoform levels and to measure differential expression of proteins and proteoforms. Bottom-up proteomic analysis of the tumor xenografts detected almost 10 times as many coding nucleotide polymorphisms and peptides resulting from novel splice junctions than top-down. For proteins in the range of 0–30 kDa, where quantitation was performed using both approaches, bottom-up proteomics quantified 3,519 protein groups from 49,185 peptides, while top-down proteomics quantified 982 proteoforms mapping to 358 proteins. Examples of both concordant and discordant quantitation were found in a ∼60:40 ratio, providing a unique opportunity for top-down to fill in missing information. The two techniques showed complementary performance, with bottom-up yielding eight times more identifications of 0–30 kDa proteins in xenograft proteomes, but failing to detect differences in certain posttranslational modifications (PTMs), such as phosphorylation pattern changes of alpha-endosulfine. This work illustrates the potency of a combined bottom-up and top-down proteomics approach to deepen our knowledge of cancer biology, especially when genomic data are available. Bottom-up proteomics relies on the use of proteases and is the method of choice for identifying thousands of protein groups in complex samples. Top-down proteomics has been shown to be robust for direct analysis of small proteins and offers a solution to the "peptide-to-protein" inference problem inherent with bottom-up approaches. Here, we describe the first large-scale integration of genomic, bottom-up and top-down proteomic data for the comparative analysis of patient-derived mouse xenograft models of basal and luminal B human breast cancer, WHIM2 and WHIM16, respectively. Using these well-characterized xenograft models established by the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium, we compared and contrasted the performance of bottom-up and top-down proteomics to detect cancer-specific aberrations at the peptide and proteoform levels and to measure differential expression of proteins and proteoforms. Bottom-up proteomic analysis of the tumor xenografts detected almost 10 times as many coding nucleotide polymorphisms and peptides resulting from novel splice junctions than top-down. For proteins in the range of 0–30 kDa, where quantitation was performed using both approaches, bottom-up proteomics quantified 3,519 protein groups from 49,185 peptides, while top-down proteomics quantified 982 proteoforms mapping to 358 proteins. Examples of both concordant and discordant quantitation were found in a ∼60:40 ratio, providing a unique opportunity for top-down to fill in missing information. The two techniques showed complementary performance, with bottom-up yielding eight times more identifications of 0–30 kDa proteins in xenograft proteomes, but failing to detect differences in certain posttranslational modifications (PTMs), such as phosphorylation pattern changes of alpha-endosulfine. This work illustrates the potency of a combined bottom-up and top-down proteomics approach to deepen our knowledge of cancer biology, especially when genomic data are available. Recent advances in high-throughput genomics have allowed deep characterization of cancer at the DNA and RNA level. Large-scale initiatives, such as The Cancer Genome Atlas at the National Cancer Institute, have provided comprehensive genomic analyses of human tumors from many cancer types and, thus, the prospect for novel insights into the pathways leading to cancer and new possibilities for medical advances. It is well known that genomic aberrations and an inability to properly maintain and repair genetic material enable tumor initiation and progression (1.Balmain A. Gray J. Ponder B. The genetics and genomics of cancer.Nat. Genet. 2003; 33: 238-244Crossref PubMed Scopus (456) Google Scholar). The large-scale mapping of cancer genomes has provided a detailed catalogue of mutations and polymorphisms that may translate into proteome variation and has left researchers wondering which genomic abnormalities drive tumor biology and which are functionally irrelevant. Although RNA sequencing can provide supporting evidence for the translation of DNA-level mutations into the proteome and alternative splicing, events, including signal peptide cleavage and a multitude of biologically active posttranslational modifications (PTMs) can significantly increase protein variation that RNA-seq data could not reliably predict. Recent studies have also shown that RNA transcript measurements poorly predict protein abundance differences between tumors (2.Zhang B. Wang J. Wang X. Zhu J. Liu Q. Shi Z. Chambers M.C. Zimmerman L.J. Shaddox K.F. Kim S. Davies S.R. Wang S. Wang P. Kinsinger C.R. Rivers R.C. Rodriguez H. Townsend R.R. Ellis M.J. Carr S.A. Tabb D.L. Coffey R.J. Slebos R.J. Liebler D.C. Proteogenomic characterization of human colon and rectal cancer.Nature. 2014; 513: 382-387Crossref PubMed Scopus (937) Google Scholar). Thus, detection of mutations and PTMs at the protein level provides a direct readout of the biological impact of cancer-related genomic abnormalities. Proteomic technologies, especially those based on mass spectrometry (MS), have the potential to detect genetic aberrations at the protein level. These technologies aim to identify the genes that give rise to proteins, characterize any modifications from the primary amino acid sequence, and quantify differences in relative expression levels between samples. Ideally, these techniques would be operable for all the proteins expressed in a cell, tissue, or other complex protein mixture; however, this is not the case. Different technologies exist, each with its unique strengths and weaknesses. Two forms of proteomics analyses are shotgun bottom-up (BU) 1The abbreviations used are:BUbottom upCPTACClinical Proteomic Tumor Analysis ConsortiumDEdifferential expressionGELFrEEgel-eluted liquid fraction entrapment electrophoresisK2C8Type 2 cytoskeletal keratin 8NGSnext-generation sequencingPDXpatient-derived xenograftPFRproteoform recordPTMposttranslational modificationSNPsingle nucleotide polymorphismTDtop downWHIMWashington University Human-in-Mousepsipounds per square inch. and top-down (TD) (3.Zhang Z. Wu S. Stenoien D.L. Pasa-Tolic L. High-throughput proteomics.Annu. Rev. Anal. Chem. 2014; 7: 427-454Crossref PubMed Scopus (147) Google Scholar). In BU proteomics, the proteins are digested with a protease, such as trypsin, prior to peptide detection and sequencing using tandem mass spectrometry. Protease digestion results in a complex mixture of peptides between 500–3,500 Da that are usually separated by reverse phase liquid chromatography or multidimensional chromatography in-line with a mass spectrometer (4.Lu B. Motoyama A. Ruse C. Venable J. Yates 3rd., J.R. Improving protein identification sensitivity by combining MS and MS/MS information for shotgun proteomics using LTQ-Orbitrap high mass accuracy data.Anal. Chem. 2008; 80: 2018-2025Crossref PubMed Scopus (54) Google Scholar, 5.Wiśniewski J.R. Zougman A. Mann M. Combination of FASP and StageTip-based fractionation allows in-depth analysis of the hippocampal membrane proteome.J. Proteome Res. 2009; 8: 5674-5678Crossref PubMed Scopus (437) Google Scholar). Precursor mass measurements, along with MS/MS fragmentation information, allow inference of the protein composition of the sample via these peptides. Extremely sensitive BU methods have been developed and are capable of identifying >5,000 protein groups within a single sample, with some peptide sequences present in multiple proteins or isoforms. Such shared peptides can lead to ambiguities in identifying the unique proteins present in the sample, the so called protein parsimony problem (6.Nesvizhskii A.I. Aebersold R. Interpretation of shotgun proteomic data: The protein inference problem.Mol. Cell. Proteomics. 2005; 4: 1419-1440Abstract Full Text Full Text PDF PubMed Scopus (791) Google Scholar). Also, enzymatic digestion can result in the loss of information about combinatorial PTMs and sequence variants. bottom up Clinical Proteomic Tumor Analysis Consortium differential expression gel-eluted liquid fraction entrapment electrophoresis Type 2 cytoskeletal keratin 8 next-generation sequencing patient-derived xenograft proteoform record posttranslational modification single nucleotide polymorphism top down Washington University Human-in-Mouse pounds per square inch. Top-down (TD) proteomics, on the other hand, does not rely on the use of proteases and examines proteins as a whole. In doing so, top-down proteomics can fully characterize the composition of individual proteoforms (7.Smith L.M. Kelleher N.L. Proteoform: A single term describing protein complexity.Nat. Methods. 2013; 10: 186-187Crossref PubMed Scopus (884) Google Scholar), including proteolysis products, signal peptide cleavage, sequence variants, and PTMs co-occurring on the same molecule. A typical TD workflow consists of single or multi-step protein separations, such as reverse-phase liquid chromatography (8.Ansong C. Wu S. Meng D. Liu X. Brewer H.M. Deatherage Kaiser B.L. Nakayasu E.S. Cort J.R. Pevzner P. Smith R.D. Heffron F. Adkins J.N. Pasa-Tolic L. Top-down proteomics reveals a unique protein S-thiolation switch in Salmonella typhimurium in response to infection-like conditions.Proc. Natl. Acad. Sci. U.S.A. 2013; 110: 10153-10158Crossref PubMed Scopus (124) Google Scholar) and GELFrEE (9.Tran J.C. Zamdborg L. Ahlf D.R. Lee J.E. Catherman A.D. Durbin K.R. Tipton J.D. Vellaichamy A. Kellie J.F. Li M. Wu C. Sweet S.M. Early B.P. Siuti N. LeDuc R.D. Compton P.D. Thomas P.M. Kelleher N.L. Mapping intact protein isoforms in discovery mode using top-down proteomics.Nature. 2011; 480: 254-258Crossref PubMed Scopus (509) Google Scholar), and the resulting protein fractions are further separated by liquid chromatography in line with a mass spectrometer. Advances in MS instruments and protein separations have allowed TD proteomics to become a robust technique for the identification and characterization of ∼2,000–3,000 proteoforms (8.Ansong C. Wu S. Meng D. Liu X. Brewer H.M. Deatherage Kaiser B.L. Nakayasu E.S. Cort J.R. Pevzner P. Smith R.D. Heffron F. Adkins J.N. Pasa-Tolic L. Top-down proteomics reveals a unique protein S-thiolation switch in Salmonella typhimurium in response to infection-like conditions.Proc. Natl. Acad. Sci. U.S.A. 2013; 110: 10153-10158Crossref PubMed Scopus (124) Google Scholar, 9.Tran J.C. Zamdborg L. Ahlf D.R. Lee J.E. Catherman A.D. Durbin K.R. Tipton J.D. Vellaichamy A. Kellie J.F. Li M. Wu C. Sweet S.M. Early B.P. Siuti N. LeDuc R.D. Compton P.D. Thomas P.M. Kelleher N.L. Mapping intact protein isoforms in discovery mode using top-down proteomics.Nature. 2011; 480: 254-258Crossref PubMed Scopus (509) Google Scholar, 10.Catherman A.D. Li M. Tran J.C. Durbin K.R. Compton P.D. Early B.P. Thomas P.M. Kelleher N.L. Top down proteomics of human membrane proteins from enriched mitochondrial fractions.Anal. Chem. 2013; 85: 1880-1888Crossref PubMed Scopus (58) Google Scholar). Unlike BU, TD proteomics routinely links proteins to their parental genes without the problem of protein inference. With the recent advent of methods for differential quantitation using TD on proteins below 30 kDa (11.Ntai I. Kim K. Fellers R.T. Skinner O.S. Smith 4th, A.D. Early B.P. Savaryn J.P. LeDuc R.D. Thomas P.M. Kelleher N.L. Applying label-free quantitation to top down proteomics.Anal. Chem. 2014; 86: 4961-4968Crossref PubMed Scopus (70) Google Scholar), it is now possible to begin comparing BU and TD techniques for three primary proteomic tasks: gene identification, whole proteoform characterization, and detection of differential expression. While some efforts have explored the complementarity of BU and TD technologies in the study of less complex proteomes (12.Hung C.-W. Jung S. Grötzinger J. Gelhaus C. Leippe M. Tholey A. Determination of disulfide linkages in antimicrobial peptides of the macin family by combination of top-down and bottom-up proteomics.J. Proteomics. 2014; 103: 216-226Crossref PubMed Scopus (19) Google Scholar, 13.Inserra I. Iavarone F. Martelli C. D'Angelo L. Delfino D. Rossetti D.V. Tamburrini G. Massimi L. Caldarelli M. Di Rocco C. Messana I. Castagnola M. Desiderio C. Proteomic study of pilocytic astrocytoma pediatric brain tumor intracystic fluid.J. Proteome Res. 2014; 13: 4594-4606Crossref PubMed Scopus (12) Google Scholar) and the structural analysis of antibodies (14.Dekker L. Wu S. Vanduijn M. Tolić N. Stingl C. Zhao R. Luider T. Pǎsa-Tolić L. An integrated top-down and bottom-up proteomic approach to characterize the antigen-binding fragment of antibodies.Proteomics. 2014; 14: 1239-1248Crossref PubMed Scopus (16) Google Scholar, 15.Liu X. Dekker L.J. Wu S. Vanduijn M.M. Luider T.M. Tolić N. Kou Q. Dvorkin M. Alexandrova S. Vyatkina K. Pǎsa-Tolić L. Pevzner P.A. De novo protein sequencing by combining top-down and bottom-up tandem mass spectra.J. Proteome Res. 2014; 13: 3241-3248Crossref PubMed Scopus (41) Google Scholar), herein we describe the first evaluation of the complementarity of BU and TD technologies for the qualitative and quantitative analysis of cancer proteomes. To accomplish this task, we employed two samples from patient-derived xenografts (PDXs) established from a basal-like (WHIM2-P32) and luminal B (WHIM16-P33) breast cancer (16.Ding L. Ellis M.J. Li S. Larson D.E. Chen K. Wallis J.W. Harris C.C. McLellan M.D. Fulton R.S. Fulton L.L. Abbott R.M. Hoog J. Dooling D.J. Koboldt D.C. Schmidt H. Kalicki J. Zhang Q. Chen L. Lin L. Wendl M.C. McMichael J.F. Magrini V.J. Cook L. McGrath S.D. Vickery T.L. Appelbaum E. Deschryver K. Davies S. Guintoli T. Lin L. Crowder R. Tao Y. Snider J.E. Smith S.M. Dukes A.F. Sanderson G.E. Pohl C.S. Delehaunty K.D. Fronick C.C. Pape K.A. Reed J.S. Robinson J.S. Hodges J.S. Schierding W. Dees N.D. Shen D. Locke D.P. Wiechert M.E. Eldred J.M. Peck J.B. Oberkfell B.J. Lolofie J.T. Du F. Hawkins A.E. O'Laughlin M.D. Bernard K.E. Cunningham M. Elliott G. Mason M.D. Thompson Jr., D.M. Ivanovich J.L. Goodfellow P.J. Perou C.M. Weinstock G.M. Aft R. Watson M. Ley T.J. Wilson R.K. Mardis E.R. Genome remodelling in a basal-like breast cancer metastasis and xenograft.Nature. 2010; 464: 999-1005Crossref PubMed Scopus (988) Google Scholar, 17.Li S. Shen D. Shao J. Crowder R. Liu W. Prat A. He X. Liu S. Hoog J. Lu C. Ding L. Griffith O.L. Miller C. Larson D. Fulton R.S. Harrison M. Mooney T. McMichael J.F. Luo J. Tao Y. Goncalves R. Schlosberg C. Hiken J.F. Saied L. Sanchez C. Giuntoli T. Bumb C. Cooper C. Kitchens R.T. Lin A. Phommaly C. Davies S.R. Zhang J. Kavuri M.S. McEachern D. Dong Y.Y. Ma C. Pluard T. Naughton M. Bose R. Suresh R. McDowell R. Michel L. Aft R. Gillanders W. DeSchryver K. Wilson R.K. Wang S. Mills G.B. Gonzalez-Angulo A. Edwards J.R. Maher C. Perou C.M. Mardis E.R. Ellis M.J. Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts.Cell Rep. 2013; 4: 1116-1130Abstract Full Text Full Text PDF PubMed Scopus (451) Google Scholar, 18.The Cancer Genome Atlas Network Comprehensive molecular portraits of human breast tumours.Nature. 2012; 490: 61-70Crossref PubMed Scopus (8299) Google Scholar). Patient-derived breast cancer xenografts have been established as reliable models of human tumors that provide a renewable resource for studying the human disease (16.Ding L. Ellis M.J. Li S. Larson D.E. Chen K. Wallis J.W. Harris C.C. McLellan M.D. Fulton R.S. Fulton L.L. Abbott R.M. Hoog J. Dooling D.J. Koboldt D.C. Schmidt H. Kalicki J. Zhang Q. Chen L. Lin L. Wendl M.C. McMichael J.F. Magrini V.J. Cook L. McGrath S.D. Vickery T.L. Appelbaum E. Deschryver K. Davies S. Guintoli T. Lin L. Crowder R. Tao Y. Snider J.E. Smith S.M. Dukes A.F. Sanderson G.E. Pohl C.S. Delehaunty K.D. Fronick C.C. Pape K.A. Reed J.S. Robinson J.S. Hodges J.S. Schierding W. Dees N.D. Shen D. Locke D.P. Wiechert M.E. Eldred J.M. Peck J.B. Oberkfell B.J. Lolofie J.T. Du F. Hawkins A.E. O'Laughlin M.D. Bernard K.E. Cunningham M. Elliott G. Mason M.D. Thompson Jr., D.M. Ivanovich J.L. Goodfellow P.J. Perou C.M. Weinstock G.M. Aft R. Watson M. Ley T.J. Wilson R.K. Mardis E.R. Genome remodelling in a basal-like breast cancer metastasis and xenograft.Nature. 2010; 464: 999-1005Crossref PubMed Scopus (988) Google Scholar, 19.Wu M. Jung L. Cooper A.B. Fleet C. Chen L. Breault L. Clark K. Cai Z. Vincent S. Bottega S. Shen Q. Richardson A. Bosenburg M. Naber S.P. DePinho R.A. Kuperwasser C. Robinson M.O. Dissecting genetic requirements of human breast tumorigenesis in a tissue transgenic model of human breast cancer in mice.Proc. Natl. Acad. Sci. U.S.A. 2009; 106: 7022-7027Crossref PubMed Scopus (48) Google Scholar, 20.Zhang X. Claerhout S. Prat A. Dobrolecki L.E. Petrovic I. Lai Q. Landis M.D. Wiechmann L. Schiff R. Giuliano M. Wong H. Fuqua S.W. Contreras A. Gutierrez C. Huang J. Mao S. Pavlick A.C. Froehlich A.M. Wu M.F. Tsimelzon A. Hilsenbeck S.G. Chen E.S. Zuloaga P. Shaw C.A. Rimawi M.F. Perou C.M. Mills G.B. Chang J.C. Lewis M.T. A renewable tissue resource of phenotypically stable, biologically and ethnically diverse, patient-derived human breast cancer xenograft models.Cancer Res. 2013; 73: 4885-4897Crossref PubMed Scopus (323) Google Scholar). These patient-derived xenograft tumor lines are genomically well-characterized (16.Ding L. Ellis M.J. Li S. Larson D.E. Chen K. Wallis J.W. Harris C.C. McLellan M.D. Fulton R.S. Fulton L.L. Abbott R.M. Hoog J. Dooling D.J. Koboldt D.C. Schmidt H. Kalicki J. Zhang Q. Chen L. Lin L. Wendl M.C. McMichael J.F. Magrini V.J. Cook L. McGrath S.D. Vickery T.L. Appelbaum E. Deschryver K. Davies S. Guintoli T. Lin L. Crowder R. Tao Y. Snider J.E. Smith S.M. Dukes A.F. Sanderson G.E. Pohl C.S. Delehaunty K.D. Fronick C.C. Pape K.A. Reed J.S. Robinson J.S. Hodges J.S. Schierding W. Dees N.D. Shen D. Locke D.P. Wiechert M.E. Eldred J.M. Peck J.B. Oberkfell B.J. Lolofie J.T. Du F. Hawkins A.E. O'Laughlin M.D. Bernard K.E. Cunningham M. Elliott G. Mason M.D. Thompson Jr., D.M. Ivanovich J.L. Goodfellow P.J. Perou C.M. Weinstock G.M. Aft R. Watson M. Ley T.J. Wilson R.K. Mardis E.R. Genome remodelling in a basal-like breast cancer metastasis and xenograft.Nature. 2010; 464: 999-1005Crossref PubMed Scopus (988) Google Scholar, 17.Li S. Shen D. Shao J. Crowder R. Liu W. Prat A. He X. Liu S. Hoog J. Lu C. Ding L. Griffith O.L. Miller C. Larson D. Fulton R.S. Harrison M. Mooney T. McMichael J.F. Luo J. Tao Y. Goncalves R. Schlosberg C. Hiken J.F. Saied L. Sanchez C. Giuntoli T. Bumb C. Cooper C. Kitchens R.T. Lin A. Phommaly C. Davies S.R. Zhang J. Kavuri M.S. McEachern D. Dong Y.Y. Ma C. Pluard T. Naughton M. Bose R. Suresh R. McDowell R. Michel L. Aft R. Gillanders W. DeSchryver K. Wilson R.K. Wang S. Mills G.B. Gonzalez-Angulo A. Edwards J.R. Maher C. Perou C.M. Mardis E.R. Ellis M.J. Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts.Cell Rep. 2013; 4: 1116-1130Abstract Full Text Full Text PDF PubMed Scopus (451) Google Scholar) and have been used to generate Comparison Reference (CompRef) samples within the Clinical Proteomic Tumor Analysis Consortium (CPTAC) (21.Ellis M.J. Gillette M. Carr S.A. Paulovich A.G. Smith R.D. Rodland K.K. Townsend R.R. Kinsinger C. Mesri M. Rodriguez H. Liebler D.C. Connecting genomic alterations to cancer biology with proteomics: The NCI Clinical Proteomic Tumor Analysis Consortium.Cancer Discov. 2013; 3: 1108-1112Crossref PubMed Scopus (170) Google Scholar) for performance validation of mass spectrometry protocols and workflows. Genome and RNA sequencing of the xenografts has provided us with lists of sequence variants, due to single nucleotide polymorphisms (SNPs), and novel splice junctions. Using these well-characterized xenograft models, we compared and contrasted the performance of BU and TD proteomic approaches to detect cancer-specific aberrations at the peptide and proteoform levels and to measure differential expression of proteins and proteoforms. This work represents the first large-scale integration of genomic, BU, and TD proteomic data for comparative analysis of PDXs comprised of the studies described in Table I. In brief, Study 1 was designed to provide information on the ability to detect tumor-specific features informed by prior RNA-seq data of these samples (16.Ding L. Ellis M.J. Li S. Larson D.E. Chen K. Wallis J.W. Harris C.C. McLellan M.D. Fulton R.S. Fulton L.L. Abbott R.M. Hoog J. Dooling D.J. Koboldt D.C. Schmidt H. Kalicki J. Zhang Q. Chen L. Lin L. Wendl M.C. McMichael J.F. Magrini V.J. Cook L. McGrath S.D. Vickery T.L. Appelbaum E. Deschryver K. Davies S. Guintoli T. Lin L. Crowder R. Tao Y. Snider J.E. Smith S.M. Dukes A.F. Sanderson G.E. Pohl C.S. Delehaunty K.D. Fronick C.C. Pape K.A. Reed J.S. Robinson J.S. Hodges J.S. Schierding W. Dees N.D. Shen D. Locke D.P. Wiechert M.E. Eldred J.M. Peck J.B. Oberkfell B.J. Lolofie J.T. Du F. Hawkins A.E. O'Laughlin M.D. Bernard K.E. Cunningham M. Elliott G. Mason M.D. Thompson Jr., D.M. Ivanovich J.L. Goodfellow P.J. Perou C.M. Weinstock G.M. Aft R. Watson M. Ley T.J. Wilson R.K. Mardis E.R. Genome remodelling in a basal-like breast cancer metastasis and xenograft.Nature. 2010; 464: 999-1005Crossref PubMed Scopus (988) Google Scholar, 17.Li S. Shen D. Shao J. Crowder R. Liu W. Prat A. He X. Liu S. Hoog J. Lu C. Ding L. Griffith O.L. Miller C. Larson D. Fulton R.S. Harrison M. Mooney T. McMichael J.F. Luo J. Tao Y. Goncalves R. Schlosberg C. Hiken J.F. Saied L. Sanchez C. Giuntoli T. Bumb C. Cooper C. Kitchens R.T. Lin A. Phommaly C. Davies S.R. Zhang J. Kavuri M.S. McEachern D. Dong Y.Y. Ma C. Pluard T. Naughton M. Bose R. Suresh R. McDowell R. Michel L. Aft R. Gillanders W. DeSchryver K. Wilson R.K. Wang S. Mills G.B. Gonzalez-Angulo A. Edwards J.R. Maher C. Perou C.M. Mardis E.R. Ellis M.J. Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts.Cell Rep. 2013; 4: 1116-1130Abstract Full Text Full Text PDF PubMed Scopus (451) Google Scholar). Study 2 tested the applicability of the recently established label-free top-down quantitative proteomics platform (11.Ntai I. Kim K. Fellers R.T. Skinner O.S. Smith 4th, A.D. Early B.P. Savaryn J.P. LeDuc R.D. Thomas P.M. Kelleher N.L. Applying label-free quantitation to top down proteomics.Anal. Chem. 2014; 86: 4961-4968Crossref PubMed Scopus (70) Google Scholar) for the analysis of tumors. Finally, Study 3 sought to detect differential expression of proteins and proteoforms between basal and luminal B breast cancer samples for the low molecular weight proteome (<30 kDa).Table ISummary of experiments comparing the performance of TD and BU proteomics to detect and quantify cancer specific aberrationsStudyDescriptionBottom-upTop-down1Qualitative comparison of WHIM2 and WHIM16 (BU/TD) protein MW range 0–100 kDaaProteins were fractionated using GELFrEE. Representative fractionations for each study are illustrated in Supplemental Fig. S1.10,453 proteinsbThe term proteins corresponds to protein groups as defined by Peak Studio, ver. 7. (82,156 peptides) 197 SNPs/11 NSJsdIdentification required a spectrum count of 3 within a single LC/MS run.2,006 proteoforms (370 proteinscthe term proteins corresponds to a single RefSeq identifier.) 5 SNPs/0 NSJs2Label-free TD quantitation of WHIM2 vs WHIM16 protein MW range 0–30 kDaaProteins were fractionated using GELFrEE. Representative fractionations for each study are illustrated in Supplemental Fig. S1.N/Penot performed.1,334 proteoforms (218 proteinscthe term proteins corresponds to a single RefSeq identifier.) 3 SNPs/1 NSJs3Quantitative comparison of WHIM2 and WHIM16 protein MW range 0–30 kDaaProteins were fractionated using GELFrEE. Representative fractionations for each study are illustrated in Supplemental Fig. S1.3,367 proteinsbThe term proteins corresponds to protein groups as defined by Peak Studio, ver. 7. (49,185 peptides) 41 SNPs / 11 NSJsdIdentification required a spectrum count of 3 within a single LC/MS run.3,125 proteoforms (438 proteinscthe term proteins corresponds to a single RefSeq identifier.) 7 SNPs/1 NSJsa Proteins were fractionated using GELFrEE. Representative fractionations for each study are illustrated in Supplemental Fig. S1.b The term proteins corresponds to protein groups as defined by Peak Studio, ver. 7.c the term proteins corresponds to a single RefSeq identifier.d Identification required a spectrum count of 3 within a single LC/MS run.e not performed. Open table in a new tab Cryopulverization of tumor xenografts was performed at Washington University in St. Louis using the established protocols of CPTAC as previously described (22.Mertins P. Yang F. Liu T. Mani D.R. Petyuk V.A. Gillette M.A. Clauser K.R. Qiao J.W. Gritsenko M.A. Moore R.J. Levine D.A. Townsend R. Erdmann-Gilmore P. Snider J.E. Davies S.R. Ruggles K.V. Fenyo D. Kitchens R.T. Li S. Olvera N. Dao F. Rodriguez H. Chan D.W. Liebler D. White F. Rodland K.D. Mills G.B. Smith R.D. Paulovich A.G. Ellis M. Carr S.A. Ischemia in tumors induces early and sustained phosphorylation changes in stress kinase pathways but does not affect global protein levels.Mol. Cell. Proteomics. 2014; 13: 1690-1704Abstract Full Text Full Text PDF PubMed Scopus (257) Google Scholar). One of the driving motivations for creating the CompRef samples was to evaluate the capacity for mass spectrometry protocols to consistently provide both qualitative and quantitative data between samples. The two Washington University Human-in-Mouse (WHIM) models chosen for this purpose represent two subtypes of breast cancer with very different intrinsic biologies (17.Li S. Shen D. Shao J. Crowder R. Liu W. Prat A. He X. Liu S. Hoog J. Lu C. Ding L. Griffith O.L. Miller C. Larson D. Fulton R.S. Harrison M. Mooney T. McMichael J.F. Luo J. Tao Y. Goncalves R. Schlosberg C. Hiken J.F. Saied L. Sanchez C. Giuntoli T. Bumb C. Cooper C. Kitchens R.T. Lin A. Phommaly C. Davies S.R. Zhang J. Kavuri M.S. McEachern D. Dong Y.Y. Ma C. Pluard T. Naughton M. Bose R. Suresh R. McDowell R. Michel L. Aft R. Gillanders W. DeSchryver K. Wilson R.K. Wang S. Mills G.B. Gonzalez-Angulo A. Edwards J.R. Maher C. Perou C.M. Mardis E.R. Ellis M.J. Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts.Cell Rep. 2013; 4: 1116-1130Abstract Full Text Full Text PDF PubMed Scopus (451) Google Scholar, 18.The Cancer Genome Atlas Network Comprehensive molecular portraits of human breast tumours.Nature. 2012; 490: 61-70Crossref PubMed Scopus (8299) Google Scholar). WHIM2 is derived from a basal-like (ER-, PR+, Her2-) breast cancer whereas WHIM16 is derived from a luminal B (ER+, PR+, Her2-) breast cancer (16.Ding L. Ellis M.J. Li S. Larson D.E. Chen K. Wallis J.W. Harris C.C. McLellan M.D. Fulton R.S. Fulton L.L. Abbott R.M. Hoog J. Dooling D.J. Koboldt D.C. Schmidt H. Kalicki J. Zhang Q. Chen L. Lin L. Wendl M.C. McMichael J.F. Magrini V.J. Cook L. McGrath S.D. Vickery T.L. Appelbaum E. Deschryver K. Davies S. Guintoli T. Lin L. Crowder R. Tao Y. Snider J.E. Smith S.M. Dukes A.F. Sanderson G.E. Pohl C.S. Delehaunty K.D. Fronick C.C. Pape K.A. Reed J.S. Robinson J.S. Hodges J.S. Schierding W. Dees N.D. Shen D. Locke D.P. Wiechert M.E. Eldred J.M. Peck J.B. Oberkfell B.J. Lolofie J.T. Du F. Hawkins A.E. O'Laughlin M.D. Bernard K.E. Cunningham M. Elliott G. Mason M.D. Thompson Jr., D.M. Ivanovich J.L. Goodfellow P.J. Perou C.M. Weinstock G.M. Aft R. Watson M. Ley T.J. Wilson R.K. Mardis E.R. Genome remodelling in a basal-like breast cancer metastasis and xenograft.Nature. 2010; 464: 999-1005Crossref PubMed Scopus (988) Google Scholar, 17.Li S. Shen D. Shao J. Crowder R. Liu W. Prat A. He X. Liu S. Hoog J. Lu C. Ding L. Griffith O.L. Miller C. Larson D. Fulton R.S. Harrison M. Mooney T. McMichael J.F. Luo J. Tao Y. Goncalves R. Schlosberg C. Hiken J.F. Saied L. Sanchez C. Giuntoli T. Bumb C. Cooper C. Kitchens R.T. Lin A. Phommaly C. Davies S.R. Zhang J. Kavuri M.S. McEachern D. Dong Y.Y. Ma C. Pluard T. Naughton M. Bose R. Suresh R. McDowell R. Michel L. Aft R. Gillanders W. DeSchryver K. Wilson R.K. Wang S. Mills G.B. Gonzalez-Angulo A. Edwards J.R. Maher C. Perou C.M. Mardis E.R. Ellis M.J. Endocrine-therapy-resistant ESR1 variants revealed by genomic characterization of breast-cancer-derived xenografts.Cell Rep. 2013; 4: 1116-1130Abstract Full Text Full Text PDF PubMed Scopus (451) Google Scholar). To prepare the samples, tumors were harvested from established xenografts, pooled, and subjected to cryopulverization to create two different homogeneous samples, P32 (WHIM2) and P33 (WHIM16). The pulverized tissue from each CompRef sample (263 mg WHIM16, P33) and (257 mg WHIM2, P32) was solubilized in 1,200 μl or 1,100 μl lysis buffer (4% sodium dodecyl sulfate, 100 mm Tris-HCl, pH 7.5) supplemented wit
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