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Shao S, Neely BA, Kao TC, Eckhaus J, Bourgeois J, Brooks J, Jones EE, Drake RR, Zhu K. Proteomic Profiling of Serial Prediagnostic Serum Samples for Early Detection of Colon Cancer in the U.S. Military. Cancer Epidemiol Biomarkers Prev 2016; 26:711-718. [PMID: 28003179 DOI: 10.1158/1055-9965.epi-16-0732] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Revised: 11/23/2016] [Accepted: 12/07/2016] [Indexed: 01/24/2023] Open
Abstract
Background: Serum proteomic biomarkers offer a promising approach for early detection of cancer. In this study, we aimed to identify proteomic profiles that could distinguish colon cancer cases from controls using serial prediagnostic serum samples.Methods: This was a nested case-control study of active duty military members. Cases consisted of 264 patients diagnosed with colon cancer between 2001 and 2009. Controls were matched to cases on age, gender, race, serum sample count, and collection date. We identified peaks that discriminated cases from controls using random forest data analysis with a 2/3 training and 1/3 validation dataset. We then included epidemiologic data to see whether further improvement of model performance was obtainable. Proteins that corresponded to discriminatory peaks were identified.Results: Peaks with m/z values of 3,119.32, 2,886.67, 2,939.23, and 5,078.81 were found to discriminate cases from controls with a sensitivity of 69% and a specificity of 67% in the year before diagnosis. When smoking status was included, sensitivity increased to 76% while histories of other cancer and tonsillectomy raised specificity to 76%. Peaks at 2,886.67 and 3,119.32 m/z were identified as histone acetyltransferases while 2,939.24 m/z was a transporting ATPase subunit.Conclusions: Proteomic profiles in the year before cancer diagnosis have the potential to discriminate colon cancer patients from controls, and the addition of epidemiologic information may increase the sensitivity and specificity of discrimination.Impact: Our findings indicate the potential value of using serum prediagnostic proteomic biomarkers in combination with epidemiologic data for early detection of colon cancer. Cancer Epidemiol Biomarkers Prev; 26(5); 711-8. ©2016 AACR.
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Affiliation(s)
- Stephanie Shao
- Division of Epidemiology and Biostatistics, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland.,John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Bethesda, Maryland
| | - Benjamin A Neely
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics and MUSC Proteomics Center, Medical University of South Carolina, Charleston, South Carolina
| | - Tzu-Cheg Kao
- Division of Epidemiology and Biostatistics, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Janet Eckhaus
- Division of Epidemiology and Biostatistics, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Jolie Bourgeois
- Division of Epidemiology and Biostatistics, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland
| | - Jasmin Brooks
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics and MUSC Proteomics Center, Medical University of South Carolina, Charleston, South Carolina
| | - Elizabeth E Jones
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics and MUSC Proteomics Center, Medical University of South Carolina, Charleston, South Carolina
| | - Richard R Drake
- Department of Cell and Molecular Pharmacology and Experimental Therapeutics and MUSC Proteomics Center, Medical University of South Carolina, Charleston, South Carolina
| | - Kangmin Zhu
- Division of Epidemiology and Biostatistics, Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Sciences, Bethesda, Maryland. .,John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Bethesda, Maryland
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Potjer TP, Mertens BJ, Nicolardi S, van der Burgt YEM, Bonsing BA, Mesker WE, Tollenaar RAEM, Vasen HFA. Application of a Serum Protein Signature for Pancreatic Cancer to Separate Cases from Controls in a Pancreatic Surveillance Cohort. Transl Oncol 2016; 9:242-7. [PMID: 27267843 PMCID: PMC4907893 DOI: 10.1016/j.tranon.2016.03.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Revised: 03/02/2016] [Accepted: 03/08/2016] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Pancreatic cancer (PC) surveillance is currently offered to individuals with a genetic predisposition to PC, but routinely used radiological screening modalities are not entirely reliable in detecting early-stage PC or its precursor lesions. We recently identified a discriminating PC biomarker signature in a sporadic patient cohort. In this study, we investigated if protein profiling can accurately distinguish PC from non-PC in a pancreatic surveillance cohort of genetically predisposed individuals. METHODS Serum samples of 66 individuals with a CDKN2A germline mutation who participated in the pancreatic surveillance program (5 cases, 61 controls) were obtained following a standardized protocol. After sample clean-up, peptide and protein profiles were obtained on an ultrahigh-resolution matrix-assisted laser desorption/ionization-Fourier transform ion cyclotron resonance mass spectrometry platform. A discriminant score for each sample was calculated with a previously designed prediction rule, and the median discriminant scores of cases and controls were compared. Individuals with precursor lesions of PC (n = 4) and individuals with a recent diagnosis of melanoma (n = 4) were also separately considered. RESULTS Cases had a higher median discriminant score than controls (0.26 vs 0.016; P = .001). The only individual with pathologically confirmed precursor lesions of PC could also be clearly distinguished from controls, and having a (recent) medical history of melanoma did not influence the protein signatures. CONCLUSIONS Peptide and protein signatures are able to accurately distinguish PC cases from controls in a pancreatic surveillance setting. Mass spectrometry-based protein profiling therefore seems to be a promising candidate for implementation in the pancreatic surveillance program as an additional screening modality.
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Affiliation(s)
- Thomas P Potjer
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands.
| | - Bart J Mertens
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands
| | - Simone Nicolardi
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
| | - Yuri E M van der Burgt
- Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, the Netherlands
| | - Bert A Bonsing
- Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands
| | - Wilma E Mesker
- Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands
| | - Rob A E M Tollenaar
- Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands
| | - Hans F A Vasen
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Leiden, the Netherlands
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Nicolardi S, Bogdanov B, Deelder AM, Palmblad M, van der Burgt YEM. Developments in FTICR-MS and Its Potential for Body Fluid Signatures. Int J Mol Sci 2015; 16:27133-44. [PMID: 26580595 PMCID: PMC4661870 DOI: 10.3390/ijms161126012] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2015] [Revised: 11/03/2015] [Accepted: 11/05/2015] [Indexed: 01/01/2023] Open
Abstract
Fourier transform mass spectrometry (FTMS) is the method of choice for measurements that require ultra-high resolution. The establishment of Fourier transform ion cyclotron resonance (FTICR) MS, the availability of biomolecular ionization techniques and the introduction of the Orbitrap™ mass spectrometer have widened the number of FTMS-applications enormously. One recent example involves clinical proteomics using FTICR-MS to discover and validate protein biomarker signatures in body fluids such as serum or plasma. These biological samples are highly complex in terms of the type and number of components, their concentration range, and the structural identity of each species, and thus require extensive sample cleanup and chromatographic separation procedures. Clearly, such an elaborate and multi-step sample preparation process hampers high-throughput analysis of large clinical cohorts. A final MS read-out at ultra-high resolution enables the analysis of a more complex sample and can thus simplify upfront fractionations. To this end, FTICR-MS offers superior ultra-high resolving power with accurate and precise mass-to-charge ratio (m/z) measurement of a high number of peptides and small proteins (up to 20 kDa) at isotopic resolution over a wide mass range, and furthermore includes a wide variety of fragmentation strategies to characterize protein sequence and structure, including post-translational modifications (PTMs). In our laboratory, we have successfully applied FTICR “next-generation” peptide profiles with the purpose of cancer disease classifications. Here we will review a number of developments and innovations in FTICR-MS that have resulted in robust and routine procedures aiming for ultra-high resolution signatures of clinical samples, exemplified with state-of-the-art examples for serum and saliva.
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Affiliation(s)
- Simone Nicolardi
- Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), PO Box 9600, 2300 RC Leiden, The Netherlands.
| | - Bogdan Bogdanov
- Perkin Elmer, San Jose Technology Center, San Jose, CA 95134, USA.
| | - André M Deelder
- Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), PO Box 9600, 2300 RC Leiden, The Netherlands.
| | - Magnus Palmblad
- Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), PO Box 9600, 2300 RC Leiden, The Netherlands.
| | - Yuri E M van der Burgt
- Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), PO Box 9600, 2300 RC Leiden, The Netherlands.
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Bladergroen MR, van der Burgt YEM. Solid-phase extraction strategies to surmount body fluid sample complexity in high-throughput mass spectrometry-based proteomics. JOURNAL OF ANALYTICAL METHODS IN CHEMISTRY 2015; 2015:250131. [PMID: 25692071 PMCID: PMC4322654 DOI: 10.1155/2015/250131] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2014] [Revised: 01/08/2015] [Accepted: 01/08/2015] [Indexed: 05/08/2023]
Abstract
For large-scale and standardized applications in mass spectrometry- (MS-) based proteomics automation of each step is essential. Here we present high-throughput sample preparation solutions for balancing the speed of current MS-acquisitions and the time needed for analytical workup of body fluids. The discussed workflows reduce body fluid sample complexity and apply for both bottom-up proteomics experiments and top-down protein characterization approaches. Various sample preparation methods that involve solid-phase extraction (SPE) including affinity enrichment strategies have been automated. Obtained peptide and protein fractions can be mass analyzed by direct infusion into an electrospray ionization (ESI) source or by means of matrix-assisted laser desorption ionization (MALDI) without further need of time-consuming liquid chromatography (LC) separations.
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Affiliation(s)
- Marco R. Bladergroen
- Leiden University Medical Center (LUMC), Center for Proteomics and Metabolomics, P.O. Box 9600, 2300 RC Leiden, Netherlands
| | - Yuri E. M. van der Burgt
- Leiden University Medical Center (LUMC), Center for Proteomics and Metabolomics, P.O. Box 9600, 2300 RC Leiden, Netherlands
- *Yuri E. M. van der Burgt:
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