Literature DB >> 16322896

Serum biomarkers for detection of breast cancers: A prospective study.

Carole Mathelin1, Anne Cromer, Corinne Wendling, Catherine Tomasetto, Marie-Christine Rio.   

Abstract

Using surface-enhanced laser desorption/ionization-time of flight (SELDI-TOF), Li et al. [Clin Chem 48(8): 1296-1304, 2002] identified 3 serum biomarkers, BC1 (4.3 kDa), BC2 (8.1 kDa) and BC3 (8.9 kDa), whose combination significantly detects breast cancer patients from non-cancer controls. This work aimed to validate these biomarkers in an independent prospective study. We screened 89 serum samples including 49 breast cancers at pT1-4N0M0 (n = 23), pT1-4N1-3M0 (n = 17) or pT1-4N0-3M1 (n = 9) stages, 13 benign breast diseases and 27 healthy women. The BC2 biomarker significance was not recovered. However, we found 2 peaks that we named BC1a (4286 Da) and BC1b (4302 Da), that could correspond to Li's BC1 since they significantly decrease in breast cancers (p < 0.00007 and p < 0.0002, respectively). Similarly, BC3a (8919 Da) and BC3b (8961 Da) are significantly increased in breast cancers (p < 0.02 and p < 0.0002, respectively) and could correspond to the Li's BC3. For each biomarker we defined stringent (no errors) and flexible (less than 10% errors) cut-off values and tested the power of the combined BC1a/BC1b/BC3a/BC3b stringent and flexible profiles to discriminate breast cancers. They identified 33% and 45% cancers, respectively. Applied to the same series, Ca 15.3 test identified 22% patients. Interestingly, in association with the BC1a/BC1b/BC3a/BC3b profiles, Ca 15.3 improved the number of detected cancers indicating that it is an independent parameter. Collectively, our data partially validate those of Li's study and confirm that the BC1 and BC3 biomarkers are helpful for breast cancer diagnosis.

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Year:  2006        PMID: 16322896     DOI: 10.1007/s10549-005-9046-2

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  20 in total

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2.  Search for breast cancer biomarkers in fractionated serum samples by protein profiling with SELDI-TOF MS.

Authors:  Annemieke W J Opstal-van Winden; Jos H Beijnen; Arnoud Loof; Waander L van Heerde; Roel Vermeulen; Petra H M Peeters; Carla H van Gils
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Review 3.  Challenges to developing proteomic-based breast cancer diagnostics.

Authors:  Richard R Drake; Lisa H Cazares; E Ellen Jones; Thomas W Fuller; O John Semmes; Christine Laronga
Journal:  OMICS       Date:  2011-02-19

4.  Mass spectrometry-based serum proteome pattern analysis in molecular diagnostics of early stage breast cancer.

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Journal:  J Transl Med       Date:  2009-07-13       Impact factor: 5.531

5.  Methodology and applications of disease biomarker identification in human serum.

Authors:  Ziad J Sahab; Suzan M Semaan; Qing-Xiang Amy Sang
Journal:  Biomark Insights       Date:  2007-02-14

6.  An efficient biomarker panel for diagnosis of breast cancer using surface-enhanced laser desorption ionization time-of-flight mass spectrometry.

Authors:  Turkan Yigitbasi; Gizem Calibasi-Kocal; Nihal Buyukuslu; Murat Kemal Atahan; Hakan Kupeli; Seyran Yigit; Ercument Tarcan; Yasemin Baskin
Journal:  Biomed Rep       Date:  2018-01-15

7.  Analysis of the differences of serum protein mass spectrometry in patients with triple negative breast cancer and non-triple negative breast cancer.

Authors:  Ai-Na Liu; Ping Sun; Jian-Nan Liu; Cai-Yan Yu; Hua-Jun Qu; Ai-Hong Jiao; Liang-Ming Zhang
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8.  Comparison of tear protein levels in breast cancer patients and healthy controls using a de novo proteomic approach.

Authors:  Daniel Böhm; Ksenia Keller; Julia Pieter; Nils Boehm; Dominik Wolters; Wulf Siggelkow; Antje Lebrecht; Marcus Schmidt; Heinz Kölbl; Norbert Pfeiffer; Franz-Hermann Grus
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9.  Mass spectrometry-based analysis of therapy-related changes in serum proteome patterns of patients with early-stage breast cancer.

Authors:  Monika Pietrowska; Joanna Polanska; Lukasz Marczak; Katarzyna Behrendt; Elzbieta Nowicka; Maciej Stobiecki; Andrzej Polanski; Rafal Tarnawski; Piotr Widlak
Journal:  J Transl Med       Date:  2010-07-11       Impact factor: 5.531

10.  Non-invasive proteomics-thinking about personalized breast cancer screening and treatment.

Authors:  Manuel Debald; Matthias Wolfgarten; Gisela Walgenbach-Brünagel; Walther Kuhn; Michael Braun
Journal:  EPMA J       Date:  2010-07-14       Impact factor: 6.543

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