Proteomic Classification of Acute Leukemias by Alignment-Based Quantitation of LC鈥揗S/MS Data Sets
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- 作者：Eric J. Foss ; Dragan Radulovic ; Derek L. Stirewalt ; Jerald Radich ; Olga Sala-Torra ; Era L. Pogosova-Agadjanyan ; Shawna M. Hengel ; Keith R. Loeb ; H. Joachim Deeg ; Soheil Meshinchi ; David R. Goodlett ; Antonio Bedalov
- 刊名：Journal of Proteome Research
- 出版年：2012
- 出版时间：October 5, 2012
- 年：2012
- 卷：11
- 期：10
- 页码：5005-5010
- 全文大小：289K
- 年卷期：v.11,no.10(October 5, 2012)
- ISSN：1535-3907
文摘
Despite immense interest in the proteome as a source of biomarkers in cancer, mass spectrometry has yet to yield a clinically useful protein biomarker for tumor classification. To explore the potential of a particular class of mass spectrometry-based quantitation approaches, label-free alignment of liquid chromatography coupled to tandem mass spectrometry (LC鈥揗S/MS) data sets, for the identification of biomarkers for acute leukemias, we asked whether a label-free alignment algorithm could distinguish known classes of leukemias on the basis of their proteomes. This approach to quantitation involves (1) computational alignment of MS1 peptide peaks across large numbers of samples; (2) measurement of the relative abundance of peptides across samples by integrating the area under the curve of the MS1 peaks; and (3) assignment of peptide IDs to those quantified peptide peaks on the basis of the corresponding MS2 spectra. We extracted proteins from blasts derived from four patients with acute myeloid leukemia (AML, acute leukemia of myeloid lineage) and five patients with acute lymphoid leukemia (ALL, acute leukemia of lymphoid lineage). Mobilized CD34+ cells purified from peripheral blood of six healthy donors and mononuclear cells (MNC) from the peripheral blood of two healthy donors were used as healthy controls. Proteins were analyzed by LC鈥揗S/MS and quantified with a label-free alignment-based algorithm developed in our laboratory. Unsupervised hierarchical clustering of blinded samples separated the samples according to their known biological characteristics, with each sample group forming a discrete cluster. The four proteins best able to distinguish CD34+, AML, and ALL were all either known biomarkers or proteins whose biological functions are consistent with their ability to distinguish these classes. We conclude that alignment-based label-free quantitation of LC鈥揗S/MS data sets can, at least in some cases, robustly distinguish known classes of leukemias, thus opening the possibility that large scale studies using such algorithms can lead to the identification of clinically useful biomarkers.r>