Proteomic classification of acute leukemias by alignment-based quantitation of LC-MS/MS data sets.
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-MS/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-MS/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-MS/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.
Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, United States.
Eric J
EJ
Foss
Dragan
D
Radulovic
Derek L
DL
Stirewalt
Jerald
J
Radich
Olga
O
Sala-Torra
Era L
EL
Pogosova-Agadjanyan
Shawna M
SM
Hengel
Keith R
KR
Loeb
H Joachim
HJ
Deeg
Soheil
S
Meshinchi
David R
DR
Goodlett
Antonio
A
Bedalov
D018952:Antigens, CD34 / Q000378:metabolism; D016022:Case-Control Studies; D016000:Cluster Analysis; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000097:blood* / Q000145:classification; D007963:Leukocytes, Mononuclear / Q000378:metabolism*; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000097:blood* / Q000145:classification; D040901:Proteomics; D053719:Tandem Mass Spectrometry
D018952:Antigens, CD34