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.