Proteomic classification of acute leukemias by alignment-based quantitation of LC-MS/MS data sets

Abstract

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.

Figure 1

Dendrogram and heat map showing similarities between 9 patient (ALL and AML) and 8 healthy (CD34+ and MNC) samples. Levels of 639 proteins were used to cluster samples according to Euclidean distance. Visual inspection of both the dendrogram and heat map demonstrate clear clustering of the four sample types, with AML and CD34+ showing the greatest degrees of similarity, as is expected. Colors indicate Euclidean distances between pairs of samples.

Figure 2

A. Heat map depicting relative protein levels and dendrogram constructed on the basis of Euclidean distances between these protein levels showing the 91 proteins we identified as capable of distinguishing the 5 ALL samples from the 6 CD34+ samples. Each column represents a patient and each row represents a protein. Higher and lower protein levels are indicated in red and green, respectively. Gray boxes indicate missing values. The dendrograms on the top and left show unsupervised hierarchical clustering of patient samples and proteins, respectively. The "all or none" proteins are indicated by asterixes on the right. B. Heat map and dendrogram as in figure 2A showing the 71 proteins we identified as capable of separating 5 ALL samples from 4 AML samples. C. Heat map and dendrogram as in figure 2A showing the 17 proteins we identified as capable of separating 6 CD34+ samples from 4 AML samples.

Figure 2

A. Heat map depicting relative protein levels and dendrogram constructed on the basis of Euclidean distances between these protein levels showing the 91 proteins we identified as capable of distinguishing the 5 ALL samples from the 6 CD34+ samples. Each column represents a patient and each row represents a protein. Higher and lower protein levels are indicated in red and green, respectively. Gray boxes indicate missing values. The dendrograms on the top and left show unsupervised hierarchical clustering of patient samples and proteins, respectively. The "all or none" proteins are indicated by asterixes on the right. B. Heat map and dendrogram as in figure 2A showing the 71 proteins we identified as capable of separating 5 ALL samples from 4 AML samples. C. Heat map and dendrogram as in figure 2A showing the 17 proteins we identified as capable of separating 6 CD34+ samples from 4 AML samples.

Figure 2

A. Heat map depicting relative protein levels and dendrogram constructed on the basis of Euclidean distances between these protein levels showing the 91 proteins we identified as capable of distinguishing the 5 ALL samples from the 6 CD34+ samples. Each column represents a patient and each row represents a protein. Higher and lower protein levels are indicated in red and green, respectively. Gray boxes indicate missing values. The dendrograms on the top and left show unsupervised hierarchical clustering of patient samples and proteins, respectively. The "all or none" proteins are indicated by asterixes on the right. B. Heat map and dendrogram as in figure 2A showing the 71 proteins we identified as capable of separating 5 ALL samples from 4 AML samples. C. Heat map and dendrogram as in figure 2A showing the 17 proteins we identified as capable of separating 6 CD34+ samples from 4 AML samples.