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Review
. 2018 Jul;15(7):613-622.
doi: 10.1080/14789450.2018.1487781. Epub 2018 Jul 6.

Shining a light on cell signaling in leukemia through proteomics: relevance for the clinic

Fieke W Hoff  1 , Chenyue W Hu  2 , Amina A Qutub  2 , Eveline S J M de Bont  1 , Terzah M Horton  3 , Steven M Kornblau  4
Affiliations
Review

Shining a light on cell signaling in leukemia through proteomics: relevance for the clinic

Fieke W Hoff et al. Expert Rev Proteomics. 2018 Jul.

Abstract

Although cure rates for acute leukemia have steadily improved over the past decades, leukemia remains a deadly disease. Enhanced risk stratification and new therapies are needed to improve outcome. Extensive genetic analyses have identified many mutations that contribute to the development of leukemia. However, most mutations occur infrequently and most gene alterations have been difficult to _target. Most patients have more than one driver mutation in combination with secondary mutations, that result in a leukemic transformation via the alteration of proteins. The proteomics of acute leukemia could more directly identify proteins to facilitate risk stratification, predict chemoresistance and aid selection of therapy. Areas covered: This review discusses aberrantly expressed proteins identified by mass spectrometry and reverse phase protein arrays and their relationship to survival. In addition, we will discuss proteins in the context of functionally related protein groups. Expert commentary: Proteomics is a powerful tool to analyze protein abundance and functional alterations simultaneously for large numbers of patients. In the forthcoming years, validation of tools to quickly assess protein levels to enable routine rapid profiling of proteins with differential abundance and functional activation may be used as adjuncts to aid in therapy selection and to provide additional prognostic insights.

Keywords: Leukemia; Reverse Phase Protein Array (RPPA); biomarkers (or) biomarker discovery.

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Conflict of interest statement

Declaration of interest

The authors declare no conflict of interest.

Figures

Figure 1.
Figure 1.. Computational workflow.
The foundation for modeling the protein expression was the establishment of 31 “Protein Functional Groups” (PFG); groups of protein pathways known from the existing literature. (A) Samples were collected from bone marrow or peripheral blood and protein lysates prepared from the blast population. (B) Protein clusters were identified within each PFG using the “Progeny clustering” algorithm [55]. Principal component analysis was used to determine if a protein cluster was similar or different from the normal CD34+ samples. Protein clusters were associated with therapy response and patient demographics. (C) Protein clusters from all 31 PFG were then assembled into a large binary dataset; 1 (blue) if a member of a protein cluster, 0 (yellow) if not. Block clustering analysis was used to search for correlation between protein clusters. Protein clusters from various PFG that were strongly correlated with each other were defined as “Protein Constellation” (horizontally). Patients that expressed similar combinations of protein constellations were grouped into patient signatures (vertically). Protein signatures were correlated with outcome and disease and patient characteristics. Within each protein signature and constellation, lists of proteins that were significantly up or down regulated compared to the normal CD34+ samples were identified.
Figure 2.
Figure 2.
Functional patterning in (A) 205 adult AML patients, (B) 73 pediatric ALL, (C) 95 pediatric AML patients, and the (D) combination of the 95 AML and 73 ALL patients. Each horizontal line represents a single protein cluster. Each vertical line represents an individual patient. Blue points indicate protein cluster membership of a patient. For each disease group, block clustering identified strong correlation between protein clusters and identified 10–12 “Protein Constellations” (denoted by the colored annotation bar on the left of the figures). Patients that expressed similar patterns of constellation membership were defined as protein signature (denoted by the colored annotation bar on the top of the figures).
Figure 3.
Figure 3.
Example of the protein _target identification in adult AML patients. Proteins that were statistically differentially expressed compared to the healthy CD34+ samples were identified for signature 2. Colors represent the median expression within that particular signature compared to the normal CD34+ samples. Yellow-orange-red proteins were significantly overexpressed versus those in cyan-blue-navy that were significantly under expressed.
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