Fieke W. Hoff

Histone Modification Patterns Using RPPA-Based Profiling Predict Outcome in Acute Myeloid Leukemia Patients

Posttranslational histone tail modifications are known to play a role in leukemogenesis and are therapeutic targets. A global analysis of the level and patterns of expression of multiple histone‐modifying proteins (HMP) in acute myeloid leukemia (AML) and the effect of different patterns of expression on outcome and prognosis has not been investigated in AML patients. Here we analyzed 20 HMP by reverse phase protein array (RPPA) in a cohort of 205 newly diagnosed AML patients. Protein levels were correlated with patient and disease characteristics, including survival and mutational state. We identified different protein clusters characterized by higher (more on) or lower (more off) expression of HMP, relative to normal CD34+ cells. On state of HMP was associated with poorer outcome compared to normal‐like and a more off state. FLT3 mutated AML patients were significantly overrepresented in the more on state. DNA methylation related mutations showed no correlation with the different HMP states. In this study, we demonstrate for the first time that HMP form recurrent patterns of expression and that these significantly correlate with survival in newly diagnosed AML patients.

Identifying Cancer Specific Metabolic Signatures Using Constraint-Based Models

Cancer metabolism differs remarkably from the metabolism of healthy surrounding tissues, and it is extremely heterogeneous across cancer types. While these metabolic differences provide promising avenues for cancer treatments, much work remains to be done in understanding how metabolism is rewired in malignant tissues. To that end, constraint-based models provide a powerful computational tool for the study of metabolism at the genome scale. To generate meaningful predictions, however, these generalized human models must first be tailored for specific cell or tissue sub-types. Here we first present two improved algorithms for (1) the generation of these context-specific metabolic models based on omics data, and (2) Monte-Carlo sampling of the metabolic model ux space. By applying these methods to generate and analyze context-specific metabolic models of diverse solid cancer cell line data, and primary leukemia pediatric patient biopsies, we demonstrate how the methodology presented in this study can generate insights into the rewiring differences across solid tumors and blood cancers.

Recognition of Recurrent Protein Expression Patterns in Pediatric Acute Myeloid Leukemia Identified New Therapeutic Targets

Heterogeneity in the genetic landscape of pediatric acute myeloid leukemia (AML) makes personalized medicine challenging. As genetic events are mediated by the expression and function of proteins, recognition of recurrent protein patterns could enable classification of pediatric AML patients and could reveal crucial protein dependencies. This could help to rationally select combinations of therapeutic targets. To determine whether protein expression levels could be clustered into functionally relevant groups, custom reverse-phase protein arrays were performed on pediatric AML (n = 95) and CD34+ normal bone marrow (n = 10) clinical specimens using 194 validated antibodies. To analyze proteins in the context of other proteins, all proteins were assembled into 31 protein functional groups (PFG). For each PFG, an optimal number of protein clusters was defined that represented distinct transition states. Block clustering analysis revealed strong correlations between various protein clusters and identified the existence of 12 protein constellations stratifying patients into 8 protein signatures. Signatures were correlated with therapeutic outcome, as well as certain laboratory and demographic characteristics. Comparison of acute lymphoblastic leukemia specimens from the same array and AML pediatric patient specimens demonstrated disease-specific signatures, but also identified the existence of shared constellations, suggesting joint protein deregulation between the diseases. Implication: Recognition of altered proteins in particular signatures suggests rational combinations of targets that could facilitate stratified targeted therapy. Mol Cancer Res; 16(8); 1275–86. ©2018 AACR. See related article by Hoff et al., p. 1263

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

ABSTRACT Introduction: 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.

Mycoplasma contamination of leukemic cell lines alters protein expression determined by reverse phase protein arrays

Mycoplasma contamination is a major problem in cell culturing, potentially altering the results of cell line-based experiments in largely uncharacterized ways. To define the consequences of mycoplasma infection at the level of protein expression we utilized the reverse phase protein array technology to analyze the expression of 235 proteins in mycoplasma infected, uninfected post treatment, and never-infected leukemic cell lines. Overall, protein profiles of cultured cells remained relatively stable after mycoplasma infection. However, paired comparisons for individual proteins identified that 18.7% of the proteins significantly changed between the infected and the never-infected cell line samples, and that 14.0% of the proteins significantly altered between the infected and the post treatment samples. Six percent of the proteins were affected in the post treatment samples compared to the never-infected samples, and 7.2% compared to treated cells that had never had mycoplasma infection before. Proteins that were significantly altered in the infected cells were enriched for apoptotic signaling processes and auto-phosphorylation, suggesting an increased cellular stress and a decreased growth rate. In conclusion, this study shows that mycoplasma infection of leukemic cell lines alters the proteins expression levels, potentially confounding experimental results. This reinforces the need for regular testing of mycoplasma.