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

Proteogenomic analysis of acute myeloid leukemia associates relapsed disease with reprogrammed energy metabolism both in adults and children

Despite improvement of current treatment strategies and novel targeted drugs, relapse and treatment resistance largely determine the outcome for acute myeloid leukemia (AML) patients. To identify the underlying molecular characteristics, numerous studies have been aimed to decipher the genomic- and transcriptomic landscape of AML. Nevertheless, further molecular changes allowing malignant cells to escape treatment remain to be elucidated. Mass spectrometry is a powerful tool enabling detailed insights into proteomic changes that could explain AML relapse and resistance. Here, we investigated AML samples from 47 adult and 22 pediatric patients at serial time-points during disease progression using mass spectrometry-based in-depth proteomics. We show that the proteomic profile at relapse is enriched for mitochondrial ribosomal proteins and subunits of the respiratory chain complex, indicative of reprogrammed energy metabolism from diagnosis to relapse. Further, higher levels of granzymes and lower levels of the anti-inflammatory protein CR1/CD35 suggest an inflammatory signature promoting disease progression. Finally, through a proteogenomic approach, we detected novel peptides, which present a promising repertoire in the search for biomarkers and tumor-specific druggable targets. Altogether, this study highlights the importance of proteomic studies in holistic approaches to improve treatment and survival of AML patients.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Lymphocytes from B-acute lymphoblastic leukemia patients present differential regulation of the adenosinergic axis depending on risk stratification

PURPOSE

Although risk-stratified chemotherapy regimens improve B-cell acute lymphoblastic leukemia (B-ALL) clinical outcome, relapse occurs in a significant number of cases. The identification of new therapeutic targets as well as prognostic and diagnostic biomarkers can improve B-ALL patients' clinical outcomes. Purinergic signaling is an important pathway in cancer progression, however the expression of ectonucleotidases and their impact on immune cells in B-ALL lacks exploration. We aimed to analyze the expression of ectonucleotidases in B-ALL patients' lymphocyte subpopulations.

METHODS

Peripheral blood samples from 15 patients diagnosed with B-ALL were analyzed. Flow cytometry was used to analyze cellularity, expression level of CD38, CD39, and CD73, and frequency of [Formula: see text], and [Formula: see text] in lymphocyte subpopulations. Plasma was used for cytokines (by CBA kit) and adenine nucleosides/nucleotides detection (by HPLC).

RESULTS

Comparing B-ALL patients to health donors, we observed an increase of CD4 + and CD8 + T-cells. In addition, a decrease in CD38 expression in B and Treg subpopulations and an increase in CD39⁺ CD73⁺  frequency in Breg and CD8+ T-cells. Analyzing cytokines and adenine nucleosides/nucleotides, we found a decrease in TNF, IL-1β, and ADO concentrations, together with an increase in AMP in B-ALL patients' plasma.

CONCLUSION

As immunomodulators, the expression of ectonucleotidases might be associated with activation states, as well as the abundance of different cellular subsets. We observed a pro-tumor immunity expression profile in B-ALL patients at diagnosis, being associated with cell exhaustion and immune evasion in B-ALL.

A comparative study of evaluating missing value imputation methods in label-free proteomics

The presence of missing values (MVs) in label-free quantitative proteomics greatly reduces the completeness of data. Imputation has been widely utilized to handle MVs, and selection of the proper method is critical for the accuracy and reliability of imputation. Here we present a comparative study that evaluates the performance of seven popular imputation methods with a large-scale benchmark dataset and an immune cell dataset. Simulated MVs were incorporated into the complete part of each dataset with different combinations of MV rates and missing not at random (MNAR) rates. Normalized root mean square error (NRMSE) was applied to evaluate the accuracy of protein abundances and intergroup protein ratios after imputation. Detection of true positives (TPs) and false altered-protein discovery rate (FADR) between groups were also compared using the benchmark dataset. Furthermore, the accuracy of handling real MVs was assessed by comparing enriched pathways and signature genes of cell activation after imputing the immune cell dataset. We observed that the accuracy of imputation is primarily affected by the MNAR rate rather than the MV rate, and downstream analysis can be largely impacted by the selection of imputation methods. A random forest-based imputation method consistently outperformed other popular methods by achieving the lowest NRMSE, high amount of TPs with the average FADR < 5%, and the best detection of relevant pathways and signature genes, highlighting it as the most suitable method for label-free proteomics.

Proteome and Phosphoproteome Changes Associated with Prognosis in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a hematological cancer that mainly affects the elderly. Although complete remission (CR) is achieved for the majority of the patients after induction and consolidation therapies, nearly two-thirds relapse within a short interval. Understanding biological factors that determine relapse has become of major clinical interest in AML. We utilized liquid chromatography tandem mass spectrometry (LC-MS/MS) to identify the protein changes and protein phosphorylation events associated with AML relapse in primary cells from 41 AML patients at time of diagnosis. Patients were defined as relapse-free if they had not relapsed within a five-year clinical follow-up after AML diagnosis. Relapse was associated with increased expression of RNA processing proteins and decreased expression of V-ATPase proteins. We also observed an increase in phosphorylation events catalyzed by cyclin-dependent kinases (CDKs) and casein kinase 2 (CSK2). The biological relevance of the proteome findings was supported by cell proliferation assays using inhibitors of V-ATPase (bafilomycin), CSK2 (CX-4945), CDK4/6 (abemaciclib) and CDK2/7/9 (SNS-032). While bafilomycin preferentially inhibited the cells from relapse patients, the kinase inhibitors were less efficient in these cells. This suggests that therapy against the upregulated kinases could also target the factors inducing their upregulation rather than their activity. This study, therefore, presents markers that could help predict AML relapse and direct therapeutic strategies.

Proteomic tools and new insights for the study of B-cell precursor acute lymphoblastic leukemia

B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is a hematological malignancy of immature B-cell precursors, affecting children more often than adults. The etiology of BCP-ALL is still unknown, but environmental factors, sex, race or ethnicity, and genomic alterations influence the development of the disease. Tools based on protein detection, such as flow cytometry, mass spectrometry, mass cytometry and reverse phase protein array, represent an opportunity to investigate BCP-ALL pathogenesis and to identify new biomarkers of disease. This review aims to document the recent advancements with respect to applications of proteomic technologies to study mechanisms of leukemogenesis, how this information could be used in the discovery of biological targets, and finally we describe the challenges of application of proteomic tools for the approach of BCP-ALL.

A comprehensive evaluation of popular proteomics software workflows for label-free proteome quantification and imputation

Label-free mass spectrometry (MS) has developed into an important tool applied in various fields of biological and life sciences. Several software exist to process the raw MS data into quantified protein abundances, including open source and commercial solutions. Each software includes a set of unique algorithms for different tasks of the MS data processing workflow. While many of these algorithms have been compared separately, a thorough and systematic evaluation of their overall performance is missing. Moreover, systematic information is lacking about the amount of missing values produced by the different proteomics software and the capabilities of different data imputation methods to account for them.In this study, we evaluated the performance of five popular quantitative label-free proteomics software workflows using four different spike-in data sets. Our extensive testing included the number of proteins quantified and the number of missing values produced by each workflow, the accuracy of detecting differential expression and logarithmic fold change and the effect of different imputation and filtering methods on the differential expression results. We found that the Progenesis software performed consistently well in the differential expression analysis and produced few missing values. The missing values produced by the other software decreased their performance, but this difference could be mitigated using proper data filtering or imputation methods. Among the imputation methods, we found that the local least squares (lls) regression imputation consistently increased the performance of the software in the differential expression analysis, and a combination of both data filtering and local least squares imputation increased performance the most in the tested data sets.

Global Cell Proteome Profiling, Phospho-signaling and Quantitative Proteomics for Identification of New Biomarkers in Acute Myeloid Leukemia Patients

The identification of protein biomarkers for acute myeloid leukemia (AML) that could find applications in AML diagnosis and prognosis, treatment and the selection for bone marrow transplant requires substantial comparative analyses of the proteomes from AML patients. In the past years, several studies have suggested some biomarkers for AML diagnosis or AML classification using methods for sample preparation with low proteome coverage and low resolution mass spectrometers. However, most of the studies did not follow up, confirm or validate their candidates with more patient samples. Current proteomics methods, new high resolution and fast mass spectrometers allow the identification and quantification of several thousands of proteins obtained from few tens of μg of AML cell lysate. Enrichment methods for posttranslational modifications (PTM), such as phosphorylation, can isolate several thousands of site-specific phosphorylated peptides from AML patient samples, which subsequently can be quantified with high confidence in new mass spectrometers. While recent reports aiming to propose proteomic or phosphoproteomic biomarkers on the studied AML patient samples have taken advantage of the technological progress, the access to large cohorts of AML patients to sample from and the availability of appropriate control samples still remain challenging.

Mass spectrometry in leukemia research and treatment

Mass spectrometry (MS) is a complex analytical chemistry tool that allows qualitative and quantitative assessments of the components of complex chemical compounds. Applications of MS in medicine include the identification and quantification of drugs and metabolites; identification of proteins, biopolymers and disease markers and investigation of differential protein expression and proteins altered by mutations and/or post-translational changes. A variety of MS methods and technologies now play valuable and expanding roles in the diagnosis and monitoring of acute leukemia, as well as in identification of therapeutic targets and biomarkers, drug discovery, and other important areas of leukemia research. The objective of this review is to present a clinically oriented review of the roles of MS in the research, diagnosis and therapy of acute leukemia.

Proteomics reveals the importance of the dynamic redistribution of the subcellular location of proteins in breast cancer cells

At the molecular level, living cells are enormously complicated complex adaptive systems in which intertwined genomic, transcriptomic, proteomic and metabolic networks all play a crucial role. At the same time, cells are spatially heterogeneous systems in which subcellular compartmentalization of different functions is ubiquitous and requires efficient cross-compartmental communication. Dynamic redistribution of multitudinous proteins to different subcellular locations in response to cellular functional state is increasingly recognized as a crucial characteristic of cellular function that seems to be at least as important as overall changes in protein abundance. Characterization of the subcellular spatial dynamics of protein distribution is a major challenge for proteomics and recent results with MCF7 breast cancer cells suggest that this may be of particular importance for cancer cells.

Modern LC in therapeutic drug monitoring and diagnosis of pediatric leukemia

The approaches of combining analytical and pharmacokinetic tools can currently assist clinicians in routinely providing more effective and individualized treatment, including in complicated cases of pediatric acute myeloid leukemia. Anticancer drug dosing based on synchronized drug monitoring and pharmacokinetic analysis can provide a better estimation of the drug systemic exposure than that obtained with the stable dosing plan. Leukemia is difficult to treat and, in the case of children, has the most dramatic and tragic course. Therefore, it is important to search for new biomarkers of leukemia that will enable early diagnosis. Hence, a completed strategy with chromatographic methods as the core element of analytical procedures provides an efficient tool that is beneficial for clinicians involved in the treatment and diagnosis of leukemia.

Statistical detection of quantitative protein biomarkers provides insights into signaling networks deregulated in acute myeloid leukemia

The increasing coverage and sensitivity of LC-MS/MS-based proteomics have expanded its applications in systems medicine. In particular, label-free quantitation approaches are enabling biomarker discovery in terms of statistical comparison of proteomic profiles across large numbers of clinical samples. However, it still remains poorly understood how much protein markers can add novel insights compared to markers derived from mRNA transcriptomic profiling. Using paired label-free LC-MS/MS and gene expression microarray measurements from primary samples of patients with acute myeloid leukemia (AML), we demonstrate here that while the quantitative proteomic and transcriptomic profiles were highly correlated, in general, the marker panels showing statistically significant expression changes across the disease and healthy groups were profoundly different between protein and mRNA levels. In particular, the proteomic assay enabled unique links to known leukemic processes, which were missed when using the transcriptomic profiling alone, as well as identified additional links to metabolic regulators and chromatin remodelers, such as GPX1, fumarate hydratase, and SET oncogene, which have subsequently been evaluated in independent AML samples. Overall, these results highlighted the complementary and informative view obtained from the quantitative LC-MS/MS approach into the AML deregulated signaling networks.

Proteomics in immunity and herpes simplex encephalitis

The genetic theory of infectious diseases has proposed that susceptibility to life-threatening infectious diseases in childhood, occurring in the course of primary infection, results mostly from individually rare but collectively diverse single-gene variants. Recent evidence of an ever-expanding spectrum of genes involved in susceptibility to infectious disease indicates that the paradigm has important implications for diagnosis and treatment. One such pathology is childhood herpes simplex encephalitis, which shows a pattern of rare but diverse disease-disposing genetic variants. The present report shows how proteomics can help to understand susceptibility to childhood herpes simplex encephalitis and other viral infections, suggests that proteomics may have a particularly important role to play, emphasizes that variation over the population is a critical issue for proteomics and notes some new challenges for proteomics and related bioinformatics tools in the context of rare but diverse genetic defects.

Protein biomarkers distinguish between high- and low-risk pediatric acute lymphoblastic leukemia in a tissue specific manner

The current study evaluated the differential expression detected in the proteomic profiles of low risk- and high risk- ALL pediatric patients to characterize candidate biomarkers related to diagnosis, prognosis and patient targeted therapy. Bone marrow and peripheral blood plasma and cell lysates samples were obtained from pediatric patients with low- (LR) and high-risk (HR) ALL at diagnosis. As controls, non-leukemic pediatric patients were studied. Cytogenetic analysis was carried out by G- banding and interphase fluorescent in situ hybridization. Differential proteomic analysis was performed using two-dimensional gel electrophoresis and protein identification by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. The differential expression of certain proteins was confirmed by Western blot analysis. The obtained data revealed that CLUS, CERU, APOE, APOA4, APOA1, GELS, S10A9, AMBP, ACTB, CATA and AFAM proteins play a significant role in leukemia prognosis, potentially serving as distinctive biomarkers for leukemia aggressiveness, or as suppressor proteins in HR-ALL cases. In addition, vitronectin and plasminogen probably contributed to leukemogenesis, whilst bicaudal D-related protein 1 could afford a significant biomarker for pediatric ALL therapeutics.

Acute lymphocitic leukaemia and AIDS

Patients with AIDS have become more and more prevalent. Therefore, the onset of diseases that coexist with this condition must be studied in order to establish proper diagnosis and treatment. We report a case of a patient who reported with loss of strength in the lower limbs and a progressive worsening in his clinical picture. He was diagnosed with acute lymphocytic leukaemia, an unusual form of association with the AIDS condition. Despite the diagnosis, he evolved into pulmonary sepsis and so staging and chemotherapy treatment could not be performed.

The proteomics big challenge for biomarkers and new drug-targets discovery

In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets.