Integrative multi-omics and drug response profiling of childhood acute lymphoblastic leukemia cell lines

A multiomic characterization of the leukemia cell line REH using short- and long-read sequencing

The B-cell acute lymphoblastic leukemia (ALL) cell line REH, with the t(12;21) ETV6::RUNX1 translocation, is known to have a complex karyotype defined by a series of large-scale chromosomal rearrangements. Taken from a 15-yr-old at relapse, the cell line offers a practical model for the study of pediatric B-ALL. In recent years, short- and long-read DNA and RNA sequencing have emerged as a complement to karyotyping techniques in the resolution of structural variants in an oncological context. Here, we explore the integration of long-read PacBio and Oxford Nanopore whole-genome sequencing, IsoSeq RNA sequencing, and short-read Illumina sequencing to create a detailed genomic and transcriptomic characterization of the REH cell line. Whole-genome sequencing clarified the molecular traits of disrupted ALL-associated genes including CDKN2A, PAX5, BTG1, VPREB1, and TBL1XR1, as well as the glucocorticoid receptor NR3C1 Meanwhile, transcriptome sequencing identified seven fusion genes within the genomic breakpoints. Together, our extensive whole-genome investigation makes high-quality open-source data available to the leukemia genomics community.

Multi Omics Applications in Biological Systems

Traditional methodologies often fall short in addressing the complexity of biological systems. In this regard, system biology omics have brought invaluable tools for conducting comprehensive analysis. Current sequencing capabilities have revolutionized genetics and genomics studies, as well as the characterization of transcriptional profiling and dynamics of several species and sample types. Biological systems experience complex biochemical processes involving thousands of molecules. These processes occur at different levels that can be studied using mass spectrometry-based (MS-based) analysis, enabling high-throughput proteomics, glycoproteomics, glycomics, metabolomics, and lipidomics analysis. Here, we present the most up-to-date techniques utilized in the completion of omics analysis. Additionally, we include some interesting examples of the applicability of multi omics to a variety of biological systems.

Characterization of Human B Cell Hematological Malignancies Using Protein-Based Approaches

The maturation of B cells is a complex, multi-step process. During B cell differentiation, errors can occur, leading to the emergence of aberrant versions of B cells that, finally, constitute a malignant tumor. These B cell malignancies are classified into three main groups: leukemias, myelomas, and lymphomas, the latter being the most heterogeneous type. Since their discovery, multiple biological studies have been performed to characterize these diseases, aiming to define their specific features and determine potential biomarkers for diagnosis, stratification, and prognosis. The rise of advanced -omics approaches has significantly contributed to this end. Notably, proteomics strategies appear as promising tools to comprehensively profile the final molecular effector of these cells. In this narrative review, we first introduce the main B cell malignancies together with the most relevant proteomics approaches. Then, we describe the core studies conducted in the field and their main findings and, finally, we evaluate the advantages and drawbacks of flow cytometry, mass cytometry, and mass spectrometry for the profiling of human B cell disorders.

Targeting autophagy as a therapeutic strategy in pediatric acute lymphoblastic leukemia

Autophagy is activated in response to a variety of stress conditions including anti-cancer therapies, and tumors cells often depend on autophagy for survival. In this study, we have evaluated inhibition of autophagy as therapeutic strategy in acute lymphoblastic leukemia (ALL) in children, both as a single treatment and in combination with glucocorticoid (GC) Dexamethasone (Dexa). Analysis of proteomics and RNA-seq of ALL cell lines and primary samples identified an upregulation of Vps34 and ATG14 proteins and autophagy and lysosomal pathway enrichment in a genetic subgroup with a recurrent t(12;21) translocation. Cells from this sugbroup were also significantly more sensitive to the selective autophagy or lysosomal inhibitors than cells with other genetic rearrangements. Further, combination of Dexa with either lysosomal or autophagy inhibitors was either synergistic or additive in killing leukemic cells across various genetic and lineage backgrounds, for both cell lines and primary samples, as assessed using viability assays and SynergyFinder as well as apoptotic caspase 3/7-based live-cell assays. Our data demonstrate that targeting autophagy represents a promising strategy for the treatment of pediatric ALL, both as a selective modality for the t(12;21) pre-B-ALL subgroup, and in combination treatments to sensitize to GC-induced cytotoxicity.

A machine learning and deep learning-based integrated multi-omics technique for leukemia prediction

In recent years, scientific data on cancer has expanded, providing potential for a better understanding of malignancies and improved tailored care. Advances in Artificial Intelligence (AI) processing power and algorithmic development position Machine Learning (ML) and Deep Learning (DL) as crucial players in predicting Leukemia, a blood cancer, using integrated multi-omics technology. However, realizing these goals demands novel approaches to harness this data deluge. This study introduces a novel Leukemia diagnosis approach, analyzing multi-omics data for accuracy using ML and DL algorithms. ML techniques, including Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), Logistic Regression (LR), Gradient Boosting (GB), and DL methods such as Recurrent Neural Networks (RNN) and Feedforward Neural Networks (FNN) are compared. GB achieved 97 % accuracy in ML, while RNN outperformed by achieving 98 % accuracy in DL. This approach filters unclassified data effectively, demonstrating the significance of DL for leukemia prediction. The testing validation was based on 17 different features such as patient age, sex, mutation type, treatment methods, chromosomes, and others. Our study compares ML and DL techniques and chooses the best technique that gives optimum results. The study emphasizes the implications of high-throughput technology in healthcare, offering improved patient care.

Proteomics Applications in Toxoplasma gondii: Unveiling the Host-Parasite Interactions and Therapeutic Target Discovery

Toxoplasma gondii, a protozoan parasite with the ability to infect various warm-blooded vertebrates, including humans, is the causative agent of toxoplasmosis. This infection poses significant risks, leading to severe complications in immunocompromised individuals and potentially affecting the fetus through congenital transmission. A comprehensive understanding of the intricate molecular interactions between T. gondii and its host is pivotal for the development of effective therapeutic strategies. This review emphasizes the crucial role of proteomics in T. gondii research, with a specific focus on host-parasite interactions, post-translational modifications (PTMs), PTM crosstalk, and ongoing efforts in drug discovery. Additionally, we provide an overview of recent advancements in proteomics techniques, encompassing interactome sample preparation methods such as BioID (BirA*-mediated proximity-dependent biotin identification), APEX (ascorbate peroxidase-mediated proximity labeling), and Y2H (yeast two hybrid), as well as various proteomics approaches, including single-cell analysis, DIA (data-independent acquisition), targeted, top-down, and plasma proteomics. Furthermore, we discuss bioinformatics and the integration of proteomics with other omics technologies, highlighting its potential in unraveling the intricate mechanisms of T. gondii pathogenesis and identifying novel therapeutic targets.

High-throughput screening as a drug repurposing strategy for poor outcome subgroups of pediatric B-cell precursor Acute Lymphoblastic Leukemia

Although a great cure rate has been achieved for pediatric BCP-ALL, approximately 15% of patients do not respond to conventional chemotherapy and experience disease relapse. A major effort to improve the cure rates by treatment intensification would result in an undesirable increase in treatment-related toxicity and mortality, raising the need to identify novel therapeutic approaches. High-throughput (HTP) drug screening enables the profiling of patients' responses in vitro and allows the repurposing of compounds currently used for other diseases, which can be immediately available for clinical application. The aim of this study was to apply HTP drug screening to identify potentially effective compounds for the treatment of pediatric BCP-ALL patients with poor prognosis, such as patients with Down Syndrome (DS) or carrying rearrangements involving PAX5 or KMT2A/MLL genes. Patient-derived Xenografts (PDX) samples from 34 BCP-ALL patients (9 DS CRLF2r, 15 PAX5r, 10 MLLr), 7 human BCP-ALL cell lines and 14 hematopoietic healthy donor samples were screened on a semi-automated HTP drug screening platform using a 174 compound library (FDA/EMA-approved or in preclinical studies). We identified 9 compounds active against BCP-ALL (ABT-199/venetoclax, AUY922/luminespib, dexamethasone, EC144, JQ1, NVP-HSP990, paclitaxel, PF-04929113 and vincristine), but sparing normal cells. Ex vivo validations confirmed that the BCL2 inhibitor venetoclax exerts an anti-leukemic effect against all three ALL subgroups at nanomolar concentrations. Overall, this study points out the benefit of HTP screening application for drug repurposing to allow the identification of effective and clinically translatable therapeutic agents for difficult-to-treat childhood BCP-ALL subgroups.

FORALL: an interactive shiny/R web portal to navigate multi-omics high-throughput data of pediatric acute lymphoblastic leukemia

Motivation

Pediatric acute lymphoblastic leukemia (ALL) is the most common cancer among children worldwide. The availability of easily accessible multi-omics data provides unprecedented resources and opportunities for discovering and refining disease biology, cancer biomarkers, and drug mechanisms of action. This has led to exponential growth of omics data available in public repositories. However, delivering the useful information and knowledge extraction from this data is one of the bottlenecks of multi-omics. Presenting, navigating, and downloading ALL omics data in a user-friendly interface provide a valuable platform for biologists and clinicians to get most of the omics data. Our in-house data provides in-depth mass spectrometry-based protein abundance data for a large panel of commercially available ALL cell lines. Providing this data to the scientific community in the form of a user-friendly web-portal allows for easy and detailed exploration of the data.

Results

We have developed the Functional Omics Resource of Acute Lymphoblastic Leukemia (FORALL) web-portal. FORALL is a shiny-based web portal designed to navigate in-depth mass spectrometry-based proteomics data of 51 cell lines. The proteomics data can be navigated and visualized along with matched RNA expression data as well as drug sensitivity data of 528 investigational and approved drugs.

Availability and implementation

FORALL is available at https://proteomics.se/forall/.

Targeting pan-essential pathways in cancer with cytotoxic chemotherapy: challenges and opportunities

Cytotoxic chemotherapy remains a key modality in cancer treatment. These therapies, successfully used for decades, continue to transform the lives of cancer patients daily. With the high attrition rate of current oncology drug development, combined with the knowledge that most new therapies do not displace standard-of-care treatments and that many healthcare systems cannot afford these new therapies; cytotoxic chemotherapies will remain an important component of cancer therapy for many years to come. The clinical value of these therapies is often under-appreciated within the pre-clinical cancer research community, where this diverse class of agents are often grouped together as non-specific cellular poisons killing tumor cells based solely upon proliferation rate; however, this is inaccurate. This review article seeks to reaffirm the importance of focusing research efforts upon improving our basic understanding of how these drugs work, discussing their ability to target pan-essential pathways in cancer cells, the relationship of this to the chemotherapeutic window, and highlighting basic science approaches that can be employed towards refining their use.

Transcription factor abnormalities in B-ALL leukemogenesis and treatment

The biological regulation of transcription factors (TFs) and repressor proteins is an important mechanism for maintaining cell homeostasis. In B cell acute lymphoblastic leukemia (B-ALL) TF abnormalities occur at high frequency and are often recognized as the major driving factor in carcinogenesis. We provide an in-depth review of molecular mechanisms of six major TF rearrangements in B-ALL, including DUX4-rearranged (DUX4-R), MEF2D-R, ZNF384-R, ETV6-RUNX1 and TCF3-PBX1 fusions, and KMT2A-R. In addition, the therapeutic options and prognoses for patients who harbor these TF abnormalities are discussed. This review aims to provide an up-to-date panoramic view of how TF-based oncogenic fusions might drive carcinogenesis and impact on potential therapeutic exploration of B-ALL treatments.

Mass Spectrometry-Based Proteomics: Analyses Related to Drug-Resistance and Disease Biomarkers

Mass spectrometry-based proteomics is a key player in research efforts to characterize aberrant epigenetic alterations, including histone post-translational modifications and DNA methylation. Data generated by this approach complements and enrich datasets generated by genomic, epigenetic and transcriptomics approaches. These combined datasets can provide much-needed information on various mechanisms responsible for drug resistance, the discovery and validation of potential biomarkers for different diseases, the identification of signaling pathways, and genes and enzymes to be targeted by future therapies. The increasing use of high-resolution, high-accuracy mass spectrometers combined with more refined protein labeling and enrichment procedures enhanced the role of this approach in the investigation of these epigenetic modifications. In this review, we discuss recent MS-based studies, which are contributing to current research efforts to understand certain mechanisms behind drug resistance to therapy. We also discuss how these MS-based analyses are contributing to biomarkers discovery and validation.

The NCOR-HDAC3 co-repressive complex modulates the leukemogenic potential of the transcription factor ERG

The ERG (ETS-related gene) transcription factor is linked to various types of cancer, including leukemia. However, the specific ERG domains and co-factors contributing to leukemogenesis are poorly understood. Drug targeting a transcription factor such as ERG is challenging. Our study reveals the critical role of a conserved amino acid, proline, at position 199, located at the 3' end of the PNT (pointed) domain, in ERG's ability to induce leukemia. P199 is necessary for ERG to promote self-renewal, prevent myeloid differentiation in hematopoietic progenitor cells, and initiate leukemia in mouse models. Here we show that P199 facilitates ERG's interaction with the NCoR-HDAC3 co-repressor complex. Inhibiting HDAC3 reduces the growth of ERG-dependent leukemic and prostate cancer cells, indicating that the interaction between ERG and the NCoR-HDAC3 co-repressor complex is crucial for its oncogenic activity. Thus, targeting this interaction may offer a potential therapeutic intervention.

Proteomics in Childhood Acute Lymphoblastic Leukemia: Challenges and Opportunities

Acute lymphoblastic leukemia (ALL) is the most common cancer in children and one of the success stories in cancer therapeutics. Risk-directed therapy based on clinical, biologic and genetic features has played a significant role in this accomplishment. Despite the observed improvement in survival rates, leukemia remains one of the leading causes of cancer-related deaths. Implementation of next-generation genomic and transcriptomic sequencing tools has illustrated the genomic landscape of ALL. However, the underlying dynamic changes at protein level still remain a challenge. Proteomics is a cutting-edge technology aimed at deciphering the mechanisms, pathways, and the degree to which the proteome impacts leukemia subtypes. Advances in mass spectrometry enable high-throughput collection of global proteomic profiles, representing an opportunity to unveil new biological markers and druggable targets. The purpose of this narrative review article is to provide a comprehensive overview of studies that have utilized applications of proteomics in an attempt to gain insight into the pathogenesis and identification of biomarkers in childhood ALL.

Functional impact of protein-RNA variation in clinical cancer analyses

Comprehensive molecular characterization of tumors aims to uncover cancer vulnerabilities, drug resistance mechanisms and biomarkers. Identification of cancer drivers was suggested as the basis for patient-tailored therapy, and transcriptomic analyses were proposed to reveal the phenotypic outcome of cancer mutations. With the maturation of the proteomic field, studies of protein-RNA discrepancies suggested that RNA analyses are insufficient to predict cellular functions. In this manuscript we discuss the importance of direct mRNA-protein comparisons in clinical cancer studies. We make use of the large amount of data generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which includes protein and mRNA expression analyses from the exact same samples. Analysis of protein-RNA correlations showed marked differences among cancer types, and highlighted the protein-RNA similarities and discrepancies among functional pathways and drug targets. Additionally, unsupervised clustering of the data based on protein or RNA showed substantial differences in tumor classification and the cellular processes that differentiate between clusters. These analyses show the difficulty to predict protein levels from mRNAs, and the critical role of protein analyses for phenotypic tumor characterization.

Deep thermal profiling for detection of functional proteoform groups

The complexity of the functional proteome extends considerably beyond the coding genome, resulting in millions of proteoforms. Investigation of proteoforms and their functional roles is important to understand cellular physiology and its deregulation in diseases but challenging to perform systematically. Here we applied thermal proteome profiling with deep peptide coverage to detect functional proteoform groups in acute lymphoblastic leukemia cell lines with different cytogenetic aberrations. We detected 15,846 proteoforms, capturing differently spliced, cleaved and post-translationally modified proteins expressed from 9,290 genes. We identified differential co-aggregation of proteoform pairs and established links to disease biology. Moreover, we systematically made use of measured biophysical proteoform states to find specific biomarkers of drug sensitivity. Our approach, thus, provides a powerful and unique tool for systematic detection and functional annotation of proteoform groups.

Knockdown of MEF2D inhibits the development and progression of B-cell acute lymphoblastic leukemia

Background

Myocyte enhancer factor 2D (MEF2D) is involved in the progression of various malignant tumors. However, its impact on B-cell acute lymphoblastic leukemia (B-ALL) has not been elucidated.

Methods

In this study, the expression level of MEF2D in B-ALL patients was validated through the Gene Expression Omnibus (GEO) database and clinical specimens. MEF2D-knockdown B-ALL cell lines were constructed by lentivirus transfection, and the effects of MEF2D on the viability, apoptosis, cycle progression, and drug sensitivity of B-ALL cells were verified by Cell Counting Kit-8 (CCK-8) and flow cytometry (FCM). The effect of MEF2D on the proliferation of B-ALL cells in vivo was verified via the construction of a xenograft mouse model. The mechanism of MEF2D regulating B-ALL cells was explored by RNA sequencing analysis, quantitative reverse transcription polymerase chain reaction (qRT-PCR), western blotting, and immunohistochemical (IHC).

Results

In this study, overexpression of MEF2D was observed in B-ALL patients and was remarkably correlated to disease progression in ALL patients. The knockdown of MEF2D expression suppressed cell viability, induced cell apoptosis, blockaded cell cycle progression, enhanced drug sensitivity of B-ALL cells in vitro, and reduced the tumor load in vivo. Furthermore, mechanistic studies revealed that MEF2D knockdown downregulated the expression of the phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)-protein kinase B (AKT) signaling pathway.

Conclusions

Our research demonstrated that MEF2D was markedly expressed in B-ALL. MEF2D knockdown inhibited cancer progression of B-ALL both in vitro and in vivo, which may be related to the downregulation of the PI3K-AKT signaling pathway. The data suggest that MEF2D plays a vital role in the process of tumorigenesis and may be a potential novel target for B-ALL therapy.

Multi-Omic Approaches to Classify, Predict, and Treat Acute Leukemias

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer, in which nearly 5% of the cases are diagnosed before the first year of age [...].

A Focused Review of Ras Guanine Nucleotide-Releasing Protein 1 in Immune Cells and Cancer

Four Ras guanine nucleotide-releasing proteins (RasGRP1 through 4) belong to the family of guanine nucleotide exchange factors (GEFs). RasGRPs catalyze the release of GDP from small GTPases Ras and Rap and facilitate their transition from an inactive GDP-bound to an active GTP-bound state. Thus, they regulate critical cellular responses via many downstream GTPase effectors. Similar to other RasGRPs, the catalytic module of RasGRP1 is composed of the Ras exchange motif (REM) and Cdc25 domain, and the EF hands and C1 domain contribute to its cellular localization and regulation. RasGRP1 can be activated by a diacylglycerol (DAG)-mediated membrane recruitment and protein kinase C (PKC)-mediated phosphorylation. RasGRP1 acts downstream of the T cell receptor (TCR), B cell receptors (BCR), and pre-TCR, and plays an important role in the thymocyte maturation and function of peripheral T cells, B cells, NK cells, mast cells, and neutrophils. The dysregulation of RasGRP1 is known to contribute to numerous disorders that range from autoimmune and inflammatory diseases and schizophrenia to neoplasia. Given its position at the crossroad of cell development, inflammation, and cancer, RASGRP1 has garnered interest from numerous disciplines. In this review, we outline the structure, function, and regulation of RasGRP1 and focus on the existing knowledge of the role of RasGRP1 in leukemia and other cancers.

Xanthotoxol suppresses non-small cell lung cancer progression and might improve patients' prognosis

BACKGROUND

Developing novel and effective drugs with less toxicity is urgent for non-small cell lung cancer (NSCLC) therapy. Xanthotoxol (Xan) is the major natural component of the medical plant Angelica dahurica with potential anti-cancer activities.

PURPOSE

In this study, we aimed to demonstrate the effect and underlying mechanism of Xan in NSCLC and evaluate the effectiveness of Xan in NSCLC patients.

METHODS

CCK8, colony formation, EdU, flow cytometry, and transwell assays were carried out to investigate the anti-NSCLC activity of Xan in vitro. In addition, the xenograft mouse model was established to evaluate the anti-NSCLC effect of Xan in vivo. Moreover, bioinformatics analysis was performed to establish a prediction model based on RNA sequencing data. Furthermore, Western blot was used to detect the expression of proteins regulated by Xan.

RESULTS

Xan inhibited the cell viability, colony formation capacity, DNA replication, cell cycle transition, migration and invasion, as well as inducing apoptosis of NSCLC cells. In addition, Xan suppressed NSCLC xenograft growth in vivo without obvious toxicity. Interestingly, bioinformatics analyses based on the RNA sequencing data indicated that Xan exerted inhibitory effects on NSCLC cells by down-regulating signals contributing to NSCLC progression and demonstrated that Xan was effective in ameliorating the prognosis of NSCLC patients with a new proposed prediction model. Moreover, Xan was shown to regulate cell cycle arrest, apoptosis, and epithelial-mesenchymal transition (EMT)-associated genes through downregulating PI3K-AKT signaling, thus suppressing NSCLC proliferation and metastasis.

CONCLUSIONS

Taken together, our work proved that Xan induced cell cycle arrest, facilitated apoptosis, and inhibited EMT processes through downregulating the PI3K-AKT pathway to suppress NSCLC progress. Moreover, we also proposed a new model for evaluating Xan as a novel and effective drug in NSCLC treatments.