Xputer: bridging data gaps with NMF, XGBoost, and a streamlined GUI experience.

The rapid proliferation of data across diverse fields has accentuated the importance of accurate imputation for missing values. This task is crucial for ensuring data integrity and deriving meaningful insights. In response to this challenge, we present Xputer, a novel imputation tool that adeptly integrates Non-negative Matrix Factorization (NMF) with the predictive strengths of XGBoost. One of Xputer's standout features is its versatility: it supports zero imputation, enables hyperparameter optimization through Optuna, and allows users to define the number of iterations. For enhanced user experience and accessibility, we have equipped Xputer with an intuitive Graphical User Interface (GUI) ensuring ease of handling, even for those less familiar with computational tools. In performance benchmarks, Xputer often outperforms IterativeImputer in terms of imputation accuracy. Furthermore, Xputer autonomously handles a diverse spectrum of data types, including categorical, continuous, and Boolean, eliminating the need for prior preprocessing. Given its blend of performance, flexibility, and user-friendly design, Xputer emerges as a state-of-the-art solution in the realm of data imputation.

AlphaML: A clear, legible, explainable, transparent, and elucidative binary classification platform for tabular data.

Leveraging the potential of machine learning and recognizing the broad applications of binary classification, it becomes essential to develop platforms that are not only powerful but also transparent, interpretable, and user friendly. We introduce alphaML, a user-friendly platform that provides clear, legible, explainable, transparent, and elucidative (CLETE) binary classification models with comprehensive customization options. AlphaML offers feature selection, hyperparameter search, sampling, and normalization methods, along with 15 machine learning algorithms with global and local interpretation. We have integrated a custom metric for hyperparameter search that considers both training and validation scores, safeguarding against under- or overfitting. Additionally, we employ the NegLog2RMSL scoring method, which uses both training and test scores for a thorough model evaluation. The platform has been tested using datasets from multiple domains and offers a graphical interface, removing the need for programming expertise. Consequently, alphaML exhibits versatility, demonstrating promising applicability across a broad spectrum of tabular data configurations.

Discovery of a small molecule that inhibits Bcl-3-mediated cyclin D1 expression in melanoma cells.

Molecular targeted therapy using a drug that suppresses the growth and spread of cancer cells via inhibition of a specific protein is a foundation of precision medicine and treatment. High expression of the proto-oncogene Bcl-3 promotes the proliferation and metastasis of cancer cells originating from tissues such as the colon, prostate, breast, and skin. The development of novel drugs targeting Bcl-3 alone or in combination with other therapies can cure these patients or prolong their survival. As a proof of concept, in the present study, we focused on metastatic melanoma as a model system. High-throughput screening and in vitro experiments identified BCL3ANT as a lead molecule that could interfere with Bcl-3-mediated cyclin D1 expression and cell proliferation and migration in melanoma. In experimental animal models of melanoma, it was demonstrated that the use of a Bcl-3 inhibitor can influence the survival of melanoma cells. Since there are no other inhibitors against Bcl-3 in the clinical pipeline for cancer treatment, this presents a unique opportunity to develop a highly specific drug against malignant melanoma to meet an urgent clinical need.

Understanding the Role of Activation Loop Mutants in Drug Efficacy for FLT3-ITD.

The type III receptor tyrosine kinase FLT3 is a pivotal kinase for hematopoietic progenitor cell regulation, with significant implications in acute myeloid leukemia (AML) through mutations like internal tandem duplication (ITD). This study delves into the structural intricacies of FLT3, the roles of activation loop mutants, and their interaction with tyrosine kinase inhibitors. Coupled with this, the research leverages molecular contrastive learning and protein language modeling to examine interactions between small molecule inhibitors and FLT3 activation loop mutants. Utilizing the ConPLex platform, over 5.7 million unique FLT3 activation loop mutants-small molecule pairs were analyzed. The binding free energies of three inhibitors were assessed, and cellular apoptotic responses were evaluated under drug treatments. Notably, the introduction of the Xepto50 scoring system provides a nuanced metric for drug efficacy. The findings underscore the modulation of molecular interactions and cellular responses by Y842 mutations in FLT3-KD, highlighting the need for tailored therapeutic approaches in FLT3-ITD-related malignancies.

PLK1 as a cooperating partner for BCL2-mediated antiapoptotic program in leukemia.

The deregulation of BCL2 family proteins plays a crucial role in leukemia development. Therefore, pharmacological inhibition of this family of proteins is becoming a prevalent treatment method. However, due to the emergence of primary and acquired resistance, efficacy is compromised in clinical or preclinical settings. We developed a drug sensitivity prediction model utilizing a deep tabular learning algorithm for the assessment of venetoclax sensitivity in T-cell acute lymphoblastic leukemia (T-ALL) patient samples. Through analysis of predicted venetoclax-sensitive and resistant samples, PLK1 was identified as a cooperating partner for the BCL2-mediated antiapoptotic program. This finding was substantiated by additional data obtained through phosphoproteomics and high-throughput kinase screening. Concurrent treatment using venetoclax with PLK1-specific inhibitors and PLK1 knockdown demonstrated a greater therapeutic effect on T-ALL cell lines, patient-derived xenografts, and engrafted mice compared with using each treatment separately. Mechanistically, the attenuation of PLK1 enhanced BCL2 inhibitor sensitivity through upregulation of BCL2L13 and PMAIP1 expression. Collectively, these findings underscore the dependency of T-ALL on PLK1 and postulate a plausible regulatory mechanism.

Venetoclax-Resistant T-ALL Cells Display Distinct Cancer Stem Cell Signatures and Enrichment of Cytokine Signaling.

Therapy resistance remains one of the major challenges for cancer treatment that largely limits treatment benefits and patient survival. The underlying mechanisms that lead to therapy resistance are highly complicated because of the specificity to the cancer subtype and therapy. The expression of the anti-apoptotic protein BCL2 has been shown to be deregulated in T-cell acute lymphoblastic leukemia (T-ALL), where different T-ALL cells display a differential response to the BCL2-specific inhibitor venetoclax. In this study, we observed that the expression of anti-apoptotic BCL2 family genes, such as BCL2, BCL2L1, and MCL1, is highly varied in T-ALL patients, and inhibitors targeting proteins coded by these genes display differential responses in T-ALL cell lines. Three T-ALL cell lines (ALL-SIL, MOLT-16, and LOUCY) were highly sensitive to BCL2 inhibition within a panel of cell lines tested. These cell lines displayed differential BCL2 and BCL2L1 expression. Prolonged exposure to venetoclax led to the development of resistance to it in all three sensitive cell lines. To understand how cells developed venetoclax resistance, we monitored the expression of BCL2, BCL2L1, and MCL1 over the treatment period and compared gene expression between resistant cells and parental sensitive cells. We observed a different trend of regulation in terms of BCL2 family gene expression and global gene expression profile including genes reported to be expressed in cancer stem cells. Gene set enrichment analysis (GSEA) showed enrichment of cytokine signaling in all three cell lines which was supported by the phospho-kinase array where STAT5 phosphorylation was found to be elevated in resistant cells. Collectively, our data suggest that venetoclax resistance can be mediated through the enrichment of distinct gene signatures and cytokine signaling pathways.

A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer.

Cisplatin, a platinum-based chemotherapeutic agent, is widely used as a front-line treatment for several malignancies. However, treatment outcomes vary widely due to intrinsic and acquired resistance. In this study, cisplatin-perturbed gene expression and pathway enrichment were used to define a gene signature, which was further utilized to develop a cisplatin sensitivity prediction model using the TabNet algorithm. The TabNet model performed better (>80 % accuracy) than all other machine learning models when compared to a wide range of machine learning algorithms. Moreover, by using feature importance and comparing predicted ovarian cancer patient samples, BCL2L1 was identified as an important gene contributing to cisplatin resistance. Furthermore, the pharmacological inhibition of BCL2L1 was found to synergistically increase cisplatin efficacy. Collectively, this study developed a tool to predict cisplatin sensitivity using cisplatin-perturbed gene expression and pathway enrichment knowledge and identified BCL2L1 as an important gene in this setting.

A Receptor Tyrosine Kinase Inhibitor Sensitivity Prediction Model Identifies AXL Dependency in Leukemia.

Despite incredible progress in cancer treatment, therapy resistance remains the leading limiting factor for long-term survival. During drug treatment, several genes are transcriptionally upregulated to mediate drug tolerance. Using highly variable genes and pharmacogenomic data for acute myeloid leukemia (AML), we developed a drug sensitivity prediction model for the receptor tyrosine kinase inhibitor sorafenib and achieved more than 80% prediction accuracy. Furthermore, by using Shapley additive explanations for determining leading features, we identified AXL as an important feature for drug resistance. Drug-resistant patient samples displayed enrichment of protein kinase C (PKC) signaling, which was also identified in sorafenib-treated FLT3-ITD-dependent AML cell lines by a peptide-based kinase profiling assay. Finally, we show that pharmacological inhibition of tyrosine kinase activity enhances AXL expression, phosphorylation of the PKC-substrate cyclic AMP response element binding (CREB) protein, and displays synergy with AXL and PKC inhibitors. Collectively, our data suggest an involvement of AXL in tyrosine kinase inhibitor resistance and link PKC activation as a possible signaling mediator.

Venetoclax Drug Increases the Apoptosis of T and B Acute Lymphoblastic Leukemia Cells by Reducing the Expression of BCL-2.

Venetoclax, a specific inhibitor of the BCL2 protein, is administered for the treatment of acute lymphoblastic leukemia. However, despite being utilized in conjunction with chemotherapy, the drug exhibits instances of resistance. The exact mechanisms responsible for this resistance remain relatively obscure. Within the context of this investigation, the study aimed to explore the involvement of anti- and pro-apoptotic proteins as one of the potential mechanisms underlying this resistance phenomenon. Blast cells were extracted from patients diagnosed with B&T acute lymphoid leukemia. Subsequently, these cells were subjected to a cultivation process. Following the cultivation, treatment with the Venetoclax drug was administered to both groups of B&T cells. Additionally, one group from each cell type was designated as a control. The relative expression levels of genes BCL-2, MCL-1, and BIM were assessed in comparison to the control group. Annexin V-fluorescein isothiocyanate and propidium iodide staining was done to check cell apoptosis. The results showed a significant increase in the expression of BIM gene and a significant decrease in BCL-2 gene compared to the control group, but the change in the expression of MCL-1 gene was not significant. Also, an increase in apoptosis was observed in the treatment groups compared to the control. Although it was shown that changes in the expression of pro- and anti-apoptotic genes can lead to an increase in cell apoptosis and a decrease in the number of blast cells, more studies are needed to investigate the simultaneous effect of Venetoclax drug with other drugs and also in the form of a clinical trial.

Phosphoproteomics of primary AML patient samples reveals rationale for AKT combination therapy and p53 context to overcome selinexor resistance.

Acute myeloid leukemia (AML) is a heterogeneous disease with variable patient responses to therapy. Selinexor, an inhibitor of nuclear export, has shown promising clinical activity for AML. To identify the molecular context for monotherapy sensitivity as well as rational drug combinations, we profile selinexor signaling responses using phosphoproteomics in primary AML patient samples and cell lines. Functional phosphosite scoring reveals that p53 function is required for selinexor sensitivity consistent with enhanced efficacy of selinexor in combination with the MDM2 inhibitor nutlin-3a. Moreover, combining selinexor with the AKT inhibitor MK-2206 overcomes dysregulated AKT-FOXO3 signaling in resistant cells, resulting in synergistic anti-proliferative effects. Using high-throughput spatial proteomics to profile subcellular compartments, we measure global proteome and phospho-proteome dynamics, providing direct evidence of nuclear translocation of FOXO3 upon combination treatment. Our data demonstrate the potential of phosphoproteomics and functional phosphorylation site scoring to successfully pinpoint key targetable signaling hubs for rational drug combinations.

Phosphorylation-Dependent Regulation of WNT/Beta-Catenin Signaling.

WNT/ß-catenin signaling is a highly complex pathway that plays diverse roles in various cellular processes. While WNT ligands usually signal through their dedicated Frizzled receptors, the decision to signal in a ß-catenin-dependent or -independent manner rests upon the type of co-receptors used. Canonical WNT signaling is ß-catenin-dependent, whereas non-canonical WNT signaling is ß-catenin-independent according to the classical definition. This still holds true, albeit with some added complexity, as both the pathways seem to cross-talk with intertwined networks that involve the use of different ligands, receptors, and co-receptors. ß-catenin can be directly phosphorylated by various kinases governing its participation in either canonical or non-canonical pathways. Moreover, the co-activators that associate with ß-catenin determine the output of the pathway in terms of induction of genes promoting proliferation or differentiation. In this review, we provide an overview of how protein phosphorylation controls WNT/ß-catenin signaling, particularly in human cancer.

T cell receptor (TCR) signaling in health and disease.

Interaction of the T cell receptor (TCR) with an MHC-antigenic peptide complex results in changes at the molecular and cellular levels in T cells. The outside environmental cues are translated into various signal transduction pathways within the cell, which mediate the activation of various genes with the help of specific transcription factors. These signaling networks propagate with the help of various effector enzymes, such as kinases, phosphatases, and phospholipases. Integration of these disparate signal transduction pathways is done with the help of adaptor proteins that are non-enzymatic in function and that serve as a scaffold for various protein-protein interactions. This process aids in connecting the proximal to distal signaling pathways, thereby contributing to the full activation of T cells. This review provides a comprehensive snapshot of the various molecules involved in regulating T cell receptor signaling, covering both enzymes and adaptors, and will discuss their role in human disease.

Machine learning in the prediction of cancer therapy.

Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power, building clinically useable models remains challenging due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response prediction for clinical practice.

The Aurora kinase/ß-catenin axis contributes to dexamethasone resistance in leukemia.

Glucocorticoids, such as dexamethasone and prednisolone, are widely used in cancer treatment. Different hematological malignancies respond differently to this treatment which, as could be expected, correlates with treatment outcome. In this study, we have used a glucocorticoid-induced gene signature to develop a deep learning model that can predict dexamethasone sensitivity. By combining gene expression data from cell lines and patients with acute lymphoblastic leukemia, we observed that the model is useful for the classification of patients. Predicted samples have been used to detect deregulated pathways that lead to dexamethasone resistance. Gene set enrichment analysis, peptide substrate-based kinase profiling assay, and western blot analysis identified Aurora kinase, S6K, p38, and ß-catenin as key signaling proteins involved in dexamethasone resistance. Deep learning-enabled drug synergy prediction followed by in vitro drug synergy analysis identified kinase inhibitors against Aurora kinase, JAK, S6K, and mTOR that displayed synergy with dexamethasone. Combining pathway enrichment, kinase regulation, and kinase inhibition data, we propose that Aurora kinase or its several direct or indirect downstream kinase effectors such as mTOR, S6K, p38, and JAK may be involved in ß-catenin stabilization through phosphorylation-dependent inactivation of GSK-3ß. Collectively, our data suggest that activation of the Aurora kinase/ß-catenin axis during dexamethasone treatment may contribute to cell survival signaling which is possibly maintained in patients who are resistant to dexamethasone.

Mechanisms of Anticancer Therapy Resistance: The Role of Cancer Stem Cells.

Despite incredible progress in anticancer therapy development, resistance to therapy is the major factor limiting the cure of cancer patients [...].

Correction: The ALK inhibitor AZD3463 effectively inhibits growth of sorafenib-resistant acute myeloid leukemia.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

FMS-like Tyrosine Kinase 3/FLT3: From Basic Science to Clinical Implications.

FMS-like tyrosine kinase 3 (FLT3) is a receptor tyrosine kinase that is expressed almost exclusively in the hematopoietic compartment. Its ligand, FLT3 ligand (FL), induces dimerization and activation of its intrinsic tyrosine kinase activity. Activation of FLT3 leads to its autophosphorylation and initiation of several signal transduction cascades. Signaling is initiated by the recruitment of signal transduction molecules to activated FLT3 through binding to specific phosphorylated tyrosine residues in the intracellular region of FLT3. Activation of FLT3 mediates cell survival, cell proliferation, and differentiation of hematopoietic progenitor cells. It acts in synergy with several other cytokines to promote its biological effects. Deregulated FLT3 activity has been implicated in several diseases, most prominently in acute myeloid leukemia where around one-third of patients carry an activating mutant of FLT3 which drives the disease and is correlated with poor prognosis. Overactivity of FLT3 has also been implicated in autoimmune diseases, such as rheumatoid arthritis. The observation that gain-of-function mutations of FLT3 can promote leukemogenesis has stimulated the development of inhibitors that target this receptor. Many of these are in clinical trials, and some have been approved for clinical use. However, problems with acquired resistance to these inhibitors are common and, furthermore, only a fraction of patients respond to these selective treatments. This review provides a summary of our current knowledge regarding structural and functional aspects of FLT3 signaling, both under normal and pathological conditions, and discusses challenges for the future regarding the use of targeted inhibition of these pathways for the treatment of patients.

Glucocorticoid-resistant B cell acute lymphoblastic leukemia displays receptor tyrosine kinase activation.

The response of childhood acute lymphoblastic leukemia (ALL) to dexamethasone predicts the long-term remission outcome. To explore the mechanisms of dexamethasone resistance in B cell ALL (B-ALL), we generated dexamethasone-resistant clones by prolonged treatment with dexamethasone. Using RNA-sequencing and high-throughput screening, we found that dexamethasone-resistant cells are dependent on receptor tyrosine kinases. Further analysis with phosphokinase arrays showed that the type III receptor tyrosine kinase FLT3 is constitutively active in resistant cells. Targeted next-generation and Sanger sequencing identified an internal tandem duplication mutation and a point mutation (R845G) in FLT3 in dexamethasone-resistant cells, which were not present in the corresponding sensitive clones. Finally, we showed that resistant cells displayed sensitivity to second-generation FLT3 inhibitors both in vitro and in vivo. Collectively, our data suggest that long-term dexamethasone treatment selects cells with a distinct genetic background, in this case oncogenic FLT3, and therefore therapies targeting FLT3 might be useful for the treatment of relapsed B-ALL patients.