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.

Antagonising a novel toxin "T14" in Alzheimer's disease: Comparison of receptor blocker versus antibody effects in vitro.

A 14mer peptide, T14, is a possible signaling molecule driving neurodegeneration. Its levels are doubled in the Alzheimer brain, but its effects can be blocked at the target alpha-7 receptor by a cyclised variant, 'NBP14', which has beneficial effects, in a transgenic mouse model, on the behavioral and histochemical profile. Since the antagonism of T14 has evident therapeutic potential, we explore here an alternative method of preventing its action by comparing the efficacy of NBP14 with a proprietorial polyclonal antibody against T14, 'Ab-19', at inhibiting three distinct effects of the peptide in PC12 cells: calcium influx, cell viability and compensatory acetylcholinesterase (AChE) release. None of these three parameters was affected by either blocking agent when applied alone. However, both NBP14 and the Ab-19 exhibited a dose-dependent profile against the actions of T14 in all three scenarios: the least sensitive effect observed was in the lower dose range, for both the antibody and the receptor blocker, in antagonizing T14-triggered release of AChE: this parameter is interpreted as indirect compensation for the T14-induced compromise of cell viability, triggered by the enhanced influx of calcium through the initial binding of the peptide to an allosteric site on the alpha-7 receptor. As such, it is the most delayed and indirect index of T14 action and thus the least relatively impacted by lowest doses of either NBP14 or Ab-19. In all three scenarios however the effects of T14 are successfully offset by either agent and thus offer two potentially very different therapies against Alzheimer's disease.

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.

Understanding cell-cell communication and signaling in the colorectal cancer microenvironment.

Carcinomas are complex heterocellular systems containing epithelial cancer cells, stromal fibroblasts, and multiple immune cell-types. Cell-cell communication between these tumor microenvironments (TME) and cells drives cancer progression and influences response to existing therapies. In order to provide better treatments for patients, we must understand how various cell-types collaborate within the TME to drive cancer and consider the multiple signals present between and within different cancer types. To investigate how tissues function, we need a model to measure both how signals are transferred between cells and how that information is processed within cells. The interplay of collaboration between different cell-types requires cell-cell communication. This article aims to review the current in vitro and in vivo mono-cellular and multi-cellular cultures models of colorectal cancer (CRC), and to explore how they can be used for single-cell multi-omics approaches for isolating multiple types of molecules from a single-cell required for cell-cell communication to distinguish cancer cells from normal cells. Integrating the existing single-cell signaling measurements and models, and through understanding the cell identity and how different cell types communicate, will help predict drug sensitivities in tumor cells and between- and within-patients responses.

Repurposing Antibacterial AM404 as a Potential Anticancer Drug for Targeting Colorectal Cancer Stem-Like Cells.

Tumour-promoting inflammation is involved in colorectal cancer (CRC) development and therapeutic resistance. However, the antibiotics and antibacterial drugs and signalling that regulate the potency of anticancer treatment upon forced differentiation of cancer stem-like cell (CSC) are not fully defined yet. We screened an NIH-clinical collection of the small-molecule compound library of antibacterial/anti-inflammatory agents that identified potential candidate drugs targeting CRC-SC for differentiation. Selected compounds were validated in both in vitro organoids and ex vivo colon explant models for their differentiation induction, impediment on neoplastic cell growth, and to elucidate the mechanism of their anticancer activity. We initially focused on AM404, an anandamide uptake inhibitor. AM404 is a metabolite of acetaminophen with antibacterial activity, which showed high potential in preventing CRC-SC features, such as stemness/de-differentiation, migration and drug-resistance. Furthermore, AM404 suppressed the expression of FBXL5 E3-ligase, where AM404 sensitivity was mimicked by FBXL5-knockout. This study uncovers a new molecular mechanism for AM404-altering FBXL5 oncogene which mediates chemo-resistance and CRC invasion, thereby proposes to repurpose antibacterial AM404 as an anticancer agent.

Concise Review: Emerging Drugs Targeting Epithelial Cancer Stem-Like Cells.

Increasing evidence suggests that cancer cell populations contain a small proportion of cells that display stem-like cell properties and which may be responsible for overall tumor maintenance. These cancer stem-like cells (CSCs) appear to have unique tumor-initiating ability and innate survival mechanisms that allow them to resist cancer therapies, consequently promoting relapses. Selective targeting of CSCs may provide therapeutic benefit and several recent reports have indicated this may be possible. In this article, we review drugs targeting CSCs, in selected epithelial cell-derived cancers. Stem Cells 2017;35:839-850.

Spheroid-Formation (Colonosphere) Assay for in Vitro Assessment and Expansion of Stem Cells in Colon Cancer.

Colorectal cancers (CRCs) form a disorganized hierarchy of heterogeneous cell populations on which current chemotherapy regimens fail to exert their distinctive cytotoxicity. A small sub-population of poorly differentiated cancer stem-like cells (CSCs), also known as cancer initiating cells, may exhibit embryonic and/or adult stem-cell gene expression signatures. Self-renewal and survival signals are also dominant over differentiation in CSCs. However, inducers of differentiation exclusive to CSC may affect cellular pathways required for the formation and progression of a tumor, which are not utilized in normal adult stem-cells. Nevertheless, assays for targeting CSCs have been hindered by expanding and maintaining rare CSCs in vitro. However, CRC-CSCs are able to form floating spheroids (known as colonospheres) 3-dimentinionally (3D) in a serum-free defined medium. Therefore, great efforts have been paid to improve colonosphere forming assay as a preclinical model to study tumor biology and to conduct drug screening in cancer research. The 3D-colonosphere culture model may also represent in vivo conditions for the spontaneous aggregation of cancer cells in spheroids. This protocol describes the development of an enrichment/culture assay using CRC-CSCs to facilitate colorectal cancer research through immunofluorescence staining of colonospheres. We have developed colonospheres from HCT116 CRC cell line to compare and link CRC-CSC markers to the NANOG expression level using an immunofluorescence assay. Our data also show that the immunostaining assay of colonosphere is a useful method to explore the role and dynamics of CRC-CSCs division between self-renewal and cell lineage differentiation of cancer cells. In principle, this method is applicable to a variety of primary cells and cell lines of epithelial origin. Furthermore, this protocol may also allow screening of libraries of compounds to identify bona fide CRC-CSC differentiation inducers.