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

Role of Wnt/β-catenin pathway in cancer drug resistance: Insights into molecular aspects of major solid tumors

Adaptive resistance to conventional and targeted therapies remains one of the major obstacles in the effective management of cancer. Aberrant activation of key signaling mechanisms plays a pivotal role in modulating resistance to drugs. An evolutionarily conserved Wnt/β-catenin pathway is one of the signaling cascades which regulate resistance to drugs. Elevated Wnt signaling confers resistance to anticancer therapies, either through direct activation of its target genes or via indirect mechanisms and crosstalk over other signaling pathways. Involvement of the Wnt/β-catenin pathway in cancer hallmarks like inhibition of apoptosis, promotion of invasion and metastasis and cancer stem cell maintenance makes this pathway a potential target to exploit for addressing drug resistance. Accumulating evidences suggest a critical role of Wnt/β-catenin pathway in imparting resistance across multiple cancers including PDAC, NSCLC, TNBC, etc. Here we present a comprehensive assessment of how Wnt/β-catenin pathway mediates cancer drug resistance in majority of the solid tumors. We take a deep dive into the Wnt/β-catenin signaling-mediated modulation of cellular and downstream molecular mechanisms and their impact on cancer resistance.

Transcriptome Analysis of Beta-Catenin-Related Genes in CD34+ Haematopoietic Stem and Progenitor Cells from Patients with AML

Background

Acute myeloid leukaemia (AML) is a disease of the haematopoietic stem cells(HSCs) that is characterised by the uncontrolled proliferation and impaired differentiation of normal haematopoietic stem/progenitor cells. Several pathways that control the proliferation and differentiation of HSCs are impaired in AML. Activation of the Wnt/beta-catenin signalling pathway has been shown in AML and beta-catenin, which is thought to be the key element of this pathway, has been frequently highlighted. The present study was designed to determine beta-catenin expression levels and beta-catenin-related genes in AML.

Methods

In this study, beta-catenin gene expression levels were determined in 19 AML patients and 3 controls by qRT-PCR. Transcriptome analysis was performed on AML grouped according to beta-catenin expression levels. Differentially expressed genes(DEGs) were investigated in detail using the Database for Annotation Visualisation and Integrated Discovery(DAVID), Gene Ontology(GO), Kyoto Encyclopedia of Genes and Genomes(KEGG), STRING online tools.

Results

The transcriptome profiles of our AML samples showed different molecular signature profiles according to their beta-catenin levels(high-low). A total of 20 genes have been identified as hub genes. Among these, TTK, HJURP, KIF14, BTF3, RPL17 and RSL1D1 were found to be associated with beta-catenin and poor survival in AML. Furthermore, for the first time in our study, the ELOV6 gene, which is the most highly up-regulated gene in human AML samples, was correlated with a poor prognosis via high beta-catenin levels.

Conclusion

It is suggested that the identification of beta-catenin-related gene profiles in AML may help to select new therapeutic targets for the treatment of AML.

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.

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.

Sprouty4 at the crossroads of Trk neurotrophin receptor signaling suppression by glucocorticoids

Glucocorticoids (GC) affect neuronal plasticity, development and function of the nervous system by inhibiting neurotrophin-induced Trk signaling. It has been established that pretreatment with dexamethasone (DEX) restricts Neurotrophin-induced neurite outgrowth by inhibiting Trk-dependent activation of Ras-Erk1/2 signaling pathways. However, the precise molecular mechanism through which DEX interferes with neurotrophin signaling and Trk-mediated neurite outgrowth has not been clearly defined yet. Here, we observed that in PC12 cells DEX treatment promotes the transcription of Sprouty4, a regulatory molecule that is part of a negative feedback module that specifically abrogates Ras to Erk1/2 signaling in response to NGF. In line with this, either knockdown of Sprouty4 or overexpression of a dominant negative form of Sprouty4 (Y53A), rescue the inhibition of NGF/TrkA-promoted neurite outgrowth and Erk1/2 phosphorylation induced by DEX. Likewise, treatment of hippocampal neurons with DEX induces the expression of Sprouty4 and its knockdown abrogates the inhibitory effect of DEX on primary neurite formation, dendrite branching and Erk1/2 activation induced by BDNF. Thus, these results suggest that the induction of Sprouty4 mRNA by DEX translates into a significant inhibition of Trk to Erk1/2 signaling pathway. Together, these findings bring new insights into the crosstalk between DEX and neurotrophin signaling and demonstrate that Sprouty4 mediates the inhibitory effects of DEX on neurotrophin function.

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.

Analyses of Transcriptomics Cell Signalling for Pre-Screening Applications in the Integrated Approach for Testing and Assessment of Non-Genotoxic Carcinogens

With recent rapid advancement of methodological tools, mechanistic understanding of biological processes leading to carcinogenesis is expanding. New approach methodologies such as transcriptomics can inform on non-genotoxic mechanisms of chemical carcinogens and can be developed for regulatory applications. The Organisation for the Economic Cooperation and Development (OECD) expert group developing an Integrated Approach to the Testing and Assessment (IATA) of Non-Genotoxic Carcinogens (NGTxC) is reviewing the possible assays to be integrated therein. In this context, we review the application of transcriptomics approaches suitable for pre-screening gene expression changes associated with phenotypic alterations that underlie the carcinogenic processes for subsequent prioritisation of downstream test methods appropriate to specific key events of non-genotoxic carcinogenesis. Using case studies, we evaluate the potential of gene expression analyses especially in relation to breast cancer, to identify the most relevant approaches that could be utilised as (pre-) screening tools, for example Gene Set Enrichment Analysis (GSEA). We also consider how to address the challenges to integrate gene panels and transcriptomic assays into the IATA, highlighting the pivotal omics markers identified for assay measurement in the IATA key events of inflammation, immune response, mitogenic signalling and cell injury.

Patients with mesenchymal tumours and high Fusobacteriales prevalence have worse prognosis in colorectal cancer (CRC)

OBJECTIVES

Transcriptomic-based subtyping, consensus molecular subtyping (CMS) and colorectal cancer intrinsic subtyping (CRIS) identify a patient subpopulation with mesenchymal traits (CMS4/CRIS-B) and poorer outcome. Here, we investigated the relationship between prevalence of Fusobacterium nucleatum (Fn) and Fusobacteriales, CMS/CRIS subtyping, cell type composition, immune infiltrates and host contexture to refine patient stratification and to identify druggable context-specific vulnerabilities.

DESIGN

We coupled cell culture experiments with characterisation of Fn/Fusobacteriales prevalence and host biology/microenviroment in tumours from two independent colorectal cancer patient cohorts (Taxonomy: n=140, colon and rectal cases of The Cancer Genome Atlas (TCGA-COAD-READ) cohort: n=605).

RESULTS

In vitro, Fn infection induced inflammation via nuclear factor kappa-light-chain-enhancer of activated B cells/tumour necrosis factor alpha in HCT116 and HT29 cancer cell lines. In patients, high Fn/Fusobacteriales were found in CMS1, microsatellite unstable () tumours, with infiltration of M1 macrophages, reduced M2 macrophages, and high interleukin (IL)-6/IL-8/IL-1β signalling. Analysis of the Taxonomy cohort suggested that Fn was prognostic for CMS4/CRIS-B patients, despite having lower Fn load than CMS1 patients. In the TCGA-COAD-READ cohort, we likewise identified a differential association between Fusobacteriales relative abundance and outcome when stratifying patients in mesenchymal (either CMS4 and/or CRIS-B) versus non-mesenchymal (neither CMS4 nor CRIS-B). Patients with mesenchymal tumours and high Fusobacteriales had approximately twofold higher risk of worse outcome. These associations were null in non-mesenchymal patients. Modelling the three-way association between Fusobacteriales prevalence, molecular subtyping and host contexture with logistic models with an interaction term disentangled the pathogen-host signalling relationship and identified aberrations (including NOTCH, CSF1-3 and IL-6/IL-8) as candidate targets.

CONCLUSION

This study identifies CMS4/CRIS-B patients with high Fn/Fusobacteriales prevalence as a high-risk subpopulation that may benefit from therapeutics targeting mesenchymal biology.

SynergyFinder Plus: Toward Better Interpretation and Annotation of Drug Combination Screening Datasets

Combinatorial therapies have been recently proposed to improve the efficacy of anticancer treatment. The SynergyFinder R package is a software used to analyze pre-clinical drug combination datasets. Here, we report the major updates to the SynergyFinder R package for improved interpretation and annotation of drug combination screening results. Unlike the existing implementations, the updated SynergyFinder R package includes five main innovations. 1) We extend the mathematical models to higher-order drug combination data analysis and implement dimension reduction techniques for visualizing the synergy landscape. 2) We provide a statistical analysis of drug combination synergy and sensitivity with confidence intervals and P values. 3) We incorporate a synergy barometer to harmonize multiple synergy scoring methods to provide a consensus metric for synergy. 4) We evaluate drug combination synergy and sensitivity to provide an unbiased interpretation of the clinical potential. 5) We enable fast annotation of drugs and cell lines, including their chemical and target information. These annotations will improve the interpretation of the mechanisms of action of drug combinations. To facilitate the use of the R package within the drug discovery community, we also provide a web server at www.synergyfinderplus.org as a user-friendly interface to enable a more flexible and versatile analysis of drug combination data.

Phosphorylation-Dependent Regulation of WNT/Beta-Catenin Signaling

WNT/β-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 β-catenin-dependent or -independent manner rests upon the type of co-receptors used. Canonical WNT signaling is β-catenin-dependent, whereas non-canonical WNT signaling is β-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. β-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 β-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/β-catenin signaling, particularly in human cancer.

PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis

Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose-response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0, https://pharmacodb.ca/), we present (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug-response analysis such as tissue distribution of dose-response metrics and biomarker analysis; and (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug-response phenotypes of cancer models.

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.

DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal

Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here, we report significant updates of DrugComb, including: (i) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; (ii) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; (iii) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample and (iv) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.