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

Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges

Drug resistance is one of the main reasons for cancer treatment failure, leading to a rapid recurrence/disease progression of the cancer. Recently, artificial intelligence (AI) has empowered physicians to use its powerful data processing and pattern recognition capabilities to extract and mine valuable drug resistance information from large amounts of clinical or omics data, to study drug resistance mechanisms, to evaluate and predict drug resistance, and to develop innovative therapeutic strategies to reduce drug resistance. In this review, we proposed a feasible workflow for incorporating AI into tumor drug resistance research, highlighted current AI-driven tumor drug resistance applications, and discussed the opportunities and challenges encountered in the process. Based on a comprehensive literature analysis, we systematically summarized the role of AI in tumor drug resistance research, including drug development, resistance mechanism elucidation, drug sensitivity prediction, combination therapy optimization, resistance phenotype identification, and clinical biomarker discovery. With the continuous advancement of AI technology and rigorous validation of clinical data, AI models are expected to fuel the development of precision oncology by improving efficacy, guiding therapeutic decisions, and optimizing patient prognosis. In summary, by leveraging clinical and omics data, AI models are expected to pioneer new therapy strategies to mitigate tumor drug resistance, improve efficacy and patient survival, and provide novel perspectives and tools for oncology treatment.

Overexpressed NEK2 contributes to progression and cisplatin resistance through activating the Wnt/β-catenin signaling pathway in cervical cancer

BACKGROUND

Cervical cancer ranks as the fourth most common cancer among women, with cisplatin resistance posing a significant challenge to the long-term survival of patients.

METHODS

The roles of NEK2 in cervical cancer were examined through bioinformatics analysis. Transfection efficiency and molecular mechanisms were assessed using real-time quantitative polymerase chain reaction (qRT-PCR) and western blotting (WB). To evaluate cell functions, a series of assays, including cell counting kit-8 (CCK-8), wound healing, transwell, colony formation, and flow cytometry (FCM), were performed on HeLa, SiHa, and HeLa/DDP (cisplatin-resistant) cell lines.

RESULTS

We found that NEK2 is upregulated in cervical cancer tissues compared to normal tissues and is further elevated in cisplatin-resistant cervical cancer compared to cisplatin-sensitive cases. The overexpression of NEK2 is associated with enhanced cancer progression, poorer prognosis, and increased cisplatin resistance in cervical cancer patients. Notably, in the presence of cisplatin, the knockdown of NEK2 inhibited cell viability, proliferation, migration, invasion, and G2/M phase arrest in cervical cancer cells, while also enhancing the sensitivity of cisplatin-resistant cervical cancer cells through the inactivation of the Wnt/β-catenin signaling pathway.

CONCLUSIONS

NEK2 is upregulated in cervical squamous cell carcinoma (CESC) compared to normal tissues and exhibits higher levels in cisplatin-resistant CESC than in sensitive counterparts, correlating with disease progression and poor prognosis. Thus, NEK2 is implicated in the cisplatin resistance of CESC via the activation of the Wnt/β-catenin signaling pathway, suggesting its potential as a prognostic marker and a novel target for the diagnosis and treatment of cisplatin-resistant CESC.

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.

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.

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.