AIMC Topic: Programmed Cell Death 1 Receptor

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Deep learning-guided rational engineering of synergistic PD-1 and LAG-3 blockade for enhanced tumor immunomodulation.

Journal of computer-aided molecular design
Evolution has optimized proteins over time by the incorporation of precise and context-specific amino acid substitutions adapted to structural and functional demands. We have reconceptualized this principle using deep learning to engineer monoclonal ...

Ligand-receptor interaction profiling as a predictive biomarker for anti-PD-1 therapy response in melanoma.

Clinical and experimental medicine
Cell-to-cell communication through ligand-receptor (LR) interactions can fundamentally shape the tumor microenvironment and immune responses, but the full spectrum of these interactions in anti-PD-1 therapy remains unexplored. We developed a predicti...

Development and validation of interpretable machine learning models for predicting AKI risk in patients treated with PD-1/PD-L1: a retrospective study.

BMC medical informatics and decision making
BACKGROUND: Anti-programmed cell death protein 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunotherapy has revolutionized cancer treatment. However, it can cause immune-related adverse events, including acute kidney injury (AKI). Such adverse e...

Integrative habitat analysis and multi-instance deep learning for predictive model of PD-1/PD-L1 immunotherapy efficacy in NSCLC patients: a dual-center retrospective study.

BMC medical imaging
BACKGROUND: PD-1/PD-L1 immunotherapy represents the primary treatment for advanced NSCLC patients; however, response rates to this therapy vary among individuals. This dual-center study aimed to integrate habitat radiomics and multi-instance deep lea...

Single-cell and bulk transcriptome analyses reveal elevated amino acid metabolism promoting tumor-directed immune evasion in colorectal cancer.

Frontiers in immunology
INTRODUCTION: Colorectal cancer (CRC), the third most common cancer worldwide, often shows limited responsiveness to immunotherapy due to its predominantly immune-excluded phenotype. Despite increasing insights into the complex tumor microenvironment...

Global research trends on biomarkers for cancer immunotherapy: Visualization and bibliometric analysis.

Human vaccines & immunotherapeutics
The global burden of cancer continues to grow, posing a significant public health challenge. Although cancer immunotherapy has shown significant efficacy, the response rate is not high. Therefore, the objective of our research was to identify the lat...

Factors inducing cutaneous adverse reactions in cancer patients treated with PD-1 and PD-L1 inhibitors: a machine-learning algorithm approach.

Immunopharmacology and immunotoxicology
BACKGROUND: Immune checkpoint inhibitors (ICIs) show promise in cancer treatment but can lead to immune-related adverse events (irAEs), notably affecting the skin. Understanding the factors behind these skin reactions is crucial for effective managem...

Towards novel small-molecule inhibitors blocking PD-1/PD-L1 pathway: From explainable machine learning models to molecular dynamics simulation.

International journal of biological macromolecules
Molecular design of small-molecule inhibitors targeting programmed cell death-1 (PD-1)/programmed cell death ligand-1 (PD-L1) pathway has been recognized as an active research area by the clinical success of cancer immunotherapy. In recent years, usi...

A multi-task deep learning model based on comprehensive feature integration and self-attention mechanism for predicting response to anti-PD1/PD-L1.

International immunopharmacology
BACKGROUND: Immune checkpoint inhibitor (ICI) has been widely used in the treatment of advanced cancers, but predicting their efficacy remains challenging. Traditional biomarkers are numerous but exhibit heterogeneity within populations. For comprehe...