AIMC Topic: Immune Checkpoint Inhibitors

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Computational characterization and machine learning analysis of quantum optimized marine fungal metabolites targeting PD-L1 in cancer immunotherapy.

Journal of computer-aided molecular design
Cancer immune evasion is predominantly mediated through immune checkpoint pathways, such as the PD-1/PD-L1 axis. In this mechanism, PD-L1, which is often overexpressed on tumor cells, binds to PD-1 receptors on T cells, resulting in the inhibition of...

Decoding strategies for enhancing immunotherapy in head and neck squamous cell carcinoma.

International immunopharmacology
Immune checkpoint inhibitors have partially improved treatment outcomes for patients with head and neck squamous cell carcinoma (HNSCC), but the response rate remains low, with only a few patients benefiting. Mechanistically, HNSCC tumor cells often ...

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 ...

Metagenomic next-generation sequencing unraveled the characteristic of lung microbiota in patients with checkpoint inhibitor pneumonitis: results from a prospective cohort study.

Journal for immunotherapy of cancer
BACKGROUND: Checkpoint inhibitor pneumonitis (CIP) is among the most lethal immune-related adverse events in patients with cancer receiving immunotherapy. This study aims to characterize the lung microbiome in patients with CIP and evaluate its diagn...

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...

A Machine Learning-Based Scoring System to Identify High Immunoactivity Microsatellite Stability Tumors by Quantifying Similarity to Microsatellite Instability-High Tumors in Colorectal Cancers: Development and Quantitative Study.

JMIR formative research
BACKGROUND: Microsatellite stability (MSS) colorectal cancers (CRCs) have a limited response to immune checkpoint inhibitors (ICIs) compared to microsatellite instability-high (MSI-H) CRCs. Nevertheless, previous studies have shown that some MSS CRCs...

Gut microbiota predictive of the efficacy of consolidation immunotherapy and chemoradiotherapy toxicity in lung cancer.

Med (New York, N.Y.)
BACKGROUND: Gut microbiota (GM) predict responses to immune checkpoint inhibitors (ICIs) in patients with advanced lung cancer. However, its role in patients with locally advanced lung cancer undergoing chemoradiotherapy (CRT) combined with consolida...

Overcoming resistance to anti-PD-L1 immunotherapy: mechanisms, combination strategies, and future directions.

Molecular cancer
Cancer cells express high levels of programmed cell death-ligand 1 (PD-L1) to evade immune surveillance. PD-L1 interacts with PD-1 on T cells to make them non-functional. Thus, PD-L1 and PD-1 are pivotal targets in cancer immunotherapy. While anti-PD...

Single-cell RNA sequencing identifies CD8Teff cell activation as a predictive biomarker in triple-negative breast cancer immunotherapy.

Molecular biomedicine
Immunotherapy has emerged as a promising treatment option for triple-negative breast cancer (TNBC); however, the pronounced heterogeneity of the tumor immune microenvironment significantly hinders the prediction of therapeutic efficacy, with effectiv...

An attention-based mRNA transformer network for accurate prediction of melanoma response to immune checkpoint inhibitors.

Scientific reports
Melanoma immunotherapy urgently requires approaches that can accurately predict drug responses to minimize unnecessary treatments. Deep learning models have emerged as powerful tools in this domain due to their robust predictive capabilities. Integra...