AIMC Topic: Antigens, Neoplasm

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Neoantigen-driven cancer vaccines in personalized oncology: progress, obstacles, and translational prospects.

Molecular biology reports
The development of neoantigen-based cancer vaccines has emerged as a groundbreaking approach in the field of personalized oncology. Neoantigens, originating from tumor-specific somatic mutations, possess considerable immunogenic potential and are abs...

CIMT 2025: Report on the 22 Annual Meeting of the Association for Cancer Immunotherapy.

Human vaccines & immunotherapeutics
The 22 Annual Meeting of the Association for Cancer Immunotherapy (CIMT) was held from May 12 to May 14, 2025, in Mainz, Germany. The event brought together 674 academic and clinical professionals from 27 countries across five continents. As a centra...

Multiple Tumor-related autoantibodies test enhances CT-based deep learning performance in diagnosing lung cancer with diameters < 70 mm: a prospective study in China.

BMC pulmonary medicine
BACKGROUND: Deep learning (DL) demonstrates high sensitivity but low specificity in lung cancer (LC) detection during CT screening, and the seven Tumor-associated antigens autoantibodies (7-TAAbs), known for its high specificity in LC, was employed t...

Immunopeptidomics-guided discovery and characterization of neoantigens for personalized cancer immunotherapy.

Science advances
Neoantigens have emerged as ideal targets for personalized cancer immunotherapy. We depict the pan-cancer peptide atlas by comprehensively collecting immunopeptidomics from 531 samples across 14 cancer and 29 normal tissues, and identify 389,165 cano...

Engineering TCR-controlled fuzzy logic into CAR T cells enhances therapeutic specificity.

Cell
Chimeric antigen receptor (CAR) T cell immunotherapy represents a breakthrough in the treatment of hematological malignancies, but poor specificity has limited its applicability to solid tumors. By contrast, natural T cells harboring T cell receptors...

A comprehensive review and evaluation of machine learning-based approaches for identifying tumor T cell antigens.

Computational biology and chemistry
The precise identification of tumor T-cell antigens (TTCAs) is crucial for advancements in cancer immunotherapy and other clinical uses. In contrast to the labor-intensive and time-consuming process of experimentally identifying TTCAs, computational ...

101 Machine Learning Algorithms for Mining Esophageal Squamous Cell Carcinoma Neoantigen Prognostic Models in Single-Cell Data.

International journal of molecular sciences
Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in the digestive tract, characterized by a high recurrence rate and inadequate immunotherapy options. We analyzed mutation data of ESCC from public databases and...

Digital quantification of Ki67 and PRAME in challenging melanocytic lesions - A novel diagnostic tool.

Pathology, research and practice
The interpretation of immunohistochemical markers in melanocytic lesions possesses difficulties due to expression in non-melanocytic cells and the time-consuming, non-reproducible nature of manual assessment. A digital tool that accurately quantifies...

Unraveling the power of NAP-CNB's machine learning-enhanced tumor neoantigen prediction.

eLife
In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) using a machine learning-based platform called NAP-CNB. Unlike other TNA predictors, NAP-CNB leverag...

OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction.

Frontiers in immunology
The key roles of Major Histocompatibility Complex (MHC) Class I and II molecules in the immune system are well established. This study aims to develop a novel machine learning framework for predicting antigen peptide presentation by MHC Class I and I...