ArticleFrontiers in immunology2024
Machine learning based anoikis signature predicts personalized treatment strategy of breast cancer.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Revolutionizing breast cancer immunotherapy by integrating AI and nanotechnology approaches: review of current applications and future directions.Bioelectronic medicine · 2025Review
- Evaluating AI-driven precision oncology for breast cancer in low- and middle-income countries: a review of machine learning performance, genomic data use, and clinical feasibility.Frontiers in digital health · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
Background: Breast cancer remains a leading cause of mortality among women worldwide, emphasizing the urgent need for innovative prognostic tools to improve treatment strategies. Anoikis, a form of programmed cell death critical in preventing metastasis, plays a pivotal role in breast cancer progression. Methods: This study introduces the Artificial Intelligence-Derived Anoikis Signature (AIDAS), a novel machine learning-based prognostic tool that identifies key anoikis-related gene patterns in breast cancer. AIDAS was developed using multi-cohort transcriptomic data and validated through immunohistochemistry assays on clinical samples to ensure robustness and broad applicability. Results: AIDAS outperformed existing prognostic models in accurately predicting breast cancer outcomes, providing a reliable tool for personalized treatment. Patients with low AIDAS levels were found to be more responsive to immunotherapies, including PD-1/PD-L1 inhibitors, while high-AIDAS patients demonstrated greater susceptibility to specific chemotherapeutic agents, such as methotrexate. Conclusions: These findings highlight the critical role of anoikis in breast cancer prognosis and underscore AIDAS's potential to guide individualized treatment strategies. By integrating machine learning with biological insights, AIDAS offers a promising approach for advancing personalized oncology. Its detailed understanding of the anoikis landscape paves the way for the development of targeted therapies, promising significant improvements in patient outcomes.
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Registered trials
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