人工智能驱动的抗体-药物偶联物生物标志物

IF 48.8 1区医学 Q1 CELL BIOLOGY

Cancer Cell Pub Date : 2025-04-10 DOI:10.1016/j.ccell.2025.03.020

Sherene Loi, Roberto Salgado

引用次数: 0

摘要

目前，没有生物标志物可靠地预测抗体-药物偶联物（adc）的疗效。在这一期的Cancer Cell中，Ma等人提出了一个HER2靶向ADC疗效的预测模型，包括免疫系统成分、激素受体状态、临床分期和HER2+细胞比例。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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AI-driven biomarkers for antibody-drug conjugates

Currently, no biomarker reliably predicts the efficacy of antibody-drug conjugates (ADCs). In this issue of Cancer Cell, Ma et al. present a predictive model for HER2-targeting ADC efficacy, incorporating immune-system components, hormone receptor status, clinical staging, and HER2+ cell proportion.

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来源期刊

Cancer Cell 医学-肿瘤学

CiteScore

55.20

自引率

1.20%

发文量

179

审稿时长

4-8 weeks

期刊介绍： Cancer Cell is a journal that focuses on promoting major advances in cancer research and oncology. The primary criteria for considering manuscripts are as follows: Major advances: Manuscripts should provide significant advancements in answering important questions related to naturally occurring cancers. Translational research: The journal welcomes translational research, which involves the application of basic scientific findings to human health and clinical practice. Clinical investigations: Cancer Cell is interested in publishing clinical investigations that contribute to establishing new paradigms in the treatment, diagnosis, or prevention of cancers. Insights into cancer biology: The journal values clinical investigations that provide important insights into cancer biology beyond what has been revealed by preclinical studies. Mechanism-based proof-of-principle studies: Cancer Cell encourages the publication of mechanism-based proof-of-principle clinical studies, which demonstrate the feasibility of a specific therapeutic approach or diagnostic test.