Wang Yan, Jin Xiaoye, Qiu Rui, Ma Bo, Zhang Sheng, Song Xuyang, He Jinxi
Department of Medical Oncology, General Hospital of Ningxia Medical University, Yinchuan, China.
General Thoracic Surgery, General Hospital of Ningxia Medical University, Yinchuan, China.
Front Artif Intell. 2025 Jan 8;7:1444127. doi: 10.3389/frai.2024.1444127. eCollection 2024.
Tumor heterogeneity significantly complicates the selection of effective cancer treatments, as patient responses to drugs can vary widely. Personalized cancer therapy has emerged as a promising strategy to enhance treatment effectiveness and precision. This study aimed to develop a personalized drug recommendation model leveraging genomic profiles to optimize therapeutic outcomes.
A content-based filtering algorithm was implemented to predict drug sensitivity. Patient features were characterized by the tumor microenvironment (TME), and drug features were represented by drug fingerprints. The model was trained and validated using the Genomics of Drug Sensitivity in Cancer (GDSC) database, followed by independent validation with the Cancer Cell Line Encyclopedia (CCLE) dataset. Clinical application was assessed using The Cancer Genome Atlas (TCGA) dataset, with Best Overall Response (BOR) serving as the clinical efficacy measure. Two multilayer perceptron (MLP) models were built to predict IC values for 542 tumor cell lines across 18 drugs.
The model exhibited high predictive accuracy, with correlation coefficients () of 0.914 in the training set and 0.902 in the test set. Predictions for cytotoxic drugs, including Docetaxel ( = 0.72) and Cisplatin ( = 0.71), were particularly robust, whereas predictions for targeted therapies were less accurate ( < 0.3). Validation with CCLE (MFI as the endpoint) showed strong correlations ( = 0.67). Application to TCGA data successfully predicted clinical outcomes, including a significant association with 6-month progression-free survival (PFS, = 0.007, AUC = 0.793).
The model demonstrates strong performance across preclinical datasets, showing its potential for real-world application in personalized cancer therapy. By bridging preclinical IC and clinical BOR endpoints, this approach provides a promising tool for optimizing patient-specific treatments.
肿瘤异质性显著增加了有效癌症治疗选择的复杂性，因为患者对药物的反应可能差异很大。个性化癌症治疗已成为提高治疗效果和精准度的一种有前景的策略。本研究旨在开发一种利用基因组图谱的个性化药物推荐模型，以优化治疗结果。
实施基于内容的过滤算法来预测药物敏感性。患者特征通过肿瘤微环境（TME）来表征，药物特征由药物指纹表示。该模型使用癌症药物敏感性基因组学（GDSC）数据库进行训练和验证，随后使用癌症细胞系百科全书（CCLE）数据集进行独立验证。使用癌症基因组图谱（TCGA）数据集评估临床应用，以最佳总体反应（BOR）作为临床疗效指标。构建了两个多层感知器（MLP）模型来预测18种药物对542个肿瘤细胞系的IC值。
该模型表现出较高的预测准确性，训练集的相关系数（）为0.914，测试集为0.902。对细胞毒性药物的预测，包括多西他赛（ = 0.72）和顺铂（ = 0.71），特别稳健，而对靶向治疗的预测则不太准确（ < 0.3）。用CCLE（以MFI为终点）进行验证显示出强相关性（ = 0.67）。应用于TCGA数据成功预测了临床结果，包括与6个月无进展生存期（PFS）的显著关联（ = 0.007，AUC = 0.793）。
该模型在临床前数据集中表现出强大性能，显示出其在个性化癌症治疗实际应用中的潜力。通过连接临床前IC和临床BOR终点，这种方法为优化患者特异性治疗提供了一个有前景的工具。
