结合临床和分子数据对急性髓性白血病进行个性化治疗:机器学习方法

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computer methods and programs in biomedicine Pub Date : 2024-09-18 DOI:10.1016/j.cmpb.2024.108432
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引用次数: 0

摘要

背景和目的近 40 年来,急性髓性白血病患者的治疗标准基本未变。由于个体患者内部和之间存在复杂的突变模式,而且缺乏针对大多数突变事件的靶向药物,因此很难对急性髓细胞白血病患者实施个体化治疗。我们采用机器学习算法重新分析了 BeatAML 数据集。BeatAML项目包括在分子和临床水平上对患者进行广泛特征描述,并与药物敏感性输出相关联。我们的方法利用 BeatAML 数据集提供的分子和临床数据,预测该项目评估的 122 种药物的体内外药物敏感性。方法我们利用 ElasticNet(可生成完全可解释的模型),结合两步训练方案,缩小了计算范围。我们采用两个指标自动完成了基因筛选步骤,并评估了所有可能的数据组合,以确定每种药物的最佳训练配置设置。结果我们发现,当临床数据和 RNA 测序数据相结合时,所有药物的皮尔逊相关性为 0.36,表现最好的模型的皮尔逊相关性达到了 0.67。当我们使用单独的数据集进行训练时,我们注意到 RNA 测序数据(Pearson:0.36)的预测能力是全外显子组测序数据(Pearson:0.11)的三倍,而临床数据则介于两者之间(Pearson 0.26)。最后,我们提出了一个临床意义范例。我们将模型的预测结果作为药物敏感性评分,对个体的预期治疗反应进行排序。结论总之,我们使用机器学习算法重新分析了 BeatAML 数据集,证明了对急性髓性白血病患者进行个体化治疗预测的潜力,解决了该疾病长期以来治疗个性化的难题。通过利用分子和临床数据,我们的方法在预测的药物敏感性和实际反应之间产生了良好的相关性,在改善急性髓细胞白血病患者的治疗效果方面迈出了重要的一步。
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Combining clinical and molecular data for personalized treatment in acute myeloid leukemia: A machine learning approach

Background and Objective

The standard of care in Acute Myeloid Leukemia patients has remained essentially unchanged for nearly 40 years. Due to the complicated mutational patterns within and between individual patients and a lack of targeted agents for most mutational events, implementing individualized treatment for AML has proven difficult. We reanalysed the BeatAML dataset employing Machine Learning algorithms. The BeatAML project entails patients extensively characterized at the molecular and clinical levels and linked to drug sensitivity outputs. Our approach capitalizes on the molecular and clinical data provided by the BeatAML dataset to predict the ex vivo drug sensitivity for the 122 drugs evaluated by the project.

Methods

We utilized ElasticNet, which produces fully interpretable models, in combination with a two-step training protocol that allowed us to narrow down computations. We automated the genes’ filtering step by employing two metrics, and we evaluated all possible data combinations to identify the best training configuration settings per drug.

Results

We report a Pearson correlation across all drugs of 0.36 when clinical and RNA sequencing data were combined, with the best-performing models reaching a Pearson correlation of 0.67. When we trained using the datasets in isolation, we noted that RNA Sequencing data (Pearson: 0.36) attained three times the predictive power of whole exome sequencing data (Pearson: 0.11), with clinical data falling somewhere in between (Pearson 0.26). Lastly, we present a paradigm of clinical significance. We used our models’ prediction as a drug sensitivity score to rank an individual's expected response to treatment. We identified 78 patients out of 89 (88 %) that the proposed drug was more potent than the administered one based on their ex vivo drug sensitivity data.

Conclusions

In conclusion, our reanalysis of the BeatAML dataset using Machine Learning algorithms demonstrates the potential for individualized treatment prediction in Acute Myeloid Leukemia patients, addressing the longstanding challenge of treatment personalization in this disease. By leveraging molecular and clinical data, our approach yields promising correlations between predicted drug sensitivity and actual responses, highlighting a significant step forward in improving therapeutic outcomes for AML patients.
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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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