NeoAgDT：通过对癌细胞群进行数字孪生模拟，优化个人新抗原疫苗成分。

IF 4.4 3区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

Bioinformatics Pub Date : 2024-04-13 DOI:10.1093/bioinformatics/btae205

Anja Mösch, Filippo Grazioli, Pierre Machart, Brandon Malone

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引用次数: 0

摘要

动机新抗原疫苗利用肿瘤特异性突变使患者的免疫系统能够识别并消灭癌症。结果在这里，我们介绍了 NeoAgDT，这是一种分两步进行的方法，包括：（1）模拟单个癌细胞，创建患者肿瘤细胞群的数字孪生体；（2）在此数字孪生体的基础上，通过整数线性规划优化疫苗组成。在一项针对七名患者的研究中，NeoAgDT显示实验验证的新抗原选择比基于排序的方法有所改进。可用性NeoAgDT代码发布在Github上：https://github.com/nec-research/neoagdt.SUPPLEMENTARY 信息补充数据可在Bioinformatics online上获取。

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NeoAgDT: Optimization of personal neoantigen vaccine composition by digital twin simulation of a cancer cell population.

MOTIVATION Neoantigen vaccines make use of tumor-specific mutations to enable the patient's immune system to recognize and eliminate cancer. Selecting vaccine elements, however, is a complex task which needs to take into account not only the underlying antigen presentation pathway but also tumor heterogeneity. RESULTS Here, we present NeoAgDT, a two-step approach consisting of: (1) simulating individual cancer cells to create a digital twin of the patient's tumor cell population and (2) optimizing the vaccine composition by integer linear programming based on this digital twin. NeoAgDT shows improved selection of experimentally-validated neoantigens over ranking-based approaches in a study of seven patients. AVAILABILITY The NeoAgDT code is published on Github: https://github.com/nec-research/neoagdt. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

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来源期刊

Bioinformatics 生物-生化研究方法

CiteScore

11.20

自引率

5.20%

发文量

753

审稿时长

2.1 months

期刊介绍： The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes- focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.