Laboratory for Molecular Biomedicine, Institute of Molecular Genetics and Genetic Engineering, University of Belgrade, 11000 Belgrade, Serbia.
University Children's Hospital, 11000 Belgrade, Serbia.
Genes (Basel). 2019 Mar 1;10(3):191. doi: 10.3390/genes10030191.
Personalized medicine is focused on research disciplines which contribute to the individualization of therapy, like pharmacogenomics and pharmacotranscriptomics. Acute lymphoblastic leukemia (ALL) is the most common malignancy of childhood. It is one of the pediatric malignancies with the highest cure rate, but still a lethal outcome due to therapy accounts for 1%⁻3% of deaths. Further improvement of treatment protocols is needed through the implementation of pharmacogenomics and pharmacotranscriptomics. Emerging high-throughput technologies, including microarrays and next-generation sequencing, have provided an enormous amount of molecular data with the potential to be implemented in childhood ALL treatment protocols. In the current review, we summarized the contribution of these novel technologies to the pharmacogenomics and pharmacotranscriptomics of childhood ALL. We have presented data on molecular markers responsible for the efficacy, side effects, and toxicity of the drugs commonly used for childhood ALL treatment, i.e., glucocorticoids, vincristine, asparaginase, anthracyclines, thiopurines, and methotrexate. Big data was generated using high-throughput technologies, but their implementation in clinical practice is poor. Research efforts should be focused on data analysis and designing prediction models using machine learning algorithms. Bioinformatics tools and the implementation of artificial i Lack of association of the CEP72 rs924607 TT genotype with intelligence are expected to open the door wide for personalized medicine in the clinical practice of childhood ALL.
个体化医学专注于研究有助于治疗个体化的学科，如药物基因组学和药物转录组学。急性淋巴细胞白血病（ALL）是儿童中最常见的恶性肿瘤。它是儿科恶性肿瘤中治愈率最高的肿瘤之一，但由于治疗导致的死亡率仍为 1%-3%。通过实施药物基因组学和药物转录组学，需要进一步改进治疗方案。新兴的高通量技术，包括微阵列和下一代测序，提供了大量潜在可用于儿童 ALL 治疗方案的分子数据。在当前的综述中，我们总结了这些新技术对儿童 ALL 的药物基因组学和药物转录组学的贡献。我们介绍了负责儿童 ALL 常用药物疗效、副作用和毒性的分子标志物的数据，即糖皮质激素、长春新碱、门冬酰胺酶、蒽环类药物、硫嘌呤和甲氨蝶呤。使用高通量技术生成了大数据，但在临床实践中的应用效果不佳。研究工作应集中在使用机器学习算法进行数据分析和预测模型设计上。生物信息学工具和人工智能的应用有望为儿童 ALL 的个体化治疗打开大门。
