Department of Medicine, Division of Hematology, Cancer Institute, and Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA.
Departments of Medicine (Biomedical Informatics), and Biomedical Data Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Leukemia. 2019 Apr;33(4):826-843. doi: 10.1038/s41375-019-0387-y. Epub 2019 Feb 6.
Three mutation-specific targeted therapies have recently been approved by the FDA for the treatment of acute myeloid leukemia (AML): midostaurin for FLT3 mutations, enasidenib for relapsed or refractory cases with IDH2 mutations, and ivosidenib for cases with an IDH1 mutation. Together, these agents offer a mutation-directed treatment approach for up to 45% of de novo adult AML cases, a welcome deluge after a prolonged drought. At the same time, a number of computational tools have recently been developed that promise to further accelerate progress in mutation-specific therapy for AML and other cancers. Technical advances together with comprehensively annotated AML tissue banks have resulted in the availability of large and complex data sets for exploration by the end-user, including (i) microarray gene expression, (ii) exome sequencing, (iii) deep sequencing data of sub-clone heterogeneity, (iv) RNA sequencing of gene expression (bulk and single cell), (v) DNA methylation and chromatin, (vi) and germline quantitative trait loci. Yet few clinicians or experimental hematologists have the time or the training to access or analyze these repositories. This review summarizes the data sets and bioinformatic tools currently available to further the discovery of mutation-specific targets with an emphasis on web-based applications that are open, accessible, user-friendly, and do not require coding experience to navigate. We show examples of how available data can be mined to identify potential targets using synthetic lethality, drug repurposing, epigenetic sub-grouping, and proteomic networks while also highlighting strengths and limitations and the need for superior models for validation.
三种突变特异性靶向治疗药物最近已被 FDA 批准用于治疗急性髓系白血病 (AML)：米哚妥林用于 FLT3 突变，依维莫司用于复发或难治性 IDH2 突变病例，ivosidenib 用于 IDH1 突变病例。这些药物共同为高达 45%的新发成人 AML 病例提供了一种针对突变的治疗方法，这是在长期干旱之后的可喜进展。与此同时，最近开发了许多计算工具，有望进一步加速 AML 和其他癌症的突变特异性治疗的进展。技术进步以及全面注释的 AML 组织库使得最终用户能够探索大型和复杂的数据集，包括 (i) 微阵列基因表达，(ii) 外显子组测序，(iii) 亚克隆异质性的深度测序数据，(iv) 基因表达的 RNA 测序（批量和单细胞），(v) DNA 甲基化和染色质，(vi) 和种系数量性状基因座。然而，很少有临床医生或实验血液学家有时间或培训来访问或分析这些存储库。本综述总结了目前可用于进一步发现突变特异性靶标的数据集和生物信息学工具，重点介绍了开放、可访问、用户友好且无需编码经验即可导航的基于网络的应用程序。我们展示了如何利用可用数据来识别潜在的目标，例如使用合成致死、药物再利用、表观遗传亚群和蛋白质组网络，同时还强调了优势和局限性以及对验证的更好模型的需求。
