Department of Haematology, Singapore General Hospital, Singapore 169608, Singapore.
High Throughput Phenomics Platform, Experimental Drug Development Centre, Agency for Science, Technology and Research (A*STAR), Singapore 139632, Singapore.
Int J Mol Sci. 2022 Mar 5;23(5):2863. doi: 10.3390/ijms23052863.
Acute myeloid leukemia (AML) is a complex hematological malignancy characterized by extensive heterogeneity in genetics, response to therapy and long-term outcomes, making it a prototype example of development for personalized medicine. Given the accessibility to hematologic malignancy patient samples and recent advances in high-throughput technologies, large amounts of biological data that are clinically relevant for diagnosis, risk stratification and targeted drug development have been generated. Recent studies highlight the potential of implementing genomic-based and phenotypic-based screens in clinics to improve survival in patients with refractory AML. In this review, we will discuss successful applications as well as challenges of most up-to-date high-throughput technologies, including artificial intelligence (AI) approaches, in the development of personalized medicine for AML, and recent clinical studies for evaluating the utility of integrating genomics-guided and drug sensitivity testing-guided treatment approaches for AML patients.
急性髓系白血病（AML）是一种复杂的血液系统恶性肿瘤，其在遗传学、对治疗的反应和长期预后方面存在广泛的异质性，使其成为个性化医学发展的典型范例。鉴于血液恶性肿瘤患者样本的可及性以及高通量技术的最新进展，已经产生了大量与临床相关的用于诊断、风险分层和靶向药物开发的生物数据。最近的研究强调了在临床上实施基于基因组和表型的筛选以改善难治性 AML 患者生存的潜力。在这篇综述中，我们将讨论最新高通量技术（包括人工智能（AI）方法）在 AML 个性化医学发展中的成功应用以及挑战，以及最近评估将基因组指导和药物敏感性检测指导的治疗方法整合用于 AML 患者的临床研究。
