Genomic Insights into the Biology of T-lineage Acute Lymphoblastic Leukemia.

IF 12.2 Q1 HEMATOLOGY
Petri Pölönen, David T Teachey, Charles G Mullighan
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引用次数: 0

Abstract

Relapsed and refractory T-lineage acute lymphoblastic leukemia (T-ALL) has a poor prognosis, emphasizing the need to identify high-risk patients. T-ALL classification has relied on immunophenotype, including identification of early T-cell precursor ALL, but few genetic alterations reproducibly predict outcome in T-ALL independently of measurable residual disease. Here, the biologic and genetic basis of T-ALL is reviewed, with an emphasis on large-scale genomic analyses that have shed light on the spectrum of genetic subtypes, their drivers, mechanisms of oncogene activation, and developmental stage. We describe how genomics-based approaches can improve T-ALL classification and risk stratification.

Significance: Whole-genome and whole-transcriptome sequencing enable biologically and clinically meaningful T-ALL subtype classification with greater precision than immunophenotype-based approaches. The genomic landscape of T-ALL is heavily influenced by noncoding alterations, posing a challenge for clinical applications, as their detection often requires whole-genome sequencing. Studies have identified genomic subtypes and genetic alterations that improve the accuracy of patient outcome prediction.

t系急性淋巴细胞白血病的基因组生物学研究。
复发和难治性t系急性淋巴细胞白血病(T-ALL)预后差,强调需要识别高危患者。T-ALL的分类依赖于免疫表型,包括早期t细胞前体ALL的鉴定,但很少有遗传改变可重复地预测T-ALL的预后,而不依赖于可测量的残留疾病。本文综述了T-ALL的生物学和遗传学基础,重点介绍了大规模基因组分析,揭示了遗传亚型谱、驱动因素、癌基因激活机制和发育阶段。我们描述了基于基因组学的方法如何改善T-ALL分类和风险分层。意义:全基因组和全转录组测序使生物学和临床意义的T-ALL亚型分类比基于免疫表型的方法更精确。T-ALL的基因组图谱受到非编码改变的严重影响,这对临床应用提出了挑战,因为它们的检测通常需要全基因组测序。研究已经确定了提高患者预后预测准确性的基因组亚型和遗传改变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
12.70
自引率
1.80%
发文量
139
期刊介绍: The journal Blood Cancer Discovery publishes high-quality Research Articles and Briefs that focus on major advances in basic, translational, and clinical research of leukemia, lymphoma, myeloma, and associated diseases. The topics covered include molecular and cellular features of pathogenesis, therapy response and relapse, transcriptional circuits, stem cells, differentiation, microenvironment, metabolism, immunity, mutagenesis, and clonal evolution. These subjects are investigated in both animal disease models and high-dimensional clinical data landscapes. The journal also welcomes submissions on new pharmacological, biological, and living cell therapies, as well as new diagnostic tools. They are interested in prognostic, diagnostic, and pharmacodynamic biomarkers, and computational and machine learning approaches to personalized medicine. The scope of submissions ranges from preclinical proof of concept to clinical trials and real-world evidence. Blood Cancer Discovery serves as a forum for diverse ideas that shape future research directions in hematooncology. In addition to Research Articles and Briefs, the journal also publishes Reviews, Perspectives, and Commentaries on topics of broad interest in the field.
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