Department of Hematology, University Medical Centre Ljubljana, Zaloška 7, 1000 Ljubljana, Slovenia.
Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia.
Int J Mol Sci. 2021 Mar 25;22(7):3371. doi: 10.3390/ijms22073371.
mutations are a revolutionary discovery and represent an important hallmark of myeloproliferative neoplasms (MPN), especially essential thrombocythemia and primary myelofibrosis. To date, several mutations were identified, with only frameshift mutations linked to the diseased phenotype. It is of diagnostic and prognostic importance to properly define the type of mutation and subclassify it according to its structural similarities to the classical mutations, a 52-bp deletion (type 1 mutation) and a 5-bp insertion (type 2 mutation), using a statistical approximation algorithm (AGADIR). Today, the knowledge on the pathogenesis of -positive MPN is expanding and several cellular mechanisms have been recognized that finally cause a clonal hematopoietic expansion. In this review, we discuss the current basis of the cellular effects of mutants and the understanding of its implementation in the current diagnostic laboratorial and medical practice. Different methods of detection are explained and a diagnostic algorithm is shown that aids in the approach to -positive MPN. Finally, contemporary methods joining artificial intelligence in accordance with molecular-genetic biomarkers in the approach to MPN are presented.
突变是一项革命性的发现，代表了骨髓增殖性肿瘤（MPN）的一个重要标志，尤其是原发性血小板增多症和原发性骨髓纤维化。迄今为止，已经鉴定出了几种突变，只有移码突变与疾病表型有关。正确定义突变类型并根据其与经典突变（52bp 缺失（1 型突变）和 5bp 插入（2 型突变））的结构相似性进行亚分类，使用统计近似算法（AGADIR）具有诊断和预后意义。如今，关于阳性 MPN 的发病机制的知识正在不断扩展，已经认识到了几个最终导致克隆性造血扩张的细胞机制。在这篇综述中，我们讨论了突变体的细胞效应的当前基础，以及在当前的诊断实验室和医学实践中对其的理解。解释了不同的突变检测方法，并展示了一个有助于诊断阳性 MPN 的诊断算法。最后，介绍了将人工智能与分子遗传生物标志物结合应用于 MPN 方法的当代方法。
