Shedding light on the DICER1 mutational spectrum of uncertain significance in malignant neoplasms - Bug, D. S.; Moiseev, I. S.; Porozov, Yu. B.; Petukhova, N. V. (Frontiers in Molecular Biosciences, 2024)
Dicer protein is an indispensable player in such fundamental cell pathways as miRNA biogenesis and regulation of protein expression in a cell. Most recently, both germline and somatic mutations in DICER1 have been identified in diverse types of cancers which suggests Dicer mutation can lead to cancer progression. In addition to well-known hotspot mutations in RNAase III domains, DICER1 is characterized by the wide spectrum of variants in all the functional domains, most of them are of uncertain significance and unstated clinical effect. Moreover, new various somatic DICER1 mutations continuously appear in cancer genome sequencing. The latest contemporary methods of variant effect prediction utilize machine learning algorithms on bulk data, yielding suboptimal correlation with biological data. Consequently, such analysis should be conducted based on the functional and structural characteristics of each protein, using a well-grounded targeted dataset rather than relying on large amounts of unsupervised data. Domains are the functional and evolutionary units of a protein; therefore, the analysis of the whole protein should be based on separate and independent examination of each domain by their evolution reconstruction. Dicer represents a hallmark example of multidomain protein, and expectedly, we confirmed phylogenetic multidomain approach being beneficial for clinical effect prediction of Dicer variants. Since Dicer was suggested to have a putative role in hematological malignancies, we examined variants of DICER1 occurring outside the well-known hotspots of RNase III domain in this type of cancers using phylogenetic reconstruction of individual domain history. Examined substitutions might disrupt Dicer function that was demonstrated by molecular dynamic simulation where distinct structural alterations were observed for each mutation. Our approach can be utilized to study other multidomain proteins and to improve clinical effect evaluation.