Identification of Clinically Relevant Subgroups of Chronic Lymphocytic Leukemia Through Discovery of Abnormal Molecular Pathways

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Publikace nespadá pod Ústav výpočetní techniky, ale pod Středoevropský technologický institut. Oficiální stránka publikace je na webu muni.cz.

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TAUŠ Petr POSPÍŠILOVÁ Šárka PLEVOVÁ Karla

Rok publikování 2021
Druh Článek v odborném periodiku
Časopis / Zdroj Frontiers in Genetics
Fakulta / Pracoviště MU

Středoevropský technologický institut

Citace
www https://www.frontiersin.org/articles/10.3389/fgene.2021.627964/full
Doi http://dx.doi.org/10.3389/fgene.2021.627964
Klíčová slova chronic lymphocytic leukemia; pathway mutation score; ensemble clustering; extreme gradient boosting; mutation subtypes
Popis Chronic lymphocytic leukemia (CLL) is the most common form of adult leukemia in the Western world with a highly variable clinical course. Its striking genetic heterogeneity is not yet fully understood. Although the CLL genetic landscape has been well-described, patient stratification based on mutation profiles remains elusive mainly due to the heterogeneity of data. Here we attempted to decrease the heterogeneity of somatic mutation data by mapping mutated genes in the respective biological processes. From the sequencing data gathered by the International Cancer Genome Consortium for 506 CLL patients, we generated pathway mutation scores, applied ensemble clustering on them, and extracted abnormal molecular pathways with a machine learning approach. We identified four clusters differing in pathway mutational profiles and time to first treatment. Interestingly, common CLL drivers such as ATM or TP53 were associated with particular subtypes, while others like NOTCH1 or SF3B1 were not. This study provides an important step in understanding mutational patterns in CLL.
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