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

Petr Taus,Sarka Pospisilova,Karla Plevova

doi:10.3389/fgene.2021.627964

Abstract

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.

Highlights

Chronic lymphocytic leukemia (CLL) is a genetically and clinically heterogeneous disease
We built a combination of multiple clustering solutions through a consensus approach and applied it to the pathway mutation scores of CLL patients
We identified four clusters differing in pathway mutational profiles and time to first treatment (TTFT)

Summary

Introduction

Chronic lymphocytic leukemia (CLL) is a genetically and clinically heterogeneous disease. CLL is divided into two main diagnostic subgroups based on the somatic hypermutation status of the immunoglobulin heavy chain variable region genes (IGHV; Damle et al, 1999; Hamblin et al, 1999). Clinical heterogeneity within both groups is substantial, patients with unmutated IGHV typically experience a more aggressive disease (Sutton et al, 2017). Genomic studies in CLL have discovered several putative drivers (Landau et al, 2013, 2015; Puente et al, 2015). Mutations in some of the drivers (e.g., mutations in TP53 and ATM genes)

Objectives

Methods

Results

Conclusion