Clinical impact of the subclonal architecture and mutational complexity in chronic lymphocytic leukemia

Leukemia. 2018 Mar;32(3):645-653. doi: 10.1038/leu.2017.291. Epub 2017 Sep 19.

Abstract

Genome studies of chronic lymphocytic leukemia (CLL) have revealed the remarkable subclonal heterogeneity of the tumors, but the clinical implications of this phenomenon are not well known. We assessed the mutational status of 28 CLL driver genes by deep-targeted next-generation sequencing and copy number alterations (CNA) in 406 previously untreated patients and 48 sequential samples. We detected small subclonal mutations (0.6-25% of cells) in nearly all genes (26/28), and they were the sole alteration in 22% of the mutated cases. CNA tended to be acquired early in the evolution of the disease and remained stable, whereas the mutational heterogeneity increased in a subset of tumors. The prognostic impact of different genes was related to the size of the mutated clone. Combining mutations and CNA, we observed that the accumulation of driver alterations (mutational complexity) gradually shortened the time to first treatment independently of the clonal architecture, IGHV status and Binet stage. Conversely, the overall survival was associated with the increasing subclonal diversity of the tumors but it was related to the age of patients, IGHV and TP53 status of the tumors. In conclusion, our study reveals that both the mutational complexity and subclonal diversity influence the evolution of CLL.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Biomarkers, Tumor*
  • Clonal Evolution / genetics*
  • DNA Copy Number Variations
  • Disease Progression
  • Female
  • Follow-Up Studies
  • Humans
  • Leukemia, Lymphocytic, Chronic, B-Cell / diagnosis
  • Leukemia, Lymphocytic, Chronic, B-Cell / genetics*
  • Leukemia, Lymphocytic, Chronic, B-Cell / mortality
  • Male
  • Middle Aged
  • Mutation / genetics*
  • Neoplasm Staging
  • Prognosis
  • Proportional Hazards Models
  • Signal Transduction
  • Young Adult

Substances

  • Biomarkers, Tumor