Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing

Abstract

SummaryMost patients with acute myeloid leukemia (AML) die from progressive disease after relapse, which is associated with clonal evolution at the cytogenetic level1,2. To determine the mutational spectrum associated with relapse, we sequenced the primary tumor and relapse genomes from 8 AML patients, and validated hundreds of somatic mutations using deep sequencing; this allowed us to precisely define clonality and clonal evolution patterns at relapse. Besides discovering novel, recurrently mutated genes (e.g. WAC, SMC3, DIS3, DDX41, and DAXX) in AML, we found two major clonal evolution patterns during AML relapse: 1) the founding clone in the primary tumor gained mutations and evolved into the relapse clone, or 2) a subclone of the founding clone survived initial therapy, gained additional mutations, and expanded at relapse. In all cases, chemotherapy failed to eradicate the founding clone. The comparison of relapse-specific vs. primary tumor mutations in all 8 cases revealed an increase in transversions, probably due to DNA damage caused by cytotoxic chemotherapy. These data demonstrate that AML relapse is associated with the addition of new mutations and clonal evolution, which is shaped in part by the chemotherapy that the patients receive to establish and maintain remissions.

Navigating cancer network attractors for tumor-specific therapy

“…Rather, it is a dynamic ensemble of subpopulations with different abnormalities undergoing molecular evolution [7][8][9] . Two fundamental principles of cancer signaling networks can explain why a binary driver/passenger classification may be too simplistic to accommodate the complex dynamic nature of tumors.…”

Exome sequencing of contralateral breast cancer identifies metastatic disease

“…Exome sequencing has proved useful to identify somatic mutations and investigate the clonal relationship between primary tumors and various forms of recurrences in a wide range of cancers, including breast cancer [8][9][10][11]. Taken together, the results from exome sequencing in the field of oncology show a large genetic diversity between tumors from different patients, with only few positions in the genome being mutated in 10% or more of cancer cases.…”

PyClone: statistical inference of clonal population structure in cancer

“…Despite progress in measuring allele prevalence with deep sequencing [4][5][6][7][8] , statistical approaches to cluster deep digital sequencing of mutations into biologically relevant groupings remain under-developed, with poorly understood analytical assumptions. The allelic prevalence of a mutation is a compound measure of several factors: the proportion of contaminating normal cells, the proportion of tumour cells harbouring the mutation and the number of allelic copies of the mutation in each cell, plus uncharacterized sources of technical noise.…”

“…This is particularly exacerbated when only single sample allelic prevalence estimates are taken. Multiple sample measurements (taken in time 5,9 or space 10 ) have two distinct advantages: reduction of noise due to repeated measures and the potential to identify sets of mutations whose cellular prevalences shift together. The latter is a route to precisely identifying clones whose prevalences are changing under selective pressures.…”