Abstract: Background Accurate measurement of heterogeneity and reconstruction of evolutionary pathways are key to decipher clonal dynamics in tumour origins. Although bulk population genetic sequencing data has been used extensively to understand the life history of tumours, sequencing single cell DNA is likely to provide a much higher resolution of clonal dynamics and yield new insights into competing cellular populations with different genotypes. This is of significant importance when clonal evolution from a benign state to malignant disease is being considered, such as in the clonal evolution of MGUS to multiple myeloma (MM). Methods To this end, we further refined a multiple displacement amplification-based single cell whole-exome sequencing (scWES) protocol and applied it to examine MM cells (2 cases), and in one of these cases, probed evolution from a pre-existing MGUS stage in a paired setting. Single tumour cells were isolated from CD138+CD38+ fluorescence-activated cell sorted populations and CD3+ for T cells as germline controls. We developed a novel computational pipeline to accurately call somatic SNVs/indels and copy number aberration (CNA) events in the form of loss-of-heterozygosity (LOH) and gains in each single cell, identified drivers at the genome-wide level and reconstructed the tumour phylogeny while estimating and accounting for allelic dropout (N=176 single cells, 25× each cell). Results By focusing on the patient that allowed comparison of single cells at pre- and post-malignancy states, we show that pre-malignant subclones can be readily identified, and strikingly that malignant cells arise and expand from one of the MGUS subclones in the combined tumour phylogeny. Conclusions These results suggest that this scWES based method is a pragmatic approach to investigate clonal dynamics by allowing: 1) the analysis of genetic heterogeneity at the genome-wide level (much higher resolution than targeted panels), and 2) at low sequencing costs across large set of cells when compared with whole genome sequencing-based methods.