Cancer cells are intrinsically heterogeneous. Multiple clones with their unique variants co-exist in tumor tissues. The variants include point mutations and structural variations. Point mutations, or single nucleotide variants are those variants on one base; structural variations are variations involving sequence with length not smaller than 50 bases. Approaches to estimate the number of clones and their respective percentages from point mutations have been recently proposed. However, structural variations, although involving more reads than point mutations, have not been quantitatively studied in characterizing cancer heterogeneity. I describe in this thesis a maximum likelihood approach to estimate variant allele fraction of a putative structural variation, as a step towards the characterization of tumor heterogeneity. A software tool, BreakDown, implemented in Perl realizing this statistical model is publicly available. I studied the performance of BreakDown through both simulated and real data, and found BreakDown outperformed other methods such as THetA in estimating variant allele fractions.