(A) A toy example of clonal evolution of one patient. The evolutionary history of four alterations A, B, C, and D is shown in the left panel. We then sample different time points and analyze genomic data. Specifically, for each patient, tagged-amplicon library next generation sequencing (NGS) and fluorescence in situ hybridization (FISH) analyses are carried out at different time points to evaluate the presence and quantify the clonal abundance of possible driver genetic lesions. Then we use mutation cell frequency (MCF) to adjust and unify the data (middle panel). Based on this longitudinal data, we build sequential network of one patient (right panel). CNA: copy number abnormalities. (B) Sequential networks derived from different patients (left panel) are further pooled to generate Integrated Sequential Network (ISN), which is a cross-sectional integration of longitudinal data (middle panel). We then infer TEDG by removing the indirect interactions with network deconvolution and simplification algorithms (‘Materials and methods’). To construct TEDG, we calculate a minimal spanning tree-based on the deconvolution scores (right panel).