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As cancer cell populations evolve, they accumulate a number of somatic mutations, resulting in heterogeneous subclones in the final tumor. Understanding the mechanisms that produce intratumor heterogeneity is important for selecting the best treatment. Although some studies have involved intratumor heterogeneity simulations, their model settings differed substantially. Thus, only limited conditions were explored in each. Herein, we developed a general framework for simulating intratumor heterogeneity patterns and a simulator ( tumopp ).  Tumopp  offers many setting options so that simulations can be carried out under various settings. Setting options include how the cell division rate is determined, how daughter cells are placed, and how driver mutations are treated. Furthermore, to account for the cell cycle, we introduced a gamma function for the waiting time involved in cell division.  Tumopp  also allows simulations in a hexagonal lattice, in addition to a regular lattice that has been used in previous simulation studies. A hexagonal lattice produces a more biologically reasonable space than a regular lattice. Using  tumopp , we investigated how model settings affect the growth curve and intratumor heterogeneity pattern. It was found that, even under neutrality (with no driver mutations),  tumopp  produced dramatically variable patterns of intratumor heterogeneity and tumor morphology, from tumors in which cells with different genetic background are well intermixed to irregular shapes of tumors with a cluster of closely related cells. This result suggests a caveat in analyzing intratumor heterogeneity with simulations with limited settings, and  tumopp  will be useful to explore intratumor heterogeneity patterns in various conditions.

Simulation framework for generating intratumor heterogeneity patterns in a cancer cell population