  Most neoplastic tumors originate from a single cell, and their evolution can
be genetically traced through lineages characterized by common alterations such
as small somatic mutations (SSMs), copy number alterations (CNAs), structural
variants (SVs), and aneuploidies. Due to the complexity of these alterations in
most tumors and the errors introduced by sequencing protocols and calling
algorithms, tumor subclonal reconstruction algorithms are necessary to
recapitulate the DNA sequence composition and tumor evolution in silico. With a
growing number of these algorithms available, there is a pressing need for
consistent and comprehensive benchmarking, which relies on realistic tumor
sequencing generated by simulation tools. Here, we examine the current
simulation methods, identifying their strengths and weaknesses, and provide
recommendations for their improvement. Our review also explores potential new
directions for research in this area. This work aims to serve as a resource for
understanding and enhancing tumor genomic simulations, contributing to the
advancement of the field.
