Computational strategies in tumor phylogenetics: evaluating multimodal integration and methodological trade-offs across study designs.

Motivation: Tumor clonal evolution represents a dynamic ecosystem underpinned by genetic alterations and Darwinian selection, posing major challenges due to intratumoral heterogeneity and therapy resistance. Although computational methods have advanced significantly, current tools often focus on single data modalities, leaving important gaps in modeling spatial and non-genetic evolution. This review systematically surveys and assesses algorithmic progress across diverse study designs to identify key limitations and future directions. Results: We systematically evaluate over 20 computational tools across four study designs-cross-sectional, regional bulk, single-cell, and lineage tracing-and perform benchmarking of seven comparable tools. Multiomics integration approaches are shown to improve phylogenetic inference, yet challenges remain in mutation ordering and polyclonal detection. A novel spatiotemporal framework is proposed to link phylogenetic branch lengths with spatial transcriptomic gradients. Future efforts should prioritize multimodal data integration, scalable computational architectures, and clinically applicable models to translate evolutionary insights into precision oncology. Availability and implementation: This review provides a comprehensive survey and benchmarking of existing methods. The code and data used to generate the benchmarking figures and results are available at https://github.com/zlsys3/review_benchmark/tree/main/figure.