A High-Fidelity and Scalable Simulator for Benchmarking Cancer Subclonal Reconstruction Methods
Introduction
Cancer is a dynamic and complex disease caused by unwanted proliferation and evolution of somatic cells. The evolutionary dynamics of cancer over space and time lead to the formation of intratumor heterogeneity (ITH), which is one of the leading causes of cancer therapeutic resistance and is associated with poor prognosis. The advent of high-throughput sequencing and single-cell barcoding technologies has facilitated systematic and quantitative studies of cancer evolution. However, challenges in sampling and data generation have prohibited systematic development and assessment of computational methods for reconstruction and prediction of cancer subclonal architecture, chronology and evolution.
Methods
To address these challenges, we are developing a cancer genome evolution simulator to recapitulate the essential technical and biological characteristics of genomic changes during human cancer evolution. This simulator incorporates multiple types of somatic alterations, including single nucleotide variations (SNVs), copy number variations (CNVs), and whole-genome doublings (WGDs), into virtual cancer genomes. It then synthesizes both bulk and single-cell-level sequencing data. Additionally, the simulator models the dynamics of cancer evolution by simulating the birth-death process of cancer cells, which allows for the generation of realistic tumor growth patterns and the emergence of intratumor heterogeneity.
Results
Our simulator provides genomic ground truths underlying the synthetic sequencing data, enabling a systematic benchmarking framework to assess the performance of subclonal reconstruction methods. With its high fidelity and scalability, the simulator will serve as a reliable tool for generating benchmarking data.
Conclusion
The successful development of this simulator, in conjunction with a comprehensive benchmarking framework, offers valuable tools and resources for modeling cancer genome evolution and studying intratumor heterogeneity. This, in turn, will facilitate the development of more reliable computational tools for cancer subclonal reconstruction, thereby advancing our understanding of cancer evolution and aiding in the development of more effective therapeutic strategies. (Acknowledgment: HJ is supported by the CPRIT Research Training Grant RP210028)