MyClone: rapid and precise reconstruction of clonal population structures for tumors.

BACKGROUND: Understanding tumor heterogeneity is essential for advancing cancer treatment. Clonal reconstruction methods play a pivotal role in deciphering this heterogeneity. Our goal is to develop a clonal reconstruction approach that is clinically applicable, easy to implement, and capable of delivering both high-speed performance and excellent reconstruction accuracy. RESULTS: We present MyClone, a probabilistic method designed to reconstruct the clonal composition of tumors using deep sequencing genomic data. MyClone processes read counts and copy number information of single nucleotide variants derived from deep sequencing data, enabling it to determine the mutational composition of clones and the cancer cell fractions of these mutations. Compared to existing clonal reconstruction methods, MyClone enhances clustering accuracy and cancer cell fraction prediction when applied to deep-targeted sequencing data and bulk tumor sequencing data with deep sequencing coverage. Additionally, MyClone achieves a substantial improvement in computational speed. We rigorously validated MyClone's performance using both simulated and real clinical datasets and applied it to analyze a circulating tumor DNA sequencing dataset from 139 metastatic breast cancer patients. In this analysis, we explored mechanisms of drug resistance in metastatic breast cancer and identified 10 mutated genes potentially associated with drug resistance or sensitivity. CONCLUSIONS: For deeply sequenced data, MyClone outperforms existing methods on both targeted sequencing and bulk tumor data. Its high computational efficiency and reconstruction accuracy position MyClone as a promising tool for broad clinical application in cancer treatment. The source code is publicly available at https://github.com/Hansen0413/myclone-code .