利用单细胞CNA数据识别与肿瘤相关的靶基因，构建同一肿瘤多个细胞之间的克隆进化过程，有助于深刻理解肿瘤进化史，并为精准治疗提供理论指导。在单细胞CNA数据的基础上，融合其它生物数据是本课题提升肿瘤进化分析可靠性、风险评估能力的关键思路。本项目以融合多源数据为基础，识别与肿瘤相关的靶基因，构建肿瘤进化模型并给出风险评估：设计一种高效的单细胞肿瘤CNA检测算法，准确识别正常细胞与肿瘤细胞CNA差异，为后续肿瘤进化研究提供遗传标记；结合相关基因表达谱数据，miRNA数据和其它组学数据在全基因组范围内筛选与肿瘤相关的候选CNA；结合miRNA数据、基因表达谱数据合理推断细胞之间的CNA进化距离，设计启发式最优化算法构建肿瘤进化树；进一步分析CNA之间上位性效应，并构建肿瘤风险评估模型，以准确评估肿瘤患病风险。本项目的深入研究不仅有助于进一步理解肿瘤的发生、发展过程，也有助于癌症的早期诊疗。

The use of single-cell CNA data to identify tumor-associated target genes and to construct a clonal evolutionary process among multiple cells of the same tumor can help to deeply understand the evolutionary history of tumors and provide theoretical guidance for precise treatment. The key idea of this project is integrating other biological data with the single-cell CAN data, which can effectively improve reliability and risk assessment ability of tumor evolution analysis. This project is based on the fusion of multi-source data to identify tumor-associated target genes, construct a tumor evolution model and give a risk assessment. Some main tasks include: design an efficient single-cell tumor CNA detection algorithm to accurately identify CNA differences between normal cells and tumor cells, and provide genetic markers for follow-up tumor evolution studies; combine relevant gene expression profiling data, miRNA data, and other histological data to screen tumor-associated candidate CNAs across the entire genome; combine miRNA data, gene expression profiling data to reasonably extrapolate CNA evolution distance among cells, and design heuristic optimization algorithm for constructing tumor phylogenetic tree; and further analyze epistatic effects between CNAs, and build a risk assessment model so as to accurately assess the risk of cancer. The in-depth study of this project not only helps to further understand the occurrence and development of tumors, but also helps early diagnosis and treatment of cancer.