The development of cancer involves the accumulation of somatic mutations in several essential biological pathways. Delineating the temporal order of pathway mutations during tumorigenesis is crucial for comprehending the biological mechanisms underlying cancer development and identifying potential targets for therapeutic intervention. Several computational and statistical methods have been introduced for estimating the order of somatic mutations based on mutation profile data from a cohort of patients. However, one major issue of current methods is that they do not take into account intra-tumor heterogeneity (ITH), which limits their ability to accurately discern the order of pathway mutations. To address this problem, we propose PATOPAI, a probabilistic approach to estimate the temporal order of mutations at the pathway level by incorporating ITH information as well as pathway and functional annotation information of mutations. PATOPAI uses a maximum likelihood approach to estimate the probability of pathway mutational events occurring in a specific sequence, wherein it focuses on the orders that are consistent with the phylogenetic structure of the tumors. Applications to whole exome sequencing data from The Cancer Genome Atlas (TCGA) illustrate our method’s ability to recover the temporal order of pathway mutations in several cancer types.
癌症的发生涉及多个关键生物学通路中体细胞突变的累积。明确肿瘤发生过程中通路突变的时间顺序对于理解癌症发展的生物学机制及确定潜在治疗靶点至关重要。已有多种计算和统计方法被提出，用于基于患者队列的突变谱数据来推断体细胞突变的顺序。然而，现有方法存在一个主要问题，即未考虑肿瘤内异质性，这限制了其准确识别通路突变顺序的能力。为解决这一问题，我们提出了PATOPAI，一种概率方法，通过整合肿瘤内异质性信息以及突变的通路和功能注释信息，在通路水平上估计突变的时间顺序。PATOPAI采用最大似然法来估计通路突变事件以特定顺序发生的概率，其中重点关注与肿瘤系统发育结构一致的顺序。通过对癌症基因组图谱全外显子组测序数据的应用，证明了我们的方法能够恢复多种癌症类型中通路突变的时间顺序。
肿瘤(癌症)患者之家