基于肿瘤特异性突变模型分析癌症基因组片段的选择压强度

Human cancer results from dynamic evolutionary processes of somatic cells driven by genetic mutations or the change of epigenetic modifications. The high genetic heterogeneity of cancer cells introduces significant challenges in cancer research and therapy. Genomic regions may perform different functions and exhibit different effects on fitness in cancer and normal cell populations. The driver genes or regulatory elements of cancer are usually under positive selection, while the cancer specific negatively selected genes or regulatory elements could be considered as cancer essential genes. Both of them are capable to be potential drug targets. However, most of the current methods were developed to detect the positively selected driver genes, and a method that can precisely estimate the selective pressure intensity of a specific region in cancer genome is still highly needed. Our group have developed a specific mutation profile based new method to estimate selection pressure of protein-coding genes. Our previous work have found that the enrichment extent of the mutations changing the RNA secondary structure could be used to estimate the selection pressure of particular noncoding RNA region. This project will develop a new cancer evolutionary genomics analysis method, which is based on single-nucleotide and short Indel mutation models, to measure the selective pressure intensity on coding and non-coding genomic regions in cancer and normal cell populations, and then experimentally verify some of the results. This study will benefit our understanding of the origin and progression of cancer and its underlying molecular mechanisms, and might also generate new effective drug targets, improving personalized medicine of cancer.

癌症是遗传或表观遗传变异驱动的体细胞动态演化的结果，它的高度异质性给研究和治疗带来了巨大的挑战。基因组区段可能在肿瘤和正常个体中行使不同的功能，从而受到不同的进化选择压。癌症驱动基因通常受到正选择作用，而癌症特异性负选择基因是潜在的癌症必需基因，两者都可能作为有效的抗癌药物靶点。然而，现有大部分方法都用于筛选受正选择的编码蛋白基因，用于筛选包括受负选择的非编码基因组区段的方法还很缺乏。本课题组前期开发了一种基于特异性单核苷酸突变谱计算编码基因选择压强度的方法，并发现改变RNA二级结构的突变的富集程度可用于估算非编码RNA区段的选择压。本项目将进一步开发一套基于单核苷酸和短的Indel突变模型的基因组进化分析新方法来估算癌症和正常人群中编码蛋白基因及非编码区段的选择压，并进行细胞实验验证。该研究能有助于进一步了解肿瘤发生发展过程及其内在机制，并可能提供新的有效药物靶点，促进癌症的个性化治疗。

遗传物质的突变是由各种内因和外因共同驱动的，不同的因素可能会产生不同突变频谱，对突变频谱的了解，有助于分子进化的研究。在生殖细胞基因组突变方面探究了不同生殖发育过程中动态变化的甲基化水平与胚系突变率之间的相关性，发现生殖细胞系基因组中CpG位点的胞嘧啶突变率主要受甲基化水平影响.在体细胞突变方面，我们探究了各种突变频谱的可能因素，发现饮酒造成的特定突变频谱，以及个体遗传背景对该突变频谱的影响。基于肿瘤基因组的特殊突变频谱，我们开发了非编码区选择压计算的工具SNIPER，用于预测通过影响RNA二级结构影响肿瘤发生发展的基因组原件。本课题还对一类特殊的非编码区突变，即影响多聚腺苷酸化位点信号（PAS）的突变。开发相应的PAS预测计算流程，并对预测的结果进行分子生物学实验验证。本该研究能有助于进一步了解肿瘤发生发展过程及其内在机制，并可能提供新的有效药物靶点，促进癌症的个性化治疗。