Computational analysis of transcriptomic data to identify PTEN-loss associated alternative splicing (AS) derived neoantigens in cancer datasets

对转录组数据进行计算分析，以识别癌症数据集中与 PTEN 丢失相关的选择性剪接 (AS) 衍生的新抗原

• 批准号: 2442093
• 金额: --
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2442093
• 关键词: Computational; analysis; transcriptomic; data; identify

Neoantigens are a type of tumour specific antigen which can result from aberrations to the DNA or RNA. To date, the most well studied neoantigens have been those arising at the DNA level which predominantly include those arising from genomic variants such as single-nucleotide variants (SNVs) and insertions and deletions (indels) however, exploring other sources of neoantigens for example, from alternative splicing (AS) is important especially in low tumour mutational burden cancers, such as prostate cancer. New non-catalytic functions for PTEN, a tumour suppressor gene, is emerging which include in AS, where PTEN interacting with splicing machinery and the spliceosome has been described. Furthermore, PTEN-loss associated alterations to the immune tumour microenvironment (TME) has been observed. Preliminary findings in our group have found a role for PTEN-loss associated aberrant AS and we hypothesise neoantigens arising from these events could alter tumour cell recognition by immune cells and/or AS-derived proteins from these events could affect immune cell function in the TME. Many machine learning (ML) neoantigen prediction algorithms exist which are trained on data from different stages of the peptide presentation and processing mechanism however, those able to identify AS derived neoantigens are limited. Using a published AS neoantigen prediction algorithm which we updated to predict neoantigens using the latest state-of-the-art ML method, we have extracted putative PTEN-loss associated MHC-I AS-derived neoantigens from DU145 cell-line data that have been depleted of PTEN. Matched mass spectrometry data has been processed to identify translated neoantigens in the samples' own reference proteome. Finally, TCGA prostate cancer data is currently being processed through the same pipelines to identify strong computationally predicted neoantigens in PTEN-loss conditions which will be correlated to the altered immune TME. This research is novel and will elucidate the PTEN-loss associated alterations on the transcriptome and proteome through antigens presented by MHC-I molecules.

新抗原是一种肿瘤特异性抗原，可由DNA或RNA的异常引起。到目前为止，研究最多的是那些在DNA水平上产生的新抗原，其中主要包括那些来自基因组变异的新抗原，如单核苷酸变异(SNV)和插入和缺失(INDELs)，然而，探索其他来源的新抗原，例如来自选择性剪接(AS)的新抗原，在低肿瘤突变负担的癌症中尤其重要，如前列腺癌。PTEN是一种肿瘤抑制基因，其新的非催化功能正在出现，包括在AS中，其中PTEN与剪接机制和剪接体相互作用已被描述。此外，还观察到PTEN缺失对免疫肿瘤微环境(TME)的影响。我们小组的初步发现已经发现PTEN缺失相关的异常AS的作用，我们假设这些事件产生的新抗原可能改变免疫细胞对肿瘤细胞的识别，和/或这些事件衍生的AS蛋白可能影响TME中的免疫细胞功能。许多机器学习(ML)新抗原预测算法都是基于多肽呈递和处理机制的不同阶段的数据进行训练的，然而，能够识别为衍生新抗原的算法是有限的。使用我们更新的AS新抗原预测算法，使用最新的最新ML方法预测新抗原，我们已经从PTEN缺失的DU145细胞系数据中提取了假定的与PTEN缺失相关的MHC-I作为衍生的新抗原。匹配的质谱学数据已被处理，以识别样本自身参考蛋白质组中翻译的新抗原。最后，TCGA前列腺癌数据目前正在通过相同的管道进行处理，以确定在PTEN缺失条件下通过计算预测的强新抗原，这些新抗原将与改变的免疫TME相关。这项研究是新颖的，将通过MHC-I分子呈递的抗原来阐明PTEN缺失对转录组和蛋白质组的影响。