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

新抗原是一种肿瘤特异性抗原，可由DNA或RNA畸变引起。迄今为止，研究最充分的新抗原是那些在DNA水平上产生的新抗原，主要包括那些由基因组变异如单核苷酸变异（snv）和插入和缺失（indels）产生的新抗原。然而，探索其他来源的新抗原，例如，从选择性剪接（as），特别是在低肿瘤突变负担的癌症，如前列腺癌是重要的。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，PTEN与剪接机制和剪接体相互作用已被描述。此外，已观察到pten缺失相关的免疫肿瘤微环境（TME）改变。我们小组的初步发现发现了pten缺失与异常AS相关的作用，我们假设这些事件产生的新抗原可以改变免疫细胞对肿瘤细胞的识别，并且/或这些事件产生的AS衍生蛋白可以影响TME中的免疫细胞功能。存在许多机器学习（ML）新抗原预测算法，这些算法是根据肽呈现和处理机制的不同阶段的数据进行训练的，然而，那些能够识别AS衍生新抗原的算法是有限的。使用已发表的AS新抗原预测算法，我们使用最新的最先进的ML方法更新以预测新抗原，我们从PTEN缺失的DU145细胞系数据中提取了推定的PTEN缺失相关的MHC-I AS衍生新抗原。匹配的质谱数据已被处理，以鉴定样品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pten丢失条件下强计算预测的新抗原，这些新抗原将与免疫TME改变相关。这项研究是新颖的，将通过mhc - 1分子呈递抗原阐明pten缺失相关的转录组和蛋白质组改变。

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.