Vittrant Benjamin, Bergeron Alain, Molina Oscar Eduardo, Leclercq Mickael, Légaré Xavier-Philippe, Hovington Hélène, Picard Valérie, Martin-Magniette Marie-Laure, Livingstone Julie, Boutros Paul C, Collins Colin, Fradet Yves, Droit Arnaud
Computational Biology Laboratory, CHU de Québec - Université Laval Research Center, Québec City, QC, Canada.
Laboratoire d'Uro-Oncologie Expérimentale, Axe Oncologie, Centre de Recherche Du CHU de Québec-Université Laval, Québec, Canada.
Oncoimmunology. 2020 Dec 1;9(1):1851950. doi: 10.1080/2162402X.2020.1851950.
Prostate cancer (PCa) immunotherapy has shown limited efficacy so far, even in advanced-stage cancers. The success rate of PCa immunotherapy might be improved by approaches more adapted to the immunobiology of the disease. The objective of this study was to perform a multi-omics analysis to identify immune genes associated with PCa progression to better characterize PCa immunobiology and propose new immunotherapeutic targets. mRNA, miRNA, methylation, copy number aberration, and single nucleotide variant datasets from The Cancer Genome Atlas PRAD cohort were analyzed after filtering for genes associated with immunity. Sparse partial least squares-discriminant analyses were performed to identify features associated with biochemical recurrence (BCR) in each type of omics data. Selected features predicted BCR with a balanced error rate (BER) of 0.20 to 0.51 in single-omics and of 0.05 in multi-omics analyses. Amongst features associated with BCR were genes from the Immunoglobulin Ig-like Receptor (LILR) family which are immune checkpoints with immunotherapeutic potential. Using Multivariate INTegrative (MINT) analysis, the association of five genes with BCR was quantified in a combination of three RNA-seq datasets and confirmed with Kaplan-Meier analysis in both these and in an independent RNA-seq dataset. Finally, immunohistochemistry showed that a high number of LILRB1 positive cells within the tumors predicted long-term adverse outcomes. Thus, tumors characterized by abnormal expression of genes have an elevated risk of recurring after definitive local therapy. The immunotherapeutic potential of these regulators to stimulate the immune response against PCa should be evaluated in pre-clinical models.
前列腺癌(PCa)免疫疗法目前显示出的疗效有限,即使在晚期癌症中也是如此。通过更适应该疾病免疫生物学的方法,PCa免疫疗法的成功率可能会提高。本研究的目的是进行多组学分析,以识别与PCa进展相关的免疫基因,从而更好地描述PCa免疫生物学特征并提出新的免疫治疗靶点。对来自癌症基因组图谱PRAD队列的mRNA、miRNA、甲基化、拷贝数变异和单核苷酸变异数据集进行筛选,以找出与免疫相关的基因,然后进行分析。进行了稀疏偏最小二乘判别分析,以识别每种组学数据中与生化复发(BCR)相关的特征。在单组学分析中,选定的特征预测BCR的平衡错误率(BER)为0.20至0.51,在多组学分析中为0.05。与BCR相关的特征中包括免疫球蛋白Ig样受体(LILR)家族的基因,这些基因是具有免疫治疗潜力的免疫检查点。使用多变量整合(MINT)分析,在三个RNA测序数据集的组合中对五个基因与BCR的关联进行了量化,并在这些数据集以及一个独立的RNA测序数据集中通过Kaplan-Meier分析得到了证实。最后,免疫组织化学显示肿瘤内大量LILRB1阳性细胞预示着长期不良预后。因此,以这些基因异常表达为特征的肿瘤在确定性局部治疗后复发风险升高。这些调节因子刺激针对PCa免疫反应的免疫治疗潜力应在临床前模型中进行评估。