Single cell transcriptomic characterisation of ovarian tumour infiltrating leukocytes to identify biomarkers of chemotherapy resistance.

项目概述：自Zhang等（2003）以来，宿主免疫反应以肿瘤浸润白细胞（tumor浸润leukocyte， til）的形式出现，对卵巢癌患者具有重要的临床意义。众所周知，肿瘤部位免疫细胞的存在对于患者对化疗的反应至关重要，并且通常表明预后较好。然而，大多数卵巢癌患者会复发，并对化疗产生耐药性（Matsuo et al., 2010）。同样明显的是，并非所有的免疫反应在帮助根除癌症方面都同样有效，肿瘤微环境重新编程til以降低宿主抗肿瘤反应在治疗过程中的功效。在撰写本文时，尽管我们知道免疫反应是卵巢癌患者预后和生存的重要因素，但还没有免疫细胞反应的分类或类别赋予卵巢癌患者临床意义。因此，在卵巢癌患者的背景下，对免疫细胞特征的需求尚未得到满足，并且了解在哪些情况下免疫细胞反应会促进化疗后患者的缓解，或者化疗耐药病例的标志是什么。该博士项目的目标是使用单细胞RNA测序来表征化疗敏感和耐药卵巢癌肿瘤微环境中免疫细胞的转录组，以识别TILs中可能与临床表型相关的众多细胞类型中的任何模式。其次，我们将定义在化疗过程中，当患者的肿瘤从治疗反应到治疗抵抗时，til是如何重新编程的。单细胞转录组学的主要优势在于，它可以在卵巢癌样本中分离出单个免疫细胞群，并表征这些单个免疫细胞的基因表达，从而识别它们并解析它们的功能（即哪些蛋白质被翻译）。该方法需要在铬10x单细胞测序之前，从新鲜卵巢肿瘤中优化单细胞解离。这个项目需要新鲜的肿瘤样本，每个样本将被分成两个臂，单细胞测序臂，这是我们实验室目前正在开发的一种方法；并批量加工手臂。我们的单细胞测序方法的目的是开发一种复杂的生物信息学算法，该算法可以识别从单细胞RNA序列中发现的免疫细胞特征，并可以外推到批量处理的肿瘤样本（自下而上的方法）。这与典型的自上而下的寻找基因表达簇并将其与免疫细胞群相关联的方法不同。此外，从免疫细胞中分离出来的肿瘤样本将具有完整的临床注释，即对化疗的反应（敏感、不敏感或难治性）。我的假设是，免疫细胞景观是预测患者对化疗反应的重要表型，单细胞测序将使我们能够阐明这些肿瘤样本的免疫细胞景观的最高分辨率。有了这些信息，我们可以研究患者肿瘤的免疫景观在治疗过程中是如何变化的，并找到可以用来指示患者肿瘤是否可能对化疗或生物治疗有反应的特征。参考文献：Zhang， L., Conejo-Garcia， J., Katsaros， D., Gimotty， P., Massobrio， M., Regnani， G., Makrigiannakis， A., Gray， H., Schlienger， K., Liebman， M., Rubin， S.和Coukos， G.（2003）。上皮性卵巢癌的肿瘤内T细胞、复发和生存。新英格兰医学杂志，348(3),pp.203-213。Matsuo， K, Lin， Y., Roman， L.和Sood， A.（2010）。克服卵巢癌的铂耐药性。《临床试验用药专家意见》，19(11)，页1339-1354

Project Summary: Since Zhang et al (2003) it is apparent that the host immune response in the form of tumour infiltrating leukocytes (TILs) has significant clinical importance for ovarian cancer patients. It is known that the presence of immune cells at the tumour sites is crucial for patients to respond to chemotherapy and is generally indicative of a better prognosis. However, most ovarian cancer patients develop recurrent cancers which are resistant to chemotherapy (Matsuo et al., 2010). It is also apparent that not all immune responses are equally effective in aiding to eradicate the cancer, and the tumour microenvironment reprograms TILs to reduce the efficacy of the host anti-tumour response over the course of therapy. As of writing there are no classification or categories of immune cell response(s) which confer clinical meaning to ovarian cancer patients, despite knowing that the immune response is an essential element in ovarian cancer patient prognosis and survival. Therefore, there is an unmet need for immune cell characterisation in the context of ovarian cancer patients, and the understanding to infer in which cases the immune cell response will either promote remission in patients following chemotherapy or what the hallmarks are in chemotherapy resistance cases. The goal of this PhD project is to use single cell RNA sequencing to characterise the transcriptomes of immune cells within the tumour microenvironment of chemotherapy sensitive and resistant ovarian cancers to identify any pattern within multitude of cell types within the TILs which may correlate to the clinical phenotype. Secondly, we will define how TILs are reprogrammed during the course of chemotherapy as the patient's tumour goes from therapy responsive to therapy resistant. The main strength of single cell transcriptomics, is it can isolate single immune cell populations in ovarian cancer samples, and characterise the gene expression of these single immune cells to both identify them and resolve their function (i.e. what proteins are translated). This method requires optimisation of single cell dissociation from fresh ovarian tumours prior to Chromium 10x single cell sequencing.This project will require fresh tumour samples, and each sample will be split into 2 arms, the single cell sequencing arm, which is a method currently in development in our lab; and batched processed arm. The aim of our single cell sequencing method is to develop a sophisticated bioinformatic algorithm which can identify the immune cell signatures found from single cell RNA sequences and can extrapolated to batched processed tumour samples (a bottom-up approach). This is different from the typical top-down approach of finding gene expression clusters and correlating them to an immune cell population. In addition, the tumour samples which the immune cells are isolated from will have full clinical annotation, i.e. response to chemotherapy (sensitive, non-sensitive or refractory). It is my hypothesis that the immune cell landscape is an important phenotype in predicting patient response to chemotherapy and that single cell sequencing will allow us to elucidate the highest resolution of the immune cell landscape of these tumour samples. With this information, we can study how the immune landscape of a patient's tumour changes over the course of treatment, and find signatures which can be used to indicate whether or not a patient's tumour is likely to respond to chemotherapy or biological therapy.Ref:Zhang, L., Conejo-Garcia, J., Katsaros, D., Gimotty, P., Massobrio, M., Regnani, G., Makrigiannakis, A., Gray, H., Schlienger, K., Liebman, M., Rubin, S. and Coukos, G. (2003). Intratumoral T Cells, Recurrence, and Survival in Epithelial Ovarian Cancer. New England Journal of Medicine, 348(3), pp.203-213.Matsuo, K., Lin, Y., Roman, L. and Sood, A. (2010). Overcoming platinum resistance in ovarian carcinoma. Expert Opinion on Investigational Drugs, 19(11),), pp.1339-1354