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

项目摘要：自Zhang et al（2003）以来，肿瘤浸润性白细胞（TIL）形式的宿主免疫应答对卵巢癌患者具有显著的临床重要性。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，大多数卵巢癌患者发展出对化疗有抗性的复发性癌症（Matsuo et al.，2010年）。同样明显的是，并非所有免疫应答在帮助根除癌症方面都同样有效，并且肿瘤微环境重新编程TIL以降低治疗过程中宿主抗肿瘤应答的功效。截至撰写本文时，尽管知道免疫应答是卵巢癌患者预后和生存的重要因素，但没有赋予卵巢癌患者临床意义的免疫细胞应答的分类或类别。因此，在卵巢癌患者的背景下，存在对免疫细胞表征的未满足的需求，以及推断在哪些情况下免疫细胞应答将促进化疗后患者的缓解或化疗耐药病例的标志是什么的理解。这个博士项目的目标是使用单细胞RNA测序来识别化疗敏感和耐药卵巢癌的肿瘤微环境中免疫细胞的转录组，以识别TIL中可能与临床表型相关的多种细胞类型中的任何模式。其次，我们将定义TIL在化疗过程中如何重新编程，因为患者的肿瘤从治疗反应到治疗抵抗。单细胞转录组学的主要优势在于它可以分离卵巢癌样本中的单个免疫细胞群体，并检测这些单个免疫细胞的基因表达，以识别它们并解析它们的功能（即翻译哪些蛋白质）。该方法需要在Chromium 10 x单细胞测序之前优化新鲜卵巢肿瘤的单细胞解离。该项目将需要新鲜肿瘤样本，每个样本将分为2组，单细胞测序组，这是我们实验室目前正在开发的方法;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，该算法可以识别从细胞中发现的免疫细胞特征。单细胞RNA序列，并可以外推到批量处理的肿瘤样品（自下而上的方法）。这不同于典型的自上而下的方法，即发现基因表达簇并将其与免疫细胞群体相关联。此外，从中分离免疫细胞的肿瘤样品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如何变化，并找到可用于指示患者肿瘤是否可能对化疗或生物疗法有反应的特征。Conejo-Garcia，J.，卡察罗斯，D.，Gimotty，P.，Massobrio，M.，Regnani，G.，Makrigiannakis，A.，格雷，H.，Schlienger，K.，Liebman，M.，Rubin，S.和Coukos，G.（2003年）的报告。上皮性卵巢癌的瘤内T细胞、复发和存活。新英格兰医学杂志，348（3），第203 - 213页。Lin，Y.，（1996年），罗曼湖和Sood，A.（2010年）。克服卵巢癌铂类耐药。研究药物的专家意见，19（11），），第1339 -1354页

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