Chronic Lymphocytic Leukaemia: uncovering immunity during first-line treatment

B细胞慢性淋巴细胞白血病（CLL）是西方国家最常见的血液系统癌症。健康的B细胞会重新排列免疫球蛋白基因，制造对抗感染的特异性抗体，有些会变成记忆B细胞，在再次接触时可以回忆起抗体。这种功能使B细胞在我们习得的"体液"免疫中发挥核心作用。我们暴露于许多不同的感染，所以我们的身体产生不同的抗体产生B细胞的"库"。当CLL发展时，这些B细胞之一中的基因突变，并且细胞增殖，通常产生很少或不产生功能性抗体。这导致体液免疫受损，以及癌症症状。最近，与传统的“细胞毒性”化疗相比，称为“小分子抑制剂”的抗CLL新药延长了反应时间。它们靶向CLL细胞中使其增殖的分子，例如BTK和BCL-2（分别是伊曲替尼和维奈托克的靶点）。这些分子的抑制导致CLL细胞死亡，并且健康的B细胞生长回来（"重建"）。然而，长期接受小分子抑制剂治疗的患者的B细胞比正常人少，仍然容易感染和疫苗接种反应差。最近的大流行凸显了这一点的重要性：使用伊克替尼和维奈托克的患者对COVID-19疫苗接种的反应率较低，抗体水平也较低。我的目的是揭示现代CLL治疗如何影响重建的B细胞，因此如何治疗或治疗后的体液免疫是通过分析正常个体和治疗前CLL病例中表达的DNA（RNA）的差异，与治疗病例相比。我将在FLAIR（CLL的一线治疗：含伊匹替尼方案的评估）试验的背景下这样做，这是英国最大的现代CLL治疗试验（1576例患者）。比较了伊布替尼方案与传统化疗：氟达拉滨、环磷酰胺和利妥昔单抗;伊布替尼单药;伊布替尼和维奈托克。虽然伊曲替尼和维奈托克增加了治疗效果，但它们靶向的途径在健康的B细胞中是活跃的，导致B细胞减少和免疫功能受损。这一点很重要，因为伊曲替尼作为单药治疗或联合治疗有效且耐受性良好，而维奈托克现已被纳入一线和复发治疗方案。因此，为了在治疗期间保持健康的缓解，我们需要了解正常的重建B细胞是如何存活的，如果治疗影响了这些细胞，以及我们是否可以优化它们的功能以增强体液免疫，包括疫苗应答。我将使用一种称为简约基因相关网络分析（PGCNA）的生物信息学方法来识别重要的RNA，并将其应用于CLL和B细胞RNA数据库，以建立一个基于证据的小组。我将对年龄匹配的健康对照和CLL治疗前样本以及FLAIR伊布替尼和伊布替尼-维奈托克治疗组进行scRNAseq。我已经设计了一种细胞分离策略来分离和定量小群体的正常B细胞，然后将使用scRNAseq进行靶向测序。然后，我将覆盖单细胞B细胞库序列和面板结果，以证明库如何与RNA表达变化相关，并使用生物信息学分析来揭示治疗后B细胞重建是否显示与正常年龄匹配或治疗前病例相同的免疫模式。我将把这些结果与FLAIR试验的临床数据叠加，以显示重建的B细胞是否执行正常的体液免疫功能，以及如何增强这些功能。

B cell Chronic Lymphocytic Leukaemia (CLL) is the most common haematological cancer in Western countries. In health B cells rearrange immunoglobulin genes to make specific antibodies to fight an infection, and some change into memory B cells that can recall the antibody on re-exposure. This function gives B cells a central role in our learnt 'humoral' immunity. We are exposed to many different infections, so our bodies generate a 'repertoire' of different antibody-producing B cells. When CLL develops, the genes in one of these B cells become mutated and the cell proliferates often producing little or no functional antibody. This results in impaired humoral immunity, alongside cancer symptoms. Recently new drugs called 'small molecule inhibitors' that fight CLL have lengthened responses compared to traditional 'cytotoxic' chemotherapy. They target molecules in CLL cells that make them proliferate for example BTK and BCL-2 (the targets of ibrutinib and venetoclax respectively). Inhibition of these molecules causes the CLL cells to die, and healthy B cells grow back ('reconstitute'). However, patients on prolonged small molecule inhibitor treatment have fewer B cells than normal and remain susceptible to infection and poor vaccination response. The importance of this has been highlighted by the recent pandemic: patients on ibrutinib and venetoclax have been shown to have lower rates of response to COVID-19 vaccination alongside lower antibody levels. I aim to uncover how modern CLL treatment affect reconstituted B cells, and therefore how on or post-treatment humoral immunity is impacted by analysing differences in expressed DNA (RNA) in normal individuals and pre-treatment CLL cases, compared to treated cases. I will do this in the context of the FLAIR (Front-Line therapy in CLL: Assessment of Ibrutinib-containing Regimes) trial, the largest UK trial of modern CLL treatment (1576 patients). Ibrutinib regimens are compared to traditional chemotherapy: fludarabine, cyclophosphamide and rituximab; ibrutinib alone; ibrutinib and venetoclax. Although ibrutinib and venetoclax increase treatment efficacy, the pathways they target are active in healthy B cells, resulting in fewer B cells and immunity impairment. This is important because ibrutinib is efficacious and well tolerated as monotherapy or in combinations and venetoclax is now included in front-line and relapse treatment regimens. Therefore, to maintain a healthy remission while on treatment we need to understand how the normal reconstituting B cells are surviving, if the repertoire is affected by treatment, and whether we can optimise their function to enhance humoral immunity including vaccine responses.I will use targeted single cell RNA sequencing (scRNAseq) to determine the effect of treatment on humoral immunity. I will identify the important RNAs using a bioinformatic method called parsimonious gene correlation network analysis (PGCNA) and apply it to both CLL and B cell RNA databases to build an evidence-based panel. I will perform scRNAseq on age-matched healthy controls and CLL pre-treatment samples and the FLAIR ibrutinib and ibrutinib-venetoclax treatment groups. I have already devised a cell separation strategy to separate and quantify small populations of normal B cells and will then use scRNAseq to perform targeted sequencing. I will then overlay single-cell B cell repertoire sequences and the panel results to demonstrate how repertoire is linked to RNA expression changes, and use bioinformatic analysis to uncover whether B cell reconstitution post treatment shows the same patterns of immune as normal age-matched or pre-treatment cases. I will overlay these results alongside FLAIR trial clinical data, to show whether reconstituting B cells are performing normal humoral immune functions and how these can be enhanced.