Development of genetic prognostication models in newly diagnosed chronic lymphocytic leukaemia (CLL) patients

新诊断慢性淋巴细胞白血病（CLL）患者遗传预测模型的建立

• 批准号: 2752818
• 金额: --
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2752818
• 关键词: Development; genetic; prognostication; models; newly

Chronic lymphocytic leukaemia (CLL) is the most common leukaemia in people of European ancestry with more than 10 new cases per day in the UK alone. CLL has a highly heterogeneous clinical course and most patients are diagnosed with early stage asymptomatic disease that does not initially require treatment. Some patients live with asymptomatic disease for several years while others progress quickly requiring treatment. CLL is inherently incurable and a significant cause of mortality and morbidity, including high risk of recurrent infections.CLL therapy has been transformed by highly effective and well tolerated B-cell receptor signalling pathway inhibitors (BCRi) which improve patient outcomes for those with advanced disease. Given the success in treating symptomatic CLL, focus has recently shifted towards addressing whether pre-emptive treatment can improve outcomes for patients with early-stage asymptomatic disease but at high-risk of progressing. Preliminary results from the German CLL12 trial report remarkable improvements in outcomes for high-risk CLL patients treated early with the BCRi ibrutinib, where time to death was 4-5 times longer in high-risk patients treated early. Despite this success, current prognostication models are inadequate and identify only a minority of high-risk patients.The aim of this project is to develop accurate prognostication models for early-stage CLL in order to identify high-risk patients who might benefit from earlier treatment. To this end, we recently published a genome-wide association study utilising early-stage CLL cases and identified two common germline genetic variants that significantly associate with high-risk CLL (Lin et al, 2021, Nature Communications, 12: 665. doi: 10.1038/s41467-020-20822-9). These variants have prognostic value equivalent to established clinical markers and provide proof of concept that the incorporation of germline genetic markers can significantly improve prognostication models for the majority of CLL patients.We have recently expanded our CLL cohort to approximately 2000 patients with a median follow-up of 15 years that includes data on infections and bleeding events as well as death. In addition to identifying new germline genetic variants predicting high-risk disease, this project will use machine learning to identify leukaemia-specific insertions and deletions (somatic alterations) from high-density array data already generated by our group. This project will also provide an opportunity to functionally interrogate novel somatic alternations using cell-based models.Critically, this will be the first study to incorporate patient germline and leukaemia somatic genetic data along with established clinical markers for accurate prognostication in early-stage CLL patients. Implementation of the resulting model into clinical practise is predicted to improve outcomes for the tens of thousands of patients living with CLL and could also lead to similar approaches being adopted to improve prognostication in other cancers.

慢性淋巴细胞白血病（CLL）是欧洲血统人群中最常见的白血病，仅在英国每天就有超过10例新发病例。CLL具有高度异质性的临床过程，并且大多数患者被诊断为早期无症状疾病，最初不需要治疗。一些患者无症状地生活了几年，而另一些患者则进展迅速，需要治疗。慢性淋巴细胞白血病（CLL）是一种无法治愈的疾病，是导致死亡和发病的重要原因，包括复发性感染的高风险。CLL治疗已经被高效且耐受性良好的B细胞受体信号通路抑制剂（BCRi）所改变，这些抑制剂可以改善晚期疾病患者的预后。鉴于在治疗症状性CLL方面的成功，最近的重点已经转移到解决先发制人的治疗是否可以改善早期无症状疾病但进展风险高的患者的结局。德国CLL 12试验的初步结果报告了早期接受BCRi伊曲替尼治疗的高风险CLL患者的结局显著改善，早期接受治疗的高风险患者的死亡时间延长了4-5倍。尽管取得了这一成功，目前的预测模型是不够的，并确定只有少数的高风险patients.The项目的目的是开发准确的预测模型，为早期CLL，以确定高风险患者谁可能受益于早期治疗。为此，我们最近发表了一项利用早期CLL病例的全基因组关联研究，并鉴定了与高风险CLL显著相关的两种常见种系遗传变异（Lin et al，2021，Nature Communications，12：665）。doi：10.1038/s41467-020-20822-9）。这些变异具有与已建立的临床标记物相当的预后价值，并提供了概念证明，即生殖系遗传标记物的掺入可以显着改善大多数CLL patients.We的预测模型最近扩大了我们的CLL队列约2000例患者的中位随访时间为15年，其中包括感染和出血事件以及死亡的数据。除了识别预测高风险疾病的新生殖系遗传变异外，该项目还将使用机器学习从我们小组已经生成的高密度阵列数据中识别白血病特异性插入和缺失（体细胞改变）。该项目还将提供一个机会，功能审问新的体细胞alternations使用基于细胞的models.Critically，这将是第一个研究，将患者生殖系和白血病体细胞遗传数据沿着与建立临床标记物，在早期CLL患者的准确prestication。预计将由此产生的模型应用于临床实践将改善数万名CLL患者的预后，并可能导致采用类似的方法来改善其他癌症的预后。