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

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.