Molecular classification and dynamic prognostication of Diffuse Large B Cell Lymphoma (DLBCL) by circulating cell-free DNA sequencing

点击翻译按钮获取中文摘要

Diffuse large B-cell Lymphoma accounts for most cases of Non Hodgkin Lymphoma and is generally a curable disease. However, more than one third of patients cannot be cured by standard immunochemotherapy and the prognosis at relapse is dismal. Risk stratification is currently carried out by the international prognostic index (IPI). However, this risk stratification lacks discriminatory power. Therefore, the prognosis differs significantly even within the different risk groups and is furthermore highly dependent on the response to therapy. Consequently, risk-adapted treatment in DLBCL is currently very limited leading to over-treatment of low-risk and under-treatment of high-risk patients.Recently, more sophisticated risk classifications relying on genetically defined clusters were developed using next-generation sequencing (NGS). However, the methods needed for these classifications are costly, technically complicated, only partly standardized and need tumor biopsy material as a prerequisite. Peripheral blood of cancer patients including DLBCL-patients contains fragments of cell-free tumor-DNA This cell-free DNA can easily be extracted and analyzed through Cancer Personalized Profiling by deep sequencing (CAPP-Seq). Our aim is to identify recurrent genetic aberrations by CAPP-Seq from peripheral blood of DLBCL patients that are prognostic. Building on that, we will develop a molecular risk classification of DLBCL based purely on a liquid biopsy. Next, an identified high-risk signature will be validated within a set of relapsed / refractory DLBCL patients. We hypothesize that the high-risk signature will be enriched within this patient set. Additionally, we will validate the detected high-risk signature within autochthonous mouse models of DLBCL. Lastly, CAPP-Seq will be applied to early follow-up samples after initiation of systemic therapy to perform minimal residual disease assessment on a molecular basis with high sensitivity. Combined with the initial risk model, this will result in a dynamic, adaptive risk classification.In summary, we plan to establish a DLBCL risk model allowing for dynamic risk assessment in DLBCL patients based on recurrent genetic aberrations, early molecular response and clinical data. This model will likely aid future efforts to develop treatment algorithms that involve early adaption of treatment in high-risk patients, for example by adding novel treatment modalities such as CAR-T cells, bispecific antibodies or targeted agents to conventional treatment. We are convinced that this will ultimately improve treatment outcome in DLBCL and consequently patient survival.