Exploiting circulating nucleic acids to enhance disease stratification, risk prediction and therapeutic monitoring in mantle cell lymphoma.

• Applicant: Dr. Julia Kühn
• Subject Area: Hematology, Oncology
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 569948274

Mantle cell lymphoma (MCL) is a clinically and molecularly heterogeneous B-cell malignancy with poor prognosis. Genetic and transcriptomic classifiers have refined prognostication and highlighted the need for personalized treatment approaches. However, current stratification frameworks rely on tissue biopsies. Moreover, spatial and systemic heterogeneity, including divergent subclonal evolution between tumor compartments, remains poorly understood. Non-invasive liquid biopsies offer a safer, more practical alternative for capturing dynamic tumor evolution. These methods enable repeated monitoring across anatomically distinct lesions without the procedural risks of invasive biopsies, especially in clinically vulnerable patients. Recent advances in next-generation sequencing technologies, such as CAPP-Seq and EPIC-Seq, now enable detailed analysis of circulating nucleic acids to assess both genetic and epigenetic tumor features. This project aims to apply and refine these technologies to improve non-invasive risk stratification, lymphoma classification, characterization of heterogeneity, and disease monitoring in MCL. Our first aim is to develop a novel classifier that predicts prognostically relevant MCL genetic subtypes through non-invasive profiling of circulating tumor DNA (ctDNA) in blood plasma. In parallel, we aim to optimize the performance of EPIC-Seq for predicting prognostically informative gene expression profiles based on epigenetic analysis of plasma-derived cell-free DNA. We will also evaluate the clinical utility of these tools for outcome prediction. The second aim focuses on investigating intra- and inter-site heterogeneity. We will use high-resolution spatial transcriptomic technologies to map previously described gene expression signatures to individual malignant B-cells and their spatial distribution within tumor samples. Inter-site heterogeneity will be explored by comparing ctDNA profiles from pre-treatment plasma to tumor genotyping, leveraging ctDNA’s ability to reflect genetic alterations across all tumor sites. This will help identify shared "truncal" mutations and subclonal aberrations unique to plasma, providing insights into MCL’s genetic diversity and assess the clinical relevance. MCL patients stratified to have a low risk of relapse may benefit from treatment de-escalation, requiring accurate biomarkers to avoid misclassification and suboptimal treatment. To this end, we will develop a personalized, tumor-informed whole-genome sequencing (WGS) approach for ultrasensitive ctDNA detection, aimed at improving minimal residual disease (MRD) monitoring. Together, this research will create novel tools for non-invasive MCL classification and provide deeper insights into its spatial and systemic complexity. These findings may inform risk-adapted therapies and contribute to the implementation of precision oncology in MCL.

• DFG Programme: WBP Fellowship
• International Connection: USA
• Host: Professor Arash Ash Alizadeh, Ph.D.