Project Details
Projekt
Measurable residual disease characteristics and its immune system interaction in the context of treatment response
Subject Area
Hematology, Oncology
Term
since 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 444949889
T-cell-based immunotherapies have improved outcomes in acute lymphoblastic leukemia (ALL), but in a significant proportion of patients persistence of measurable residual disease (MRD) leads to a relapse of the disease. In our first funding phase we investigated leukemia-immune cell interactions under chemo- and immunotherapy. Under chemotherapy, low proliferative states of leukemic cells contribute to resistance. In contrast, under immunotherapy the loss of the target antigen CD19, a lineage switch of the leukemia and the failure of effective T-cell response appear an important mechanisms for MRD persistence. In addition, preleukemic clonal hematopoiesis (CH) was found to be relevant. CH mutations occur at the preleukemic stage at the level of multipotent progenitors and are not restricted to the leukemia compartment. CH contributes to disease progression, impacts immune fitness, and can affect T-cell functionality. While CH is well-studied in myeloid neoplasms, its implications in ALLs are less understood. Understanding the distribution, dynamics, and the direct impact of CH on leukemogenesis and immune fitness under different therapeutic regimens remains crucial. Therefore, our goal within the proposed project is to study how preceding chemotherapy, target antigen density and age-driven immune variation affect T-cell fitness. By performing T-cell functionality assays and epigenetic profiling, we aim to define measurable biomarkers of T-cell fitness across age groups. This will enable a better prediction of blinatumomab response and further deepen the understanding of mechanisms of response and resistance. Further, we will retrospectively evaluate target expression profiles on pediatric and adult ALL samples and generate an age-overriding atlas of leukemic phenotypes using machine learning approaches to understand target-based mechanisms of response to immunotherapies. These AI-driven methods promise to uncover novel disease patterns and refine diagnostic and therapeutic decisions. Lastly, we will investigate the distribution and clonal trajectories of CH in various hematopoietic compartments, especially in T cells, under chemo- and immunotherapies. Using single-cell genomic and methylation analyses, we will trace mutation-specific clones to understand how CH influences immune fitness, drives ALL evolution, and impacts responses to T-cell–engaging immunotherapies. By integrating robust immunophenotypic, metabolic, epigenetic, and single-cell data, the proposed work will generate clinically relevant biomarkers and mechanistic insights to personalize therapy, optimize treatment sequences, and ultimately eradicate MRD and improve survival in pediatric and adult ALL.
DFG Programme
Clinical Research Units
