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Identification of new cancer specific genetic and epigenetic biomarkers for cancer evolution and Minimal Residual Disease (MRD) in peripheral blood samples

Subject Area Hematology, Oncology
General Genetics and Functional Genome Biology
Human Genetics
Term from 2021 to 2024
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 493951700
 
Final Report Year 2024

Final Report Abstract

Multiple Myeloma (MM) is a genetically heterogeneous disease and clonal evolution is known to play an important role in its progression. Even though a plethora of potent drugs are available and novel immunotherapies are currently being integrated into the treatment landscape, resistance remains one of the biggest clinical challenges with the underlying mechanisms largely unknown. In the course of the disease, patients frequently acquire genetic and epigenetic alterations that either transmit a fitness advantage to the tumor or a drug escape mechanism. Therefore, the quantification of cancer cells remaining after therapy (MRD) is important to predict the clinical outcome of a patient, but universal biomarkers for NGS-MRD are lacking. In this work, we have identified new drug resistance mechanisms to standard and novel therapies. Prone regions harboring (epi)genetic alterations are the drug binding site, associated genes in the network, interaction partners, or general mechanisms affecting drug tolerance e.g. efflux pumps or apoptosis receptors. Moreover, in recent years multi-omic analysis techniques have undergone huge technical advances and new insights can be gained by the integration of different regulatory levels e.g. with the Oxford Nanopore Sequencing Technology (ONT) the full genome and the full DNA methylome can be sequenced from the same sample in parallel. As MRD markers, DNA methylation marks have two big advantages in comparison to SNVs. First, tumor cells of the same type tend to exhibit similar DNA methylation changes and second the sensitivity can be increased by taking the status of several adjacent CpGs into account. Applying machine learning algorithms on a DNA methylation dataset comprising over 1,800 individuals with different hematological cancers and healthy controls, a total of 66 hypomethylated loci were identified, separating Multiple Myeloma from healthy controls and exhibiting an intermediate state in monoclonal gammopathy of undetermined significance (MGUS). These hits are suitable markers for the development of a non-invasive liquid biopsy test and the translational applicability of such a test is broad, reaching from early diagnosis to drug resistance assessment, MRD monitoring, and relapse management.

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