Comprehensive (Epi)Genetic Tumor Characterization and Disease Follow-Up in Multiple Myeloma Using Blood-Derived Cell-Free DNA

Introduction: Although the survival of multiple myeloma (MM) patients has considerably improved over the past decades, MM is still considered to be an incurable hematological malignancy. Research has shown that MM is characterized by a complex and heterogeneous mutation and DNA methylation profile, affecting prognosis and response to (targeted) therapy. Hence, a flexible and reliable biomarker allowing to comprehensively characterize and follow-up these (epi)genetic profiles during the MM disease course would be of paramount clinical importance. The analysis of bone marrow (BM) samples in MM patients can lead to an underestimation of the spatiotemporal (epi)genetic heterogeneity in MM. Therefore, there is an increasing interest in the development of peripheral blood-based monitoring methods. This would allow a more practical and non-invasive sampling procedure, facilitating a more frequent disease follow-up. We recently identified cell-free DNA (cfDNA) as the most promising biomarker for mutation profiling in MM in a comparative study (Heestermans et al., Cancers 2022). However, data about epigenetic analysis in MM using liquid biopsies and cfDNA in particular is very scarce and fragmented at this moment. In this study, we evaluated whether cfDNA can be used to track changes in the mutation profile over time during MM disease progression and to detect alterations in the tumor methylation profile.

Methods: In total, matched blood and BM samples were collected in 17 MM patients with active disease. For serial follow-up of the mutation profile, we performed targeted gene sequencing (using a 165-gene panel) on 35 matched BM and cfDNA samples collected in 15 MM patients at 2 or more different time points during a 5-year follow-up period. Classification of Single Nucleotide Variants (SNVs) was done based on the Belgian ComPerMed guidelines. For the methylation profiling, we investigated the genome-wide methylation status of CpG islands in paired BM and cfDNA samples of 5 MM patients with advanced disease using the NEBNext Enzymatic Methyl-seq kit (EM-seq^TM) (New England Biolabs Inc., Massachusetts, USA). Pooled sequencing data from mononuclear cells-derived DNA from a group of healthy controls (n = 4) was used as reference material. Both genomic DNA (gDNA) and cfDNA from human myeloma cell lines (HMCLs) cultured in vitro (OPM-2, U266, XG-7 and RPMI-8226) was used as a positive control. The bioinformatics analysis of the raw sequencing data involved the identification of differentially methylated regions (DMRs), with further annotation to the reference genome using the methylKit R package.

Results: When focusing on the serial mutation analysis, our results indicate that cfDNA outperforms BM-DNA, as cfDNA and BM-DNA permitted the detection of 38/41 (93%) and 35/41 (85%) of unique SNVs found in this cohort, respectively. We detected 28 mutated genes, with the highest mutation frequencies observed in NRAS (27%), DNMT3A (20%), TP53 (20%) and DIS3 (20%). Notably, cfDNA permitted the detection of 4 (likely) pathogenic variants that were undetectable in BM-DNA. Additionally, our data in cfDNA confirm the previously made observation that the extent of changes at the genetic level is correlated with the depth of clinical response in MM. When performing the epigenetic profiling, we detected previously described hypermethylation in CDKN2A, RASSF4, CDH1 and RASSF1A in cfDNA and gDNA of HMCLs using EM-Seq. In total, 9794 DMRs were identified in the 5 MM patients we studied so far. Importantly, cfDNA permitted to detect 3349/4093 (82%) of DMRs found in matched BM-DNA.

Conclusion: Our results demonstrate the potential of cfDNA as a tool to monitor the dynamic mutation profile in MM and to correlate these results with the clinical response. The presence of SNVs only detected in cfDNA and not in matched BM-DNA highlights the added value of this strategy. In addition, our results indicate that cfDNA has the potential to be used as a biomarker for methylation profiling in MM. Future research should confirm these observations on larger patient cohorts. The introduction of serial cfDNA analysis into clinical settings could improve existing risk stratification and inform clinicians about the emergence of prognostically relevant and therapy resistance-associated (epi)genetic alterations, hence contributing to personalized medicine in MM.