Minimal Residual Disease Detection By Single Cell DNA Sequencing Technology: A Feasible Approach for Clinical Application and Identification of the Landscape of MRD Clones

Introduction:Despite recent advances in AML treatment, patients continue to relapse and become chemotherapy resistant, leading to poor long-term overall survival. The identification of this measurable residual disease (MRD), defined as the post-therapy presence of leukemic cells, currently stands as one of the most well-established risk factors. The abnormal phenotypic and molecular characteristics of AML cells offer an opportunity for disease monitoring using techniques like multiparameter flow cytometry (MFC) and qPCR, which are currently considered the gold standard for MRD detection. However, these techniques have limitations. The aim of this study was to validate a different approach using single-cell DNA sequencing (scDNAseq) technology to detect MRD in patients achieving complete response (CR) and to characterize the genomic landscape of MRD clones and the potential identification of clonal evolution.

Methods:We selected 24 cryopreserved bone marrow samples from 15 AML patients who participated in the QUIWI-PETHEMA clinical trial (NCT04107727) and achieved CR after induction and consolidation (n=20). 4 diagnosis samples were analyzed to determine genomic differences between the MRD clone and the initial leukemic population. All patients had bulk targeted NGS data available at diagnosis. Selection of CD34+ and/or CD117+ cells was performed using magnetic beads for enrichment of blasts and Mission Bio multiome single cell DNA+protein was performed using an AML-related 469 amplicon panel and 19 surface antibody mix. Multiplex of 3 independent samples was performed in each library preparation. Analysis was done according to manufacturer's instructions. MRD was also analyzed using MFC (n=14) EuroFlow panel with a limit of detection of 0.1% aberrant cells. RNA qRT-PCR was used in cases with NPM1 mutations (n=6). Quantification of MRD by scDNAseq was calculated according to the enrichment performance obtained from the manufacturer and the percentage of mutant cells.

Results: The concordance between gold standard techniques for MRD and scDNAseq was 75% (15/20) (Table 1). Concordance between MFC and scDNAseq was 78% (11/14). The 3 discordant cases, positives by scDNAseq, MRD levels ranged between 0.04-0.09%, below the consensus cutoff of 0.1% to define MRD+. These results suggest that scDNAseq may complement MFC in the detection of very low levels of MRD. Concordance with qRT-PCR was 66% (4/6), but we only detected one patient with a persistent NPM1 clone.

Taking advantage of the single cell approach, we were able to assess the genomic landscape of MRD clones and the clonal evolution in sequential samples. The number of clones/subclones and the number of variants per clone varied between patients with positive MRD as shown in Table 1. Interestingly, we analyzed 4 samples at diagnosis by scDNAseq and observed 2 cases in which MRD mutation was already present at diagnosis but was not informed as VAF was < 1%. In 4 patients, we detected small clones (around 1%) that remained unchanged in size despite treatment, suggesting that they likely represent clonal hematopoiesis.

In 6 cases, consecutive samples were obtained showing clearance of some clones with other ones remaining stable (UPN2), progressive clearance of clones (UPN3, UPN4), acquisition of new clones (UPN9) and clearance of some clones and acquisition of new ones (UPN15) (Figure 1).

Integration of scDNA and cell surface protein expression in the same cell allowed us to perform mutation-clone specific immunophenotypic analysis. Some cases showed a clear pattern in which one of the mutant clones had a significantly higher expression of some markers that was correlated with previous flow cytometry data (e.g. UPN1).

Conclusions: Our study suggests that the use of scDNA technology is a feasible approach for detection of MRD in AML patients. Moreover, the use of this approach in sequential samples may allow deciphering clonal evolution, the co-occurrence of different mutations including potential clonal hematopoiesis mutations and identifying winner clones potentially responsible for disease persistence and relapse. Finally, the integration of mutations and surface antibody markers in the same cell provides a means for identifying the presence of mutations in different cell populations. Validation of these results in larger series of patients and correlation with clinical outcome are the next steps for validation of this technology.