PS1015 SINGLE‐CELL RNA‐SEQUENCING & GENOTYPING OF PATIENTS WITH WT1‐SUBCLONAL AML TO ELUCIDATE CLONAL HETEROGENEITY

Background: Acute myeloid leukemia (AML) is a heterogeneous clonal disorder of hematopoietic progenitor cells. Most patients reach a complete remission (CR) defined by morphological criteria after chemotherapy (CT), but ultimately, two‐thirds of the patients relapse. Only half of the relapsed patients reach a second CR, suggesting clonal evolution and possible selection of more aggressive subclones. Smaller clones present at initial diagnosis (e.g. WT1 mut ) are often found to be predominant in relapse. The mechanisms underlying resistance of certain clones are largely unknown. Aims: Here we aim to elucidate clonal heterogeneity on the transcriptome level in AML patients with subclonal WT1 mutations using a novel single‐cell RNA‐sequencing method (mcSCRBseq), combined with single‐cell transcriptome genotyping to reveal the WT1 mutation status in each cell. Methods: Cryopreserved cells from seven AML patients with subclonal WT1 mutations (variant allele frequency [VAF] 15–40%, as determined by targeted amplicon sequencing) were FACS‐sorted, enriching for viable myeloid blasts (CD33 + , CD45 low , Annexin V low , live‐dead‐stain). RNA‐sequencing libraries from up to 576 single cells per patients were prepared according to the mcSCRBseq protocol, which includes cellular and mRNA‐molecule barcoding and is powerful and cost‐efficient. Libraries were sequenced at ∼ 250.000 reads per cell. Single‐cell genotyping is performed from full‐length cDNA using the scTAGseq protocol, which allows bringing the mutation site in close proximity of the cell and molecular barcodes via a circularization step. Results: We obtained single‐cell RNA‐sequencing data of >4000 cells from seven AML patients with subclonal WT1 mutations at the time point of primary diagnosis, and additionally at relapse for one patient. Batch effects were very low regarding the three library preparation days and the 5–6 plates per patient. On average, we detected >2500 genes per cell. UMAP clustering of all single cells reveals a homogeneous distrubition of cells with a moderate clustering by patient and, when clustered per patient individually, potential subclonal architectures. Although we only see very little coverage of WT1 in the expression data (0–12 UMI‐reads/cell, mean 0.04 in 4149 cells), we detected WT1 in the full‐length cDNA of our samples. Per‐cell genotype information for WT1 is currently being included into the dataset. We will then study differential gene expression according to WT1 status, and, for samples with more than one WT1 mutation, presence of biallelic WT1 mutations. Summary/Conclusion: Here, we present a high quality transcriptome data set of >4000 single cells from seven patients with AML at primary diagnosis and, for one patient, additionally at relapse. By combining single‐cell genotype and expression data, we aim to detect specific expression patterns for WT1 mut & WT1 wt populations, and study differentially expressed genes and pathways in subclones with different WT1 mutation status.