Clonal evolution is cancer is a critical part of disease progression, but it is difficult to understand which clonal populations are present with bulk sequencing techniques. Myeloid malignancies are increasing among our aging veteran population and are a group of blood cancers where clonal evolution even prior to inception is apparent. This proposal uses cutting-edge single-cell DNA sequencing, in addition to other single cell modalities, to better characterize clonal evolution in signaling mutant MDS and secondary AML. Our preliminary data has identified patterns of clonal architecture change that are typical of transformation of MDS to sAML. These patterns, defined as either dynamic or static clonal change, blast increase and are enriched for signaling mutations. We propose to profile signaling mutant sAML and MDS deeply to trace clonal histories with single cell DNA sequencing combined with surface marker characterization. We will also sequence mitochondrial DNA from scDNAseq and scRNAseq data to connect genotype to transcriptome. This represents a powerful tool to better probe cell ontogeny and clonality in myeloid malignancy. Further we will characterize how mutation status effects cell type output. Next, we will use single cell RNA sequencing combined with antibody detection of surface markers (CITE- seq) to identify transcriptional properties of clonal populations, with the focus of distinguishing between signaling mutant cells and non-mutant cells in the same sample. We will test the hypothesis here that there are common gene regulatory networks within clonal evolution patterns with both methylation gene mutations (e.g., TET2) and signaling gene mutations (e.g., FLT3) in MDS and sAML that arises out of MDS. In addition to CITE-seq, we will perform phospho-specific mass cytometry to find aberrant signaling responses to inflammatory perturbations. We have used this modality to probe extracellular and intracellular proteins simultaneously in AML and MDS in the past. Here, we will probe signaling responses by subjecting samples to multiple perturbations. This technique enables cell type assignment via a large panel of >24 surface markers combined with 10 intracellular signaling readouts. Ultimately, we propose to computationally align data from the common surface markers in each of the three single cell modalities to discover correlations between signaling, transcriptional, and clonal identity. In order to dissect the emergence of signaling mutations more accurately, we will use ex vivo CRISPR/Cas9-edited cells that model this clonal change. We will investigate the underlying epigenetic alterations that can give rise to signaling mutant clonal evolution and investigate signaling changes associated with a new signaling mutation. This proposal will identify molecular mechanisms involved in clonal advantage and potential therapeutic targets.