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Introduction: Despite achievement of complete remission (CR) following chemotherapy, Acute Myelogenous Leukemia (AML) relapses in the majority of adult patients. While relapsed AML is almost always clonally related to the disease at diagnosis, the actual molecular and cellular contributors to chemotherapy resistance and to AML relapse remain incompletely understood. Some molecular determinants of relapse have been identified in genomic, epigenetic and proteomic aberrations, while cellular relapse reservoirs have been identified in leukemia stem cells as well as in more mature leukemic cell compartments. Here, we set out to determine the cellular composition, gene mutation status and gene expression of paired AML specimens procured at diagnosis and at relapse aiming at a better understanding of the AML relapse process. Methods: We employed the drop-seq 3' single cell RNA sequencing (scRNA-seq) method (Macosko 2015) with minor modifications to analyze the mRNA expression in single cells derived from 12 paired AML specimens procured at diagnosis and at relapse from prior CR. We obtained scRNA-seq data on 1000-2000 single cells per sample detecting approximately 2000-3000 unique molecular identifiers (UMIs) and 800-1500 genes per cell. Using WES or panel-based sequencing we determined mutations in known driver genes. Subsequently, we optimized novel methods for detection and mapping of mutated driver genes to individual cells using mutation specific PCR conditions and novel bioinformatics approaches. We annotated scRNA-seq expression profiles of the diagnosis and relapsed AML specimens individually using publicly available data for cell type-specific RNA markers derived from sorted normal cell populations and further compared the scRNA-seq data to scRNA-seq data of 5 pooled normal human bone marrows generated for this study. Results: Through analyses of scRNA-seq data of paired diagnosis and relapse AML specimens via principle components analyses (PCA) or t-distributed stochastic neighbor embedding (t-SNE) we detected varying degrees of separation of cell clusters in all cases analyzed indicative of substantial changes in single cell gene expression between AML diagnosis and relapse. A few of these observed cluster shifts were paralleled by gain or loss of mutated genes (e.g. FLT3-ITD) at relapse while most others lacked obvious clonal genomic markers. Through subsequent comparison of the expression similarities of single AML cells to sorted normal human bone marrow cells we detected two distinct AML relapse patterns: i) a pattern of relapse suggesting simple leukemia regrowth as evidenced by similar proportions of leukemia cells mapping onto discrete normal bone marrow cells (e.g. monocyte-like or GMPs or CMPs), and, ii) a pattern of relapse whereby the gene expression of relapsed cells (but not diagnosis cells) had similarity to normal hematopoietic cells that are conventionally placed more apical in the classical hematopoiesis differentiation cascade (HSCs, MPPs, CMPs; a phenotypic shift to immaturity). In addition, no leukemia sample mapped to just one classically defined bone marrow cell type but instead to multiple cell types, suggesting that most AML leukemia cells harbor aberrant hybrid cell gene expression patterns. Finally, we detected quantitative shifts in T cells and NK cells in some samples at relapse, which will be analyzed in greater detail. Conclusions: The comparative analysis of scRNA-seq data of paired AML specimens procured at diagnosis and relapse, identifies frequent and previously unrecognized changes in gene expression in leukemia cells at relapse. Through a comparison of gene mutation and gene expression at single cell resolution we identify two distinct AML relapse patterns in adult AML. Disclosures No relevant conflicts of interest to declare.

Plasminogen is the precursor of the serine protease plasmin, a central enzyme of the fibrinolytic system. Plasma levels of plasminogen vary by almost 2-fold among healthy individuals, yet little is known about its heritability or genetic determinants in the general population. In order to identify genetic factors affecting the natural variation of plasminogen levels, we performed a genome-wide association study and linkage analysis in a sample of 3456 young healthy individuals who participated in the Genes and Blood Clotting Study (GABC) or the Trinity Student Study (TSS). Heritability of plasminogen levels was 48.1% to 60.0%. Tobacco smoking and female sex were associated with higher levels of plasminogen. In the meta-analysis, 11 single-nucleotide polymorphisms (SNPs) in 2 regions reached genome-wide significance (P < 5.0E-8). Of these, 9 SNPs were near the PLG or LPA genes on Chr6q26, whereas 2 were on Chr19q13 and 5′ upstream of SIGLEC14. These 11 SNPs represented 4 independent signals and collectively explained 6.8% of plasminogen level variation in the study populations. The strongest association was observed for a nonsynonymous SNP in the PLG gene (R523W). Individuals bearing an additional copy of this allele had an average decrease of 13.4% in plasma plasminogen level.

Homeostatic processing of von Willebrand factor (VWF) in the circulation relies on proteolysis of ultra-large VWF (ULVWF) multimers by the metalloprotease ADAMTS13. Deficiency in ADAMTS13 results in Thrombotic Thrombocytopenic Purpura (TTP) due to the accumulation of thrombogenic ULVWF. ADAMTS13 is secreted into the circulation in its active form and has no known inhibitor. Regulation of ADAMTS13 activity occurs in part through plasma ADAMTS13 levels and by the hemodynamic exposure of the scissile bond in VWF. To identify the genetic determinants of ADAMTS13 levels we performed genome-wide association studies (GWAS) and linkage analysis in two independent cohorts of young healthy individuals. This study recruited 3206 young healthy individuals of European descent who participated in the Genes and Blood Clotting Study (GABC, n=940) and the Trinity Student Study (TSS, n=2266). All subjects provided written informed consent and all genotyping and phenotyping was performed on de-identified samples. Details of the genotyping and data cleaning process for these cohorts were previously described. (Desch KC et al. PNAS 2013, 110:588-593) ADAMTS13 levels were measured by a custom AlphaLISA (Perkin-Elmer, Waltham, MA) assay using polyclonal anti-ADAMTS13 antibodies (a gift from X. Long Zheng, University of Pennsylvania). In GABC, the median ADAMTS13 levels were 115.4 IU/dL and demonstrated a 2.0-fold variation between the 5th and 95th percentiles. Heritability of ADAMTS13 levels was estimated at 77% in GABC and 43% in the TSS. ADAMTS13 levels were associated smoking in both cohorts and was associated with an 8.9% increase in levels of ADAMTS13 (P < 1.5E-11, GABC). GWAS identified 10 SNPs in GABC and 33 SNPs in TSS that were significantly associated with ADAMTS13 levels (P Although ABO mediated glycosylation patterns affect VWF clearance rates, no signal in ABO was detected in our multiple association studies suggesting that the common ABO haplotypes are not associated with ADAMTS13 levels. Although other mutations tagged by the significantly associated SNPs cannot be excluded, a potential candidate mutation (rs2301612, Q448E) was identified in ADAMTS13 and may be responsible for the decrease in ADAMTS13 levels through alteration of synthesis/secretion or plasma clearance rates. Aditionally, linkage analysis confirmed the chromosome 9q34 signal and identified a region on chromosome 5p13 that was not detected by GWAS. This finding suggests that rare variants in this genomic region also contribute to ADAMTS13 levels. The identification of mutations that determine normal ADAMTS13 variation may lead to a better understanding of the genetic risk for TTP and mechanisms of von Willebrand Factor homeostasis. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

The conversion of plasminogen (PLG) to plasmin is a critical step in the activation of the fibrinolytic system. Due to its broad substrate specificity, plasmin plays a pleotropic role in the circulation and extravascular space, controlling the degradation of fibrin clots as well as potential functions in regulating cell migration and angiogenesis. PLG activation is regulated through the interaction of tPA or uPA with plasminogen activator inhibitor type one (PAI-1). Genome-wide association studies (GWAS) have identified polymorphisms associated with the plasma levels of tPA and PAI-1, but to date, no studies have directly examined the control of PLG levels. In order to identify genetic determinants of PLG levels we performed GWAS in a cohort of 3244 young healthy individuals (557 sibships) who participated in the Genes and Blood Clotting Study (GABC) or the Trinity Student Study (TSS).(Desch KC et al. PNAS 2013, 110:588-593) All subjects provided written informed consent. Details of the genotyping and data cleaning process for these cohorts are as previously described. (Desch KC et al. PNAS 2013, 110:588-593) PLG levels were measured in both cohorts by AlphaLISA (Perkin-Elmer, Waltham, MA). Levels were natural log transformed and adjusted for participant age, gender and BMI (TSS only) prior to use in heritability estimates and GWAS, which were performed using the PLINK, EMMAX, MERLIN, GCTA and METAL software packages. The median PLG levels were 101.7 IU/dL and 104.7 IU/dL in the GABC and TSS cohorts respectively and had a 1.7 fold variation between the 5th and 95th percentiles. The narrow-sense heritability using intra-class correlation was 59.7% based on 1277 siblings in GABC and TSS, which was similar to the estimate of 60.2% based on the genotype data and the MERLIN software package. GWAS for PLG in the European only subset of GABC (n=940) did not reveal any genome-wide significant signals. However, GWAS for PLG levels in TSS (n=2304) revealed 9 significant SNPs on chromosome 6q26 at the PLG (plasminogen) and LPA (lipoprotein A) locus that together explained 7.8% of the variance in PLG levels according to the GCTA method, Figure 1. The top SNP was rs4252129 (p = 3.47E-25) which results in a single amino acid substitution (R523W) in PLG. The next strongest associations were with two SNPs near LPA, rs1084651 and rs783149 (p = 1.60E-13 and 1.07E-12 respectively). These SNPs are located between LPA and PLG (Figure 1). To determine if the LPA SNPs were independent from the top PLG SNP, a conditional analysis was performed using rs4252129 as a covariate. Rs1084651 and rs783149 remained significant (p= 7.73E-16 and 6.48E-15 respectively), verifying that they represent a second, independent association signal. Meta-analysis of both cohorts (using 723,715 common SNPs) confirmed the 6q26 signal and revealed another significantly associated region on 19q13.41, Figure 2. Two SNPs, rs10412972 and rs11084102 (p = 2.16E-09 and 1.14E-08, respectively) are intergenic variants 4.8 and 12.6 Kb downstream from SIGLEC14. The significant SNPs in the meta-analysis, 6 on 6q26 and 2 on 19q13.41, explained 5.8% of the variance for PLG. The top SNP in the TSS GWAS, rs4252129, had a minor allele frequency (MAF) of 1.4% in GABC and was not included in the GABC GWAS or meta-analysis. In summary, our data identify two loci (at PLG/LPA and SIGLEC14) that harbor genetic variants contributing to the variation of plasma PLG levels in healthy young adults. The strongest association was observed for a non-synonymous SNP in the PLG gene itself, with a MAF of 1.3% in the HapMap CEU population and 2.2% in TSS. The minor allele (W523) is associated with a 14.1% decrease in mean plasma PLG level. This low frequency, non-synonymous SNP, or another SNP in LD with it, may be the functional mutation leading to decreased PLG levels through unknown mechanisms. Whether the significant SNPs near LPA affect lipoprotein A or PLG is not clear. However, the association with causal variants at LPA is possible and consistent with previous biochemical studies demonstrating competitive inhibition of PLG binding to endothelial surfaces by lipoprotein A, or an effect of lipoprotein A on endothelial PAI-1 expression or the PAI-1 interaction with tPA/uPA. Finally, these studies identified two significant SNPs near SIGLEC14. This gene encodes a sialic acid lectin type receptor protein, closely related to SIGLEC 5 recently implicated in the clearance of von Willebrand factor. Disclosures: Ginsburg: Shire plc: Membership on an entity’s Board of Directors or advisory committees; Portola Pharmaeuticals: Membership on an entity’s Board of Directors or advisory committees; Catalyst Biosciences: Membership on an entity’s Board of Directors or advisory committees; Merck pharmaceuticals: Consultancy; Boston Children's Hospital: From Baxter to Children's Hosp (VWF), From Baxter to Children's Hosp (VWF) Patents & Royalties; University of Michigan: From Baxter to University of Michigan (ADAMTS13) Patents & Royalties.