Role of Matrix Metalloproteinase MMP-2, MMP-9 and Tissue Inhibitor of Metalloproteinase (TIMP-1) in Clinical Progression of Pediatric Acute Lymphoblastic Leukemia

Background: Matrix metalloproteinases (MMPs) play a crucial role in cancer progression and metastasis, however their role in pediatric Acute lymphoblastic leukemia (ALL) is still unrevealed.Methods: The diagnostic, prognostic and predictive value of tissue inhibitor of metalloproteinase (TIMP-1), MMP-2, MMP-9 and CD34+CD38- CSCs were assessed in bone marrow (BM) samples of 76 ALL children using Flow Cytometry analysis. Results: There was a significant increase in TIMP-1 [1.52 (0.41-10) versus 0.91(0.6-1.12); respectively, P<0.001], and CSCs CD84+CD38- [1 (0.03-18.6) versus 0.3 (0.01-1.1), P<0.001] expression in ALL patients compared to controls. While there were no significant differences regarding MMP-2 and MMP-9 expression between the two groups. The sensitivity, specificity, AUC of MMP-2 were (80.3%, 53.3% and 0.568, P=0.404), and that of MMP-9 were (53.9%, 40% and 0.660, P=0.053). While that of TIMP-1 were (78.9%, 100% and 0.892, P<0.001), and that of CSCs CD34+ CD38- were (78.9%, 73.3% and 0.855, P<0.001). There was a significant association between MMP-2 overexpression and MRD at day-15, increased BM blast cell count at diagnosis and at day-15, (P=0.020, P=0.047 and P=0.001). Increased TIMP-1 expression associated with the high-risk disease (P<0.001), increased BM blast cell count at diagnosis and at day-15 (P=0.033 and P=0.001), as well as MRD at day 15 and day 42 (P<0.001 for both). CD34+CD38- CSCs associated with MRD at day-15, increased BM blast cell count at diagnosis and at day-15 (P=0.015, P=0.005 and P=0.003). TIMP-1 overexpression associated with shorter DFS and OS rates (P=0.009 and P=0.048). Multivariate logistic regression analysis showed that both TIMP-1 [OR: 4.224, P=0.046], and CD34+CD38- CSCs [OR: 6.873, P=0.005] are independent diagnostic factors for pediatric ALL.Conclusion: TIMP-1 and CD34+CD38- CSCs could be useful independent diagnostic markers for pediatric ALL. Also, TIMP-1 is a promising prognostic marker for poor outcome of the patients.

Joint profiling of DNA and proteins in single cells to dissect genotype-phenotype associations in leukemia

Studies of acute myeloid leukemia rely on DNA sequencing and immunophenotyping by flow cytometry as primary tools for disease characterization. However, leukemia tumor heterogeneity complicates integration of DNA variants and immunophenotypes from separate measurements. Here we introduce DAb-seq, a technology for simultaneous capture of DNA genotype and cell surface phenotype from single cells at high throughput, enabling direct profiling of proteogenomic states in tens of thousands of cells. To demonstrate the approach, we analyze the disease of three patients with leukemia over multiple treatment timepoints and disease recurrences. We observe complex genotype-phenotype dynamics that illustrate the subtlety of the disease process and the degree of incongruity between blast cell genotype and phenotype in different clinical scenarios. Our results highlight the importance of combined single-cell DNA and protein measurements to fully characterize the heterogeneity of leukemia.

Comparative analysis of chromosomal aberrations in children with acute leukemia

Leukemia is a hematopoetic tissue tumor with a primary lesion of the bone marrow, where the morphological substrate is the blast cell. Chromosomal and molecular genetic aberrations play a major role in the acute leukemia pathogenesis, determing the morphological, immunological and clinical features of the disease. Our study was aimed to to analyze retrospectively the structure and frequency of chromosomal aberrations in children with initially diagnosed acute leukemia. Material and methods. Medical histories retrospective analysis of children charged to oncohematology department of the «Scientific Center of Pediatrics and Pediatric Surgery» in Almaty for the period 2015 - 2017 was carried out. 310 histories with primary diagnosed acute leukemia were studied. Results and discussion. Among 310 patients different chromosome aberrations were isolated in 158 patients (51%) during cytogenetic and molecular cytogenetic (in situ hybridization) studies of bone marrow blast cells. A normal karyotype was observed in 102 patients (33%). Conclusion. The lymphoblastic variant of acute leukemia was determined in 75.5%, that indicates its leading role in AL structure among the children of different ages. AML was determined in 22.6% of all OL cases. The most frequent chromosomal rearrangement in ALL patients was blast cell chromosome hyperdiploidy (10,6%) and t(12;21)(p13;q22)/ETV6-RUNX1,which was detected in 37 (16%) patients. The most frequent AML abberation was t (8;21) (q22;q22)/RUNX1-RUNX1T1, identified in 15 (21.4%) patients. Keywords: acute leukemia, bone marrow, blast cells, karyotype, chromosomal aberrations, cytogenetic study.

Preliminary process in blast cell morphology identification based on image segmentation methods

The diagnosis of blood disorders in developing countries usually uses the diagnostic procedure Complete Blood Count (CBC). This is due to the limitations of existing health facilities so that examinations use standard microscopes as required in CBC examinations. However, the CBC process still poses a problem, namely that the procedure for manually counting blood cells with a microscope requires a lot of energy and time, and is expensive. This paper will discuss alternative uses of image processing technology in blast cell identification by using microscope images. In this paper, we will discuss in detail the morphological measurements which include the diameter, circumference and area of blast cell cells based on watershed segmentation methods and active contour. As a basis for further development, we compare the performance between the uses of both methods. The results show that the active contour method has an error percentage of 5.15% while the watershed method has an error percentage of 8.25%.

Analysis of Clonal Evolution of AML Using Simultaneous Single-Cell DNA/RNA Analysis

Background Acute myeloid leukemia (AML) was defined by an increase of immature myeloid cells, or blasts that exceed ≥20% in bone marrow or peripheral blood. Many lines of evidence suggest that the development of AML is shaped by clonal evolution through multiple rounds of positive selection driven by newly acquired mutations, ultimately leading to an increased blast count. This process has been analyzed in detail in the case of progression from myelodysplastic syndromes (MDS) to secondary AML (sAML), which is invariably accompanied by expansion of cells that acquired new driver alterations, generating clonal substructures in many cases (Walter et al. NEJM. 2012, Makishima et al. Nat. Genet. 2015). However, it has not been fully elucidated how these newly acquired mutations contribute to increased blast cells that define AML. Results In order to understand how driver mutations contribute to the phenotype of blasts, we first focused on the driver mutations that have known to be enriched in sAML, including those in IDH1/2, NPM1, FLT3,NRAS, KRAS, PTPN11, CBL and WT1, and compared BM blast count (BC) and mutant cell fraction (MCF) of each driver mutation in 27 cases with sAML. Compared with BC, IDH1- or IDH2-mutated cells exhibited a larger MCF in most cases, suggesting that newly acquired IDH1/2 mutations contribute clonal expansion but only a part of the expanded cells undergo differentiation block and the remaining cells can differentiate into mature cells. Of interest, we observed lower MCFs than BC in approximately half of the cases with signaling pathway mutations, including FLT3 and RAS pathway (NRAS, KRAS, PTPN11 and CBL) mutations, in which MCFs for signaling pathway mutation accounted for less than 2/3 of BC, which was also observed in de novo AML cases. In fact, signaling pathway mutations in two representative cases were confirmed to account only for 30.4% and 3.4% of blast cells, using ddPCR of the blast cells collected as the CD45dim SSClow fraction, which were confirmed to show a blast morphology. These results suggest a possibility that the presence of mutant cells might affect the phenotype of the surrounding unmutated cells. Thus, to investigate the mechanism of such non-cell autonomous effects of mutations on blast cell morphology, we developed an advanced single-cell sequencing platform that enables simultaneous measurements of both mutations and gene expression profiles at a single-cell level and applied this to the analysis of immature (CD34+ Lin-) BM cells from 2 sAML cases with multiple RAS pathway mutations showing disproportionately small MCF compared to BC, in which gene expression of mutated and unmutated cells were evaluated separately. The same BM faction in 13 healthy donors was also analyzed as normal control. In single-cell mutation analysis, multiple RAS pathway mutations in both cases represented independent clones. As expected, cells carrying each RAS pathway mutation at sAML showed an immature myeloid phenotype. However, most of the cells, even carrying MDS mutations alone, also exhibited an immature myeloid phenotype similar to the RAS pathway mutated cells, although the latter cells showed upregulated RAS signaling compared with the former cells. Cells solely carrying MDS mutations in MDS phase showed multi-lineage differentiation, which was no longer observed in those cells in sAML phase. This was in contrast to another case who acquired MYC amplification on sAML progression, where nearly all cells having MYC-amplification showed an immature myeloid phenotype, whereas the remaining MDS clones lacking MYC-amplification retained multilineage differentiation even at the sAML phase. These results suggest that RAS mutants might have a non-cell autonomous effect on the surrounding cells including those hematopoietic cells lacking those mutations and other stromal cells, preventing their differentiation to mature cells, although we cannot exclude another possibility that altered BM microenvironment could influence the phenotype of both mutated and unmutated cells. Conclusions Although an acquisition of new mutations is essential for the progression of MDS to sAML, our results suggest that the blast cell phenotype may not solely be determined by cell-intrinsic effects of such mutations, but non-cell autonomous effects of mutated cells (and possibly also of an altered BM microenvironment) may have a role in increased blast count and therefore AML progression. Disclosures Inagaki: Sumitomo Dainippon Pharma Co., Ltd.: Current Employment. Nakagawa:Sumitomo Dainippon Pharma Co., Ltd.: Research Funding. Ogawa:KAN Research Institute, Inc.: Membership on an entity's Board of Directors or advisory committees, Research Funding; Eisai Co., Ltd.: Research Funding; Sumitomo Dainippon Pharma Co., Ltd.: Research Funding; Asahi Genomics Co., Ltd.: Current equity holder in private company; Otsuka Pharmaceutical Co., Ltd.: Research Funding; Chordia Therapeutics, Inc.: Membership on an entity's Board of Directors or advisory committees, Research Funding.

Single Cell Transcriptomics Revealed AML and Non-AML Cell Clusters Relevant to Relapse and Remission in Pediatric AML

Introduction: While advances in front-line conventional chemotherapy have increased the likelihood of attaining remission in pediatric AML, relapse rates remain high (25-35%), and novel therapies are needed (Zhang, Savage et al. 2019). The clinical and molecular heterogeneity of AML makes it complex to study and creates challenges for the development of novel therapies (Bolouri, Farrar et al. 2018). It is important to identify cells and pathways underlying relapse to facilitate development of novel therapies. Single-cell RNA Sequencing (scRNA-Seq) allows in-depth analysis of the heterogeneous AML landscape to provide a detailed view of the tumor microenvironment, revealing populations of blasts and immune cells which may be relevant to relapse or complete remission. Methods: We analyzed ~36,500 cells from 14 pediatric AML bone marrow samples in our institutional biorepository, spanning different AML subtypes and 3 healthy children to generate a comprehensive scRNA-Seq landscape of immature AML-associated blasts and microenvironment cells. Samples collected at the time of diagnosis (Dx), end of induction (EOI), and relapse (Rel) were used to generate scRNA-Seq data using a droplet-based barcoding technique (Panigrahy, Gartung et al. 2019). After normalization of scRNA-Seq data, the cell clusters were identified using principal component analysis and Uniform Manifold Approximation and Projection (UMAP) approach (Becht et al, 2018). Differential expression, pathways and systems biology analysis between relapsed and remission patients reveal differences for specific cell clusters (Panigrahy, Gartung et al. 2019). To determine the clinical outcome association of our AML blast specific markers, survival analysis was performed on AML TARGET data (https://ocg.cancer.gov/programs/target) using cox proportional hazard survival approach. To characterize AML blast cells with high accuracy, we used support vector machine (SVM), an Artificial Intelligence based feature extraction and model development approach (Bhasin, Ndebele et al. 2016). Results:ScRNA-Seq analysis of paired Dx and EOI samples using UMAP identified three blast cell clusters with significant gene expression differences among different patients, indicating heterogeneity of AML blast cells (Fig 1a, b). Comparative analysis of the three Dx enriched blast cell clusters with other cells identified a "core blast cell signature" with overexpression of genes like AZU1, CLEC11A, FLT3, and NREP (Fig 1c). These core AML-blast genes were linked to significant activation of the Wnt/Ca2+, Phospholipase C, and integrin signaling pathways (Z score &gt;2 and P-value &lt;.001). A subset of AML blast-specific genes also depicted significant association with poorer overall survival (CLEC11A Hazard ratio (HR)=1.9 ;95% CI=1.1-3.4;log-rank P=.03, FLT3 HR=2.4(1.5-3.9);P&lt;0.001,NREP HR=1.9(1.2-3.1); P&lt;0.008) (Fig 1d). Furthermore, we developed a highly sensitive 7-gene AML blast cell signature that distinguished AML blasts from normal myeloblasts and other hematopoietic cells (Area Under Curve of 0.94) using SVM. The scRNA-Seq of AML specific blast cells from relapsed and remission samples exhibited a different clustering pattern indicating different transcriptome landscapes. Relapse-associated AML cell clusters expressed high levels of AZU1, S100A4, LGALS1, and GRK2 genes (Fig 2a). Analysis of the non-AML tumor microenvironment demonstrated enrichment of T/NK in relapsed samples, with differential expression of T cell regulatory/activation genes (Fig 2b, c). ScRNA-Seq showed enrichment of monocyte/macrophage cell clusters in remission samples with distinct relapse- and remission-specific clusters. Remission associated macrophage/monocyte clusters showed overexpression of S100A10, FTH1, CST3 and IFITM2 genes (Fig 2d). Similarly, enrichment of T cell and monocyte/macrophage clusters was observed in relapse and remission samples respectively during EOI. Conclusions: Using single cell transcriptomics we developed a novel potential gene signature to characterize heterogenous AML blast populations with high sensitivity. These genes and the pathways they regulate implicate potential therapeutic targets in pediatric AML. Single cell transcriptome analysis also enabled identification of cell clusters with modulated gene expression at both Dx and EOI that may be useful in predicting relapse/remission. Disclosures Bhasin: Canomiiks Inc: Current equity holder in private company, Other: Co-Founder.

Characterization of T-ALL-Specific Heterogenous Blast Populations Using High Resolution Single Cell Profiling

Introduction: T-cell acute lymphoblastic leukemia (T-ALL) is characterized by proliferation of immature T-cells and accounts for ~15% of pediatric ALL. T-ALL blasts are phenotypically diverse and are sub-classified into pro-, pre-, cortical and mature T-ALL based on the stage of differentiation of the leukemic clone. Early T precursor ALL (ETP-ALL) is a T-ALL subtype associated with higher risk of relapse (Raetz and Teachey, 2016). Bulk sequencing approaches have revealed valuable information about modulated genes in T-ALL; however, little is understood about the interplay between tumor cells and the immune microenvironment. We present a comprehensive single cell RNA sequencing (scRNASeq) analysis of T-ALL samples with the purpose of characterizing the heterogenous tumor and microenvironment cells in order to identify dysregulated genes in leukemia cells and investigate oncogenic signaling pathways. Methods: We profiled 16,280 cells from 5 diagnostic pediatric T-ALL bone marrow samples using the Chromium single cell transcriptomics platform (10x Genomics, Pleasanton, CA). To compare T-ALL versus healthy bone marrow profiles and identify T-ALL-specific malignant blast cell populations, we included data from 3 healthy pediatric bone marrow samples from a recent study (Caron et al, 2020). Dimension reduction using the Uniform Manifold Approximation and Projection (UMAP) approach was used to identify unique cell type clusters (Becht et al, 2018). Using the TARGET dataset (https://ocg.cancer.gov/programs/target), we further evaluated the prognostic significance of identified malignant blast-specific genes by performing Kaplan-Meier survival analysis and compared gene expression patterns and tumor microenvironment makeup between ETP-ALL and non-ETP T-ALL patient samples. Results: We successfully characterized leukemic blasts (CD7+, CD99+ and CD3D+) and other major immune cell types (T cells, B cells, monocytes, erythroid precursors) using the expression of established marker genes (Fig 1A, C). Clustering analysis revealed patient-specific leukemia blast cell clusters (Fig 1B). Differential expression analysis between CD3D+ patient-specific leukemia clusters and CD3D+ clusters comprised of normal T-cells identified a set of 385 promiscuous genes that are significantly differentially expressed between malignant and normal T-cells (p-value &lt;0.05) despite tumor cell heterogeneity. Among the top genes upregulated in the leukemia clusters are HES4 (a downstream target of NOTCH1), CD99, RACK1, and TUBB (Fig 1D), which have previously been implicated in T-ALL leukemogenesis and chemoresistance (DeDecker et al, 2020; Cox et al, 2016; Lei et all 2016). Further, pathways analysis demonstrated significant activation (Z score &gt;1.3, p-value &lt;0.05) of multiple pathways associated with cancer stemness, cellular growth and proliferation including PI3K/AKT, unfolded protein response, and glycolysis, implicating these genes as oncogenic mediators in T-ALL blasts (Fig 2A). Overexpression of S100A4, IFITM2, and CD74 were significantly associated with poor survival in the TARGET cohort of 241 patients (hazard ratio 2.2, 5.8, and 7.1, respectively, p-value &lt;0.05), indicating their prognostic significance (Fig 2B). We additionally evaluated the expression of our gene set across ETP-ALL and non-ETP T-ALL groups in the TARGET dataset. Multiple leukemic blast-specific genes including ARMH1, CD44, CD74, DNAJC1, and IFITM2 were significantly upregulated in ETP-ALL. Further, deconvolution analysis performed on ETP-ALL vs non-ETP T-ALL samples determined that tumor microenvironment cell types are differentially enriched in these T-ALL groups. We observed enrichment of memory B cells, dendritic cells, NK cells, and M2 macrophages in ETP-ALL samples as compared to non-ETP T-ALL samples. Conversely,lower levels of mast cells and T-regulatory cells were observed in ETP-ALL samples. Conclusions: We identified a gene signature characterizing heterogenous T-ALL blast populations. External validation using the TARGET dataset identified genes within the signature that are associated with poor outcomes in T-ALL. Differences in the composition of the immune microenvironment in ETP-ALL versus non-ETP T-ALL samples provide a promising area for future study. Further studies will be carried out with relapsed and non-relapsed T-ALL samples to validate this leukemia signature. Disclosures Bhasin: Canomiiks Inc: Current equity holder in private company, Other: Co-Founder.