DOI: 10.6084/m9.figshare.24320007
title: Additional file 1 of Single-cell RNA sequencing distinctly characterizes the wide heterogeneity in pediatric mixed phenotype acute leukemia

description: Additional file 1: Table S1. Detailed patient characteristics for pediatric MPAL samples analyzed in our study. For each MPAL sample analyzed, the sample ID, data source, diagnosis subtype, initial WBC (white blood cell count), PB (peripheral blood) blast percentage, BM (bone marrow) blast percentage, timepoint, induction regimen, EOI (end of induction) MRD (minimal residual disease) status, BM blast percentage at EOI, post induction therapy course, whether the patient relapsed or had refractory disease, whether the patient was alive or deceased at last f/u (follow up), and the time to death or last follow up (in days). Table S2. Flow cytometry characteristics of MPAL samples. The information includes the sample ID, diagnosis subtype, whether multiple blast populations were found, whether there was ETP-ALL or near ETP-ALL features, a description of the peripheral blood flow analysis, a description of the bone marrow flow analysis, the bone marrow blast percentage by morphology, the distribution of immune cells in bone marrow based on flow, and the percentage of immune cell subsets in the single-cell dataset (percentage of entire sample). Table S3. Stemness signature that was used for stem cell enrichment analysis. The stem cell enriched signature of 189 genes associated with high expression in stem cells across multiple tissue and disease types was obtained from Palmer et al. [34] study. Table S4. MPAL subtype blast biomarker genes. The table list the gene after each filtering step described in Additional file 2: Fig. S1. Table S5. Sample information for bulk RNA-seq TARGET samples. The sample ID, acute leukemia subtype, vital status, overall survival, and event-free survival for each sample were retrieved from the TARGET web portal ( https://www.cancer.gov/ccg/research/genome-sequencing/target ). Table S6. Top 40 differentially expressed genes (DEGs) between blast populations within T/My sample M2 and B/My sample M3. The top differentially expressed genes between blast subpopulations in B/My sample M3 (M3-My and M3-B) and in T/My sample M2 (M2-My and M2-T). DEGs analysis was performed for sample M3, comparing sample M3’s cells in clusters 0 versus 4, and for sample M2, comparing sample M2’s cells in clusters 4 and 7 versus 5. Please refer to Additional file 2: Fig. S2 for cluster information. Genes with adjusted p-value&lt;0.05 and avg. log2FC&gt;0.25 were considered significantly differently expressed, and the top 40 over-expressed genes were identified based on the highest avg. log2FC. Table S7. Significantly overexpressed genes in MPAL subtype blasts compared to healthy cells. The overexpressed genes were selected based on fold change (avg. log2FC &gt; 0.25) and multiple test corrected p-value (Bonferroni correction, adjusted p-value&lt;0.05). Table S8. Commonly overexpressed genes between MPAL subtypes. A) The significantly overexpressed genes (n=146, avg. log2FC &gt; 0.25, adjusted p-value&lt;0.05) in MPAL blast cells compared to healthy progenitor cells. B) The commonly significantly overexpressed genes (n = 75) between B/My and T/My MPAL, when compared to healthy BM cells (Additional file 1: Table S7). Table S9. Sample information for comparison of MPAL with other pediatric acute and adult MPAL. The sample ID, acute leukemia subtype, data source, and cell count for each sample are listed that were used for analysis shown in Figs. 2, 3, and 4. Table S10. Enriched pathway for B/My MPAL and T/My MPAL blast biomarker gene sets. The final MPAL blast biomarker genes (Additional file 1: Table S4) were used for pathway enrichment analysis using the MetaCore platform that contains functions, pathways, and network models derived by systematically exploring peer-reviewed scientific literature and public databases. The pathways with p-value&lt;0.05 were considered significant. Table S11. Flow cytometry characteristics of T-ALL samples. The information includes the sample ID, flow cytometry characterization, along with non-ETP, the ETP-,ALL, and near ETP-ALL classification.