Abstract Background The molecular landscape of pediatric B-cell precursor acute lymphoblastic leukemia (BCP-ALL) has been extensively characterized through single-modality studies. However, the interplay between molecular modalities and their collective influence on treatment response and outcomes remains poorly understood. Methods We integrated genomic, epigenomic, transcriptomic, and ex vivo drug response data from 1231 patients diagnosed with BCP-ALL. Using Multi-Omics Factor Analysis, we identified signatures explaining key aspects of the integrative molecular landscape, referred to as cross-modal elements (CMEs). The CME-derived signatures were introduced into pathway and intermodal network analyses, while their impact on patient outcomes was assessed through survival modeling. Results Pathway and network analyses annotate the resulting integrative CMEs, linking them to key biological processes, including disease development, cellular regulatory processes, metabolic pathways, and drug response. By leveraging correlations between DNA methylation and ex vivo response to doxorubicin, we stratify patients with hyperdiploidy into subgroups that differ in relapse-free survival. These signatures are independent of clinical variables. Survival models incorporating CME-selected ex-vivo drug responses combined with clinical data improve risk prediction compared to clinical models alone (FDR < 0.05), demonstrating the potential of integrative multiomics in refining risk stratification. Conclusions Our study highlights the importance of multimodal data integration in BCP-ALL to provide biological insights with potential relevance for precision medicine.

FLT3-mutated acute myeloid leukemia (AML) remains difficult to treat due to frequent resistance to FLT3 inhibitors like midostaurin. In this study, we observed a progenitor-like CD38+CD45RA+ leukemic cell population that may be associated with midostaurin resistance. Midostaurin-resistant cells display disrupted membrane architecture and a shift in signaling from STAT5 to PI3K/AKT, favoring survival over apoptosis. Functional drug testing was consistent with clinical response to midostaurin, and together with multi-omic profiling, including single-cell and proteomic analyses, indicated the presence and relevance of this resistant phenotype. Drug combination screening revealed that co-targeting with SMAC mimetics restores apoptotic competence and selectively depletes the resistant population when combined with midostaurin. In contrast, venetoclax combinations preferentially affected CD34hi cells, underscoring distinct subpopulation vulnerabilities. These findings may point to a biologically relevant mechanism underlying midostaurin resistance.

Abstract The circulating blood proteome provides a systemic readout of disease biology and holds promise for advancing diagnostics and disease monitoring in pediatric leukemia. Here, we profiled 3072 proteins in diagnostic serum from 54 children with acute lymphoblastic leukemia (ALL), 21 with acute myeloid leukemia (AML), and 12 healthy controls using the Olink Proximity Extension Assay. We observed profound alterations in circulating protein levels in leukemia patients compared with controls and identified immunophenotype-specific proteins, including SIGLEC15 in B-cell precursor ALL (BCP-ALL), NOTCH1 in T-ALL, and CEBPA in AML, all which remained high even in patients with low (<20%) or no peripheral blood blasts. Within BCP-ALL, molecular subtypes were reflected in the circulating proteome; for example, DSC2 and PTPRK were elevated in ETV6 :: RUNX1 -positive cases, while IL-6R and ADAM8 were higher in High Hyperdiploid cases. Angiogenic growth factors decreased across all leukemia patients compared with controls, suggesting a fragile peripheral vasculature at diagnosis. Integration with external datasets revealed the likely cellular source of abundant proteins and examination of an external cohort validated our subtype-specific findings. Together, these results define shared and distinct proteomic signatures across pediatric acute leukemias and highlight candidate biomarkers for diagnostics and disease monitoring. Graphical abstract

Ex vivo drug response profiling is emerging as a valuable tool for identifying drug resistance mechanisms and advancing precision medicine in hematological cancers. However, the functional impact of dysregulation of the epigenome and transcriptome in this context remains poorly understood. In this study, we combined ex vivo drug sensitivity profiling with transcriptomic and epigenomic analyses in diagnostic samples from 597 pediatric B-cell precursor acute lymphoblastic leukemia (BCP-ALL) patients. Ex vivo resistance to antimetabolites (e.g., cytarabine, thioguanine), glucocorticoids (e.g., dexamethasone, prednisolone), and doxorubicin was independently associated with reduced relapse-free survival (P < 0.05). Molecular profiling identified pretreatment DNA methylation and gene expression patterns distinguishing resistant from sensitive cases, revealing key drug resistance signatures. These included aberrant expression of genes related to heme metabolism (e.g., ATPV06A) and KRAS signaling (e.g., GS02). Notably, we also observed atypical expression of genes usually restricted to T cells and other immune cells (e.g., ITK) in resistant BCP-ALL cells. Our findings demonstrate that ex vivo drug response patterns are predictive of clinical outcomes and reflect intrinsic molecular states associated with drug tolerance. This integrative multi-omics approach highlights potential therapeutic targets and underscores the value of functional precision medicine in identifying treatment vulnerabilities in pediatric ALL.

Functional precision medicine (FPM) aims to optimize patient-specific drug selection based on the unique characteristics of their cancer cells. Recent advancements in high throughput ex vivo drug profiling have accelerated interest in FPM. Here, we present a proof-of-concept study for an integrated experimental system that incorporates ex vivo treatment response with a single-cell gene expression output enabling barcoding of several drug conditions in one single-cell sequencing experiment. We demonstrate this through a proof-of-concept investigation focusing on the glucocorticoid-resistant acute lymphoblastic leukemia (ALL) E/R+ Reh cell line. Three different single-cell transcriptome sequencing (scRNA-seq) approaches were evaluated, each exhibiting high cell recovery and accurate tagging of distinct drug conditions. Notably, our comprehensive analysis revealed variations in library complexity, sensitivity (gene detection), and differential gene expression detection across the methods. Despite these differences, we identified a substantial transcriptional response to fludarabine, a highly relevant drug for treating high-risk ALL, which was consistently recapitulated by all three methods. These findings highlight the potential of our integrated approach for studying drug responses at the single-cell level and emphasize the importance of method selection in scRNA-seq studies. Finally, our data encompassing 27 327 cells are freely available to extend to future scRNA-seq methodological comparisons.

The B-cell acute lymphoblastic leukemia (ALL) cell line REH, with the t(12;21)ETV6::RUNX1translocation, is known to have a complex karyotype defined by a series of large-scale chromosomal rearrangements. Taken from a 15-yr-old at relapse, the cell line offers a practical model for the study of pediatric B-ALL. In recent years, short- and long-read DNA and RNA sequencing have emerged as a complement to karyotyping techniques in the resolution of structural variants in an oncological context. Here, we explore the integration of long-read PacBio and Oxford Nanopore whole-genome sequencing, IsoSeq RNA sequencing, and short-read Illumina sequencing to create a detailed genomic and transcriptomic characterization of the REH cell line. Whole-genome sequencing clarified the molecular traits of disrupted ALL-associated genes includingCDKN2A,PAX5,BTG1,VPREB1, andTBL1XR1, as well as the glucocorticoid receptorNR3C1. Meanwhile, transcriptome sequencing identified seven fusion genes within the genomic breakpoints. Together, our extensive whole-genome investigation makes high-quality open-source data available to the leukemia genomics community.

Recent advancements in spatial biology have revolutionized our understanding of the organization and functional dynamics of cells and tissues. In this study, we applied Molecular Pixelation (MPX), a single-cell spatial proteomics assay, to investigate the modulation of the cell surface proteome in an in vitro drug screening model using the ETV6::RUNX1 acute lymphoblastic leukemia (ALL) cell line, Reh . Specifically, we focused on the in vitro response to fludarabine, a chemotherapeutic agent used prior to allogenic stem cell transplantation and chimeric antigen receptor (CAR)-T cell therapy in high-risk, refractory, or relapsed ALL patients. Using MPX, we quantified changes in protein abundance, spatial distribution, and colocalization of 76 targeted cell surface proteins in Reh cells before and after fludarabine treatment. Our analysis revealed 25 proteins with altered abundance, 24 proteins with increased polarity, and 138 protein pairs with modified colocalization following treatment. Notably, the tetraspanins CD82 and CD53, which are known for their roles in chemotherapy resistance, exhibited increased abundance, polarization, and colocalization post-treatment, suggesting their potential as a therapeutic scaffold. These findings underscore the unique ability of spatially resolved single-cell proteomics to uncover nuanced cellular responses that would otherwise remain undetected. ### Competing Interest Statement The authors have declared no competing interest.

Acute myeloid leukemia (AML) is a disease caused by abnormal proliferations of myeloid progenitor cells in the bone marrow. About 25-30% of AML patients have a mutation in the FLT3 gene, which is associated with a poor prognosis. Although FLT3-inhibitors (FLT3i) that can target this mutation are clinically approved, approximately 40% of FLT3mut patients do not respond to FLT3i. In this study we performed high-throughput functional ex vivo drug testing (n=528) in 63 FLT3mut patients, with paired MS-proteomics (n=20), RNA-seq (n=20), plasma proteome (n=16), spatial single-cell proteomics (n=6) and mass cytometry (n=3) data with the aim to uncover the functional and molecular landscape linked to FLT3i response. First, we compared clinical and ex vivo response to FLT3i (midostaurin) to determine the clinical predictability of our ex vivo drug testing. All five ex vivo responders had a complete remission (CR) after first treatment with conventional induction therapy and midostaurin, while four out of five non-responders eventually relapsed or had no CR. Furthermore, non-responders had an increased immune activation and displayed immature stem cell phenotypes, as well as decreased expression of surface markers associated with mature myeloid cells (e.g., CD64, CD11c, CD33) compared to responders. Moreover, soluble CD40, CD244, PD-L1, CD4, and IL12RB1 were increased in responders, indicating an immune suppressive environment consistent with RNA and protein data. Surprisingly, the T cell receptor proteins CD200 and CD45RA were found to be increased in FLT3i responders and non-responders, respectively. None of the other 527 drugs tested ex vivo were more effective in the non-responder group, including other FLT3i. Thus, we performed drug combination screening on FLT3mut and FLT3i resistant cell lines and patient cells to identify effective drug combinations. Combinatorial drug screening in resistant FLT3mut cells revealed an increase in drug sensitivity to apoptotic modulators, as well as PI3K/AKT inhibitors in combination with midostaurin. Subsequent validation confirmed synergy between the midostaurin and the SMAC-mimetics birinapant and LCL161, while the BH3 mimetic venetoclax and the PI3K inhibitor idelalisib only showed an additive effect. Our data shows specific differences in myeloid maturation and LSC (Leukemic Stem Cell) phenotype between midostaurin responders and non-responders, together with a potential functional shift in cell signaling in immune signaling and anti-apoptotic pathways. Moreover, ex vivo drug testing data demonstrates that while there is less overall sensitivity to drug treatment in non-responders, combination therapies including apoptotic modulators such as the SMAC mimetics could overcome FLT3i resistance and improve FLT3mut patient outcomes.

Genomic analyses have redefined the molecular subgrouping of pediatric acute lymphoblastic leukemia (ALL). Molecular subgroups guide risk-stratification and targeted therapies, but outcomes of recently identified subtypes are often unclear, owing to limited cases with comprehensive profiling and cross-protocol studies. We developed a machine learning tool (ALLIUM) for the molecular subclassification of ALL in retrospective cohorts as well as for up-front diagnostics. ALLIUM uses DNA methylation and gene expression data from 1131 Nordic ALL patients to predict 17 ALL subtypes with high accuracy. ALLIUM was used to revise and verify the molecular subtype of 281 B-cell precursor ALL (BCP-ALL) cases with previously undefined molecular phenotype, resulting in a single revised subtype for 81.5% of these cases. Our study shows the power of combining DNA methylation and gene expression data for resolving ALL subtypes and provides a comprehensive population-based retrospective cohort study of molecular subtype frequencies in the Nordic countries.

ABSTRACTThe B-cell acute lymphoblastic leukemia (ALL) cell line REH, with the t(12;21)ETV6-RUNX1translocation, is known to have a complex karyotype defined by a series of large-scale chromosomal rearrangements. Taken from a 15-year-old at relapse, the cell line offers a practical model for the study of high-risk pediatric B-ALL patients. In recent years, short-read DNA and RNA sequencing have emerged as a complement to analog karyotyping techniques in the resolution of structural variants in an oncological context. However, it is challenging to create a comprehensive digital karyotype of a genome with these techniques alone. Here, we explore the integration of long-read PacBio and Oxford Nanopore whole genome sequencing (WGS), IsoSeq RNA-sequencing, and short-read sequencing to create a detailed digital karyotype of the REH cell line. WGS refined the breakpoints of known aberrations and clarified the molecular traits of disrupted ALL-associated genesBTG1andTBL1XR1, as well as the glucocorticoid receptorNR3C1. Several previously underreported structural variants were also uncovered, including deletions affecting the ALL-associated genesVPREB1andNFATC1. Meanwhile, transcriptome sequencing identified seven fusion genes within the genomic breakpoints. Together, our extensive whole-genome investigation makes high-quality open-source data available to the leukemia genomics community.KEY POINTSA complete digital karyotype of the REH cell line was produced with short- and long-read DNA and RNA sequencing technologies.The study enabled precise identification of structural variants, and the fusion genes expressed as the result of these variants.

DNA methylation is a central epigenetic mark that has diverse roles in gene regulation, development, and maintenance of genome integrity. 5 methyl cytosine (5mC) can be interrogated at base resolution in single cells by using bisulfite sequencing (scWGBS). Several different scWGBS strategies have been described in recent years to study DNA methylation in single cells. However, there remain limitations with respect to cost-efficiency and yield. Herein, we present a new development in the field of scWGBS library preparation; single cell Splinted Ligation Adapter Tagging (scSPLAT). scSPLAT employs a pooling strategy to facilitate sample preparation at a higher scale and throughput than previously possible. We demonstrate the accuracy and robustness of the method by generating data from 225 single K562 cells and from 309 single liver nuclei and compare scSPLAT against other scWGBS methods.

The mechanisms driving clonal heterogeneity and evolution in relapsed pediatric acute lymphoblastic leukemia (ALL) are not fully understood. We performed whole genome sequencing of samples collected at diagnosis, relapse(s) and remission from 29 Nordic patients. Somatic point mutations and large-scale structural variants were called using individually matched remission samples as controls, and allelic expression of the mutations was assessed in ALL cells using RNA-sequencing. We observed an increased burden of somatic mutations at relapse, compared to diagnosis, and at second relapse compared to first relapse. In addition to 29 known ALL driver genes, of which nine genes carried recurrent protein-coding mutations in our sample set, we identified putative non-protein coding mutations in regulatory regions of seven additional genes that have not previously been described in ALL. Cluster analysis of hundreds of somatic mutations per sample revealed three distinct evolutionary trajectories during ALL progression from diagnosis to relapse. The evolutionary trajectories provide insight into the mutational mechanisms leading relapse in ALL and could offer biomarkers for improved risk prediction in individual patients.

Structural chromosomal rearrangements that can lead to in-frame gene-fusions are a leading source of information for diagnosis, risk stratification, and prognosis in pediatric acute lymphoblastic leukemia (ALL). Traditional methods such as karyotyping and FISH struggle to accurately identify and phase such large-scale chromosomal aberrations in ALL genomes. We therefore evaluated linked-read WGS for detecting chromosomal rearrangements in primary samples of from 12 patients diagnosed with ALL. We assessed the effect of input DNA quality on phased haplotype block size and the detectability of copy number aberrations and structural variants in the ALL genomes. We found that biobanked DNA isolated by standard column-based extraction methods was sufficient to detect chromosomal rearrangements even at low 10x sequencing coverage. Linked-read WGS enabled precise, allele-specific, digital karyotyping at a base-pair resolution for a wide range of structural variants including complex rearrangements and aneuploidy assessment. With use of haplotype information from the linked-reads, we also identified previously unknown structural variants, such as a compound heterozygous deletion of ERG in a patient with the DUX4 - IGH fusion gene. We conclude that linked-read WGS allows detection of important pathogenic variants in ALL genomes at a resolution beyond that of traditional karyotyping and FISH.

Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and a global resource for epigenetics research from the FDA’s Epigenomics Quality Control (EpiQC) Group. The study design leverages seven human cell lines that are designated as reference materials and publicly available from the National Institute of Standards and Technology (NIST) and Genome in a Bottle (GIAB) consortium. These samples were subject to a variety of genome-wide methylation interrogation approaches across six independent laboratories, with a primary focus was on 5-methylcytosine modifications. Each sample was processed in two or more technical replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), one enzymatic deamination method (EMseq), targeted methylation sequencing (Illumina Methyl Capture EPIC), and single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, BWAMeth, and GemBS), we found overall high concordance between assays (R=0.87-R0.93), differences in efficency of read mapping and CpG capture and coverage, and platform performance. The data provided herein can guide continued used of these reference materials in epigenomics assays, as well as provide best practices for epigenomics research and experimental design in future studies.

High-throughput sequencing was applied to investigate the mutation/methylation patterns on 1q and gene expression profiles in pediatric B-cell precursor acute lymphoblastic leukemia (BCP ALL) with/without (w/wo) dup(1q). Sequencing of the breakpoint regions and all exons on 1q in seven dup(1q)-positive cases revealed non-synonymous somatic single nucleotide variants (SNVs) in BLZF1, FMN2, KCNT2, LCE1C, NES, and PARP1. Deep sequencing of these in a validation cohort w (n = 17)/wo (n = 94) dup(1q) revealed similar SNV frequencies in the two groups (47% vs. 35%; P = 0.42). Only 0.6% of the 36,259 CpGs on 1q were differentially methylated between cases w (n = 14)/wo (n = 13) dup(1q). RNA sequencing of high hyperdiploid (HeH) and t(1;19)(q23;p13)-positive cases w (n = 14)/wo (n = 52) dup(1q) identified 252 and 424 differentially expressed genes, respectively; only seven overlapped. Of the overexpressed genes in the HeH and t(1;19) groups, 23 and 31%, respectively, mapped to 1q; 60-80% of these encode nucleic acid/protein binding factors or proteins with catalytic activity. We conclude that the pathogenetically important consequence of dup(1q) in BCP ALL is a gene-dosage effect, with the deregulated genes differing between genetic subtypes, but involving similar molecular functions, biological processes, and protein classes.

AIM:To identify regions of aberrant DNA methylation in acute lymphoblastic leukemia (ALL) cells of different subtypes on a genome-wide scale.MATERIALS & METHODS:Whole-genome bisulfite sequencing (WGBS) was used to determine the DNA methylation levels in cells from four pediatric ALL patients of different subtypes. The findings were confirmed by 450k DNA methylation arrays in a large patient set.RESULTS:Compared with mature B or T cells WGBS detected on average 82,000 differentially methylated regions per patient. Differentially methylated regions are enriched to CpG poor regions, active enhancers and transcriptional start sites. We also identified approximately 8000 CpG islands with variable intermediate DNA methylation that seems to occur as a result of stochastic de novo methylation.CONCLUSION:WGBS provides an unbiased view and novel insights into the DNA methylome of ALL cells.

To characterize the mutational patterns of acute lymphoblastic leukemia (ALL) we performed deep next generation sequencing of 872 cancer genes in 172 diagnostic and 24 relapse samples from 172 pediatric ALL patients. We found an overall greater mutational burden and more driver mutations in T-cell ALL (T-ALL) patients compared to B-cell precursor ALL (BCP-ALL) patients. In addition, the majority of the mutations in T-ALL had occurred in the original leukemic clone, while most of the mutations in BCP-ALL were subclonal. BCP-ALL patients carrying any of the recurrent translocations ETV6-RUNX1, BCR-ABL or TCF3-PBX1 harbored few mutations in driver genes compared to other BCP-ALL patients. Specifically in BCP-ALL, we identified ATRX as a novel putative driver gene and uncovered an association between somatic mutations in the Notch signaling pathway at ALL diagnosis and increased risk of relapse. Furthermore, we identified EP300, ARID1A and SH2B3 as relapse-associated genes. The genes highlighted in our study were frequently involved in epigenetic regulation, associated with germline susceptibility to ALL, and present in minor subclones at diagnosis that became dominant at relapse. We observed a high degree of clonal heterogeneity and evolution between diagnosis and relapse in both BCP-ALL and T-ALL, which could have implications for the treatment efficiency.

We applied genome-wide allele-specific expression analysis of monocytes from 188 samples. Monocytes were purified from white blood cells of healthy blood donors to detect cis-acting genetic variation that regulates the expression of long non-coding RNAs. We analysed 8929 regions harboring genes for potential long non-coding RNA that were retrieved from data from the ENCODE project. Of these regions, 60% were annotated as intergenic, which implies that they do not overlap with protein-coding genes. Focusing on the intergenic regions, and using stringent analysis of the allele-specific expression data, we detected robust cis-regulatory SNPs in 258 out of 489 informative intergenic regions included in the analysis. The cis-regulatory SNPs that were significantly associated with allele-specific expression of long non-coding RNAs were enriched to enhancer regions marked for active or bivalent, poised chromatin by histone modifications. Out of the IncRNA regions regulated by cis-acting regulatory SNPs, 20% (n = 52) were co-regulated with the closest protein coding gene. We compared the identified cis-regulatory SNPs with those in the catalog of SNPs identified by genome-wide association studies of human diseases and traits. This comparison identified 32 SNPs in loci from genome-wide association studies that displayed a strong association signal with allele-specific expression of non-coding RNAs in monocytes, with p-values ranging from 6.7x10(-7) to 9.5x10(-89). The identified cis-regulatory SNPs are associated with diseases of the immune system, like multiple sclerosis and rheumatoid arthritis.

To detect genes with CpG sites that display methylation patterns that are characteristic of acute lymphoblastic leukemia (ALL) cells, we compared the methylation patterns of cells taken at diagnosis from 20 patients with pediatric ALL to the methylation patterns in mononuclear cells from bone marrow of the same patients during remission and in non-leukemic control cells from bone marrow or blood. Using a custom-designed assay, we measured the methylation levels of 1,320 CpG sites in regulatory regions of 413 genes that were analyzed because they display allele-specific gene expression (ASE) in ALL cells. The rationale for our selection of CpG sites was that ASE could be the result of allele-specific methylation in the promoter regions of the genes. We found that the ALL cells had methylation profiles that allowed distinction between ALL cells and control cells. Using stringent criteria for calling differential methylation, we identified 28 CpG sites in 24 genes with recurrent differences in their methylation levels between ALL cells and control cells. Twenty of the differentially methylated genes were hypermethylated in the ALL cells, and as many as nine of them (AMICA1, CPNE7, CR1, DBC1, EYA4, LGALS8, RYR3, UQCRFS1, WDR35) have functions in cell signaling and/or apoptosis. The methylation levels of a subset of the genes were consistent with an inverse relationship with the mRNA expression levels in a large number of ALL cells from published data sets, supporting a potential biological effect of the methylation signatures and their application for diagnostic purposes.

Coronary artery disease (CAD) is the commonest cause of death. Here, we report an association analysis in 63,746 CAD cases and 130,681 controls identifying 15 loci reaching genome-wide significance, taking the number of susceptibility loci for CAD to 46, and a further 104 independent variants (r(2) < 0.2) strongly associated with CAD at a 5% false discovery rate (FDR). Together, these variants explain approximately 10.6% of CAD heritability. Of the 46 genome-wide significant lead SNPs, 12 show a significant association with a lipid trait, and 5 show a significant association with blood pressure, but none is significantly associated with diabetes. Network analysis with 233 candidate genes (loci at 10% FDR) generated 5 interaction networks comprising 85% of these putative genes involved in CAD. The four most significant pathways mapping to these networks are linked to lipid metabolism and inflammation, underscoring the causal role of these activities in the genetic etiology of CAD. Our study provides insights into the genetic basis of CAD and identifies key biological pathways.