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Despite the critical role of DNA methylation, clinical implementations harnessing its promise have not been described in acute myeloid leukemia. Utilizing DNA methylation from 3314 leukemia patient samples across 11 harmonized cohorts, we describe the Acute Leukemia Methylome Atlas, which includes robust models capable of accurately predicting AML subtypes. A genome-wide prognostic model as well as a targeted panel of 38 CpGs significantly predict five-year survival in our pediatric and adult test cohorts. To accelerate rapid clinical utility, we develop a specimen-to-result protocol that uses long-read nanopore sequencing and machine learning to characterize patients’ whole genomes and epigenomes. Clinical validation on patient samples confirms high concordance between epigenomic signatures and genomic lesions, though uniquely rare karyotypes remained challenging due to limited available training data. These results unveil the potential for increased affordability, speed, and accuracy for patients in need of complex molecular diagnosis and prognosis. The prognostic and diagnostic roles of DNA methylation in acute myeloid leukemia remains to be explored. Here, the authors develop DNA methylation-based models for the prediction of five-year survival and clinical molecular subtypes in both pediatric and adult test cohorts.

Background Cancer is the leading cause of disease-related mortality in children. Causes of leukemia, the most common form, are largely unknown. Growing evidence points to an origin in-utero , when global redistribution of DNA methylation occurs driving tissue differentiation. Methods Epigenome-wide DNA methylation was profiled in surrogate (blood) and target (bone marrow) tissues at birth, diagnosis, remission and relapse of pediatric pre-B acute lymphoblastic leukemia (pre-B ALL) patients. Double-blinded analyses was performed between prospective cohorts extending from birth to diagnosis and retrospective studies backtracking from clinical disease to birth. Validation was carried out using independent technologies and populations. Results The imprinted and immuno-modulating VTRNA2-1 was hypermethylated (FDR<0.05) at birth in nested cases relative to controls in all tested populations (totaling 317 cases and 483 controls), including European and Hispanic ancestries. VTRNA2-1 methylation was stable over follow-up years after birth and across surrogate, target and other tissues ( n =5,023 tissues; 30 types). When profiled in leukemic tissues from two clinical cohorts (totaling 644 cases), VTRNA2-1 methylation exhibited higher levels at diagnosis relative to controls, it reset back to normal levels at remission, and then re-increased to above control levels at relapse. Hypermethylation was significantly associated with worse pre-B ALL patient survival and with reduced VTRNA2-1 expression ( n =2,294 tissues; 26 types), supporting a functional and translational role for VTRNA2-1 methylation. Conclusion This study provides proof-of-concept to detect at birth epigenetic precursors of pediatric pre-B ALL. These alterations were reproducible with different technologies, in three continents and in two ethnicities, and can offer biomarkers for early detection and prognosis as well as actionable targets for therapy. Key points • Precursors of pediatric acute lymphoblastic leukemia may be of epigenetic origin, detectable since birth and affecting patient prognosis. • These epigenetic precursors can be robust over several years and across several populations, ethnicities and surrogate and target tissues.

Acute lymphoblastic leukemia (ALL) is the most prevalent cancer in children, and despite considerable progress in treatment outcomes, relapses still pose significant risks of mortality and long-term complications. To address this challenge, we employed a supervised machine learning technique, specifically random survival forests, to predict the risk of relapse and mortality using array-based DNA methylation data from a cohort of 763 pediatric ALL patients treated in Nordic countries. The relapse risk predictor (RRP) was constructed based on 16 CpG sites, demonstrating c-indexes of 0.667 and 0.677 in the training and test sets, respectively. The mortality risk predictor (MRP), comprising 53 CpG sites, exhibited c-indexes of 0.751 and 0.754 in the training and test sets, respectively. To validate the prognostic value of the predictors, we further analyzed two independent cohorts of Canadian ( n  = 42) and Nordic ( n  = 384) ALL patients. The external validation confirmed our findings, with the RRP achieving a c-index of 0.667 in the Canadian cohort, and the RRP and MRP achieving c-indexes of 0.529 and 0.621, respectively, in an independent Nordic cohort. The precision of the RRP and MRP models improved when incorporating traditional risk group data, underscoring the potential for synergistic integration of clinical prognostic factors. The MRP model also enabled the definition of a risk group with high rates of relapse and mortality. Our results demonstrate the potential of DNA methylation as a prognostic factor and a tool to refine risk stratification in pediatric ALL. This may lead to personalized treatment strategies based on epigenetic profiling.

Pediatric acute myeloid leukemia (AML) is a heterogeneous disease composed of clinically relevant subtypes defined by recurrent cytogenetic aberrations. The majority of the aberrations used in risk grouping for treatment decisions are extensively studied, but still a large proportion of pediatric AML patients remain cytogenetically undefined and would therefore benefit from additional molecular investigation. As aberrant epigenetic regulation has been widely observed during leukemogenesis, we hypothesized that DNA methylation signatures could be used to predict molecular subtypes and identify signatures with prognostic impact in AML. To study genome-wide DNA methylation, we analyzed 123 diagnostic and 19 relapse AML samples on Illumina 450k DNA methylation arrays. We designed and validated DNA methylation-based classifiers for AML cytogenetic subtype, resulting in an overall test accuracy of 91%. Furthermore, we identified methylation signatures associated with outcome in t(8;21)/RUNX1-RUNX1T1, normal karyotype, and MLL/KMT2A-rearranged subgroups (p < 0.01). Overall, these results further underscore the clinical value of DNA methylation analysis in AML.

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.

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.

Pediatric acute lymphoblastic leukemia (ALL), the most common childhood cancer, is characterized by aberrant lymphopoiesis that disrupts normal hematopoiesis in the bone marrow. Diverse genetic drivers, and epigenetic and transcriptomic alterations contribute to disease heterogeneity. By applying standard clinical diagnostic procedures and next generation sequencing (NGS) methods, 22 B-cell precursor ALL (BPC-ALL) and 15 T-cell ALL (T-ALL) subtypes have been identified. The aim of this thesis is to integrate molecular, clinical, and ex vivo drug response data from pediatric ALL patients diagnosed and treated in the Nordic countries to identify cross-modal interactions underlying therapy responses and clinical outcomes. Ultimately, this work aims to provide insights that support treatment optimization, with the dual goal of preventing relapses and reducing toxicity. The datasets included DNA methylation (DNAm), gene expression (GEX), somatic single nucleotide variants (SNVs), fusion genes, copy number alterations (CNA), ex vivo drug response (EVDR) data, and clinical variables from over 1,000 patients. Bioinformatic approaches included supervised classification, survival analysis and modelling, differential DNAm and GEX analyses, and data integration.In paper I, we built ALLIUM, a multimodal machine learning classifier that predicts 17 ALL subtypes using either DNAm or GEX data. Applied to 1,131 ALL patients, ALLIUM successfully subtyped previously unclassified cases, usually aligning with existent molecular findings. In paper II, we designed two DNAm-based risk prediction models, the relapse risk predictor (RRP) and the mortality risk predictor (MRP), which outperformed the traditional risk stratification models and generalized well to independent cohorts. In paper III, we integrated EVDR data with DNAm and GEX data and found pre-treatment epigenetic and transcriptomic differences between ex vivo resistant and sensitive BCP-ALL patients. The resulting signatures were enriched in pathways commonly altered in cancer. In paper IV, we utilized a framework to integrate DNAm, GEX, SNV, and EVDR data from 1,231 BCP-ALL patients. We identified cross-modal signatures and associations, reducing feature dimensionality and enabling pathway, correlation, and network analyses. These signatures improved risk modelling beyond standard clinical stratification.Together, these studies enhance our understanding of the molecular underpinnings of ALL and may form a basis for supporting future clinical decision-making, particularly in ambiguous cases.

PURPOSE Recent advances in machine learning (ML) have led to the development of classifiers that predict molecular subtypes of acute lymphoblastic leukemia (ALL) using RNA sequencing (RNA-seq) data. While these models have shown promising results, they often lack robust performance guarantees. The aim of this study was three-fold: to quantify the uncertainty of these classifiers; to provide prediction sets that control the false negative rate (FNR); and to perform implicit reduction by transforming incorrect predictions into uncertain predictions. METHODS Conformal prediction is a distribution-agnostic framework for generating statistically calibrated prediction sets whose size reflects model uncertainty. In this study, we applied an extension called conformal risk control to ALLIUM, an RNA-seq ALL subtype classifier. Leveraging RNA-seq data from 1042 patient samples taken at diagnosis, we developed a multi-class conformal predictor, ALLCoP, which generates statistically guaranteed FNR-controlled prediction sets. RESULTS ALLCoP was able to create prediction sets with specified FNR tolerances ranging from 7.5-30%. In a validation cohort, ALLCoP successfully reduced the FNR of the ALLIUM classifier from 8.95% to 3.5%. For cases whose subtype was not previously known, the use of ALLCoP was able to reduce the occurrence of empty predictions from 37% to 17%. Notably, up to 34% of the multiple-class prediction sets included the PAX5alt subtype, suggesting that increased prediction set size may reflect secondary aberrations and biological complexity, contributing to classifier uncertainty. Finally, ALLCoP was validated on two additional RNA-seq ALL subtype classifiers, ALLSorts and ALLCatchR. CONCLUSION Our results highlight the potential of conformal prediction in enhancing the use of oncological RNA-seq subtyping classifiers and also in uncovering additional molecular aberrations of potential clinical importance.Competing Interest StatementThe authors have declared no competing interest.

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.