Ex Vivo Drug Responses and Molecular Profiles of 597 Pediatric Acute Lymphoblastic Leukemia Patients

Ex Vivo Drug Responses and Molecular Profiles of 597 Pediatric Acute Lymphoblastic Leukemia Patients | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Ex Vivo Drug Responses and Molecular Profiles of 597 Pediatric Acute Lymphoblastic Leukemia Patients Anna Pia Enblad , View ORCID Profile Olga Krali , Henrik Gezelius , Anders Lundmark , Kristin Blom , Claes Andersson , Josefine Palle , Britt-Marie Frost , Samppa Ryhänen , Trond Flægstad , Ólafur G Jónsson , Kjeld Schmiegelow , Mats Heyman , Arja Harila , Peter Nygren , Rolf Larsson , Gudmar Lönnerholm , View ORCID Profile Jessica Nordlund doi: https://doi.org/10.1101/2024.12.17.24319138 Anna Pia Enblad 1 Department of Medical Sciences, Uppsala University , Uppsala, Sweden 2 Science for Life Laboratory, Uppsala University , Uppsala, Sweden 3 Department of Women’s and Children’s Health, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Olga Krali 1 Department of Medical Sciences, Uppsala University , Uppsala, Sweden 2 Science for Life Laboratory, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Olga Krali Henrik Gezelius 1 Department of Medical Sciences, Uppsala University , Uppsala, Sweden 2 Science for Life Laboratory, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anders Lundmark 1 Department of Medical Sciences, Uppsala University , Uppsala, Sweden 2 Science for Life Laboratory, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kristin Blom 1 Department of Medical Sciences, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Claes Andersson 1 Department of Medical Sciences, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Josefine Palle 3 Department of Women’s and Children’s Health, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Britt-Marie Frost 3 Department of Women’s and Children’s Health, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Samppa Ryhänen 4 Division of Hematology, Oncology and Stem Cell Transplantation, Children’s Hospital, and Pediatric Research Center, The New Children’s Hospital, University of Helsinki and Helsinki University Central Hospital , Helsinki, Finland 10 For the Nordic Society of Pediatric Hematology and Oncology (NOPHO) Find this author on Google Scholar Find this author on PubMed Search for this author on this site Trond Flægstad 5 Department of Pediatrics, Tromsø University and University Hospital , Tromsø, Norway 10 For the Nordic Society of Pediatric Hematology and Oncology (NOPHO) Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ólafur G Jónsson 6 Pediatric Hematology-Oncology, Children’s Hospital, Barnaspitali Hringsins, Landspitali University Hospital , Reykjavik, Iceland 10 For the Nordic Society of Pediatric Hematology and Oncology (NOPHO) Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kjeld Schmiegelow 7 Department of Pediatrics and Adolescent Medicine, Rigshospitalet, and the Medical Faculty, Institute of Clinical Medicine, University of Copenhagen , Copenhagen, Denmark 10 For the Nordic Society of Pediatric Hematology and Oncology (NOPHO) Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mats Heyman 8 Childhood Cancer Research Unit, Karolinska Institutet, Astrid Lindgren Children’s Hospital, Karolinska University Hospital , Stockholm, Sweden 10 For the Nordic Society of Pediatric Hematology and Oncology (NOPHO) Find this author on Google Scholar Find this author on PubMed Search for this author on this site Arja Harila 3 Department of Women’s and Children’s Health, Uppsala University , Uppsala, Sweden 10 For the Nordic Society of Pediatric Hematology and Oncology (NOPHO) Find this author on Google Scholar Find this author on PubMed Search for this author on this site Peter Nygren 9 Department of Immunology, Genetics and Pathology, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rolf Larsson 1 Department of Medical Sciences, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gudmar Lönnerholm 3 Department of Women’s and Children’s Health, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jessica Nordlund 1 Department of Medical Sciences, Uppsala University , Uppsala, Sweden 2 Science for Life Laboratory, Uppsala University , Uppsala, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jessica Nordlund For correspondence: jessica.nordlund{at}medsci.uu.se Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF 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 (RFS, p < 0.05). Molecular profiling identified pre-treatment 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. ABCB1 ). 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. Key points Ex vivo drug response profiling provides predictors of outcome that complement standard clinical risk stratification, identifying high-risk phenotypes not captured by traditional methods. Pre-treatment transcriptional and epigenetic profiles reveal resistance-primed molecular states detectable at diagnosis, mechanisms driving treatment resistance and offer potential therapeutic targets to counteract drug tolerance. Introduction Therapeutic advances in pediatric acute lymphoblastic leukemia (ALL) have significantly improved patient outcomes, with modern treatment protocols leveraging risk stratification based on clinical presentation, genetic markers, and minimal residual disease (MRD). 1 , 2 However, 10-20% of patients still relapse or develop resistant disease 3 , including a notable proportion of those initially classified as low-risk. 2 , 4 Moreover, while most patients are cured, the intense treatment regimens often result in significant short- and long-term toxicities. 5 – 8 Evidence indicates many standard-risk patients could benefit from less intensive chemotherapy, highlighting the limitations of current risk stratification methods. 9 , 10 Emerging immune therapies, such as blinatumomab and CAR-T cells, have shown promise in the relapsed and refractory settings 11 , 12 and are increasingly explored as alternatives or supplements in ALL therapy. 13 However, the results of such options are affected by the remaining tumor burden 14 , 15 , thus initial response to chemotherapeutic agents is key for achieving lasting remission. Addressing these challenges requires a deeper understanding of the mechanisms underlying treatment response and resistance. Ex vivo drug screening evaluates the response of patient-derived leukemia cells to therapeutic agents in vitro , offering a direct measure of cellular drug efficacy. This approach has potential as a predictor of clinical outcomes in pediatric ALL. 16 – 19 Combining ex vivo drug screening with omics-based assays has demonstrated the clinical feasibility of “functional precision medicine”, enabling the identification of therapeutic options across various malignancies, including pediatric ALL. 20 – 23 However, these efforts have primarily focused on relapsed, treatment-refractory, or high-risk cases. While whole-genome sequencing is a powerful tool for identifying genetic variants associated with disease progression 24 , it fails to capture the full spectrum of regulatory mechanisms and cellular processes driving drug resistance. 25 Among these, epigenetic alterations, which are increasingly recognized a contributors to drug resistance 26 – 28 , remain under explored. Promising work has linked epigenetic profiles to ex vivo drug resistance in pediatric ALL. 25 , 29 However, these studies have largely focused on single-drug responses, which do not reflect the multi-drug chemotherapy regimens used in clinical practice. Expanding these investigations, particularly in diagnostic samples and multi-drug contexts will be essential to evaluate the future utility of such approaches for functional precision medicine strategies. In this study, we performed ex vivo drug response profiling on a Nordic cohort of diagnostic samples from 597 pediatric BCP-ALL patients, integrating the responses to 10 therapeutic agents with transcriptomic and epigenomic data. This approach provided clinically relevant risk stratification information and identified molecular markers and pathways associated with drug resistance. These findings underscore the potential of integrative strategies to uncover pre-treatment molecular states that predispose patients to drug resistance. Methods Patient cohort The study consisted of 597 children (1–18 years of age) diagnosed with BCP-ALL between 1992 and 2008 at Nordic centers for pediatric oncology. Patients were treated according to the Nordic Society of Pediatric Hematology and Oncology (NOPHO) treatment protocols (NOPHO ALL-92 or NOPHO ALL-2000) and were stratified into risk groups at the time of diagnosis: standard risk (SR), intermediate risk (IR) or high risk (HR) according to established criteria 30 , including age, white blood cell count at diagnosis, extramedullary involvement and molecular genetic subtype. The patients and/or guardians provided informed consent. The study was approved by the Regional Ethical Review Board in Uppsala, Sweden, and was conducted according to the guidelines of the Declaration of Helsinki. Clinical outcome Follow up data and clinical outcome was reported by the participating clinics to the NOPHO registry at the Childhood Cancer Research Unit in Stockholm. Complete remission (CR) was defined as less than 5% leukemic blasts in a representative bone marrow sample and no other manifestation of leukemia. Relapse was defined as more than 5% blast cells in bone marrow and/or manifestation of leukemia at any other location. Relapse-free survival (RFS) was defined as the time from diagnosis to leukemic relapse at any location and overall survival (OS) was defined as the time from diagnosis to death by any cause. Sample processing and ex vivo drug screening procedure Bone marrow samples were collected at diagnosis in heparinized glass tubes and sent to Uppsala, Sweden for analysis. Ex vivo drug responses were assessed by the fluorometric microculture cytotoxicity assay (FMCA) 31 and the experimental procedures including handling of ALL samples follows Frost et al. 17 In brief, FMCA generates a survival index (SI%) for each drug per patient, where a high numerical SI% indicates ex vivo resistance to the drug. Ten drugs were tested at concentrations chosen to give high SI% variability between samples, based on previous work. 32 The drugs represent different drug classes, including compounds used in the NOPHO ALL-92 and ALL-2000 treatment protocols (dexamethasone, prednisolone, doxorubicin, vincristine, L-asparaginase, cytarabine, thioguanine) as well as three additional drugs (mitoxantrone, amsacrine, etoposide). Drugs, concentrations, and the number of patients screened per drug summarized in Supplemental table 1. DNA methylation and RNA sequencing data DNA and RNA were extracted as previously described from drug-naïve diagnostic samples. 33 Genome-wide DNA methylation (DNAm) data were generated with the Infinium HumMeth450K BeadChip assay (450k array, n = 382 patients) or the MethylationEPIC v2.0 BeadChip assay (EPIC array, n = 55 patients). 34 , 35 RNA-seq data (GEX) was generated for 119 patients. 34 , 36 The computational approaches to analyze the DNAm and GEX datasets are described in detail previously 34 , 35 and further summarized in the Supplemental methods. After filtering and normalization, a total of 372,264 CpG sites (DNAm) and 15,019 genes (GEX) were available downstream analyses. Molecular genetic subtype revision At the time of ALL diagnosis, patients were divided into subtype groups based on immunophenotype and recurrent genetic aberrations as established by FISH, PCR, or G-band karyotyping. 30 Since the patients were diagnosed before the introduction of high throughput sequencing techniques, complete information was unavailable for 224 patients (B-other, unknown, or uncertain subtype). We previously performed additional molecular analysis and machine learning-based classification 34 , resulting in subtypes re-classification of 106 patients to the current International Consensus Classification. 37 A summary of molecular subtypes is provided in Table 1 . View this table: View inline View popup Download powerpoint Table 1. Patient cohort characteristics. Bioinformatics and statistical analysis Data processing and statistical analyses were performed using RStudio (RStudio 2022.07.2 “Spotted Wakerobin” and RStudio 2023.03.15 “Shortstop Beagle”) or Python. For all analyses, a p-value < 0.05 was considered statistically significant, if not otherwise specified. Survival analysis was performed using the R package ggsurvfit to create Kaplan-Meier curves. Log-rank tests were used to test for differences in survival between groups. Multivariate Cox regression was performed using the R package survival , with ex vivo drug response group, risk group and sex as covariates. Unsupervised hierarchical clustering was performed using the Python package scipy . Missing values for ex vivo drug responses were imputed patient-wise with the most frequent response category for each patient (sensitive, intermediate or resistant). Spearman’s rank correlation coefficient was performed to assess the relationship between drug responses (SI%). Differentially methylated CpG sites (DMCs) were determined in the R package limma 38 using a FDR adjusted p-value of 0.15 between groups. Differentially expressed genes (DEGs) were determined using DeSeq2 39 using FDR adjusted p-value 1 between groups. ALL molecular subtype was adjusted for when testing for DMCs and DEGs. DMCs were annotated to genes using GREAT 4.0.4. 40 Gene set analysis was performed using the MSigDB Hallmark 41 and gene ontology biological processes. 42 Single-cell analysis Bone marrow samples from four BCP-ALL patients diagnosed with ETV6::RUNX1- positive pre-B ALL were selected for single-cell RNA-sequencing (scRNA-seq). Vital frozen ampules after Ficoll gradient separation were collected from NOPHO Leukemia Biobank in Uppsala, Sweden. The cells were thawed and seeded to microwell plates. Each of the wells were incubated with 0.45 vol% DMSO for 72 hours before collection. Cells were harvested by gentle centrifugation (300 rpm 10 minutes) and washed twice with PBS. ScRNA-seq libraries were generated using the 10x Genomics FLEX kit following the manufacturer’s protocol. The libraries were sequenced on an Illumina NovaSeq X plus 10B flowcell with the following read setup: 28 bp -10 bp-10 bp-90 bp. Cellranger 8.0.1 was used to demultiplex single cell sequence data into UMI count matrices. Downstream analysis of expression data was performed in R 4.4.1 using Seurat 5.1.0 . 43 Cells were called using the inflection point determined by barcodeRanks from the package DropletUtils 1.25.2. 44 Remaining cells with a mitochondrial gene percentage greater than ten percent were removed. Cell types were assigned using BoneMarrowMap 0.1.0. 45 Results Patient characteristics A total of 597 children with BCP-ALL, treated according to the NOPHO ALL-1992 (45.6%, n = 272) or ALL-2000 (54.4%, n = 325) protocols, were included in the study ( Table 1 ). Leukemic relapse occurred in 134 patients, with proportions varying between risk groups; 21% of SR patients, 18% of IR patients and 32% of HR patients relapsed. During follow-up, 82 patients died; 61 after relapse, ten in complete remission, seven following induction failure, three following secondary malignant neoplasm and one due to resistant disease. The outcomes of the selection of patients in this study is similar to what was reported overall for all patients treated on NOPHO ALL-1992 and -2000. 30 For most of the drugs ∼90% of the patients were screened for ex vivo response, however the proportion was lower for cytarabine (73%), mitoxantrone (72%), and L-asparaginase (42%) (Supplemental table 1). Ex vivo drug resistance is an independent predictor of clinical outcome Patients were stratified into three ex vivo drug response categories (sensitive, intermediate, and resistant) based on tertiles of surviving cells (SI%) for each drug ( Figure 1A ). The proportions of samples in each tertile group were similar across countries of origin, indicating that transportation time did not impact the ex vivo screening results (Supplemental table 2). Download figure Open in new tab Figure 1. Ex-vivo drug response and clinical outcome in pediatric BCP-ALL patients. A) SI% distribution (y-axis) per drug (x-axis). The patients are color-coded by their ex vivo drug response group (sensitive, intermediate, resistant). B) Kaplan Meier curves depicting relapse free survival, with patients stratified by the ex vivo drug response groups (sensitive, intermediate or resistant), p-values calculated using log-rank test. Forest plots showing hazard ratio and 95% confidence interval for relapse in groups with different ex vivo drug response, adjusted for sex and risk group. C) Relationship between the distribution of patients in different ex vivo drug response groups and the different clinical risk groups. D) Unsupervised hierarchical clustering of patients based on ex vivo drug response to the ten individual drugs, rendering three main clusters deemed multi-drug sensitive, intermediate and resistant. E) Relationship between the distribution of patients in different levels of ex vivo multi-drug response and the different clinical risk groups. F) Correlation plot displaying the Spearman’s rank correlation coefficient pairwise between different drugs, to assess the relationship between drug responses. Dexa = dexamethasone, pred = prednisolone, cyta = cytarabine, thio = thioguanine, doxo = doxorubicin, ams = amsacrine, eto = etoposide, mito = mitoxantrone, asp = L-asparaginase, vcr = vincristine. Patients in the ex vivo sensitive group demonstrated superior relapse-free survival (RFS) compared to those in the resistant group for a majority of the drugs, while the outcomes for the intermediate group varied by drug (log rank test p < 0.01; Figure 1B ). Notably, ex vivo responses to dexamethasone, doxorubicin, and thioguanine were significantly associated with overall survival (OS, log rank test p < 0.01, Supplemental Figure 1). These associations remained significant in multivariate Cox regression analyses, adjusted for clinical risk group and sex ( Figure 1B , Supplemental Figure 1). Next, we assessed the distribution of clinical risk groups within the three-tiered ex vivo response categories. Surprisingly, there was no enrichment of high-risk patients in the ex vivo drug-resistant groups. Instead, resistant patients were evenly distributed across all clinical risk groups ( Figure 1C ). When responses to all 10 drugs were considered collectively, a pattern of ex vivo multidrug resistance emerged ( Figure 1D ). Similar to single-drug observations, multidrug resistance was not associated with clinical risk groups ( Figure 1E ), but was strongly correlated with inferior clinical outcomes, including RFS and OS (Supplemental Figure 2). Additionally, patients displayed similar ex vivo responses to drugs with shared mechanisms of action ( Figure 1F ), suggesting that intrinsic cellular properties influence drug responses based on their mechanisms. Together, these findings demonstrate that ex vivo drug responses reflect in vivo drug efficacy and may complement traditional clinical risk stratification methods. Pre-treatment molecular profiles are associated with ex vivo response We next investigated molecular profiles of drug-naïve diagnostic samples in relation to ex vivo responses. To focus on the most pronounced differences, the resistant and sensitive groups were compared, excluding samples from the intermediate response group from this analysis. This analysis identified 1,779 unique DMCs and 1,158 unique DEGs between the sensitive and resistant groups across the 10 drugs ( Figure 2A-D , Supplemental Tables 3 and 4). Notably, the most pronounced differences in molecular profiles were observed in cells resistant to the glucocorticoids and topoisomerase II inhibitors, while few DMCs or DEGs were correlated with response to antimetabolites and L-asparaginase. Download figure Open in new tab Figure 2. Molecular profiles related to ex vivo drug responses. A) Numbers of differentially methylated CpG sites (DMCs) per drug, with total numbers indicated in parenthesis and number of hypo- and hypermethylated CpG sites shown left and right of the bar graph. B) Pairwise comparison between drugs displaying the number of overlapping DMCs between drugs, colored according to percentage of DMCs overlapping. C) Numbers of differentially expressed genes (DEGs) per drug, with total numbers indicated in parenthesis and number of downregulated and overexpressed genes shown left and right of the bar graph. D) Pairwise comparison between drugs displaying the number of overlapping DEGs between drugs, colored according to percentage of DEGs overlapping. The number of drug-related DMCs varied by drug, ranging from none for L-asparaginase and cytarabine to 1,200 for etoposide ( Figure 2A ). These DMCs were enriched in low-CpG content, open sea regions (Supplemental figure 3). Notably, 24% of DMCs overlapped between at least two drugs, with the greatest overlap observed among drugs with similar mechanisms of action ( Figure 2B ). For example, 34 DMCs were shared by the three topoisomerase II inhibitors (doxorubicin, etoposide, amacrine) and 66 by the glucocorticoids (dexamethasone, prednisolone). Similarly, the number of DEGs varied by drug, from 21 for L-asparaginase to 682 for etoposide ( Figure 2C ). Up to 49% of DEGs were shared between drugs, with notable overlaps observed for glucocorticoids and topoisomerase II inhibitors ( Figure 2D ). For instance, 398 DEGs were shared between etoposide and amsacrine, 75 between dexamethasone and prednisolone, and a significant overlap was also found between vincristine, glucocorticoids, and topoisomerase II inhibitors ( Figure 2D ). These findings suggest distinct molecular profiles in drug-naïve diagnostic samples, with shared, as well as drug-specific signatures. To explore the interplay between DNA methylation and gene expression, we focused on 47 genes with DMCs and DEGs linked to the same drug (Supplemental table 5). Most were associated with resistance to etoposide, amsacrine, dexamethasone and doxorubicin. Notably, three genes that were hypomethylated and upregulated in the resistant samples where ABCB1 , encoding the multidrug resistance-associated ABC transporter P-gp1; ADHFE1 , a MYC-related oncogene linked to cell survival; and CXCL2 that has been associated with increased leukemic cell survival and poor prognosis in ALL and other cancer s. 46 – 48 These finding further emphasize the potential mechanistic link between epigenetic regulation and gene expression in the context of intrinsic cellular resistance. Pathways priming resistance To identify pathways influencing drug response, we performed gene set analysis, for the genes mapped to DMCs and DEGs independently. Overlap in pathways was observed within and across drug classes and data modality ( Figure 3A , Supplemental Table 6). Among the top-ranking pathways was upregulation of KRAS signaling (including e.g. ABCB1, GS02) , identified for cells resistant to topoisomerase II inhibitors, as has been previously shown for etoposide 49 and doxorubicin. 50 Additionally, heme metabolism was affected, with for example upregulation of OSBP2 and ATP6V0A1 in resistant patients ( Figure 3B ). These two genes have previously been linked to chemoresistance. 51 , 52 Immune response-related pathways were also upregulated in patients resistant to topoisomerase II inhibitors and vincristine. Increased inflammatory activity, previously tied to chemotherapy resistance and poor outcomes in leukemia, supports the relevance of these findings. 53 Download figure Open in new tab Figure 3. Pathways priming resistance in BPC-ALL. A) The top three most significantly enriched pathways per drug (lowest FDR adjusted p-value) and, in case of the same pathways being found for other drugs, this is also displayed, even if it was not in the top three most significantly enriched pathways for that drug. Round shape indicates that the pathway was related DMCs and triangle shape that the pathway was associated with genes related to DEGs. Size indicates how many genes were enriched to each pathway, colored according to the -log 10 FDR score. B) Heatmaps showing the different expression levels of genes (log2 expression) related to heme metabolism and C) T-cell pathways (bottom), between glucocorticoid (dexamethasone and prednisolone) resistant and sensitive patients. Interestingly, pathways related to T-cell linage were enriched in the samples resistant to four important anti-leukemia drugs (dexamethasone, prednisolone, thioguanine and vincristine). The associated genes were either upregulated or hypomethylated in resistant patients and included genes typically expressed by non-B-cell lineage immune cells (T-cells and NK-cells), such as ITK and TXK ( Figure 3C ). Validation by single-cell RNA-sequencing To confirm that the DEGs identified in the drug-related pathways were intrinsic to leukemic cells and not artifacts due to other cell types present in bulk samples, we performed single-cell RNA sequencing (scRNA-seq) on diagnostic bone marrow samples from four ALL patients ( Figure 4A , patient characteristics in Supplemental table 7). While most cells in these samples were annotated to the B-cells linage, consistent with the leukemic population, also additional cell types could be detected ( Figure 4B ). Still, key genes from our pathway analysis, including ABCB1 and G0S2 (KRAS pathway) , OSBP2 and ATP6V0A1 (heme metabolism), ITK and TXK (T-cell related genes) were expressed in the B-lineage leukemic cells ( Figure 4C ). Notably, we observed variability in gene expression across the cells from the four patients, reflecting heterogeneity within the leukemic cell population. These results support that aberrant gene expression identified in bulk analyses originates from BCP-ALL cells rather than contamination by non-leukemic cells, strengthening the relevance of our findings. Download figure Open in new tab Figure 4. Single-cell (sc) profiling of pediatric BCP-ALL samples. A) sc-UMAP plot color-coded by patient ID. B) sc-UMAP plot colored by cell linage. C) UMAP plots demonstrating gene-specific expression for genes involved in KRAS (ABCB1, G0S2 ), heme ( OSBP2 , ATP6V0A1 ) and T-cell related pathways ( ITK and TXK ). Discussion In this study, we combined ex vivo drug screening with molecular profiling of drug-naïve samples from 597 pediatric BCP-ALL patients. Our findings highlight two key insights: first, ex vivo drug responses serve as robust predictors of clinical outcomes; second, molecular signatures identified in diagnostic samples reveal intrinsic cellular states associated with increased tolerance or resistance to treatment. Together, these observations strengthen the case for functional precision medicine in treating ALL. Early identification of patients resistant to conventional therapies through ex vivo approaches could potentially enable the upfront recognition of high-risk cellular phenotypes and guide the selection of alternative therapeutic strategies. Our findings provide orthogonal evidence supporting the potential benefit integrating such approaches for ALL. Consistent with previous studies, out data suggest that these types of functional assays offer additional information that can cooperate with genetic analyses risk grouping. 20 – 22 , 54 – 56 Further, our observation of patients resistant to one drug were more likely to exhibit resistance across multiple drugs with different mechanisms, underscoring the relevance of addressing multidrug resistance in ALL. 57 , 58 The idea of drug response-associated states that exists prior to any chemotherapy have been seen before. 59 Using bulk molecular profiling (RNA-seq and DNAm arrays) in drug-naïve samples, we identified putative molecular states, priming cells for resistance, highlighting several candidate genes and pathways for future investigation. Among the genes we identified was ABCB1 , encoding P-gp1, an efflux transporter that mediates the efflux of diverse drugs from cancer cells. 60 , 61 Notably, we found ABCB1 to be variably expressed in BCP-ALL cells, with significantly higher levels in resistance-primed patients. These findings prioritize ABC modulators, including specifically modulators of ABCB1 62 for further exploration as a potential treatment in combination with standard chemotherapeutic drugs to augment therapeutic efficacy in BCP-ALL. We also found three pathways of particular interest: KRAS signaling, heme metabolism and expression of T-cell related genes. Mutations affecting the Ras pathway, including KRAS mutations, are common in BCP-ALL and has previously been linked to poor outcome. 63 – 65 Herein, we found that KRAS activation was correlated with resistance to topoisomerase II inhibitors. Although inhibiting KRAS has long been deemed difficult, new approaches for targeting KRAS in cancer therapy are emerging, and our results further support continuing this avenue for ALL. 66 , 67 The importance of heme metabolism in chemotherapy resistance has been noted in AML 68 as well as other non-hematological cancer types, 69 , 70 but to our knowledge has not been observed previously in BCP-ALL. Upregulation of ATP6V0A1 , which encodes a subunit of a vacuolar ATPase (V-ATPase) proton pump, was a particularly interesting observation as it has functionally established role in chemotherapy resistance, 52 , 71 where targeted inhibitors exist. 72 , 73 Finally, we observed increased expression of T-cell lineage genes in patients ex vivo resistant to the drugs that are central elements of ALL treatment (glucocorticoids, vincristine and thioguanine). Aberrant T-lineage cell surface marker expression in B-cell malignancies has previously been linked to poor outcome, 74 but the expression of T-cell lineage genes in BCP-ALL cells and the relationship to drug response has not been described. One gene in particular, ITK, was upregulated in ex vivo resistant patients. Ibrutinib, a BTK inhibitor effective in chronic lymphocytic leukemia 75 and in combination with dasatinib for of CNS-infiltrating BCP-ALL 76 , has also shown promising results in inhibiting ITK 77 . Our results broaden the potential scope of BTK inhibition for treatment of BCP-ALL. Challenges faced by our approach include sample handling and cell viability. 78 Furthermore, our study primarily assessed responses to individual drugs, whereas BCP-ALL treatment involves complex, multi-drug regimens administered over extended timeframes. These regimens may produce synergistic or antagonistic effects that single-drug assays cannot fully replicate. However, advancements in functional assay technologies are making high-throughput platforms increasingly robust and scalable, enabling the simultaneous screening of hundreds of drugs in various combinations. 79 – 81 These developments hold promise for addressing the limitations of single-drug analyses in future studies. Here, our bulk analysis approach may additionally obscure heterogeneity within leukemic cell populations by averaging signals across the sample, which likely includes both leukemic and normal cells as well as sub-populations with distinct molecular and genetic diversity 82 . Single-cell technologies combined with ex vivo drug screening can address some of these limitations by allowing the investigation of drug responses at the resolution of individual cells. 83 – 85 Moreover, sequential drug treatment studies have demonstrated promise in blocking non-genetic cancer evolution, underscoring the importance of capturing dynamic cellular responses over time. 86 Integrating these advanced approaches with multi-omics profiling in future studies could provide deeper insights into treatment resistance mechanisms and therapeutic vulnerabilities that remain beyond the scope of bulk analyses. However, it is important to note that in our study, we identified molecular signatures in bulk samples that were also observed at the single-cell level, affirming the relevance and robustness of our findings despite the limitations of bulk approaches. In conclusion, our findings demonstrate that BCP-ALL patient cells exhibit distinct molecular signatures associated with drug resistance, highlighting pre-treatment mechanisms that may prime cells for therapy failure. These results underscore the value of integrating ex vivo drug screening with molecular profiling to identify resistance mechanisms and uncover potential therapeutic targets, paving the way for improved treatment strategies and outcomes. Data Availability The DNA methylation data were available from their original studies and the metadata are available at the Gene Expression Omnibus (GEO) under accession number GSE49031, and under controlled access via 10.17044/scilifelab.22303531 and 10.17044/scilifelab.26096371 ( https://figshare.scilifelab.se/ ). The gene expression data are available under GSE227832. Data requests may be submitted to Jessica Nordlund ( jessica.nordlund{at}medsci.uu.se ). Authorship Contributions G.L., and J.N. conceived the study. A.P.E, O.K., A.L, H.G, J.N and G.L. analyzed the data. A.P.E, O.K., A.L. and J.N. designed the figures. K.B. and H.G. performed experiments. J.P., B.M.F., S.R., T.F., O.G.J., K.S., M.H., A.H. and G.L. provided clinical material, patient data and clinical expertise. P.N. and R.L. established methods. K.B., C.A., P.N. and R.L. provided ex vivo drug screening expertise. A.P.E., O.K., and J.N. wrote the manuscript. All authors read and approved the final version of the manuscript. Data sharing statement The DNA methylation data were available from their original studies and the metadata are available at the Gene Expression Omnibus (GEO) under accession number GSE49031, and under controlled access via 10.17044/scilifelab.22303531 and 10.17044/scilifelab.26096371 ( https://figshare.scilifelab.se/ ). The gene expression data are available under GSE227832. Data requests may be submitted to Jessica Nordlund ( jessica.nordlund{at}medsci.uu.se ). Conflict of Interest Disclosures The authors declare that they have no conflicts of interest. Acknowledgements We thank Martin Åberg for cell preparations for the scRNA-seq analysis. This work was supported by grants from the Swedish Research Council (2019-01976 to JN), the Swedish Cancer Society (CAN2022-2395 to JN), the Swedish Childhood Cancer Fund (PR2022-0082 and HFT2023-0011 to JN, TJ2020-0039 to AH), and the Göran Gustafssons Foundation (to JN). The genomics data was generated with assistance from the SciLifeLab National Genomics Infrastructure, SNP&SEQ Technology Platform, which is funded by the Swedish Research Council and the Knut and Alice Wallenberg Foundation. Computational resources were provided by the Swedish National Infrastructure for Computing (SNIC) and National Academic Infrastructure for Supercomputing in Sweden (NAISS). Footnotes Email addresses of co-authors: Anna Pia Enblad: anna_pia.enblad{at}uu.se , Olga Krali: olga.krali{at}medsci.uu.se , Henrik Gezelius: henrik.gezelius{at}medsci.uu.se , Anders Lundmark: anders.lundmark{at}medsci.uu.se , Kristin Blom: kristin.blom{at}medsci.uu.se , Claes Andersson: claes.andersson{at}medsci.uu.se , Josefine Palle: josefine.palle{at}uu.se , Britt-Marie Frost: britt_marie.frost{at}uu.se , Samppa Ryhänen: samppa.ryhanen{at}helsinki.fi , Trond Flægstad: trond.flaegstad{at}unn.no , Ólafur G Jónsson: olafurgi{at}landspitali.is , Kjeld Schmiegelow: kjeld.Schmiegelow{at}regionh.dk , Mats Heyman: mats.heyman{at}ki.se , Arja Harila: arja.harila{at}uu.se , Peter Nygren: peter.nygren{at}igp.uu.se , Rolf Larsson: rolf.larsson{at}medsci.uu.se , Gudmar Lönnerholm: gudmar.lonnerholm{at}uu.se References 1. ↵ Pui CH , Pei D , Coustan-Smith E , et al. Clinical utility of sequential minimal residual disease measurements in the context of risk-based therapy in childhood acute lymphoblastic leukaemia: a prospective study . The Lancet Oncology . 2015 ; 16 ( 4 ): 465 – 474 . doi: 10.1016/S1470-2045(15)70082-3 OpenUrl CrossRef PubMed 2. ↵ Toft N , Birgens H , Abrahamsson J , et al. Results of NOPHO ALL2008 treatment for patients aged 1–45 years with acute lymphoblastic leukemia . Leukemia . 2018 ; 32 ( 3 ): 606 – 615 . doi: 10.1038/leu.2017.265 OpenUrl CrossRef PubMed 3. ↵ Oskarsson T , Soderhall S , Arvidson J , et al. Relapsed childhood acute lymphoblastic leukemia in the Nordic countries: prognostic factors, treatment and outcome . Haematologica . 2016 ; 101 ( 1 ): 68 – 76 . doi: 10.3324/haematol.2015.131680 OpenUrl Abstract / FREE Full Text 4. ↵ Maloney KW , Devidas M , Wang C , et al. Outcome in Children With Standard-Risk B-Cell Acute Lymphoblastic Leukemia: Results of Children’s Oncology Group Trial AALL0331 . JCO . 2020 ; 38 ( 6 ): 602 – 612 . doi: 10.1200/JCO.19.01086 OpenUrl CrossRef PubMed 5. ↵ Toft N , Birgens H , Abrahamsson J , et al. Toxicity profile and treatment delays in NOPHO ALL2008-comparing adults and children with Philadelphia chromosome-negative acute lymphoblastic leukemia . Eur J Haematol . 2016 ; 96 ( 2 ): 160 – 169 . doi: 10.1111/ejh.12562 OpenUrl CrossRef PubMed 6. Bhakta N , Liu Q , Ness KK , et al. The cumulative burden of surviving childhood cancer: an initial report from the St Jude Lifetime Cohort Study (SJLIFE) . The Lancet . 2017 ; 390 ( 10112 ): 2569 – 2582 . doi: 10.1016/S0140-6736(17)31610-0 OpenUrl CrossRef PubMed 7. Christensen MS , Heyman M , Mottonen M , et al. Treatment-related death in childhood acute lymphoblastic leukaemia in the Nordic countries: 1992-2001 . Br J Haematol . 2005 ; 131 ( 1 ): 50 – 58 . doi: 10.1111/j.1365-2141.2005.05736.x OpenUrl CrossRef PubMed Web of Science 8. ↵ Lund B , Åsberg A , Heyman M , et al. Risk factors for treatment related mortality in childhood acute lymphoblastic leukaemia . Pediatric Blood & Cancer . 2011 ; 56 ( 4 ): 551 – 559 . doi: 10.1002/pbc.22719 OpenUrl CrossRef PubMed 9. ↵ Kato M , Ishimaru S , Seki M , et al. Long-term outcome of 6-month maintenance chemotherapy for acute lymphoblastic leukemia in children . Leukemia . 2017 ; 31 ( 3 ): 580 – 584 . doi: 10.1038/leu.2016.274 OpenUrl CrossRef PubMed 10. ↵ Sidhom I , Shaaban K , Youssef SH , et al. Reduced-intensity therapy for pediatric lymphoblastic leukemia: impact of residual disease early in remission induction . Blood . 2021 ; 137 ( 1 ): 20 – 28 . doi: 10.1182/blood.2020007977 OpenUrl CrossRef PubMed 11. ↵ Maude SL , Laetsch TW , Buechner J , et al. Tisagenlecleucel in Children and Young Adults with B-Cell Lymphoblastic Leukemia . N Engl J Med . 2018 ; 378 ( 5 ): 439 – 448 . doi: 10.1056/NEJMoa1709866 OpenUrl CrossRef PubMed 12. ↵ Locatelli F , Zugmaier G , Mergen N , et al. Blinatumomab in pediatric relapsed/refractory B-cell acute lymphoblastic leukemia: RIALTO expanded access study final analysis . Blood Advances . 2022 ; 6 ( 3 ): 1004 – 1014 . doi: 10.1182/bloodadvances.2021005579 OpenUrl CrossRef PubMed 13. ↵ Hodder A , Mishra AK , Enshaei A , et al. Blinatumomab for First-Line Treatment of Children and Young Persons With B-ALL . JCO . 2024 ; 42 ( 8 ): 907 – 914 . doi: 10.1200/JCO.23.01392 OpenUrl CrossRef PubMed 14. ↵ Cabannes-Hamy A , Brissot E , Leguay T , et al. High tumor burden before blinatumomab has a negative impact on the outcome of adult patients with B-cell precursor acute lymphoblastic leukemia . A real-world study by the GRAALL. haematol . 2022 ; 107 ( 9 ): 2072 – 2080 . doi: 10.3324/haematol.2021.280078 OpenUrl CrossRef 15. ↵ Schultz LM , Baggott C , Prabhu S , et al. Disease Burden Affects Outcomes in Pediatric and Young Adult B-Cell Lymphoblastic Leukemia After Commercial Tisagenlecleucel: A Pediatric Real-World Chimeric Antigen Receptor Consortium Report . JCO . 2022 ; 40 ( 9 ): 945 – 955 . doi: 10.1200/JCO.20.03585 OpenUrl CrossRef PubMed 16. ↵ Lee SHR , Yang W , Gocho Y , et al. Pharmacotypes across the genomic landscape of pediatric acute lymphoblastic leukemia and impact on treatment response . Nat Med . 2023 ; 29 ( 1 ): 170 – 179 . doi: 10.1038/s41591-022-02112-7 OpenUrl CrossRef PubMed 17. ↵ Frost BM , Nygren P , Gustafsson G , et al. Increased in vitro cellular drug resistance is related to poor outcome in high-risk childhood acute lymphoblastic leukaemia: In vitro cellular drug resistance in childhood ALL . British Journal of Haematology . 2003 ; 122 ( 3 ): 376 – 385 . doi: 10.1046/j.1365-2141.2003.04442.x OpenUrl CrossRef PubMed Web of Science 18. Kaspers GJL , Veerman AJP , Pieters R , et al. In Vitro Cellular Drug Resistance and Prognosis in Newly Diagnosed Childhood Acute Lymphoblastic Leukemia . Blood . 1997 ; 90 ( 7 ): 2723 – 2729 . doi: 10.1182/blood.V90.7.2723 OpenUrl Abstract / FREE Full Text 19. ↵ Frismantas V , Dobay MP , Rinaldi A , et al. Ex vivo drug response profiling detects recurrent sensitivity patterns in drug-resistant acute lymphoblastic leukemia . Blood . 2017 ; 129 ( 11 ): e26 – e37 . doi: 10.1182/blood-2016-09-738070 OpenUrl Abstract / FREE Full Text 20. ↵ Acanda De La Rocha AM , Berlow NE , Fader M , et al. Feasibility of functional precision medicine for guiding treatment of relapsed or refractory pediatric cancers . Nat Med . 2024 ; 30 ( 4 ): 990 – 1000 . doi: 10.1038/s41591-024-02848-4 OpenUrl CrossRef PubMed 21. Peterziel H , Jamaladdin N , ElHarouni D , et al. Drug sensitivity profiling of 3D tumor tissue cultures in the pediatric precision oncology program INFORM . npj Precis Onc . 2022 ; 6 ( 1 ): 94 . doi: 10.1038/s41698-022-00335-y OpenUrl CrossRef 22. ↵ Malani D , Kumar A , Brück O , et al. Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia . Cancer Discovery . 2022 ; 12 ( 2 ): 388 – 401 . doi: 10.1158/2159-8290.CD-21-0410 OpenUrl Abstract / FREE Full Text 23. ↵ Liebers N , Bruch PM , Terzer T , et al. Ex vivo drug response profiling for response and outcome prediction in hematologic malignancies: the prospective non-interventional SMARTrial . Nat Cancer . 2023 ; 4 ( 12 ): 1648 – 1659 . doi: 10.1038/s43018-023-00645-5 OpenUrl CrossRef PubMed 24. ↵ Ryan SL , Peden JF , Kingsbury Z , et al. Whole genome sequencing provides comprehensive genetic testing in childhood B-cell acute lymphoblastic leukaemia . Leukemia . 2023 ; 37 ( 3 ): 518 – 528 . doi: 10.1038/s41375-022-01806-8 OpenUrl CrossRef PubMed 25. ↵ Autry RJ , Paugh SW , Carter R , et al. Integrative genomic analyses reveal mechanisms of glucocorticoid resistance in acute lymphoblastic leukemia . Nat Cancer . 2020 ; 1 ( 3 ): 329 – 344 . doi: 10.1038/s43018-020-0037-3 OpenUrl CrossRef PubMed 26. ↵ Chen CC , Lee KD , Pai MY , et al. Changes in DNA methylation are associated with the development of drug resistance in cervical cancer cells . Cancer Cell Int . 2015 ; 15 ( 1 ): 98 . doi: 10.1186/s12935-015-0248-3 OpenUrl CrossRef PubMed 27. Chan DW , Lam WY , Chen F , et al. Genome-wide DNA methylome analysis identifies methylation signatures associated with survival and drug resistance of ovarian cancers . Clin Epigenet . 2021 ; 13 ( 1 ): 142 . doi: 10.1186/s13148-021-01130-5 OpenUrl CrossRef PubMed 28. ↵ Ohnmacht AJ , Rajamani A , Avar G , et al. The pharmacoepigenomic landscape of cancer cell lines reveals the epigenetic component of drug sensitivity . Commun Biol . 2023 ; 6 ( 1 ): 825 . doi: 10.1038/s42003-023-05198-y OpenUrl CrossRef PubMed 29. ↵ Ferguson DC , McCorkle JR , Barnett KR , et al. Amino acid stress response genes promote L-asparaginase resistance in pediatric acute lymphoblastic leukemia . Blood Advances . 2022 ; 6 ( 11 ): 3386 – 3397 . doi: 10.1182/bloodadvances.2022006965 OpenUrl CrossRef PubMed 30. ↵ Schmiegelow K , Forestier E , Hellebostad M , et al. Long-term results of NOPHO ALL-92 and ALL-2000 studies of childhood acute lymphoblastic leukemia . Leukemia . 2010 ; 24 ( 2 ): 345 – 354 . doi: 10.1038/leu.2009.251 OpenUrl CrossRef PubMed Web of Science 31. ↵ Lindhagen E , Nygren P , Larsson R . The fluorometric microculture cytotoxicity assay . Nat Protoc . 2008 ; 3 ( 8 ): 1364 – 1369 . doi: 10.1038/nprot.2008.114 OpenUrl CrossRef PubMed 32. ↵ Nygren P , Kristensen J , Jonsson B , et al. Feasibility of the fluorometric microculture cytotoxicity assay (FMCA) for cytotoxic drug sensitivity testing of tumor cells from patients with acute lymphoblastic leukemia . Leukemia . 1992 ; 6 ( 11 ): 1121 – 1128 . OpenUrl PubMed Web of Science 33. ↵ Milani L , Lundmark A , Nordlund J , et al. Allele-specific gene expression patterns in primary leukemic cells reveal regulation of gene expression by CpG site methylation . Genome Res . 2009 ; 19 ( 1 ): 1 – 11 . doi: 10.1101/gr.083931.108 OpenUrl Abstract / FREE Full Text 34. ↵ Krali O , Marincevic-Zuniga Y , Arvidsson G , et al. Multimodal classification of molecular subtypes in pediatric acute lymphoblastic leukemia . npj Precis Onc . 2023 ; 7 ( 1 ): 131 . doi: 10.1038/s41698-023-00479-5 OpenUrl CrossRef PubMed 35. ↵ Nordlund J , Bäcklin CL , Wahlberg P , et al. Genome-wide signatures of differential DNA methylation in pediatric acute lymphoblastic leukemia . Genome Biol . 2013 ; 14 ( 9 ): r105 . doi: 10.1186/gb-2013-14-9-r105 OpenUrl CrossRef PubMed 36. ↵ Marincevic-Zuniga Y , Dahlberg J , Nilsson S , et al. Transcriptome sequencing in pediatric acute lymphoblastic leukemia identifies fusion genes associated with distinct DNA methylation profiles . J Hematol Oncol . 2017 ; 10 ( 1 ): 148 . doi: 10.1186/s13045-017-0515-y OpenUrl CrossRef 37. ↵ Arber DA , Orazi A , Hasserjian RP , et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data . Blood . 2022 ; 140 ( 11 ): 1200 – 1228 . doi: 10.1182/blood.2022015850 OpenUrl CrossRef PubMed 38. ↵ Ritchie ME , Phipson B , Wu D , et al. limma powers differential expression analyses for RNA-sequencing and microarray studies . Nucleic Acids Research . 2015 ; 43 ( 7 ): e47 – e47 . doi: 10.1093/nar/gkv007 OpenUrl CrossRef PubMed 39. ↵ Love MI , Huber W , Anders S . Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 . Genome Biol . 2014 ; 15 ( 12 ): 550 . doi: 10.1186/s13059-014-0550-8 OpenUrl CrossRef PubMed 40. ↵ McLean CY , Bristor D , Hiller M , et al. GREAT improves functional interpretation of cis-regulatory regions . Nat Biotechnol . 2010 ; 28 ( 5 ): 495 – 501 . doi: 10.1038/nbt.1630 OpenUrl CrossRef PubMed Web of Science 41. ↵ Liberzon A , Birger C , Thorvaldsdóttir H , Ghandi M , Mesirov JP , Tamayo P . The Molecular Signatures Database Hallmark Gene Set Collection . Cell Systems . 2015 ; 1 ( 6 ): 417 – 425 . doi: 10.1016/j.cels.2015.12.004 OpenUrl CrossRef PubMed 42. ↵ Ashburner M , Ball CA , Blake JA , et al. Gene Ontology: tool for the unification of biology . Nat Genet . 2000 ; 25 ( 1 ): 25 – 29 . doi: 10.1038/75556 OpenUrl CrossRef PubMed Web of Science 43. ↵ Hao Y , Stuart T , Kowalski MH , et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis . Nat Biotechnol . 2024 ; 42 ( 2 ): 293 – 304 . doi: 10.1038/s41587-023-01767-y OpenUrl CrossRef PubMed 44. ↵ Lun ATL , Riesenfeld S , Andrews T , et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data . Genome Biol . 2019 ; 20 ( 1 ): 63 . doi: 10.1186/s13059-019-1662-y OpenUrl CrossRef PubMed 45. ↵ Zeng AGX , Iacobucci I , Shah S , et al. Single-cell transcriptional mapping reveals genetic and non-genetic determinants of aberrant differentiation in AML . Published online December 27 , 2023 . doi: 10.1101/2023.12.26.573390 OpenUrl Abstract / FREE Full Text 46. ↵ Robey RW , Pluchino KM , Hall MD , Fojo AT , Bates SE , Gottesman MM . Revisiting the role of ABC transporters in multidrug-resistant cancer . Nat Rev Cancer . 2018 ; 18 ( 7 ): 452 – 464 . doi: 10.1038/s41568-018-0005-8 OpenUrl CrossRef PubMed 47. Mishra P , Tang W , Ambs S . ADHFE1 is a MYC-linked oncogene that induces metabolic reprogramming and cellular de-differentiation in breast cancer . Mol Cell Oncol . 2018 ; 5 ( 3 ): e1432260 . doi: 10.1080/23723556.2018.1432260 OpenUrl CrossRef 48. ↵ Li L , Zhao L , Man J , Liu B . CXCL2 benefits acute myeloid leukemia cells in hypoxia . Int J Lab Hematology . 2021 ; 43 ( 5 ): 1085 – 1092 . doi: 10.1111/ijlh.13512 OpenUrl CrossRef 49. ↵ Ohm AM , Tan AC , Heasley LE , Reyland ME . Co-dependency of PKCδ and K-Ras: inverse association with cytotoxic drug sensitivity in KRAS mutant lung cancer . Oncogene . 2017 ; 36 ( 30 ): 4370 – 4378 . doi: 10.1038/onc.2017.27 OpenUrl CrossRef PubMed 50. ↵ Ratner ES , Keane FK , Lindner R , et al. A KRAS variant is a biomarker of poor outcome, platinum chemotherapy resistance and a potential target for therapy in ovarian cancer . Oncogene . 2012 ; 31 ( 42 ): 4559 – 4566 . doi: 10.1038/onc.2011.539 OpenUrl CrossRef PubMed Web of Science 51. ↵ Wu Q , Li X , Long M , Xie X , Liu Q . Integrated analysis of histone lysine lactylation (Kla)-specific genes suggests that NR6A1, OSBP2 and UNC119B are novel therapeutic targets for hepatocellular carcinoma . Sci Rep . 2023 ; 13 ( 1 ): 18642 . doi: 10.1038/s41598-023-46057-4 OpenUrl CrossRef PubMed 52. ↵ Menyhárt O , Fekete JT , Győrffy B . Resistance to Combined Anthracycline–Taxane Chemotherapy Is Associated with Altered Metabolism and Inflammation in Breast Carcinomas . IJMS . 2024 ; 25 ( 2 ): 1063 . doi: 10.3390/ijms25021063 OpenUrl CrossRef PubMed 53. ↵ Zhong FM , Yao FY , Liu J , et al. Inflammatory response mediates cross-talk with immune function and reveals clinical features in acute myeloid leukemia . Biosci Rep . 2022 ; 42 ( 5 ): BSR20220647 . doi: 10.1042/BSR20220647 OpenUrl CrossRef PubMed 54. ↵ Dietrich S , Oleś M , Lu J , et al. Drug-perturbation-based stratification of blood cancer . Journal of Clinical Investigation . 2017 ; 128 ( 1 ): 427 – 445 . doi: 10.1172/JCI93801 OpenUrl CrossRef PubMed 55. Ferrato MH , Marsh AG , Franke KR , et al. Machine learning classifier approaches for predicting response to RTK-type-III inhibitors demonstrate high accuracy using transcriptomic signatures and ex vivo data . Bioinform Adv . 2023 ; 3 ( 1 ): vbad034 . doi: 10.1093/bioadv/vbad034 OpenUrl CrossRef 56. ↵ Wang J , Li J , Chen R , et al. DNA methylation-based profiling reveals distinct clusters with survival heterogeneity in high-grade serous ovarian cancer . Clin Epigenet . 2021 ; 13 ( 1 ): 190 . doi: 10.1186/s13148-021-01178-3 OpenUrl CrossRef 57. ↵ Tallen G , Ratei R , Mann G , et al. Long-Term Outcome in Children With Relapsed Acute Lymphoblastic Leukemia After Time-Point and Site-of-Relapse Stratification and Intensified Short-Course Multidrug Chemotherapy: Results of Trial ALL-REZ BFM 90 . JCO . 2010 ; 28 ( 14 ): 2339 – 2347 . doi: 10.1200/JCO.2009.25.1983 OpenUrl Abstract / FREE Full Text 58. ↵ Schrappe M , Hunger SP , Pui CH , et al. Outcomes after Induction Failure in Childhood Acute Lymphoblastic Leukemia . N Engl J Med . 2012 ; 366 ( 15 ): 1371 – 1381 . doi: 10.1056/NEJMoa1110169 OpenUrl CrossRef PubMed 59. ↵ Zhang K , Erkan EP , Jamalzadeh S , et al. Longitudinal single-cell RNA-seq analysis reveals stress-promoted chemoresistance in metastatic ovarian cancer . Sci Adv . 2022 ; 8 ( 8 ): eabm1831 . doi: 10.1126/sciadv.abm1831 OpenUrl CrossRef PubMed 60. ↵ Wang F , Li D , Zheng Z , et al. Reversal of ABCB1-related multidrug resistance by ERK5-IN-1 . J Exp Clin Cancer Res . 2020 ; 39 ( 1 ): 50 . doi: 10.1186/s13046-020-1537-9 OpenUrl CrossRef PubMed 61. ↵ Adorni MP , Galetti M , La Monica S , et al. A New ABCB1 Inhibitor Enhances the Anticancer Effect of Doxorubicin in Both In Vitro and In Vivo Models of NSCLC . IJMS . 2023 ; 24 ( 2 ): 989 . doi: 10.3390/ijms24020989 OpenUrl CrossRef PubMed 62. ↵ Sucha S , Sorf A , Svoren M , et al. ABCB1 as a potential beneficial target of midostaurin in acute myeloid leukemia . Biomed Pharmacother . 2022 ; 150 : 112962 . doi: 10.1016/j.biopha.2022.112962 OpenUrl CrossRef PubMed 63. ↵ Case M , Matheson E , Minto L , et al. Mutation of Genes Affecting the RAS Pathway Is Common in Childhood Acute Lymphoblastic Leukemia . Cancer Research . 2008 ; 68 ( 16 ): 6803 – 6809 . doi: 10.1158/0008-5472.CAN-08-0101 OpenUrl Abstract / FREE Full Text 64. Lindqvist CM , Lundmark A , Nordlund J , et al. Deep targeted sequencing in pediatric acute lymphoblastic leukemia unveils distinct mutational patterns between genetic subtypes and novel relapse-associated genes . Oncotarget . 2016 ; 7 ( 39 ): 64071 – 64088 . doi: 10.18632/oncotarget.11773 OpenUrl CrossRef PubMed 65. ↵ Irving J , Matheson E , Minto L , et al. Ras pathway mutations are prevalent in relapsed childhood acute lymphoblastic leukemia and confer sensitivity to MEK inhibition . Blood . 2014 ; 124 ( 23 ): 3420 – 3430 . doi: 10.1182/blood-2014-04-531871 OpenUrl Abstract / FREE Full Text 66. ↵ Huang L , Guo Z , Wang F , Fu L . KRAS mutation: from undruggable to druggable in cancer . Sig Transduct Target Ther . 2021 ; 6 ( 1 ): 386 . doi: 10.1038/s41392-021-00780-4 OpenUrl CrossRef PubMed 67. ↵ Kim D , Herdeis L , Rudolph D , et al. Pan-KRAS inhibitor disables oncogenic signalling and tumour growth . Nature . 2023 ; 619 ( 7968 ): 160 – 166 . doi: 10.1038/s41586-023-06123-3 OpenUrl CrossRef 68. ↵ Zhe N , Wang J , Chen S , et al. Heme oxygenase-1 plays a crucial role in chemoresistance in acute myeloid leukemia . Hematology . 2015 ; 20 ( 7 ): 384 – 391 . doi: 10.1179/1607845414Y.0000000212 OpenUrl CrossRef 69. ↵ Xiang L , Wang Y , Lan J , et al. HIF-1-dependent heme synthesis promotes gemcitabine resistance in human non-small cell lung cancers via enhanced ABCB6 expression . Cell Mol Life Sci . 2022 ; 79 ( 6 ): 343 . doi: 10.1007/s00018-022-04360-9 OpenUrl CrossRef PubMed 70. ↵ Kongpetch S , Kukongviriyapan V , Prawan A , Senggunprai L , Kukongviriyapan U , Buranrat B . Crucial Role of Heme Oxygenase-1 on the Sensitivity of Cholangiocarcinoma Cells to Chemotherapeutic Agents. Sim RB, ed . PLoS ONE . 2012 ; 7 ( 4 ): e34994 . doi: 10.1371/journal.pone.0034994 OpenUrl CrossRef PubMed 71. ↵ Lu Q , Lu S , Huang L , et al. The expression of V-ATPase is associated with drug resistance and pathology of non-small-cell lung cancer . Diagn Pathol . 2013 ; 8 ( 1 ): 145 . doi: 10.1186/1746-1596-8-145 OpenUrl CrossRef PubMed 72. ↵ Schneider LS , Von Schwarzenberg K , Lehr T , et al. Vacuolar-ATPase Inhibition Blocks Iron Metabolism to Mediate Therapeutic Effects in Breast Cancer . Cancer Research . 2015 ; 75 ( 14 ): 2863 – 2874 . doi: 10.1158/0008-5472.CAN-14-2097 OpenUrl Abstract / FREE Full Text 73. ↵ Zhang S , Schneider LS , Vick B , et al. Anti-leukemic effects of the V-ATPase inhibitor Archazolid A . Oncotarget . 2015 ; 6 ( 41 ): 43508 – 43528 . doi: 10.18632/oncotarget.6180 OpenUrl CrossRef PubMed 74. ↵ Hussein S , Gill KZ , Sireci AN , et al. Aberrant TDcell antigen expression in B lymphoblastic leukaemia . Br J Haematol . 2011 ; 155 ( 4 ): 449 – 456 . doi: 10.1111/j.1365-2141.2011.08870.x OpenUrl CrossRef PubMed 75. ↵ Burger JA , Barr PM , Robak T , et al. Long-term efficacy and safety of first-line ibrutinib treatment for patients with CLL/SLL: 5 years of follow-up from the phase 3 RESONATE-2 study . Leukemia . 2020 ; 34 ( 3 ): 787 – 798 . doi: 10.1038/s41375-019-0602-x OpenUrl CrossRef PubMed 76. ↵ Gentile G , Poggio T , Catalano A , et al. Development of combination therapies with BTK inhibitors and dasatinib to treat CNS-infiltrating E2A-PBX1+/preBCR+ ALL . Blood Adv . 2024 ; 8 ( 11 ): 2846 – 2860 . doi: 10.1182/bloodadvances.2023011582 OpenUrl CrossRef PubMed 77. ↵ Dubovsky JA , Beckwith KA , Natarajan G , et al. Ibrutinib is an irreversible molecular inhibitor of ITK driving a Th1-selective pressure in T lymphocytes . Blood . 2013 ; 122 ( 15 ): 2539 – 2549 . doi: 10.1182/blood-2013-06-507947 OpenUrl Abstract / FREE Full Text 78. ↵ Struyf N , Österroos A , Vesterlund M , et al. Delineating functional and molecular impact of ex vivo sample handling in precision medicine . npj Precis Onc . 2024 ; 8 ( 1 ): 38 . doi: 10.1038/s41698-024-00528-7 OpenUrl CrossRef 79. ↵ Leo IR , Aswad L , Stahl M , et al. Integrative multi-omics and drug response profiling of childhood acute lymphoblastic leukemia cell lines . Nat Commun . 2022 ; 13 ( 1 ): 1691 . doi: 10.1038/s41467-022-29224-5 OpenUrl CrossRef PubMed 80. Lau LMS , Mayoh C , Xie J , et al. In vitro and in vivo drug screens of tumor cells identify novel therapies for highDrisk child cancer . EMBO Mol Med . 2022 ; 14 ( 4 ): e14608 . doi: 10.15252/emmm.202114608 OpenUrl CrossRef PubMed 81. ↵ Cairns LV , Lappin KM , Mutch A , Ali A , Matchett KB , Mills KI . Multiplex Screening for Interacting Compounds in Paediatric Acute Myeloid Leukaemia . Int J Mol Sci . 2021 ; 22 ( 18 ): 10163 . doi: 10.3390/ijms221810163 OpenUrl CrossRef PubMed 82. ↵ Iacobucci I , Witkowski MT , Mullighan CG . Single-cell analysis of acute lymphoblastic and lineage-ambiguous leukemia: approaches and molecular insights . Blood . 2023 ; 141 ( 4 ): 356 – 368 . doi: 10.1182/blood.2022016954 OpenUrl CrossRef PubMed 83. ↵ Gezelius H , Enblad AP , Lundmark A , et al. Comparison of high-throughput single-cell RNA-seq methods for ex vivo drug screening . NAR Genomics and Bioinformatics . 2024 ; 6 ( 1 ): lqae001 . doi: 10.1093/nargab/lqae001 OpenUrl CrossRef 84. Thorsson H , Henningsson R , Puente-Moncada N , et al. Single-cell genomics details the maturation block in BCP-ALL and identifies therapeutic vulnerabilities in DUX4-r cases . Blood . 2024 ; 144 ( 13 ): 1399 – 1411 . doi: 10.1182/blood.2023021705 OpenUrl CrossRef PubMed 85. ↵ Huang X , Li Y , Zhang J , et al. Single-cell systems pharmacology identifies development-driven drug response and combination therapy in B cell acute lymphoblastic leukemia . Cancer Cell . 2024 ; 42 ( 4 ): 552 – 567 .e6. doi: 10.1016/j.ccell.2024.03.003 OpenUrl CrossRef PubMed 86. ↵ Malyukova A , Lahnalampi M , Falqués-Costa T , et al. Sequential drug treatment targeting cell cycle and cell fate regulatory programs blocks non-genetic cancer evolution in acute lymphoblastic leukemia . Genome Biol . 2024 ; 25 ( 1 ): 143 . doi: 10.1186/s13059-024-03260-4 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted December 20, 2024. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Ex Vivo Drug Responses and Molecular Profiles of 597 Pediatric Acute Lymphoblastic Leukemia Patients Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Ex Vivo Drug Responses and Molecular Profiles of 597 Pediatric Acute Lymphoblastic Leukemia Patients Anna Pia Enblad , Olga Krali , Henrik Gezelius , Anders Lundmark , Kristin Blom , Claes Andersson , Josefine Palle , Britt-Marie Frost , Samppa Ryhänen , Trond Flægstad , Ólafur G Jónsson , Kjeld Schmiegelow , Mats Heyman , Arja Harila , Peter Nygren , Rolf Larsson , Gudmar Lönnerholm , Jessica Nordlund medRxiv 2024.12.17.24319138; doi: https://doi.org/10.1101/2024.12.17.24319138 Share This Article: Copy Citation Tools Ex Vivo Drug Responses and Molecular Profiles of 597 Pediatric Acute Lymphoblastic Leukemia Patients Anna Pia Enblad , Olga Krali , Henrik Gezelius , Anders Lundmark , Kristin Blom , Claes Andersson , Josefine Palle , Britt-Marie Frost , Samppa Ryhänen , Trond Flægstad , Ólafur G Jónsson , Kjeld Schmiegelow , Mats Heyman , Arja Harila , Peter Nygren , Rolf Larsson , Gudmar Lönnerholm , Jessica Nordlund medRxiv 2024.12.17.24319138; doi: https://doi.org/10.1101/2024.12.17.24319138 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Oncology Subject Areas All Articles Addiction Medicine (573) Allergy and Immunology (865) Anesthesia (304) Cardiovascular Medicine (4457) Dentistry and Oral Medicine (445) Dermatology (383) Emergency Medicine (610) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1517) Epidemiology (15244) Forensic Medicine (30) Gastroenterology (1132) Genetic and Genomic Medicine (6620) Geriatric Medicine (669) Health Economics (1002) Health Informatics (4557) Health Policy (1372) Health Systems and Quality Improvement (1615) Hematology (543) HIV/AIDS (1272) Infectious Diseases (except HIV/AIDS) (15936) Intensive Care and Critical Care Medicine (1106) Medical Education (624) Medical Ethics (147) Nephrology (670) Neurology (6634) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1148) Occupational and Environmental Health (957) Oncology (3348) Ophthalmology (980) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1696) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5463) Public and Global Health (9257) Radiology and Imaging (2210) Rehabilitation Medicine and Physical Therapy (1371) Respiratory Medicine (1198) Rheumatology (598) Sexual and Reproductive Health (716) Sports Medicine (532) Surgery (714) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a0363d3fec15dfa9',t:'MTc4MDA2NTQ2OQ=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();