A platform of robust patient-derived leukemia models covering subgroups for which no cell lines exist

A platform of robust patient-derived leukemia models covering subgroups for which no cell lines exist | bioRxiv /* */ /* */ <!-- <!-- /*! * 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-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results A platform of robust patient-derived leukemia models covering subgroups for which no cell lines exist View ORCID Profile Binje Vick , View ORCID Profile Vindi Jurinovic , View ORCID Profile Kristina Kuhbandner , View ORCID Profile Lena Lagally , View ORCID Profile Lisa Latzko , Chiara Arnreich , View ORCID Profile Gerulf Hänel , Amelie Muth , Maja Rothenberg-Thurley , Annika M. Dufour , Stephanie Schneider , View ORCID Profile Lesca M. Holdt , Liliana Mura , Fabian Klein , Annette Frank , Maya C. André , View ORCID Profile Claudia D. Baldus , View ORCID Profile Martin Carroll , Christine Dierks , View ORCID Profile Martin Ebinger , View ORCID Profile Katharina S. Götze , Pablo Menéndez , View ORCID Profile Christian Récher , View ORCID Profile Ambrine Sahal , View ORCID Profile Jean-Emmanuel Sarry , View ORCID Profile Christian Thiede , View ORCID Profile Talía Velasco-Hernández , Xiaoyan Wei , View ORCID Profile Jan Henning Klusmann , View ORCID Profile Michael von Bergwelt-Baildon , View ORCID Profile Wolfgang Hiddemann , View ORCID Profile Klaus H. Metzeler , View ORCID Profile Philipp A. Greif , View ORCID Profile Marion Subklewe , View ORCID Profile Sebastian Vosberg , View ORCID Profile Tobias Herold , View ORCID Profile Karsten Spiekermann , View ORCID Profile Irmela Jeremias doi: https://doi.org/10.1101/2025.09.26.677299 Binje Vick 1 Research Unit Apoptosis in Hematopoietic Stem Cells, Helmholtz Munich, German Research Center for Environmental Health (HMGU) , Munich, Germany 2 German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and University Hospital LMU Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Binje Vick For correspondence: irmela.jeremias{at}helmholtz-munich.de binje.vick{at}helmholtz-munich.de Vindi Jurinovic 1 Research Unit Apoptosis in Hematopoietic Stem Cells, Helmholtz Munich, German Research Center for Environmental Health (HMGU) , Munich, Germany 3 Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Faculty of Medicine, LMU Munich , Munich, Germany 4 Department of Medicine III, University Hospital, LMU Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Vindi Jurinovic Kristina Kuhbandner 5 Hopp Children’s Cancer Center Heidelberg (KiTZ) , Heidelberg, Germany 6 Manfred Fuchs Research Group Pediatric Leukemia, Heidelberg University Hospital , Heidelberg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kristina Kuhbandner Lena Lagally 1 Research Unit Apoptosis in Hematopoietic Stem Cells, Helmholtz Munich, German Research Center for Environmental Health (HMGU) , Munich, Germany 7 Department of Paediatric Allergology, Dr. von Hauner Children’s Hospital, University Hospital, LMU Munich , Munich, Germany 8 Comprehensive Pneumology Center Munich (CPC-M), German Center for Lung Research (DZL) , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lena Lagally Lisa Latzko 2 German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and University Hospital LMU Munich , Munich, Germany 4 Department of Medicine III, University Hospital, LMU Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lisa Latzko Chiara Arnreich 1 Research Unit Apoptosis in Hematopoietic Stem Cells, Helmholtz Munich, German Research Center for Environmental Health (HMGU) , Munich, Germany 9 Department of Internal Medicine III, Hematology and Oncology, University Hospital Regensburg , Regensburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gerulf Hänel 4 Department of Medicine III, University Hospital, LMU Munich , Munich, Germany 10 Laboratory for Translational Cancer Immunology , LMU Gene Center, Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gerulf Hänel Amelie Muth 4 Department of Medicine III, University Hospital, LMU Munich , Munich, Germany 10 Laboratory for Translational Cancer Immunology , LMU Gene Center, Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maja Rothenberg-Thurley 4 Department of Medicine III, University Hospital, LMU Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Annika M. Dufour 4 Department of Medicine III, University Hospital, LMU Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Stephanie Schneider 4 Department of Medicine III, University Hospital, LMU Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lesca M. Holdt 11 Institute of Laboratory Medicine, LMU University Hospital, LMU Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lesca M. Holdt Liliana Mura 1 Research Unit Apoptosis in Hematopoietic Stem Cells, Helmholtz Munich, German Research Center for Environmental Health (HMGU) , Munich, Germany 2 German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and University Hospital LMU Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fabian Klein 1 Research Unit Apoptosis in Hematopoietic Stem Cells, Helmholtz Munich, German Research Center for Environmental Health (HMGU) , Munich, Germany 2 German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and University Hospital LMU Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Annette Frank 1 Research Unit Apoptosis in Hematopoietic Stem Cells, Helmholtz Munich, German Research Center for Environmental Health (HMGU) , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maya C. André 12 Department of Pediatric Hematology and Oncology, University Children’s Hospital, Eberhard Karls University , Tuebingen, Germany 13 University of Basel Children’s Hospital, Division of Respiratory and Critical Care Medicine , Basel, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Claudia D. Baldus 14 Department of Internal Medicine II (Hematology/Oncology), University Hospital Schleswig-Holstein , Kiel, Germany 15 Clinical Research Unit CATCH ALL (KFO 5010/1) funded by the Deutsche Forschungsgemeinschaft , Kiel, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Claudia D. Baldus Martin Carroll 16 Division of Hematology/Oncology, Department of Medicine, The Perelman School of Medicine, The University of Pennsylvania , Philadelphia, PA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Martin Carroll Christine Dierks 17 Department of Hematology and Oncology, Freiburg University Medical Center, Albert-Ludwigs-University of Freiburg , Breisgau, Germany 18 Department of Hematology/Oncology and Stem Cell Transplantation, KIM IV, Martin-Luther University Halle-Wittenberg , Halle, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martin Ebinger 12 Department of Pediatric Hematology and Oncology, University Children’s Hospital, Eberhard Karls University , Tuebingen, Germany 19 German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ) , Partner site Tübingen, Tübingen, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Martin Ebinger Katharina S. Götze 2 German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and University Hospital LMU Munich , Munich, Germany 20 Technical University of Munich, School of Medicine, TUM University Hospital, Department of Internal Medicine III , Munich, Germany 21 Bavarian Cancer Research Center (BZKF) , partner site Munich, Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Katharina S. Götze Pablo Menéndez 22 Josep Carreras Leukemia Research Institute , Barcelona, Spain 23 Red Española de Terapias Avanzadas (TERAV), Instituto de Salud Carlos III , Madrid, Spain 24 Departments of Biomedical Sciences and Medicine and Health Sciences, University of Barcelona , Barcelona, Spain 25 Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) , Madrid, Spain 26 Institució Catalana de Recerca i Estudis Avançats (ICREA) , Barcelona, Spain 27 Pediatric Cancer Centre Barcelona-Institut de Recerca Sant Joan de Deu (PCCB-SJD) , Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christian Récher 28 Cancer Research Center of Toulouse, U1037, INSERM, University of Toulouse , France 29 Equipe Labellisée LIGUE 2023 and Labex TOUCAN , Toulouse, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christian Récher Ambrine Sahal 28 Cancer Research Center of Toulouse, U1037, INSERM, University of Toulouse , France 29 Equipe Labellisée LIGUE 2023 and Labex TOUCAN , Toulouse, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ambrine Sahal Jean-Emmanuel Sarry 28 Cancer Research Center of Toulouse, U1037, INSERM, University of Toulouse , France 29 Equipe Labellisée LIGUE 2023 and Labex TOUCAN , Toulouse, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jean-Emmanuel Sarry Christian Thiede 30 Department of Internal Medicine I, University Hospital Carl Gustav Carus, University of Technology , Dresden, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Christian Thiede Talía Velasco-Hernández 22 Josep Carreras Leukemia Research Institute , Barcelona, Spain 23 Red Española de Terapias Avanzadas (TERAV), Instituto de Salud Carlos III , Madrid, Spain 24 Departments of Biomedical Sciences and Medicine and Health Sciences, University of Barcelona , Barcelona, Spain Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Talía Velasco-Hernández Xiaoyan Wei 31 Department of Pediatrics, Goethe-University Frankfurt , Frankfurt, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jan Henning Klusmann 31 Department of Pediatrics, Goethe-University Frankfurt , Frankfurt, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jan Henning Klusmann Michael von Bergwelt-Baildon 2 German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and University Hospital LMU Munich , Munich, Germany 4 Department of Medicine III, University Hospital, LMU Munich , Munich, Germany 21 Bavarian Cancer Research Center (BZKF) , partner site Munich, Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael von Bergwelt-Baildon Wolfgang Hiddemann 2 German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and University Hospital LMU Munich , Munich, Germany 4 Department of Medicine III, University Hospital, LMU Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Wolfgang Hiddemann Klaus H. Metzeler 2 German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and University Hospital LMU Munich , Munich, Germany 32 Department for Hematology, Cell Therapy, Hemostaseology and Infectious Diseases, University of Leipzig Medical Center , Leipzig, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Klaus H. Metzeler Philipp A. Greif 2 German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and University Hospital LMU Munich , Munich, Germany 4 Department of Medicine III, University Hospital, LMU Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Philipp A. Greif Marion Subklewe 2 German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and University Hospital LMU Munich , Munich, Germany 4 Department of Medicine III, University Hospital, LMU Munich , Munich, Germany 10 Laboratory for Translational Cancer Immunology , LMU Gene Center, Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marion Subklewe Sebastian Vosberg 2 German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and University Hospital LMU Munich , Munich, Germany 4 Department of Medicine III, University Hospital, LMU Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sebastian Vosberg Tobias Herold 1 Research Unit Apoptosis in Hematopoietic Stem Cells, Helmholtz Munich, German Research Center for Environmental Health (HMGU) , Munich, Germany 2 German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and University Hospital LMU Munich , Munich, Germany 4 Department of Medicine III, University Hospital, LMU Munich , Munich, Germany 33 Department of Hematology, Oncology, and Stem-Cell Transplantation, Munich Hospital Bogenhausen , Munich Municipal Hospital Group, Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tobias Herold Karsten Spiekermann 2 German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and University Hospital LMU Munich , Munich, Germany 4 Department of Medicine III, University Hospital, LMU Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karsten Spiekermann Irmela Jeremias 1 Research Unit Apoptosis in Hematopoietic Stem Cells, Helmholtz Munich, German Research Center for Environmental Health (HMGU) , Munich, Germany 2 German Cancer Consortium (DKTK), partner site Munich, a partnership between DKFZ and University Hospital LMU Munich , Munich, Germany 34 Department of Pediatrics, Dr. von Hauner Children’s Hospital, University Hospital, LMU Munich , Munich, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Irmela Jeremias For correspondence: irmela.jeremias{at}helmholtz-munich.de binje.vick{at}helmholtz-munich.de Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Preclinical cancer research requires robust model systems, especially for poor prognosis entities like acute myeloid leukemia (AML), a highly aggressive blood cancer. Here, primary tumor cells from 137 AML patients of all age groups were transplanted into immune compromised mice to generate patient-derived xenografts (PDX). From these, 23 models enable robust, virtually endless serial re-transplantation and are amenable to lentiviral genetic engineering ( * PDX AML models). These models primarily originate from patients with highly aggressive, relapsed disease. Comprehensive genomic, transcriptomic, and epigenomic analyses confirmed that they replicate primary AML biology more faithfully than conventional cell lines. Notably, * PDX AML models include AML subgroups that are underrepresented or absent in existing model systems, such as cytogenetically normal or IDH1/2 -mutant AML. They withstand freeze-thaw cycles, making them suitable for broad distribution and reproducibility across research institutions. Luciferase-based in vivo imaging enables real-time monitoring of tumor progression and treatment responses in preclinical trials. Surprisingly, long-term treatment, including repeated cytarabine therapy over a period of one year, showed a gradual reduction in leukemia cell proliferation, which decreased continuously after each treatment block. Collectively, our * PDX models represent a robust, versatile, and relevant platform that holds great promise to accelerate translational research for the benefit of cancer patients. Download figure Open in new tab Key Points We present new robust AML PDX models covering subgroups for which no cell lines exist for use in various ex vivo and in vivo applications. * PDX models enable serial transplantation, genetic engineering and better representation of primary AML biology than cell lines. One-year in vivo trials mimicking clinical chemotherapy showed surprising gradual decline in leukemia growth after each treatment block. Introduction Acute myeloid leukemia (AML) is an aggressive and heterogenous hematologic malignancy characterized by the clonal expansion of immature myeloid cells 1 . Initial chemotherapy can induce complete remission, but high relapse rates lead to a persistently unfavorable prognosis in AML, highlighting an urgent need for better treatment strategies 2 . A major challenge to develop novel therapies for treatment of cancer is the lack of convenient and faithful model systems. Primary patient samples are rare and often low in cell numbers, especially in children suffering from hematologic malignancies, and cells don’t consistently grow ex vivo 3 , 4 . Cell lines are convenient to culture, but poorly model important features of primary tumor cells as they are heavily biased towards unstable karyotypes and certain genetic abnormalities and show altered drug response 5 - 7 . While establishing a cell line from a primary tumor sample is often successful in solid tumors, this is rarely the case in liquid tumors and often accompanied by acquired confounding features 8 . Recent 3D organoid models of the bone marrow (BM) niche with multiple cell types 9 lack, for example, mechanical forces by blood flow and cannot model tumor cell migration into the BM. Patient-derived xenograft (PDX) models are highly attractive tools for cancer research. By transplanting primary human cancer cells into immunodeficient mice, they proliferate in an in vivo microenvironment. Orthotopically growing tumors as in AML considerably facilitate our understanding of disease pathogenesis, support reliable drug testing and more accurately reflect the genetic and biological heterogeneity of the human disease 10 - 12 . PDXs have offered critical insights into cancer stem cell and AML biology, including mechanisms driving disease initiation, progression and therapy resistance, (epi)genetic clonal evolution, the biology of leukemia-initiating cells and interaction with the BM niche 13 - 16 (and many more). Most PDX AML studies focus on primary engraftment of patient cells. As limitation, primary cells are only available in limited cell numbers in many countries, restricting their availability and use in repetitive assays. A decade ago, we published our first serially transplantable PDX AML models, pioneered genetic engineering of PDX AML cells and shared the models across multiple laboratories worldwide 17 . Building on these initial results, we have since increased the number of available models, characterized the models in depth and were able to generate PDX models for AML subgroups for which no cell lines exist. These s erially transplantable a nd robust (STAR), genetically engineered PDX ( * PDX) AML models represent an attractive tool for future research in the field. Results Primary AML engraftment in NSG mice is favored by aggressive disease and female mice Primary samples of 102 adult AML patients were transplanted into mice over a period of 12 years (Table S1); of these, 74 samples were collected at the University Hospital at LMU Munich (cohort 1, Table S2), and 28 samples were collected either at hospitals in Germany in the framework of the German Cancer Consortium (n=23) or at the hospital of the University of Pennsylvania (n=5) (together cohort 2). From 22 patients, PDX models were established by collaboration partners, among them five from pediatric patients (cohort 3). In an independent approach, 13 primary AML samples from children, mainly with KMT2A -rearranged AML, were transplanted at Hannover Medical School or University Hospital Halle (cohort 4). In total, cells from 137 patients were included into the study ( Figure 1A ; Table S1). Using non-irradiated NSG mice and intra-venous injection, between 40% and 60% of samples across all cohorts engrafted, in line with published data ( Figure 1A ) 18 , 19 . Download figure Open in new tab Figure 1. Both primary engraftment and robust serial engraftment are confined to a minority of PDX models. A Primary samples of adult AML patients (cohort 1, n=74 and cohort 2, n=28), xenografted models of adult or pediatric AML patients (cohort 3, n=22) or primary samples of pediatric AML patients (cohort 4, n=13) were transplanted into one to eight NSG mice via tail vein injection (2.5E5 – 2E7 cells per mouse) and positive engraftment was analyzed. If at least one mouse showed positive engraftment, the sample was classified as engrafted. R/R: relapsed/primary refractory. B , C Primary AML samples or AML PDX models were serially transplanted into NSG mice, and positive engraftment was analyzed. B Scheme illustrating the scoring system based on specific features. C Distribution of samples within Score 2 – Score 6. To assess predictive parameters for primary engraftment, we analyzed concomitant clinical data available for cohort 1 and reproduced known prediction factors such as relapse/primary refractory (R/R) disease and presence of FLT3 -ITD mutations (Table S2; Figure S1A) 18 , 20 - 24 . Additionally, BCOR -mutated samples, although low in numbers, showed a trend towards higher engraftment capacity (Figure S1A). Combining the effect of these two mutations by creating a composite binary variable did not improve the prediction model (not shown). Other examined variables such as patient age, gender, white blood cell count, or French-American-British (FAB) group did not predict engraftment (Table S1 and data not shown). In accordance with literature, xenograft potential negatively correlated with overall survival (OS) probability, with a major difference in 1-year OS between patients whose cells did or did not engraft (Figure S1B) 20 , 21 . This is consistent with the extremely poor prognosis for patients who suffer relapse, e.g., after allogeneic BM transplant, when treatment may focus on supportive care. Conflicting results have been published concerning engraftment capacity in male versus female NSG mice 25 , 26 . In our study, primary cells from male patients transplanted into male mice showed the lowest engraftment rates, while primary cells from female patients transplanted into female mice showed the highest engraftment rates; age of recipient mice, however, did not affect engraftment efficiency (Figure S1C,D). A rare subfraction of primografts allows generation of PDX models with robust serial engraftment and genetic engineering Serial re-transplantation of PDX AML cells beyond two passages has been scarcely studied, except for our own previous data 15 , 17 , 24 , 27 , 28 . Here, we aimed to establish robust, serially transplantable and fast engrafting PDX AML models which allow freeze/thaw cycles and genetic engineering as new tools for complex downstream applications ex vivo and in vivo . To identify appropriate PDX models, we defined subjective characteristics based on prior experience to categorize PDX AML models into six groups, considering the following features: (i) positive primary engraftment, (ii) serial engraftment capacity of frozen/thawed cells, (iii) high engraftment rate in the BM and in a majority of mice, (iv) passaging time (period from transplantation to end-stage leukemia) below 16 weeks, and (v) ability for lentiviral transduction ( Figure 1B ). Of the 137 AML samples included in the study, 76 (55%) showed positive primary engraftment (score ≥2), but only 52 (38%) showed positive serial engraftment (score ≥3). This contrasts with the closely related disease acute lymphoblastic leukemia (ALL), where both primary engraftment and serial passaging is feasible for virtually all samples 29 . Robust and fast serial engraftment was achieved in 35 samples (25.5%, score ≥4). While genetic manipulation was not conducted in samples of cohort 4, 23/124 (18.5%) samples of cohort 1-3 allowed genetic engineering (score ≥5, Figure 1B,C and S2A). We called those score 5/6 models * PDX AML models to indicate their ability for s erial transplantation a nd robust engraftment (STAR) and genetic engineering. In summary, * PDX AML models were generated from only around one-fifth of primary AML samples. In vivo characteristics of * PDX AML models * PDX AML models showed re-engraftment in up to 200 mice, up to 18 consecutive passages and thereby in vivo growth for up to 800 consecutive days until today, indicating the presence of non-exhausting leukemia-initiating cells (Table S1; Figure S2B,C). The passaging time was highly heterogeneous between and within models, with median passaging times ranging from 42 to 105 days, dependent on the passage number, and other factors in individual models (Figure S2D,E). The positive engraftment rate (PER) remained constant over sequential passaging and was independent of the number of injected cells, use of freshly isolated versus thawed cells, or recipient mouse age for most models (Figure S2F). On purpose, we included mice of both sexes into our studies, to prevent gender bias and ensure ethical animal use. While genetic engineering mildly affected PER, mouse gender had a more pronounced effect with higher PER in female than in male mice in some models, in line with published data (Figure S2F,G) 26 . The amount of PDX cells that could be re-isolated from BM or spleen at an end-stage leukemia was highly heterogenous within and across * PDX AML models; splenic engraftment associated with splenomegaly was observed in a subfraction and at late disease stages (Figure S2H), while we could not define any factors predicting splenic involvement (data not shown). Percentage of PDX cells in PB at advanced-stage leukemia differed between the models (Figure S2I). A particular advantage of the model is that, upon careful observation, most experiments can be terminated with mice showing no or mild symptoms (Figure S2J). * PDX AML models originate from patients with highly aggressive disease * PDX AML models might represent a biased subfraction of AML in patients; thus, we next examined variables associated with these models. As for primary engraftment (Figure S1A), disease status emerged as the most robust predictor for the ability to generate * PDX AML models ( Figure 2A and S3A) accordingly, patients whose cells gave rise to a * PDX AML model showed dismal OS ( Figure 2B and S3B) and thus represent a population of high need for better treatment. Most PDX models originating from ID samples failed to fulfill the prerequisites of score 5/6 PDX models, with four exceptions: AML-1177 and AML-1186, as well as AML-388 and AML-1226. The latter were generated from patients with primary refractory disease and are counted as “R/R samples”. Disease stage of AML-955 is unknown. Therefore, while around 60% of score 2-4 PDX models originated from ID, about 90% of score 5/6 PDX models originated from R/R disease (Figure S3A). The 23 * PDX AML models were retrieved from patients of all age groups and from both sexes and originated from five distinct AML World Health Organization (WHO) subgroups at representative proportions: 8/23 models harbor NPM1 mutations (30-35% of AML patients), 6/23 display a myelodysplasia-related phenotype (10-15% of AML cases) or have KMT2A rearrangement (KMT2Ar; about 5% in adults, 20-25% in children, respectively), and one model has a BCR::ABL1 fusion, which is also rare in patients ( Figure 2C ) 30 - 34 . As another peculiar feature, AML-491 and AML-661 are paired models derived from cells from the same patient collected at first and second relapse, respectively, allowing to study specific questions such as tumor recurrence. Interestingly, upon second relapse, a gain of an EZH2 LOF mutation was detected in this model (Table S3: Reiter 2018). Download figure Open in new tab Figure 2. Genomic characteristics of * PDX AML models better resemble primary AML cells than cell lines. A Prediction model for score 5/6 (n=23) versus Score 2-4 (n=46) was calculated by backward selection based on the Akaike information criterion, including samples of cohort 1-3. Left: percentage of models originating from initial diagnosis (ID, grey) or primary refractory / relapse (R/R, black) patient samples are depicted ( p= 8.7E-4). Right: percentage of models with no (grey) or detected mutation in KRAS or WT1 or ETV6 (black) are depicted ( p= 1.8E-05). The p -values were calculated with Fisher’s exact test. B Kaplan-Meier graph showing the survival probability of patients with R/R disease whose cells produced score 5/6 PDX models (green) or did not meet the criteria of high score models (score 2-4, blue). Log-rank test, p =0.053. C Genomic landscape of * PDX models . * PDX AML models are presented and categorized according to their WHO group. Patient (Pat.) data indicate age group, sex and disease status at time of sample collection. PDX models were characterized by cytogenetics, low coverage whole genome sequencing, RNA sequencing, methylation profiling, and DNA panel sequencing. PDX data depict cytogenetically normal (CN) models, detection of copy number alterations (CNAs), gene fusions, KMT2A rearrangements, and complex karyotype, as identified with at least two independent methods. Recurrent mutations are presented by variant allele frequency (VAF). D Relative frequencies of samples/models with specific mutational or chromosomal alterations are depicted for primary adult patient samples at relapsed disease of BEAT cohort (n=53, black), * PDX AML models generated from adult patients (n=18, green), and AML cell lines 36 generated from adult patients and having more than 500 hits in Google Scholar (n=19, grey). Events are included which are present in a minimum of 5% in one of the three cohorts and are found in primary patients. Alterations are categorized according to prevalence in cell line models and sorted according to relative frequencies in patients. Refer to Figure S3C for further categories. Genomic characteristics of * PDX AML models replicate AML biology better than cell lines Molecular alterations within serially passaged * PDX AML cells were studied using whole genome sequencing, panel sequencing, RNA sequencing, cytogenetics, methylation profiling and fluorescence in situ hybridization (Table S1). At the level of genomics and in contrast to primary engraftment, mutations in WT1, KRAS or ETV6 , but not in FLT3- ITD or BCOR , were associated with an increased likelihood to generate * PDX AML models. 60% of score 5/6 models harbor mutations in these genes compared to 5% of score 2-4 PDX models, although the presence of WT1, KRAS or ETV6 mutations does not correlate with patient OS probability ( Figure 2A and Figure S3C,D). Our * PDX AML models exhibited a wide range of chromosomal alterations and/or mutations known to be associated with AML. Transcriptome analysis revealed both known fusions and undescribed fusion genes ( Figure 2C and S3E,F; Table S1). We compared the frequency of alterations between primary AML cells, PDX models, and cell lines. In the initial analysis, we included primary AML samples from adult patients at relapse from the BEAT cohort (n=53) 35 , * PDX AML models from adults (n=18), and AML cell lines from adults which were most frequently used in research (n=19) 36 . Importantly, cytogenetically normal samples and mutations in IDH1, IDH2, PTPN11 and RAD21 can be modelled in * PDX AML models but are absent in AML cell lines or in most frequently applied cell lines, respectively. Additionally, mutations most prevalent in primary patient cells – NPM1, FLT3- ITD, DNMT3A and WT1 – are recapitulated in PDX models at similar frequencies but are severely underrepresented in cell lines. Conversely, cell lines have a strong bias towards TP53 mutations and complex karyotypes, while primary samples and * PDX AML models display these alterations at low frequencies ( Figure 2D and S3H,I). Several alterations were represented by both * PDX AML models and cell lines, with * PDX AML models overrepresenting KRAS mutations and KMT2A rearrangements. Some rare AML mutations were not found in * PDX AML models, explainable by the limited number of primary samples studied. Unfortunately, some mutations are still not represented, neither in * PDX AML models nor in cell lines, e.g. biallelic CEBPA, SF3B1 or SRSF2 , although for the latter we were able to establish a serially transplantable, although less robust PDX model of score 3 (Figure S3G-I; Table S1). When we further broadened the analyses by including additional primary patient data sets 37 , 38 , scores 3-6 AML PDX models derived from adult patients (n=45), and all adult AML cell lines (n=54) 36 , we could reproduce the results described above and additionally found that the number of AML-specific mutations was similar in * PDX AML models and primary samples (Figure S3I,J). In summary, PDX models were clearly superior to cell lines in mimicking the genetic and chromosomal alterations found in primary AML cells, including alterations for which no cell lines exist. Methylome, transcriptome and surface protein characteristics show that * PDX AML models resemble primary AML cells To further investigate the molecular classification of our models, we compared DNA methylation and gene expression (GE) patterns from * PDX AML models (Table S1) with available own data from AML patient samples at initial diagnosis (AML-CG, n=433) 39 , 40 and from AML cell lines (n=6). In the t-distributed Stochastic Neighbor Embedding (t-SNE) plots, PDX cells predominantly segregated from primary samples in both methylome and transcriptome ( Figure 3A and S4A). Of note, both methylation and GE of AMLCG samples were conducted from bulk samples with variable percentage of blasts, while pure AML cells of PDX models were analyzed; furthermore, the analyses were not available from patient cells originating from relapsed disease. Interestingly, most differentially expressed pathways were associated with immunity, possibly induced by the immunocompromised environment of * PDX AML models (Figure S4B-D). Download figure Open in new tab Figure 3. Methylome, transcriptome and surface protein characteristics show that * PDX AML models resemble primary AML cells. A t-SNE plot showing the methylation profile of primary patient samples at ID (209 patients from the AML-CG-1999 or AML-CG-2008 study and 224 patients from the AML-CG Registry (DRKS00020816), black), * PDX AML models (n=23, green), and AML cells lines (Leibniz Institute DSMZ, n=6, red). B Prediction Analysis of Microarrays (PAM) models . PAM classifiers based on DNA methylation profiling evaluated on AML-CG Registry patient samples (n=224) and * PDX AML models (n=19). Displayed are the true positive rate (TPR) and true negative rate (TNR) for genes mutated in at least 20% of PDX samples. C Immunophenotype of * PDX AML models resembles primary patient samples . Primary patient samples at initial diagnosis (ID, n=20), primary patient samples at relapse (REL, n=20), and * PDX AML models (n=23) were analyzed for surface molecule expression via flow cytometry. Primary patient samples are independent of PDX models. Violin plots depicting the percentage of antigen-positive cells within blast gate; median indicated by solid line and 25 th and 75 th percentile by dotted lines. We performed Prediction Analysis for Microarrays (PAM) with five genes mutated in at least 20% of * PDX AML models. The predictive outcomes were largely comparable between patient samples and PDX models, with no consistent trend favoring either cohort in terms of model performance, highlighting a major similarity between * PDX AML models and primary AML cells ( Figure 3B and S4E). Additionally, * PDX AML models maintained key immunophenotypic characteristics of primary AML cells, expressing myeloid and precursor antigens at heterogeneous levels and in similar percentages across all cohorts (CD33, CD123 and HLA-DR) or more biased towards samples from initial diagnosis (CD34 and CD33/CD7) or relapse (CD135 (FLT3) and CD33/CD64) ( Figure 3C and S4F). In summary, PDX models largely mimic primary patient AML cells on the level of transcriptome, methylome and immunophenotype. * PDX AML models allow a wide range of ex vivo and in vivo experiments Until today, * PDX AML models have been successfully used for a variety of applications, including functional and molecular assays and treatment trials, and were included in 45 scientific publications from collaboration partners across the globe and us ( Figure 4A and S5A-C; Table S3). To further broaden their scientific use, these models and related characteristics will be shared via the openly accessible repository CancerModels.org 41 . Download figure Open in new tab Figure 4: * PDX AML models allow a wide range of ex vivo and in vivo experiments, including long-term in vivo treatment trials. A Scheme illustrating published in vivo and ex vivo trials conducted with * PDX AML models. B-E Repetitive treatment to model induction plus consolidation chemotherapy . AML-491 PDX cells were injected into mice and tumor burden was monitored by BLI. At a total flux of around 1E9 P/sec, mice were either treated with PBS (n=7, grey) or two cycles of chemotherapy (C1 and C2, n=15, black). After re-growth, animals were sacrificed and PDX cells were re-injected into next recipient mice receiving a third cycle of chemotherapy (n=6, C3). B Scheme. Grey syringe: AraC 50 mg/kg, d1-4 + DNX 1mg/kg, d1+4, 10 days rest; black syringe: AraC 100 mg/kg, d1-4, 10 days rest. C BLI pictures of two exemplary mice are shown; numbers indicate weeks after treatment start. D Quantification of BLI. Grey box indicates treatment period, and red lines show exponential trend line during growth. E Growth rate, measured by increase in BLI signal, is depicted for untreated mice (before A/D, n=7), after stop of 1 st (C1, n=15), 2 nd (C2, n=13) or 3 rd (C3, n=6) cycle of chemotherapy. Dots indicate values of individual mice, line indicates median. n.s. not significant, * p <0.05, **** p <0.0001 as tested by two-sided unpaired t-test with Welch’s correction. F , G Long-term therapy with Azacitidine . AML-372 (n=4-5 per group, diamonds) or AML-491 (n=4 per group, circles) PDX cells were injected into mice and tumor burden was monitored by BLI. Mice were either treated with PBS (grey) or Azacitidine (black) for 11 weeks. F Quantification of BLI. Grey box indicates treatment period, and red lines show exponential trend line during growth. G Growth rate, measured by increase in BLI signal, is depicted for untreated mice (before Azacitidine, n=4) or after stop of Azacitidine therapy (n=5). Dots indicate values of individual mice. *** p <0.001 as tested by two-sided unpaired t-test with Welch’s correction. PDX cells allow functional characterization of leukemia cells and investigation of different tumor cell subpopulations. In AML primografts and with the help of secondary transplantation assays, the cancer stem cell concept had been elucidated 13 . Our serially transplantable models enable stem cell-associated tests such as limited dilution transplantation assays to determine the frequency of leukemia-initiating stem cells 17 , 42 (Table S3: Tahk 2021, Ghalandary 2023, Bahrami 2023, Zhou 2023). Moreover, they also facilitate functional assays allowing to investigate homing (Table S3: Bahrami 2023), (re-) engraftment capacity (Table S3: Yankova 2021, Zhou 2023, Able 2025) and proliferation status 42 of leukemia cells ( Figure 4A and S5A). * PDX AML cells freshly isolated from donor mice display a higher viability than frozen/thawed cells, allowing complex ex vivo studies with high cell numbers and over several weeks (Figure S5E). However, ex vivo studies could only be performed with PDX cells amplified in donor mice, as * PDX AML models show only minor proliferation rates ex vivo . For cutting edge in vivo experiments, * PDX AML models were genetically engineered to stably express enhanced firefly luciferase for sensitive and repetitive monitoring of tumor burden using BLI 17 , 43 . For molecular studies, derivative * PDX AML models were generated which express various transgenes, such as fluorochromes, Cre-ER T2 or Cas9. This allows fluorescent labelling for cell enrichment 42 , genetic barcoding to track (single cell) clones (Table S3: Zeller 2022), (inducible) knock-down (Table S3: Garg 2019, Jensen 2020, Carlet 2021, Sun 2021, Sollier 2025), CRISPR/Cas9 knock out (Table S3: Salik 2020, Ghalandary 2023, Bahrami 2023, Barbosa 2024), marker-guided competitive in vivo assays (Table S3: Carlet 2021, Zeller 2023, Ghalandary 2023, Bahrami 2023) and CRISPR/Cas9 in vivo and in vitro screens (Table S3: Ghalandary 2023, Bahrami 2023) (Figure S5B,D). Serially transplantable, luciferase-transgenic PDX models are particularly suited to perform comprehensive pre-clinical in vivo therapy trials. Importantly, in-depth characterization allows the selection of samples representing specific patient subgroups which might benefit most from specific (targeted) drugs; for example, FLT3 -ITD positive samples were specifically selected for testing a novel multi-kinase inhibitor (Table S3: Lopez-Millan 2022). The value of our PDX models has also been recognized by the leukemia community and sparked numerous collaborations with research groups from Germany, Europe and across the globe focusing on the development of new treatment options. In the last years, multiple therapeutic strategies were tested in our PDX models including targeted therapies (Table S3: Garz 2017, Lynch 2019, Janssen 2022, Lopez-Millan 2020, Yankova 2021, Shi 2025), immunotherapies (Table S3: Gottschlich 2023, Tahk 2021, Baroni 2020) and antibody-based therapies (Table S3: Krupka 2015, Roas 2022, Augsberger 2022, Able 2025; Figure S5C). Long-term in vivo experiments show reduced growth rates after treatment As a concrete example of their application, we show here that * PDX AML models and repetitive in vivo imaging enable long-term in vivo treatment trials and thus provide new insights into the disease biology. Spontaneous tumor growth cannot be studied in patients as immediate and continuous treatment is required upon cancer diagnosis. The influence of treatment on spontaneous growth rates remains largely elusive in leukemia, while certain solid tumors showed accelerated growth after chemotherapy 44 , 45 . Most treatment regimens are based on repetitive therapy cycles or constant drug application over months. These schedules are difficult to reproduce in current experimental models, among others due to the aggressiveness of cell line models or the lack of options for continuous tumor monitoring. Using an adapted form of a mouse equivalent to the “7+3” induction therapy in patients 46 , 47 , we mimicked induction- and consolidation therapy and treated mice transplanted with luciferase-transgenic * PDX AML-491 cells with repetitive cycles of chemotherapy ( Figure 4B-E ). Of note, NSG mice only tolerate low-doses of DNA-damaging agents due to their scid -background 47 . We have previously shown that PDX models differed in their response towards a 3 week-treatment with Cytarabine and DaunoXome, a liposomally encapsulated version of the anthracycline Daunorubicin, which is currently not commercially available. While therapy only had a minor effect on tumor burden in AML-393 and AML-661, AML-491 showed response (Table S3: Stief 2020, Kempf 2021, Zeller 2022). Therefore, we chose this model to simulate induction and consolidation therapy. Mice were transplanted with PDX AML-491 cells and treated with two cycles of chemotherapy ( Figure 4B ). While solvent-treated control mice rapidly progressed to advanced leukemia as monitored by BLI, the treatment significantly reduced tumor burden but – similar to what is commonly observed in the clinics – tumor re-growth occurred upon therapy cessation, both after first and second treatment cycle ( Figure 4CD ). PDX cells were isolated in the logarithmic growth phase, around 30 weeks after cell transplantation, and re-transplanted due to mouse aging into secondary recipients which received a third treatment cycle ( Figure 4B ). Repeatedly, the tumor load was effectively reduced with re-occurrence after stopping treatment ( Figure 4D ). Despite expected reduction in blood cell counts 48 , the treatment was well tolerated over prolonged periods of time (Figure S5F,G). After a total of 53 experimental weeks with nine blocks of chemotherapy in three cycles, we were surprised to find that response to treatment remained unchanged, with no signs of acquired resistance, while tumor growth rate decreased continuously with each treatment block ( Figure 4D,E and S5H). Importantly, only continuous imaging allowed to capture this additional, essential piece of information. Unfit AML patients are typically treated with hypomethylating agents 49 . Treatment of * PDX AML-491 cells with Azacitidine monotherapy for three months reduced tumor burden below the imaging detection limit, while mice carrying * PDX AML-372 showed tumor re-growth after end of treatment with an increase in BLI doubling time, indicating that a reduced growth rate after treatment represents a shared feature across different treatment modalities and PDX models ( Figure 4F,G and S5I,J). In summary, we established * PDX AML models - serially transplantable and robust genetically engineered PDX AML models - for translational and preclinical trials, harboring the potential to foster future AML research for the ultimate benefit of patients suffering AML. Discussion To advance cancer research, we established and characterized * PDX models as a sharable tool according to PDX-MI guidelines 50 . They fulfill clearly defined criteria regarding the ability of serial transplantation, robust engraftment and genetic engineering. * PDX AML models represent alterations found in primary AML patient cells, both at initial diagnosis and relapse, and include subgroups that cannot be modelled with cell lines, such as cytogenetically normal and IDH1/2 -mutant AML. They are used for complex molecular and functional studies, both ex vivo and in vivo , and are especially suitable for long-term preclinical treatment trials. * PDX AML models complement existing model systems such as primary tumor cells, primografts, and cell lines. Primary patient cells are typically available mostly in limited numbers, especially from pediatric patients and in certain countries, complicating data reproducibility and standardization of experiments. Primary patient cells and primografts don’t consistently grow ex vivo , while it will be interesting to study whether PDX AML cells do so without major skewing. Compared to primary patient samples, primografts undergo clonal selection with a bias towards “relapse-committed” clones 16 , 51 - 53 , complicating co-clinical trials for individual patients. Additionally, clones might be further enriched during serial passaging 21 . Nevertheless, our * PDX AML models, even from advanced passages and after genetic engineering, showed high overall concordance with primary patient samples regarding mutational landscape, chromosomal alterations, and methylation patterns. As shown before, neither serial passaging nor genetic engineering had a major impact on characteristics of * PDX AML models, including mutations, transcriptome, or therapy response 17 , 54 , 55 . Importantly, we do not anticipate that each PDX model represents the corresponding primary patient sample. Instead, we have established robust AML models representing various specific disease subgroups, facilitating scientific discoveries relevant for groups of patients, not for individual patients. One limitation of our cohort is that it only includes samples collected from patients in Europe and North America, thus introducing an ethnic bias. Furthermore, * PDX-AML models are biased towards high-risk subgroups. Primary leukemia cells from AML subtypes with less dismal prognosis showed positive primary engraftment in immune compromised mice only after long latency and at low percentage 22 , making it impossible to develop * PDX models as defined here. Still, generating robust PDX models from highly aggressive samples represents a valuable tool to model the clinically most challenging subtypes of the disease with most urgent need for new treatment options. The advantage of * PDX AML models lies in their ability to provide highly patient-related cells repetitively and reproducibly for a wide variety of different AML subtypes and clones across the broad heterogeneity of AML. Compared to cell lines, PDX models are time- and resource-intensive and logistically complex 56 , 57 . Nevertheless and importantly, PDX AML models harbour major advantages over cell lines which include (i) orthotopic engraftment due to a persistent dependence on the physiologic tumor microenvironment 42 , 58 ; (ii) maintained stem cell hierarchy 17 , 59 , 60 ; (iii) higher similarity to primary AML cells; (iv) availability of AML subtypes for which no cell lines exist; and (v) improved predictive value for drug responses 48 , 61 . * PDX models allow performing prolonged in vivo treatment trials. Repetitive in vivo imaging facilitates close monitoring of tumor burden under treatment and thereby enabled, to our knowledge, the first preclinical in vivo trial in acute leukemia which continued for 12 consecutive months. The technical advance allowed studying the kinetics of tumor repopulation after treatment, a topic that was often neglected so far, but is relevant to understand real world treatment outcomes 45 . In contrast to findings in solid tumors, where tumor growth was accelerated after chemotherapy 44 , 45 , we observed slower growth after treatment. Our results are consistent with the well-known fact that chemotherapy is more effective in proliferating cells than in resting cells 42 , 62 . Reduced tumor growth kinetics might be explained by efficient eradication of proliferating cells and exhaustion of cells in the stem cell compartment. However, these results need to be validated in additional PDX models and future projects are required to uncover the underlying mechanisms. Of relevance for clinical decision making, our data show that AML growth dynamics might substantially differ directly after treatment compared to disease relapse. In summary, * PDX AML models mimic the human disease more closely than cell lines and can be used in preclinical studies to generate data for subsequent clinical trials across multiple genetically distinct AML subtypes. Our vision is that * PDX AML models will be globally shared analogous to cell lines, contribute to scientific progress in the field and ultimately help improving the prognosis of patients suffering from AML. Materials and Methods Ethical Statement BM and peripheral blood (PB) samples from AML patients were collected for diagnostic purposes. Signed informed consent was obtained from the patients or from person having custody in case patients were minor. The study was performed in accordance with the Helsinki Declaration of 1975, as revised in 2013, and with the ethical standards of the responsible committees on human experimentation (Research Ethics Boards of the medical faculty of LMU Munich, #068-08, 222-10 and 20-739). Mouse work Animal trials were performed in accordance with the ethical standards of the official committee on animal experimentation and according to ARRIVE guidelines 63 . Five to 35 weeks old male and female NOD.Cg-Prkdc scid IL2rg tm1Wjl /SzJ (NSG) mice (The Jackson Laboratory, Bar Harbour, USA) were included (n=2111); these were part of diverse scientific projects (Table S3). To reduce gender bias and ensure ethical animal use, we included both female and male mice and both younger and older mice in our studies 64 . This approach minimizes the number of animals bred without scientific purpose, in line with German regulations that prohibit the killing of animals without justified scientific need. No randomization or blinding was performed. All mice, mouse-related interventions and values were documented in a customized database (My IMouse, Bioslava GbR, Berg, Germany). Transplantation of primary leukemia cells into NSG mice Depending on availability of cells, 2.5E5 to 1E7 fresh or frozen/thawed primary cells were injected into the tail vein of one to eight NSG mice. In contrast to literature 15 , 16 , 22 , 65 and to avoid treatment-induced bias, mice were not pre-irradiated prior cell injection. No enrichment or depletion steps were performed. Exclusion criteria were samples from EDTA or citrate anti-coagulated material, samples with less than 25% blasts, samples with less than 2.5E5 cells, and acute promyelocytic leukemias. Information on samples is listed in Table S1 according to PDX-MI guidelines 50 . Functional scoring system of PDX cells PDX models were categorized into classes (Score 1 – 6) according to functional engraftment characteristics. Score 1: no primary engraftment. Score 2: primary, but no secondary engraftment. Score ≥3: serial positive engraftment; positive engraftment after freeze-thawing cycles; engraftment primarily in BM and spleen. Score ≥4: detection of >80% human cells in BM at end-stage leukemia; positive engraftment in at least 75% (Score 4 and 5) or at least 90% (Score 6) of transplanted mice; median time from transplantation to end-stage leukemia below 110, 90 or 70 days (Score 4, 5 or 6, respectively). Score ≥5: successful genetic engineering. Score 6: transduction rates above 5%. Therapy trials Repetitive treatment cycles with cytarabine (AraC) and DaunoXome (DNX) AML-491 PDX cells were injected into groups of mice (5E5 luc + cells/mouse). Tumor burden was regularly monitored by bioluminescence in vivo imaging (BLI). When BLI signals reached a total flux of around 1E9 Photons/second, mice were either treated with PBS (n=7) or first cycle of chemotherapy (C1) (n=15). Treated mice showed a reduction of tumor burden followed by a re-growth of tumor cells and were again treated with a second cycle of chemotherapy (C2). When BLI signals again reached a total flux above 1E9 Photons/second, or when mice showed clinical signs of illness, the animals were sacrificed. PDX cells were re-isolated, counted, and 5E6 cells were re-injected into next recipient mice. These mice received a third cycle of chemotherapy (C3) (n=6). Chemotherapy schedule: per cycle three repetitions with four days treatment and ten days rest; C1, week 1 and 3: AraC 50 mg/kg d1-4, DNX 1 mg/kg d1+4; cycle 1, week 5: AraC 100 mg/kg d1-4; C2 and C3, week 1, 3 and 5: AraC 100 mg/kg, d1-4. AraC (Cell Pharma GmbH, Bad Vilbel, Germany) was diluted in PBS and applied i.p., DNX (Galen Ltd, Craigavon, United Kingdom) was diluted in distilled H 2 O and applied i.v. Long-term treatment with Azacitidine (Aza) AML-372 (n=3 per group) or AML-491 (n=4 per group) PDX cells were injected into groups of mice (3E5 luc+ cells/mouse). Tumor burden was regularly monitored by BLI. When BLI signals reached a total flux of around 1E8 Photons/second, mice were treated i.p. either with PBS or Azacitidine (2.5 mg/kg or 5 mg/kg, diluted in PBS) three times a week for 11 weeks. Monitoring of tumor burden was continued after end of therapy. Enrichment of AML PDX cells for sequencing AML PDX cells were enriched from murine BM via negative selection by magnetic cell sorting using mouse cell depletion kit (Miltenyi Biotech, Bergisch Gladbach, Germany). 100 μl MicroBeads were used per 1E7 BM cells isolated from mice with end-stage leukemia. Enriched PDX cells were lysed in RLT-Buffer (Qiagen, Hilden, Germany) with 1% beta-mercaptoethanol. Prediction models To develop a prediction model for positive engraftment, a backward selection based on the Akaike information criterion was performed. Variables were included that had less than 10% of missing values and a univariable p -value of ≤0.1. Statistics Statistical analyses were calculated using GraphPad Prism 10 software (Graphpad Prism, La Jolla, CA, USA) and R 66 . Statistical tests used are indicated in the figure legends. No star: not significant; * p <0.05, ** p <0.01, *** p <0.001, **** p <0.0001. Further methods are provided in the supplementary information. Author contribution B.V., V.J., G.H., A.M., L.H., L.M., F.K., and A.F. performed experiments; B.V., V.J., K.K., L.Lagally, L.Laszko, C.A., M.R.T., A.M.D., S.S., K.H.M., P.A.G., M.S., S.V., and T.H. analyzed results; M.C.A., C.D.B., M.P.C., C.D., M.E., K.S.G., P.M., J.E.S., C.R., F.V., V.d.M., A.S., C.T., T.V.H., X.W., J.H.K., M.v.B.B., W.H., M.S., T.H., and K.S. provided material and data; B.V., K.K., and I.J. made the figures; B.V., T.H., K.S., and I.J. designed the research; B.V., K.K., and I.J. wrote the paper. Conflict of interest TH: Travel support: Jazz Pharmaceuticals; Participation on Advisory Board: Servier and Jazz Pharmaceuticals; honoraria for speakers: Astellas Pharma. All other authors declare no conflict of interest. Acknowledgements We thank all team members of the Irmela Jeremias lab for helpful discussions and critically reading the manuscript; Maike Fritschle, Zahra Raffie Pour, Chantal Jekien (all Irmela Jeremias lab) and Bianka Ksienzyk (Department of Medicine III, LMU Munich) for excellent technical assistance; Stephanie Hoffmann for lab management; Markus Brielmeier and team (Research Unit Comparative Medicine) for animal care services; Véronique de Mas and François Vergez (Diagnosis laboratory, University of Toulouse) and the team of the leukemia diagnostics lab (LFL, Department of Medicine III, LMU Munich) for providing primary patient cells and diagnostics; PRoXe (Weinstock Laboratory, Dana-Farber Cancer Institute) for providing PDX models; Johannes Bagnoli and Wolfgang Enard (Faculty of Biology, LMU Munich) for primeSeq of * PDX models, Marina Vogel and Andrea Weber (Genomics & Proteomics Core Facility, German Cancer Research Center (DKFZ)), and Stefan Krebs, Helmut Blum and Alexander Graf (Laboratory for Functional Genome Analysis, LMU Munich) for providing excellent sequencing services; Isolde Summerer and Claudia Haferlach (MLL Münchner Leukämielabor GmbH) for cytogenetic characterization of * PDX samples. The work was supported by grants from the European Research Council (Consolidator Grant 681524) and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 537701335 (Reinhart Koselleck project) and SFB 1709/1 2025 – 533056198; a Mildred Scheel Professorship by German Cancer Aid; Bettina Bräu Stiftung and Dr. Helmut Legerlotz Stiftung (all to IJ) and by funding from the Deutsche José Carreras Leukämie-Stiftung (DJCLS 15 R/2021 and DJCLS 02 R/2023 to I.J. and P.M.) Funder Information Declared European Research Council, https://ror.org/0472cxd90 , 681524 Deutsche Forschungsgemeinschaft, https://ror.org/018mejw64 , 537701335 , SFB 1709/1 2025 - 533056198 German Cancer Aid, https://ror.org/01wxdd722 , Mildred Scheel Professorship Deutsche José Carreras Leukämie-Stiftung, https://ror.org/00826gz80 , DJCLS 15 R/2021 , DJCLS 02 R/2023 Bettina Bräu Stiftung Dr. Helmut Legerlotz Stiftung References 1. ↵ Döhner H , Wei AH , Appelbaum FR , et al. 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