Heritable genetic background alters survival and phenotype of Mll-AF9-induced leukemias

•Mll-AF9 knockin mice were crossed with five inbred strains, and the F1 progeny were tested.•Genetic background alters peripheral blood lineage composition.•Genetic background has an impact on overall survival.•Genetic background alters types of hematologic malignancies observed. The MLL-AF9 fusion protein occurring as a result of t(9;11) translocation gives rise to pediatric and adult acute leukemias of distinct lineages, including acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), and mixed-phenotype acute leukemia (MPAL). The mechanisms underlying how this same fusion protein results in diverse leukemia phenotypes among different individuals are not well understood. Given emerging evidence from genome-wide association studies that genetic risk factors contribute to MLL-rearranged leukemogenesis, here we tested the impact of genetic background on survival and phenotype of a well-characterized Mll-AF9 knockin mouse model. We crossed this model with five distinct inbred strains (129, A/J, C57BL/6, NOD, CAST) and tested their F1 hybrid progeny for dominant genetic effects on Mll-AF9 phenotypes. We discovered that genetic background altered peripheral blood composition, with Mll-AF9 CAST F1 having a significantly increased B-lymphocyte frequency, while the remainder of the strains exhibited myeloid-biased hematopoiesis, similar to the parental line. Genetic background also had an impact on overall survival, with Mll-AF9 A/J F1 and Mll-AF9 129 F1 having significantly shorter survival and Mll-AF9 CAST F1 having longer survival, compared with the parental line. Furthermore, we observed a range of hematologic malignancies, with Mll-AF9 A/J F1, Mll-AF9 129 F1, and Mll-AF9 B6 F1 developing exclusively myeloid cell malignancies (myeloproliferative disorder [MPD] and AML), whereas a subset of Mll-AF9 NOD F1 developed MPAL and Mll-AF9 CAST F1 developed ALL. This study provides a novel in vivo experimental model in which to evaluate the underlying mechanisms by which MLL-AF9 results in diverse leukemia phenotypes and provides definitive experimental evidence that genetic risk factors contribute to survival and phenotype of MLL-rearranged leukemogenesis. The MLL-AF9 fusion protein occurring as a result of t(9;11) translocation gives rise to pediatric and adult acute leukemias of distinct lineages, including acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), and mixed-phenotype acute leukemia (MPAL). The mechanisms underlying how this same fusion protein results in diverse leukemia phenotypes among different individuals are not well understood. Given emerging evidence from genome-wide association studies that genetic risk factors contribute to MLL-rearranged leukemogenesis, here we tested the impact of genetic background on survival and phenotype of a well-characterized Mll-AF9 knockin mouse model. We crossed this model with five distinct inbred strains (129, A/J, C57BL/6, NOD, CAST) and tested their F1 hybrid progeny for dominant genetic effects on Mll-AF9 phenotypes. We discovered that genetic background altered peripheral blood composition, with Mll-AF9 CAST F1 having a significantly increased B-lymphocyte frequency, while the remainder of the strains exhibited myeloid-biased hematopoiesis, similar to the parental line. Genetic background also had an impact on overall survival, with Mll-AF9 A/J F1 and Mll-AF9 129 F1 having significantly shorter survival and Mll-AF9 CAST F1 having longer survival, compared with the parental line. Furthermore, we observed a range of hematologic malignancies, with Mll-AF9 A/J F1, Mll-AF9 129 F1, and Mll-AF9 B6 F1 developing exclusively myeloid cell malignancies (myeloproliferative disorder [MPD] and AML), whereas a subset of Mll-AF9 NOD F1 developed MPAL and Mll-AF9 CAST F1 developed ALL. This study provides a novel in vivo experimental model in which to evaluate the underlying mechanisms by which MLL-AF9 results in diverse leukemia phenotypes and provides definitive experimental evidence that genetic risk factors contribute to survival and phenotype of MLL-rearranged leukemogenesis. Chromosomal rearrangements involving the mixed-lineage leukemia 1 (MLL1) gene, also known as Lysine [K]-specific methyltransferase 2A (KMT2A), generate fusion proteins causing aggressive acute leukemias in infants, children, and adults. MLL-rearranged leukemias constitute ∼10% of acute leukemias across all age groups [1Muntean AG Hess JL. The pathogenesis of mixed-lineage leukemia.Annu Rev Pathol. 2012; 7: 283-301Crossref PubMed Scopus (243) Google Scholar]. Patients with MLL-rearranged leukemias generally have a poor prognosis, with high-risk treatment options and frequent relapse. This underscores an unmet need for novel therapeutic approaches to improve outcomes in MLL-rearranged leukemia. Emerging evidence from genome-wide association studies (GWAS) suggest that heritable genetic polymorphisms can modify the risk of MLL-rearranged leukemia [2Winters AC Bernt KM. MLL-Rearranged leukemias—an update on science and clinical approaches.Front Pediatr. 2017; 5: 4Crossref PubMed Scopus (227) Google Scholar, 3Ross JA Linabery AM Blommer CN et al.Genetic variants modify susceptibility to leukemia in infants: a Children's Oncology Group report.Pediatr Blood Cancer. 2013; 60: 31-34Crossref PubMed Scopus (39) Google Scholar, 4Emerenciano M Barbosa TC Lopes BA et al.ARID5B polymorphism confers an increased risk to acquire specific MLL rearrangements in early childhood leukemia.BMC Cancer. 2014; 14: 127Crossref PubMed Scopus (20) Google Scholar]. To definitively test causation and build upon these findings toward development of novel therapeutic targets, use of in vivo mouse models of MLL-rearranged leukemia is ideal. However, the vast majority of genetically engineered mouse models of human leukemia are studied on a single inbred genetic background, C57BL/6, despite genetic variability having been recognized as an important modifier of leukemogenesis in mouse models [5Zhang S Ramsay ES Mock BA Cdkn2a, the cyclin-dependent kinase inhibitor encoding p16INK4a and p19ARF, is a candidate for the plasmacytoma susceptibility locus.Pctr1. Proc Natl Acad Sci USA. 1998; 95: 2429-2434Crossref PubMed Scopus (128) Google Scholar, 6Fenske TS McMahon C Edwin D et al.Identification of candidate alkylator-induced cancer susceptibility genes by whole genome scanning in mice.Cancer Res. 2006; 66: 5029-5038Crossref PubMed Scopus (41) Google Scholar, 7Hunter KW. Mouse models of cancer: does the strain matter?.Nat Rev Cancer. 2012; 12: 144-149Crossref PubMed Scopus (39) Google Scholar, 8Janke MR Baty JD Graubert TA SWR/J mice are susceptible to alkylator-induced myeloid leukemia.Blood Cancer J. 2013; 3: e161Crossref PubMed Scopus (5) Google Scholar]. The first MLL fusion protein to be modeled as an endogenous knockin allele in mice was Mll-AF9 (t(9;11)) [9Corral J Lavenir I Impey H et al.An Mll-AF9 fusion gene made by homologous recombination causes acute leukemia in chimeric mice: a method to create fusion oncogenes.Cell. 1996; 85: 853-861Abstract Full Text Full Text PDF PubMed Scopus (451) Google Scholar]. After an early myeloproliferative phase, Mll-AF9 mice primarily succumb to AML and, only in rare cases, to ALL [10Dobson CL Warren AJ Pannell R et al.The mll-AF9 gene fusion in mice controls myeloproliferation and specifies acute myeloid leukaemogenesis.EMBO J. 1999; 18: 3564-3574Crossref PubMed Scopus (178) Google Scholar,11Chen W Li Q Hudson WA Kumar A Kirchof N Kersey JH A murine Mll-AF4 knock-in model results in lymphoid and myeloid deregulation and hematologic malignancy.Blood. 2006; 108: 669-677Crossref PubMed Scopus (101) Google Scholar]. This is notably distinct from human disease, where MLL-AF9 is found in both B-cell acute lymphocytic leukemia (B-ALL) and acute myeloid leukemia (AML) in infants and children and AML in adults [12Meyer C Burmeister T Gröger D et al.The MLL recombinome of acute leukemias in 2017.Leukemia. 2018; 32: 273-284Crossref PubMed Scopus (385) Google Scholar]. Here, we have utilized this well-characterized Mll-AF9 knockin mouse model to test the extent to which dominant genetic alleles modify Mll-AF9-driven leukemogenesis using genetically diverse mouse strains [13Roberts A Pardo-Manuel de Villena F Wang W McMillan L Threadgill DW The polymorphism architecture of mouse genetic resources elucidated using genome-wide resequencing data: implications for QTL discovery and systems genetics.Mamm Genome. 2007; 18: 473-481Crossref PubMed Scopus (196) Google Scholar,14Saul MC Philip VM Reinholdt LG Center for Systems Neurogenetics of Addiction, Chesler EJ. High-diversity mouse populations for complex traits.Trends Genet. 2019; 35: 501-514Abstract Full Text Full Text PDF PubMed Scopus (63) Google Scholar]. Kmt2atm2(MLLT3)Thr/KsyJ (referred to as Mll-AF9, Stock No. 009079) mice were obtained from, and aged within, The Jackson Laboratory (Bar Harbor, ME). The Mll-AF9 model was created on a 129P2/OlaHsd background, crossed with C57BL/6NCrl females for four generations, and has been maintained at The Jackson Laboratory since 2012 on a mixed C57BL/6 and 129S1/SvlmJ background by breeding with B6129PF1/J. The Mll-AF9 original strain was crossed to the A/J, C57BL/6 (B6), 129S1/SvlmJ (129), NOD/ShiLtJ (NOD), and CAST/EiJ (CAST) strains to create F1 generation experimental mice. Male and female F1 progeny from each strain cross were included in the studies and monitored from 8 weeks of age until moribund. Female and male mice were analyzed for peripheral blood complete blood count (PB CBC) data at 6 months of age. The Jackson Laboratory's Institutional Animal Care and Use Committee (IACUC) approved all experiments. PB was collected from mice via the retro-orbital sinus, and red blood cells were lysed before staining mature lineage markers: B220 (clone RA3-6B2), CD3e (clone 145-2C11), CD11b (clone M1/70), Gr-1 (clone RB6-8C5). Stained cells were analyzed on an LSRII (BD Biosciences, San Jose, CA), and populations were analyzed using FlowJo software, version 10. Differential blood cell counts were obtained from PB using an Advia 120 Hematology Analyzer (Siemens Healthineers). RNA was isolated from whole bone marrow (BM) from moribund mice using the RNeasy mini kit (Qiagen), and quantitative polymerase chain reaction (PCR) was performed using RT2 SYBR Green ROX qPCR Mastermix (Qiagen) on a QuantStudio 7 Flex (Applied Biosystems). Mll-AF9 expression level was calculated relative to the housekeeping gene B2M. Primer sequences were as follows:MllAF9 Forward: 5′-TGTGAAGCAGAAATGTGTGGMllAF9 Reverse: 5′-TGCCTTGTCACATTCACCATB2M Forward: 5′-TTCTGGTGCTTGTCTCACTGAB2M Rev: 5′-CAGTATGTTCGGCTTCCCATTC Moribund mice identified by declining health status were euthanized, and PB, spleen, liver, and BM harvested. Single-cell suspensions of PB, spleen, and BM were analyzed by flow cytometry for mature lineage markers and c-Kit (clone 2B8), using an LSRII (BD Biosciences), and populations were analyzed using FlowJo (Ashland, OR) software, version 10. Differential blood cell counts were obtained from PB using the Advia 120 Hematology Analyzer (Siemens). Cytospin preparations of whole BM mononuclear cells (MNCs) were stained with May–Grunwald–Giemsa stain. Liver and spleens were fixed for 24 hours in 10% buffered formalin phosphate and embedded in paraffin; sections were stained with hematoxylin and eosin. Histological images of stained BM, liver, and spleen were captured on a Nikon Eclipse Ci upright microscope with SPOT imaging software (version 5.6). To determine overall survival, a log-rank (Mantel–Cox) test was performed on Kaplan–Meier survival curves. Statistical analysis of nonsurvival data was performed with the Brown–Forsythe one-way analysis of variance (ANOVA), test followed by Dunnett's multiple comparison test. All statistical tests, including evaluation of normal distribution of data and examination of variance between groups being statistically compared, were assessed using Prism 8 software (GraphPad, San Diego, CA). To determine the role of genetic diversity in MLL-AF9 leukemia, we crossed the Mll-AF9 knock-in mouse model [9Corral J Lavenir I Impey H et al.An Mll-AF9 fusion gene made by homologous recombination causes acute leukemia in chimeric mice: a method to create fusion oncogenes.Cell. 1996; 85: 853-861Abstract Full Text Full Text PDF PubMed Scopus (451) Google Scholar] with the five distinct inbred strains: A/J, C57BL/6 (B6), 129S1/SvlmJ (129), NOD/ShiLtJ (NOD), and CAST/EiJ (CAST). We studied F1 hybrid mice heterozygous for Mll-AF9 from these crosses versus the parental genetic background (Figure 1A). F1 hybrid mice heterozygous for Mll-AF9 from all crosses were born at expected Mendelian frequencies (Supplementary Figure E1, online only, available at www.exphem.org). The MLL-AF9 parental strain has been maintained as it was historically, on a mixed B6 and 129 background. While the parental Mll-AF9 strain has previously been found to develop leukemia around ∼6 months of age, detectable myeloid proliferation has been observed by 8 to 10 weeks of age [9Corral J Lavenir I Impey H et al.An Mll-AF9 fusion gene made by homologous recombination causes acute leukemia in chimeric mice: a method to create fusion oncogenes.Cell. 1996; 85: 853-861Abstract Full Text Full Text PDF PubMed Scopus (451) Google Scholar,11Chen W Li Q Hudson WA Kumar A Kirchof N Kersey JH A murine Mll-AF4 knock-in model results in lymphoid and myeloid deregulation and hematologic malignancy.Blood. 2006; 108: 669-677Crossref PubMed Scopus (101) Google Scholar]. Consistent with this observation, analysis of PB of parental Mll-AF9 mice compared with MllWT littermates at 8 weeks of age revealed a significantly increased myeloid cell frequency with concomitant reduction in T-cell frequency (Figure 1B). This same phenotype was observed in Mll-AF9 A/J F1, Mll-AF9 B6 F1, Mll-AF9 129 F1, and Mll-AF9 NOD F1 mice. In contrast, myeloid cell frequency in Mll-AF9 CAST F1 mice did not significantly differ from that in MllWT mice but, instead, a significant increase in B-cell frequency and reduction in T-cell frequency were observed. To determine whether this observation was based on baseline differences in the CAST genetic background, we examined PB composition in wild-type CAST mice versus the other strains used in this study. We found that wild-type CAST mice have no differences in PB composition compared with the other strains (Supplementary Figure E2A, online only, available at www.exphem.org), suggesting that this phenotype may be a consequence of Mll-AF9 expression. To evaluate baseline differences in PB composition between the F1 strains, we examined PB of MllWT littermates at 8 weeks of age from a subset of the F1 crosses (MllWT A/J F1, MllWT B6 F1, and MllWT CAST F1). We found that certain F1 strains differ in baseline PB composition from the parental background (Supplementary Figure E2B). MllWT CAST F1 have a reduced frequency of myeloid cells, both MllWT A/J F1 and MllWT CAST F1 have higher frequencies of B cells, and MllWT A/J F1 has a reduced frequency of T cells. Although this suggests that the F1 hybrid strains do differ in baseline PB composition, these differences do not explain the reduced myeloid cell frequency and increased B-cell frequency observed uniquely in the Mll-AF9 CAST F1 mice (Figure 1B). This observation further supports that differences between strains are Mll-AF9 dependent.Supplemental Figure E1Mendelian ratios of pups from heterozygous Mll-AF9 mouse breeding to genetically diverse inbred strains. Observed genotypes are listed as frequency followed by number of animals in parentheses. Expected genotypes are listed as frequency. Mll-AF9/+, heterozygrous mice; +/+, wild-type mice; org, original parental background strain; df, degrees of freedom. Open table in a new tab Monitoring PB composition until pathology developed revealed that Mll-AF9 CAST F1 mice maintained a significantly reduced frequency of myeloid cells and increased frequency of B cells with aging compared with the parental Mll-AF9 strain (Figure 2A). In concordance with previous studies [11Chen W Li Q Hudson WA Kumar A Kirchof N Kersey JH A murine Mll-AF4 knock-in model results in lymphoid and myeloid deregulation and hematologic malignancy.Blood. 2006; 108: 669-677Crossref PubMed Scopus (101) Google Scholar], median survival in the parental Mll-AF9 strain was 225 days. In contrast, Mll-AF9 CAST F1 had a longer median survival (361 days, p = 0.079) and Mll-AF9 A/J F1 and Mll-AF9 129 F1 had a significantly shorter median survival (172 days, p = 0.0071, and 178 days, p = 0.0179, respectively) (Figure 2B). Examining Mll-AF9 transcript expression by real-time PCR in moribund mice across the six strain backgrounds revealed no significant differences in Mll-AF9 expression (Figure 2C). These data suggest that genetic background can alter the development and progression of leukemia caused by Mll-AF9, independent of Mll-AF9 transcript expression level. Characterization of the hematologic malignancies that developed in these strains also revealed genetic background-dependent distinctions. While this in-depth phenotyping was performed on only a subset of moribund animals in Figure 2B, no other comorbidities were observed in any of the Mll-AF9 mice during postmortem analysis. Consistent with previous studies [9Corral J Lavenir I Impey H et al.An Mll-AF9 fusion gene made by homologous recombination causes acute leukemia in chimeric mice: a method to create fusion oncogenes.Cell. 1996; 85: 853-861Abstract Full Text Full Text PDF PubMed Scopus (451) Google Scholar,11Chen W Li Q Hudson WA Kumar A Kirchof N Kersey JH A murine Mll-AF4 knock-in model results in lymphoid and myeloid deregulation and hematologic malignancy.Blood. 2006; 108: 669-677Crossref PubMed Scopus (101) Google Scholar], parental Mll-AF9 mice developed myelomonocytic AML with 100% penetrance and a median latency of 210 days (Figure 3A) characterized by leukocytosis and thrombocytopenia (Figure 3B), splenomegaly (Figure 3C), >20% blasts in the BM (Figure 3D), and abundant myeloid cell infiltration into the spleen and liver (Figure 3D,E). Strains with a shorter median survival (Mll-AF9 A/J F1 and Mll-AF9 129 F1) developed either AML (median latency: 65 and 170 days, respectively) or a myeloproliferative disorder (MPD)-like disorder (median latency: 196 and 182 days, respectively) characterized by splenomegaly, <20% immature or blast-like cells in the BM, and a high frequency of Gr1-expressing granulocytes in the BM (Figure 3E). In the Mll-AF9 CAST F1 strain exhibiting the longest survival, 50% of mice developed AML (median latency: 367 days) and 50% of mice were found to have ALL (median latency: 536 days) characterized by leukocytosis, splenomegaly, spleen and liver infiltration, and a high frequency of B220lo c-Kit+ blast cells in the BM and spleen. While CAST mice have not been broadly studied in the context of leukemia or other cancer development, CAST F1 mice do have increased tumor growth in a model of neuroendocrine prostate carcinoma [15Patel SJ Molinolo AA Gutkind S Crawford NP Germline genetic variation modulates tumor progression and metastasis in a mouse model of neuroendocrine prostate carcinoma.PLoS One. 2013; 8: e61848Crossref PubMed Scopus (21) Google Scholar], suggesting that the increased survival we have observed is not due to a general tumor-resistant genetic background. Of note, one individual Mll-AF9 NOD F1 mouse in our study was found to develop mixed-phenotype acute leukemia (MPAL, 238 days) characterized by leukocytosis, thrombocytopenia, spleen and liver infiltration, and biphenotypic B220+ CD11b+ blast cells in the BM. As NOD mice are a polygenic model for autoimmune type 1 diabetes and exhibit aberrant immunophenotypes, it is interesting to speculate that this may influence development of a biphenotypic leukemia. As noted above, Mll-AF9 transcript expression level did not significantly differ between strains (Figure 2C) and did not significantly differ when these data were re-analyzed to group samples based on hematologic malignancy diagnosis (MPD vs. AML vs. MPAL vs. ALL) (Supplementary Figure E3, online only, available at www.exphem.org). These data support that genetic background can alter leukemia phenotypes caused by Mll-AF9, independent of Mll-AF9 transcript expression level.Supplemental Figure E2Peripheral blood phenotypes of MllWT inbred and F1 background strains. (A) White blood cell (WBC), red blood cell (RBC), platelet, lymphocyte, neutrophil and monocyte counts in 6 month-old inbred A/J (n=13), B6 (n=15), 129 (n=20), NOD (n=16) and CAST (n=12) mice. Data obtained from Mouse Phenome Database25 (https://phenome.jax.org). Bars represent median, min to max. *P<0.05, ***P<0.001 compared to inbred B6 values as determined by Brown-Forsythe ANOVA with Dunnett's T3 multiple comparisons test. (B) Frequency of myeloid, B and T cells. Dots represent individual mice (MllWT, n=7; Mll-AF9, n=15; MllWT A/J F1, n=8; Mll-AF9 A/J F1, n=17; MllWT B6 F1, n=2; Mll-AF9 B6 F1, n=12; MllWT CAST F1, n=4; Mll-AF9 CAST F1, n=13). Bars represent mean +/- SEM. #P<0.05 compared to MllWT parental values, *P<0.05, **P<0.01, ***P<0.001 compared to MllWT strain littermate values, as determined by Brown-Forsythe ANOVA with Dunnett's T3 multiple comparisons test.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Supplemental Figure E3Relative Mll-AF9 expression assessed by real-time PCR. Bars represent mean +/- SEM (MPD, n=4; AML, n=10; MPAL, n=1; ALL, n=2).View Large Image Figure ViewerDownload Hi-res image Download (PPT) By introducing genetic variation into the MLL-AF9 knockin mouse model, our work has identified that disease latency and leukemia phenotype are significantly affected by heritable genetic variants segregating among common inbred strains, and suggests the presence of specific, dominant-acting modifier alleles in one or more strains. These findings support that genetic background differences may play a role in how and why leukemogenesis resulting from a common fusion oncogene can result in distinct etiology among different individuals. As epigenetic dysregulation is a critical driver of MLL-rearranged leukemia [16Bernt KM Armstrong SA. Targeting epigenetic programs in MLL-rearranged leukemias.Hematology. 2011; 1: 354-360Crossref Scopus (80) Google Scholar,17Chan AKN Chen CW. Rewiring the epigenetic networks in MLL-rearranged leukemias: epigenetic dysregulation and pharmacological interventions.Front Cell Dev Biol. 2019; 7: 81Crossref PubMed Scopus (32) Google Scholar], we posit that altered survival and leukemia phenotypes may be related to differences in epigenetic or chromatin state in genetically diverse mice. This is also supported by GWAS identification of single-nucleotide polymorphisms in ARID5B, encoding part of the histone H3K9me2 demethylase complex, that modify risk for MLL-rearranged early childhood leukemia [4Emerenciano M Barbosa TC Lopes BA et al.ARID5B polymorphism confers an increased risk to acquire specific MLL rearrangements in early childhood leukemia.BMC Cancer. 2014; 14: 127Crossref PubMed Scopus (20) Google Scholar]. More broadly, apart from the presence of the fusion oncogene, data from our lab and others support that other factors strongly influence the specific outcome, including the cell type of origin [18Chen W Kumar AR Hudson WA et al.Malignant transformation initiated by Mll-AF9: gene dosage and critical target cells.Cancer Cell. 2008; 13: 432-440Abstract Full Text Full Text PDF PubMed Scopus (173) Google Scholar, 19George J Uyar A Young K et al.Leukaemia cell of origin identified by chromatin landscape of bulk tumour cells.Nat Commun. 2016; 7: 12166Crossref PubMed Scopus (40) Google Scholar, 20Krivtsov AV Figueroa ME Sinha AU et al.Cell of origin determines clinically relevant subtypes of MLL-rearranged AML.Leukemia. 2013; 27: 852-860Crossref PubMed Scopus (135) Google Scholar] and the developmental stage and context in which the chromosome translocation occurs [21Wei J Wunderlich M Fox C et al.Microenvironment determines lineage fate in a human model of MLL-AF9 leukemia.Cancer Cell. 2008; 13: 483-495Abstract Full Text Full Text PDF PubMed Scopus (256) Google Scholar,22Rowe RG Lummertz da Rocha E Sousa P et al.The developmental stage of the hematopoietic niche regulates lineage in MLL-rearranged leukemia.J Exp Med. 2019; 216: 527-538Crossref PubMed Scopus (17) Google Scholar]. Differing propensities to incur secondary mutations that accelerate Mll-AF9–mediated leukemias may be an additional variable, which we have not examined here. Our study also has not definitively determined whether differences in disease latency and leukemia phenotype are mediated by leukemia cell–intrinsic or leukemia cell–extrinsic mechanisms (e.g., systemic or immune differences between strains), or both. These cellular and molecular mechanistic studies will be key future work in determining the nature of the differences in leukemia lethality. Importantly, this study included five of the eight founder strains of the Collaborative Cross (CC) and Diversity Outbred (DO) mouse populations [23Svenson KL Gatti DM Valdar W et al.High-resolution genetic mapping using the Mouse Diversity outbred population.Genetics. 2012; 190: 437-447Crossref PubMed Scopus (309) Google Scholar,24Churchill GA Gatti DM Munger SC Svenson KL The Diversity Outbred mouse population.Mamm Genome. 2012; 23: 713-718Crossref PubMed Scopus (270) Google Scholar], complementary resources that enable one to model human genetic diversity and map genetic modifiers that underlie phenotype differences in the population. Future studies will take advantage of these powerful tools to map the genetic determinants of leukemia susceptibility and phenotype, with the goal of identifying novel gene targets for the development of new therapies for MLL-rearranged leukemia. This work was supported by National Institutes of Health (NIH), National Cancer Institute (NCI) Cancer Core Grant P30CA034196, and Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Grant T32HD007065 (KY). This work was also supported by The V Foundation V Scholar award (JJT) and grants from the Maine Cancer Foundation (JJT). KY is supported by an American Society of Hematology (ASH) Scholar Award and the Pyewacket Fund at The Jackson Laboratory. We thank Steve Munger, Jennifer SanMiguel, and members of the Trowbridge laboratory for helpful discussion and critical comments, and Rebecca Bell for experimental and laboratory support.