HOXA9/IRX1 expression pattern defines two subgroups of infant MLL-AF4-driven acute lymphoblastic leukemia

Abstract

•We identified two sub-groups of infant MLL-AF4-driven ALL, iALL-HOXA9, and iALL-IRX1.•The subgroups exhibit mutually exclusive expression of HOXA9/HOXA10 and IRX1.•The transcriptional profile of iALL-IRX1 patients revealed a more aggressive disease.•The two subgroups exhibit different expression of potential therapeutic targets. Infant t(4;11) acute lymphoblastic leukemia is the most common leukemia in infant patients and has a highly aggressive nature. The patients have a dismal prognosis, which has not improved in more than a decade, suggesting that a better understanding of this disease is required. In the study described here, we analyzed two previously published RNA-sequencing data sets and gained further insights into the global transcriptomes of two known subgroups of this disease, which are characterized by the presence or absence of a homeobox gene expression signature. Specifically, we identified a remarkable mutually exclusive expression of the HOXA9/HOXA10 and IRX1 genes and termed the two subgroups iALL-HOXA9 and iALL-IRX1. This expression pattern is critical as it suggests that there is a fundamental difference between the two subgroups. Investigation of the transcriptomes of the two subgroups reveals a more aggressive nature for the iALL-IRX1 group, which is further supported by the fact that patients within this group have a worse prognosis and are also diagnosed at a younger age. This could be reflective of a developmentally earlier cell of origin for iALL-IRX1. Our analysis further uncovered critical differences between the two groups that may have an impact on treatment strategies. In summary, after a detailed investigation into the transcriptional profiles of iALL-HOXA9 and iALL-IRX1 patients, we highlight the importance of acknowledging that these two subgroups are different and that this is of clinical importance. Infant t(4;11) acute lymphoblastic leukemia is the most common leukemia in infant patients and has a highly aggressive nature. The patients have a dismal prognosis, which has not improved in more than a decade, suggesting that a better understanding of this disease is required. In the study described here, we analyzed two previously published RNA-sequencing data sets and gained further insights into the global transcriptomes of two known subgroups of this disease, which are characterized by the presence or absence of a homeobox gene expression signature. Specifically, we identified a remarkable mutually exclusive expression of the HOXA9/HOXA10 and IRX1 genes and termed the two subgroups iALL-HOXA9 and iALL-IRX1. This expression pattern is critical as it suggests that there is a fundamental difference between the two subgroups. Investigation of the transcriptomes of the two subgroups reveals a more aggressive nature for the iALL-IRX1 group, which is further supported by the fact that patients within this group have a worse prognosis and are also diagnosed at a younger age. This could be reflective of a developmentally earlier cell of origin for iALL-IRX1. Our analysis further uncovered critical differences between the two groups that may have an impact on treatment strategies. In summary, after a detailed investigation into the transcriptional profiles of iALL-HOXA9 and iALL-IRX1 patients, we highlight the importance of acknowledging that these two subgroups are different and that this is of clinical importance. In the age of molecular medicine, transcriptional profiling of patients’ samples has become a vital component in improving our understanding of diseases. We now have the potential to dissect transcriptional variations among patients, identify unique components of their disease and provide customized treatment. This approach is particularly valuable for diseases that are rare and difficult to model as there is a scarcity of available information. A prominent example of such a disease is infant MLL-AF4-driven acute lymphoblastic leukemia (ALL). This devastating disease is known to arise in utero, and the patients have a poor prognosis [1Meyer C Burmeister T Groger D et al.The MLL recombinome of acute leukemias in 2017.Leukemia. 2018; 32: 273-284Crossref PubMed Scopus (307) Google Scholar, 2Pieters R De Lorenzo P Ancliffe P et al.Outcome of infants younger than 1 year with acute lymphoblastic leukemia treated with the Interfant-06 Protocol: results From an international phase III randomized study.J Clin Oncol. 2019; 37: 2246-2256Crossref PubMed Scopus (78) Google Scholar, 3Gale KB Ford AM Repp R et al.Backtracking leukemia to birth: identification of clonotypic gene fusion sequences in neonatal blood spots.Proc Natl Acad Sci USA. 1997; 94: 13950-13954Crossref PubMed Scopus (417) Google Scholar, 4Ford AM Ridge SA Cabrera ME et al.In utero rearrangements in the trithorax-related oncogene in infant leukaemias.Nature. 1993; 363: 358-360Crossref PubMed Scopus (365) Google Scholar]. With only a handful of patients diagnosed each year, a unique underlying biology, and a lack of accurate disease models, our understanding of this disease remains limited, which is reflected in the lack of progress in treating these patients [2Pieters R De Lorenzo P Ancliffe P et al.Outcome of infants younger than 1 year with acute lymphoblastic leukemia treated with the Interfant-06 Protocol: results From an international phase III randomized study.J Clin Oncol. 2019; 37: 2246-2256Crossref PubMed Scopus (78) Google Scholar]. Currently, we know that infant MLL-AF4-driven ALL can be divided further into two subgroups. Two classification systems can be found in the literature: one is based on the expression levels of the gene HOXA9, and the other on separating the patients based on the expression of genes in the HOXA and IRX family of proteins [5Kang H Wilson CS Harvey RC et al.Gene expression profiles predictive of outcome and age in infant acute lymphoblastic leukemia: a Children's Oncology Group study.Blood. 2012; 119: 1872-1881Crossref PubMed Scopus (81) Google Scholar, 6Stam RW Schneider P Hagelstein JA et al.Gene expression profiling-based dissection of MLL translocated and MLL germline acute lymphoblastic leukemia in infants.Blood. 2010; 115: 2835-2844Crossref PubMed Scopus (116) Google Scholar, 7Trentin L Giordan M Dingermann T Basso G Te Kronnie G Marschalek R Two independent gene signatures in pediatric t(4;11) acute lymphoblastic leukemia patients.Eur J Haematol. 2009; 83: 406-419Crossref PubMed Scopus (43) Google Scholar]. This has been reported to be of clinical relevance as patients with HOXA9high/IRXneg/low expression have a better prognosis than those with HOXA9low/IRXpos expression [5Kang H Wilson CS Harvey RC et al.Gene expression profiles predictive of outcome and age in infant acute lymphoblastic leukemia: a Children's Oncology Group study.Blood. 2012; 119: 1872-1881Crossref PubMed Scopus (81) Google Scholar, 6Stam RW Schneider P Hagelstein JA et al.Gene expression profiling-based dissection of MLL translocated and MLL germline acute lymphoblastic leukemia in infants.Blood. 2010; 115: 2835-2844Crossref PubMed Scopus (116) Google Scholar, 7Trentin L Giordan M Dingermann T Basso G Te Kronnie G Marschalek R Two independent gene signatures in pediatric t(4;11) acute lymphoblastic leukemia patients.Eur J Haematol. 2009; 83: 406-419Crossref PubMed Scopus (43) Google Scholar]. In this study, we set out to better understand these two subgroups of patients. To do this, we analyzed two previously published RNA-sequencing data sets derived from infant/pediatric patients with MLL-AF4-driven ALL [8Agraz-Doblas A Bueno C Bashford-Rogers R et al.Unraveling the cellular origin and clinical prognostic markers of infant B-cell acute lymphoblastic leukemia using genome-wide analysis.Haematologica. 2019; 104: 1176-1188Crossref PubMed Scopus (44) Google Scholar,9Andersson AK Ma J Wang J et al.The landscape of somatic mutations in infant MLL-rearranged acute lymphoblastic leukemias.Nat Genet. 2015; 47: 330-337Crossref PubMed Scopus (284) Google Scholar]. Raw reads were aligned with Kallisto (version 0.43.1) to GRCh38. The Bioconductor package Tximport was used to import transcript-level abundance, estimated counts, and transcript lengths (version 3.5) [10Soneson C Love MI Robinson MD Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences.F1000Research. 2015; 4: 1521Crossref PubMed Google Scholar]. We initially performed batch correction using limma and filtered the samples for genes with low counts across samples. After filtering, determination of the expression level of each gene and differential expression analysis were performed using the DESeq2 pipeline (version 3.5) [11Love MI Huber W Anders S Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.Genome Biol. 2014; 15: 550Crossref PubMed Scopus (24538) Google Scholar,12Ritchie ME Phipson B Wu D et al.limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucleic Acids Res. 2015; 43: e47Crossref PubMed Scopus (11766) Google Scholar]. Genes were considered differentially expressed if they had an adjusted p value ≤0.1. Library pcaExplorer was used for PCA analysis [13Marini F Binder H pcaExplorer: an R/Bioconductor package for interacting with RNA-seq principal components.BMC Bioinformatics. 2019; 20: 331Crossref PubMed Scopus (50) Google Scholar]. Gene set enrichment analysis (GSEA) was performed using the GSEA Jana Desktop tool (version 4.1) [14Mootha VK Lindgren CM Eriksson KF et al.PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes.Nat Genet. 2003; 34: 267-273Crossref PubMed Scopus (5465) Google Scholar,15Subramanian A Tamayo P Mootha VK et al.Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc Natl Acad Sci USA. 2005; 102: 15545-15550Crossref PubMed Scopus (22503) Google Scholar]. R version 3.4.3 was used. It should be noted that before processing of the Andersson et al. [9Andersson AK Ma J Wang J et al.The landscape of somatic mutations in infant MLL-rearranged acute lymphoblastic leukemias.Nat Genet. 2015; 47: 330-337Crossref PubMed Scopus (284) Google Scholar] data set, Bam files were converted to Fastq with Samtools. GraphPad Prism version 7.0 was used. We analyzed the RNA-sequencing data set of Andersson et al. [9Andersson AK Ma J Wang J et al.The landscape of somatic mutations in infant MLL-rearranged acute lymphoblastic leukemias.Nat Genet. 2015; 47: 330-337Crossref PubMed Scopus (284) Google Scholar], which contains data from 17 infant (<1 year) and 5 pediatric (11–18 years) patients with MLL-AF4-driven ALL. Principal component analysis (PCA) revealed that infant blasts formed two clusters (pink and green in Figure 1A). Intriguingly, blasts from pediatric patients (blue) clustered closely with one of the infant clusters. Investigation into the genes driving the clustering revealed HOXA9, HOXA10 and IRX1, IRX2 to be among the top PC1 loadings—in opposite directions. This confirmed previous publications reporting that clustering of the patients was driven by genes of the HOXA and IRX families [5Kang H Wilson CS Harvey RC et al.Gene expression profiles predictive of outcome and age in infant acute lymphoblastic leukemia: a Children's Oncology Group study.Blood. 2012; 119: 1872-1881Crossref PubMed Scopus (81) Google Scholar,6Stam RW Schneider P Hagelstein JA et al.Gene expression profiling-based dissection of MLL translocated and MLL germline acute lymphoblastic leukemia in infants.Blood. 2010; 115: 2835-2844Crossref PubMed Scopus (116) Google Scholar]. To further investigate the expression pattern of these genes we performed Spearman's correlation test, which revealed an inverse correlation in the expression of HOXA9, HOXA10, and IRX1, but not IRX2 (Figure 1B,C; Supplementary Table E1, online only, available at www.exphem.org). Although IRX2 was one of the top differentially expressed genes, it was not uniformly upregulated in the HOXA9low/IRXpos patients (Figure 1C). Furthermore, Fisher's exact test confirmed that the observed mutually exclusive expression was statistically significant (Figure 1D,E). These data suggest that the previously described infant ALL (iALL) HOXA9low/IRXpos subgroup would be more accurately described as iALL-IRX1. It was also noteworthy that all pediatric patients expressed HOXA9 and clustered closely with iALL-HOXA9. This may hint at the age of the patients at diagnosis as being another contributing factor to the division of infant patients into two subgroups, especially because a previous study suggested that expression patterns in infants change noticeably around the age of 90 days [5Kang H Wilson CS Harvey RC et al.Gene expression profiles predictive of outcome and age in infant acute lymphoblastic leukemia: a Children's Oncology Group study.Blood. 2012; 119: 1872-1881Crossref PubMed Scopus (81) Google Scholar]. However, although patients in the iALL-IRX1 group appeared to be diagnosed at an earlier age, this did not reach statistical significance (Figure 1F). Investigation of the expression of all HOXA cluster genes revealed that they were uniquely upregulated in the iALL-HOXA9 subgroup, in line with previous reports of their coordinated expression (Figure 1G) [5Kang H Wilson CS Harvey RC et al.Gene expression profiles predictive of outcome and age in infant acute lymphoblastic leukemia: a Children's Oncology Group study.Blood. 2012; 119: 1872-1881Crossref PubMed Scopus (81) Google Scholar,6Stam RW Schneider P Hagelstein JA et al.Gene expression profiling-based dissection of MLL translocated and MLL germline acute lymphoblastic leukemia in infants.Blood. 2010; 115: 2835-2844Crossref PubMed Scopus (116) Google Scholar,16Krivtsov AV Feng Z Lemieux ME et al.H3K79 methylation profiles define murine and human MLL-AF4 leukemias.Cancer Cell. 2008; 14: 355-368Abstract Full Text Full Text PDF PubMed Scopus (427) Google Scholar]. GSEA of the genes differentially expressed between iALL-HOXA9 and iALL-IRX1 (Supplementary Table E2, online only, available at www.exphem.org) revealed an enrichment in MYC targets, as well as oxidative phosphorylation in the iALL-IRX1 patients (Figure 1H). The same subgroup also exhibited enrichment for proliferation pathways as exemplified by G2M checkpoints, E2F targets, and MTORC1 signaling upregulation (Figure 1H). This signature is indicative of the more aggressive nature of the blasts derived from these patients, which could explain their worse prognosis compared with iALL-HOXA9 patients. This is further supported by an additional enrichment of IL2 STAT5 signaling, a key component of core cancer pathways [17Wingelhofer B Maurer B Heyes EC et al.Pharmacologic inhibition of STAT5 in acute myeloid leukemia.Leukemia. 2018; 32: 1135-1146Crossref PubMed Scopus (71) Google Scholar]. The top enriched pathway in iALL-HOXA9 patients was Allograft rejection indicative of an immune system-related response (Figure 1I). To further validate our data, we performed the same analysis with the data set of Agraz-Doblas et al. [8Agraz-Doblas A Bueno C Bashford-Rogers R et al.Unraveling the cellular origin and clinical prognostic markers of infant B-cell acute lymphoblastic leukemia using genome-wide analysis.Haematologica. 2019; 104: 1176-1188Crossref PubMed Scopus (44) Google Scholar], which contains the transcriptome sequences of the blasts of 27 infant patients, and obtained similar results (Supplementary Figure E1A–F, Supplementary Tables E1 and E3, online only, available at www.exphem.org) [8Agraz-Doblas A Bueno C Bashford-Rogers R et al.Unraveling the cellular origin and clinical prognostic markers of infant B-cell acute lymphoblastic leukemia using genome-wide analysis.Haematologica. 2019; 104: 1176-1188Crossref PubMed Scopus (44) Google Scholar]. To identify genes common to both data sets, we compared the genes differentially expressed between iALL-HOXA9 and iALL-IRX1 in both experiments (i.e., genes common between Supplementary Tables E2 and E3), which identified a total of 342 common genes (Figure 2A; Supplementary Table E4, online only, available at www.exphem.org). To obtain a general idea about these genes, we performed GSEA. There was an enrichment in Estrogen response late, which had been previously correlated with aggressive cancers, in the iALL-IRX1 group [18Deeb KK Michalowska AM Yoon CY et al.Identification of an integrated SV40 T/t-antigen cancer signature in aggressive human breast, prostate, and lung carcinomas with poor prognosis.Cancer Res. 2007; 67: 8065-8080Crossref PubMed Scopus (89) Google Scholar,19Jagannathan V Robinson-Rechavi M Meta-analysis of estrogen response in MCF-7 distinguishes early target genes involved in signaling and cell proliferation from later target genes involved in cell cycle and DNA repair.BMC Syst Biol. 2011; 5: 138Crossref PubMed Scopus (19) Google Scholar]. HOXA9-expressing blasts, on the other hand, exhibited an enrichment for Interferon gamma response, cementing our previous observation of an immune system response in these patients (Figure 2B,C). Both these signatures were present in the two individual RNA-sequencing experiments (Supplementary Figure 2A,B, online only, available at www.exphem.org). It is intriguing that Homeobox genes HOXA9, HOXA10, and IRX1 are inversely correlated in the two subgroups of infant patients with MLL-AF4-driven ALL. This mutually exclusive expression could be the result of the two subgroups having a different cell of origin. To investigate the expression pattern of these genes in human hematopoietic cells, we looked into previously published single-cell RNA-sequencing experiments with adult bone marrow and fetal liver-derived hematopoietic cells [20Hay SB Ferchen K Chetal K Grimes HL Salomonis N The Human Cell Atlas bone marrow single-cell interactive web portal.Exp Hematol. 2018; 68: 51-61Abstract Full Text Full Text PDF PubMed Scopus (60) Google Scholar,21Popescu DM Botting RA Stephenson E et al.Decoding human fetal liver haematopoiesis.Nature. 2019; 574: 365-371Crossref PubMed Scopus (158) Google Scholar]. While HOXA9 and HOXA10 were expressed in hematopoietic stem and progenitor cells (both adult and fetal), IRX1 exhibited very little expression in the hematopoietic system (Supplementary Figure E3A,B, online only, available at www.exphem.org) [20Hay SB Ferchen K Chetal K Grimes HL Salomonis N The Human Cell Atlas bone marrow single-cell interactive web portal.Exp Hematol. 2018; 68: 51-61Abstract Full Text Full Text PDF PubMed Scopus (60) Google Scholar,21Popescu DM Botting RA Stephenson E et al.Decoding human fetal liver haematopoiesis.Nature. 2019; 574: 365-371Crossref PubMed Scopus (158) Google Scholar]. Interrogation of murine gastrulation and early organogenesis data sets revealed that Irx1 was expressed predominantly in mesoderm, whereas Hoxa9 and Hoxa10 were expressed in hematoendothelial progenitors (Supplementary Figure E3C) [22Pijuan-Sala B Griffiths JA Guibentif C et al.A single-cell molecular map of mouse gastrulation and early organogenesis.Nature. 2019; 566: 490-495Crossref PubMed Scopus (227) Google Scholar]. The IRX1 expression pattern could be indicative of iALL-IRX1 arising in a developmentally earlier cell type than iALL-HOXA9, which is supported by upregulation of genes such as PDGFRB and PDGFD in the iALL-IRX1 data set (Figure E3A; Supplementary Figure E4A, online only, available at www.exphem.org). Contrary to this, genes such as and are upregulated in the iALL-HOXA9 set (Figure Supplementary Figure Supplementary Table Furthermore, as patients with iALL-IRX1 to be diagnosed at a younger age as compared with iALL-HOXA9 patients (Figure has cell it would not be if MLL-AF4 was expressed in the bone marrow of patients with iALL-IRX1. In et al. P P R et marrow stem cells from infants with acute leukemia and the MLL-AF4 fusion 2009; PubMed Scopus Google Scholar] reported that a of bone marrow cells of infant patients with MLL-AF4-driven ALL the fusion Although they do not the patients expressed HOXA9 or IRX1, they do suggest that the disease could arise from a IRX1 expression two subgroups of infant MLL-AF4-driven ALL et al. data PCA of patients by HOXA9/HOXA10 and IRX1 infants with HOXA9/HOXA10 infants with IRX1 HOXA9 and HOXA10 expression in the subgroups. RNA-sequencing data are as each a IRX1 and IRX2 expression in the subgroups. RNA-sequencing data are as each a Fisher's exact test samples based on HOXA9 and HOXA10 expression levels were if and if Fisher's exact test samples based on IRX1 expression levels were and if of HOXA cluster genes in the subgroups. are as should be noted that samples were from the analysis as two samples expressed both HOXA9 and IRX1, one of the samples did not HOXA9 or of transcript Figure set enrichment pathways upregulated in both RNA-sequencing Andersson et al. data and Agraz-Doblas et al. iALL-IRX1 enrichment for Estrogen response late, whereas iALL-HOXA9 an enrichment in Interferon gamma enrichment Figure Figure expression pattern in previously published cell RNA-sequencing data HOXA9, HOXA10 and IRX1 expression in human adult bone marrow derived obtained from Human Cell Atlas HOXA9, HOXA10 and IRX1 expression in human fetal liver-derived obtained from Human Cell Atlas Hoxa10 and Irx1 expression in mouse gastrulation and early organogenesis Figure differentially expressed between iALL-HOXA9 and iALL-IRX1 et al. PDGFRB and PDGFD expression in the subgroups. RNA-sequencing data are as each a and expression in the subgroups. RNA-sequencing data are as each a expression in the subgroups. RNA-sequencing data are as each a expression in the subgroups. RNA-sequencing data are as each a Figure PCA of patients by HOXA9/HOXA10 and IRX1 infants with HOXA9/HOXA10 infants with IRX1 HOXA9 and HOXA10 expression in the subgroups. RNA-sequencing data are as each a IRX1 and IRX2 expression in the subgroups. RNA-sequencing data are as each a Fisher's exact test samples based on HOXA9 and HOXA10 expression levels were if and if Fisher's exact test samples based on IRX1 expression levels were and if of HOXA cluster genes in the subgroups. are as should be noted that samples were from the analysis as two samples expressed both HOXA9 and IRX1, one of the samples did not HOXA9 or of transcript Andersson et al. data and Agraz-Doblas et al. iALL-IRX1 enrichment for Estrogen response late, whereas iALL-HOXA9 an enrichment in Interferon gamma enrichment HOXA9, HOXA10 and IRX1 expression in human adult bone marrow derived obtained from Human Cell Atlas HOXA9, HOXA10 and IRX1 expression in human fetal liver-derived obtained from Human Cell Atlas Hoxa10 and Irx1 expression in mouse gastrulation and early organogenesis PDGFRB and PDGFD expression in the subgroups. RNA-sequencing data are as each a and expression in the subgroups. RNA-sequencing data are as each a expression in the subgroups. RNA-sequencing data are as each a expression in the subgroups. RNA-sequencing data are as each a The differences between the two groups may also have an impact on treatment for these patients. which has been suggested to be a target for patients, was upregulated in the iALL-HOXA9 subgroup, with expression in the iALL-IRX1 group of patients (Figure Supplementary Figure C T F et for MLL leukemia: 2019; PubMed Scopus Google L S et and of the stem cell in MLL-AF4 leukemia PubMed Scopus Google Scholar]. iALL-IRX1 patients with cells not be as as for iALL-HOXA9 patients. described therapeutic target for MLL-rearranged patients is which is expressed at similar levels in both iALL-HOXA9 and iALL-IRX1 at levels than suggesting that the outcome of this treatment could be similar for both subgroups (Figure Supplementary Figure B D H et is a therapeutic target for MLL-rearranged B-cell acute lymphoblastic leukemia.Leukemia. 2019; PubMed Scopus Google Scholar]. key about the two subgroups of patients with MLL-AF4-driven ALL is that the of we have about this disease to is derived from mouse and cell such as that it is to experiments in and cell that the iALL-IRX1 We this would be critical therapeutic for these patients, as exemplified by We that should the HOXA9 and IRX1 expression of infant ALL patients. The We the Children's and Agraz-Doblas et al. [8Agraz-Doblas A Bueno C Bashford-Rogers R et al.Unraveling the cellular origin and clinical prognostic markers of infant B-cell acute lymphoblastic leukemia using genome-wide analysis.Haematologica. 2019; 104: 1176-1188Crossref PubMed Scopus (44) Google Scholar] for data from infant and pediatric patients. This was supported by a and an with files

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