Lymphoid Lineage Preference of MLL-AF4 Is Revealed in a Species-Specific Model

The Mixed Lineage Leukemia (MLL) gene on chromosome 11q23 is fused by reciprocal translocation to a diverse group of partner genes that drive both acute myeloid and acute lymphoid leukemia (AML and ALL). As a result of the t(4;11)(q21;q23), MLL fuses to AF4 (also referred to as AFF1), one of the most common MLL fusion partner proteins. Unlike several other MLL fusions that are frequently identified in AML, for example MLL-AF9 caused by t(9;11)(p22;q23), MLL-AF4 is almost exclusively associated with B-cell ALL with a pro-B immunophenotype. It is the most frequent MLL fusion in ALL and accounts for 10-15% of ALL cases. Patients with t(4;11) have a poor prognosis compared with other cytogenetically defined subsets. Although many MLL-fusion leukemia models have successfully been established, it has not been possible to generate a t(4;11) pro-B leukemia model that accurately recapitulates the human disease, hampering research into the molecular mechanisms that underlie the development of this subtype of leukemia. Here we present a faithful human cell based model of t(4;11) pro-B-ALL that fully recapitulates the immunophenotypic and molecular aspects of the human disease. Transduced with a modified MLL-AF4 fusion gene, human hematopoietic CD34+ cells successfully initiate ALL in a xenograft system with high penetrance. The leukemia cells have a CD19+CD34+ pro-B immunophenotype and are CD10(-), a common feature in MLL-AF4 patients. The effect of the oncogene is species-specific, as retroviral transduction and transplantation of murine hematopoietic cells with MLL-AF4 results in only AML using either lymphoid or myeloid conditions. An MLL-AF4 specific gene signature derived from patients is significantly enriched in our model cells, as shown by RNAseq, and the model samples group tightly with MLL-AF4 patients, even when compared with other MLL-fusions in unsupervised hierarchical clustering analysis. Interestingly, using gene profiles of normal pro-B and pre-B cells as reference, our MLL-AF4 leukemia cells show strong enrichment for pro-B genes, while instead, pre-B but not pro-B genes are overrepresented in our MLL-AF9 B-ALL leukemia cells. This differential developmental stage blockage of MLL-fusions is also reflected in patient samples. More strikingly, in accordance with the distinct lineage bias of MLL-fusions observed in the clinic, human cells expressing MLL-AF4 have a strong predilection for the lymphoid lineage and a demonstrated resistance to reprogramming in response to myeloid signals compared to human cells expressing MLL-AF9. This difference in lineage predisposition of MLL-AF4 compared to MLL-AF9 can be attributed to differential effects on lineage-specific gene expression. Under myeloid-priming conditions, phenotypically (CD33+CD19-) and morphologically myeloid MLL-AF4 cells are still able to initiate pro-B ALL in immunodeficient mice, while only AML is generated by MLL-AF9 myeloid cells. Accordingly, an active lymphoid molecular program with lower expression of critical myeloid genes is observed in MLL-AF4 myeloid cells compared to MLL-AF9 myeloid cells. Interestingly, we find that the polycomb gene BMI1, which was reported to be critical to prevent lymphoid priming in normal hematopoiesis, is expressed at significantly lower levels in MLL-AF4 than in MLL-AF9 myeloid cells. Strikingly, this decreased BMI1 expression is evident in primary B-ALL patient samples as well, with MLL-AF9 B-ALL samples demonstrating increased BMI1 relative to MLL-AF4. Reintroduction of BMI1 into MLL-AF4 cells enables AML generation with variable penetrance, while control vector transduced cells always result in B-ALL. Our results demonstrate that lineage fate in response to MLL-fusion protein expression involves a complex interplay of oncogene, intra- and extra-cellular microenvironmental cues. In addition, our data clearly demonstrate the species specificity associated with the t(4;11) oncogene and highlight the limitations of using murine cells in human disease modeling. The model provides a valuable tool to unravel the pathogenesis of MLL-AF4 leukemogenesis.