T-cell acute lymphoblastic leukemia (T-ALL) and T-cell acute lymphoblastic lymphoma (T-LBL) are aggressive hematological malignancies that are currently treated with high-dose chemotherapy. Over the last several years, the search toward novel and less-toxic therapeutic strategies for T-ALL/T-LBL patients has largely focused on the identification of cell-intrinsic properties of the tumor cell. However, non–cell-autonomous activation of specific oncogenic pathways might also offer opportunities that could be exploited at the therapeutic level. In line with this, we here show that endogenous interleukin 7 (IL7) can increase the expression of the oncogenic kinase proviral integration site for Moloney-murine leukemia 1 (PIM1) in CD127+ T-ALL/T-LBL, thereby rendering these tumor cells sensitive to in vivo PIM inhibition. In addition, using different CD127+ T-ALL/T-LBL xenograft models, we also reveal that residual tumor cells, which remain present after short-term in vivo chemotherapy, display consistent upregulation of PIM1 as compared with bulk nontreated tumor cells. Notably, this effect was transient as increased PIM1 levels were not observed in reestablished disease after abrogation of the initial chemotherapy. Furthermore, we uncover that this phenomenon is, at least in part, mediated by the ability of glucocorticoids to cause transcriptional upregulation of IL7RA in T-ALL/T-LBL patient-derived xenograft (PDX) cells, ultimately resulting in non–cell-autonomous PIM1 upregulation by endogenous IL7. Finally, we confirm in vivo that chemotherapy in combination with a pan-PIM inhibitor can improve leukemia survival in a PDX model of CD127+ T-ALL. Altogether, our work reveals that IL7 and glucocorticoids coordinately drive aberrant activation of PIM1 and suggests that IL7-responsive CD127+ T-ALL and T-LBL patients could benefit from PIM inhibition during induction chemotherapy.
T-cell lymphoblastic lymphoma (T-LBL) is a rare and aggressive lymphatic cancer, often diagnosed at a young age. Patients are treated with intensive chemotherapy, potentially followed by a hematopoietic stem cell transplantation. Although prognosis of T-LBL has improved with intensified treatment protocols, they are associated with side effects and 10–20% of patients still die from relapsed or refractory disease. Given this, the search toward less toxic anti-lymphoma therapies is ongoing. Here, we targeted the recently described DNA hypermethylated profile in T-LBL with the DNA hypomethylating agent decitabine. We evaluated the anti-lymphoma properties and downstream effects of decitabine, using patient derived xenograft (PDX) models. Decitabine treatment resulted in prolonged lymphoma-free survival in all T-LBL PDX models, which was associated with downregulation of the oncogenic MYC pathway. However, some PDX models showed more benefit of decitabine treatment compared to others. In more sensitive models, differentially methylated CpG regions resulted in more differentially expressed genes in open chromatin regions. This resulted in stronger downregulation of cell cycle genes and upregulation of immune response activating transcripts. Finally, we suggest a gene signature for high decitabine sensitivity in T-LBL. Altogether, we here delivered pre-clinical proof of the potential use of decitabine as a new therapeutic agent in T-LBL.
Background: Some rare subgroups of leukemia cells harboring relapse-inducing genes were selected after chemotherapy.Tounravel intra-tumoral heterogeneity and selective drug-resistance, single-cell RNA sequencing (scRNA-seq) was already performed on many solid tumors and blood cancer to achieve the high-resolutiontranscriptome profiling on individual cells from a larger heterogeneous population. However,the comprehensive investigation on cancer heterogeneityduring cancer development at single-cell resolution is still rare. Aims: To identify diverse subsets and molecular characteristics of acute myeloid leukemia (AML) relapse Methods: Since single-cell suspension was obtainedfrom bone marrow of acute myeloid leukemia samples, we used the 10x GenomicsChromium platform to capture transcriptomes of singlecells on barcoded mRNA capture beadsfor massively parallel scRNA-seq. Data processing followed by the Cell Ranger software pipelineto demultiplex cellularbarcodes, and map reads to the genome and transcriptome hg38 using the STAR aligner.Uniquemolecular identifier (UMI) count matrix and quality control were performed using Seurat. The t-SNE map was calculated using Rtsne package Results: We analyzed transcriptome data from near 50K single leukemia bone marrow cells across 3 patients during newly diagnosed, complete remission and relapse stages. To define the landscape of cellular heterogeneity and its association with outcome in an unbiased manner, we performed unsupervised machine learning algorithm on near 50K single cells from leukemia bone marrow and identify one robust 14-cluster solution (from 0 to 13, Figure 1A) and the hallmark genes within each clusters (Figure 1B). The pattern exhibits distinct distribution on different stages (Figures 1C), indicating intra-tumoral heterogeneity during leukemia progression. Within cluster 0, the subgroups expressing such as LILRB2, TNFAIP2 or PTAFR were chemotherapy sensitive (Figure 2A). While the subgroups expressing such as APOC1, CDKN2A, KLF1 or GATA1 were chemotherapy resistant (Figure 2B). These chemotherapy resistant subgroups may play some key roles in leukemia relapse.
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