Edited by Associate Editor Berthold Göttgens, this Review Series focuses on how the use of single-cell genomic and multiomic analyses are broadening our understanding of the complexity of leukemias ...and myeloid neoplasms. For acute myeloid leukemia, acute lymphoblastic leukemia, chronic lymphocytic leukemia, and myeloproliferative neoplasm, leading experts bring us up to date with recent data and speculate how these rapidly developing technologies may inform the directions of clinical care.
The study of hematopoiesis has been revolutionized in recent years by the application of single-cell RNA sequencing technologies. The technique coupled with rapidly developing bioinformatic analysis ...has provided great insight into the cell type compositions of many populations previously defined by their cell surface phenotype. Moreover, transcriptomic information enables the identification of individual molecules and pathways which define novel cell populations and their transitions including cell lineage decisions. Combining single-cell transcriptional profiling with molecular perturbations allows functional analysis of individual factors in gene regulatory networks and better understanding of the earliest stages of malignant transformation. In this chapter we describe a comprehensive protocol for scRNA-Seq analysis of the mouse bone marrow, using both plate-based (low throughput) and droplet-based (high throughput) methods. The protocol includes instructions for sample preparation, an antibody panel for flow cytometric purification of hematopoietic progenitors with index sorting for plate-based analysis or in bulk for droplet-based methods. The plate-based protocol described in this chapter is a combination of the Smart-Seq2 and mcSCRB-Seq protocols, optimized in our laboratory. It utilizes off-the-shelf reagents for cDNA preparation, is amenable to automation using a liquid handler, and takes 4 days from preparation of the cells for sorting to producing a sequencing-ready library. The droplet-based method (using for instance the 10× Genomics platform) relies on the manufacturer's user guide and commercial reagents, and takes 3 days from isolation of the cells to the production of a library ready for sequencing.
Acute myeloid leukemia (AML) and myeloid neoplasms develop through acquisition of somatic mutations that confer mutation-specific fitness advantages to hematopoietic stem and progenitor cells. ...However, our understanding of mutational effects remains limited to the resolution attainable within immunophenotypically and clinically accessible bulk cell populations. To decipher heterogeneous cellular fitness to preleukemic mutational perturbations, we performed single-cell RNA sequencing of eight different mouse models with driver mutations of myeloid malignancies, generating 269,048 single-cell profiles. Our analysis infers mutation-driven perturbations in cell abundance, cellular lineage fate, cellular metabolism, and gene expression at the continuous resolution, pinpointing cell populations with transcriptional alterations associated with differentiation bias. We further develop an 11-gene scoring system (Stem11) on the basis of preleukemic transcriptional signatures that predicts AML patient outcomes. Our results demonstrate that a single-cell-resolution deep characterization of preleukemic biology has the potential to enhance our understanding of AML heterogeneity and inform more effective risk stratification strategies.
Single cell genomics has revolutionised our understanding of differentiating systems and led to a reinterpretation of haematopoiesis, moving from a step-wise hierarchical tree model towards more ...continous differentiation landscapes. Recent single cell RNA sequencing (scRNA-seq) studies of Lineage- ckit+ (LK) mouse bone marrow progenitors have defined a highly granular landscape of differentiation from multipotent stem cells to committed progenitors in 8 different lineages and have served as a valuable reference landscape for comparisons with perturbation states. While these landscapes have permitted exploration and interrogation of early haematopoietic progenitors, as yet, no map of mouse total bone marrow haematopoiesis exists at single cell resolution, limiting efforts to fully understand the gene programs which specify and the cell surface phenotypes which identify maturing haematopoietic lineages nor the connection between early bifurcation decisions in progenitor cells and mature cellular outputs. To answer these questions, we performed droplet-based scRNA-seq and cell surface proteomics on whole bone marrow from 4 male and 4 female 12-week old C57Bl6 mice, using a panel of 138 antibodies against cell surface antigens. To increase representation of rare progenitor populations, we complemented total bone marrow mononuclear cells with c-kit enrichment and FACS sorted LK and LK sca1-positive (LSK) populations. After quality control filtering the single cell transcriptomes and proteomes, over 198,000 single cells were analysed separately before multimodal integration into a single UMAP embedding. Addition of cell surface proteome information to the scRNA-seq data through multimodal integration produced a representation with greater resolution of haematopoietic cell types, including improved resolution of T-cell subtypes and separation of a rare population of eosinophil progenitors which were previously indistinguishable from basophil progenitors. This integrated landscape of mouse haematopoiesis was then clustered and annotated though an iterative process of label transfer and manual annotation into 41 cell type states (Figure 1). Using cell fate analysis, which utilises transcriptional similarity and pseudotemporal ordering to estimate the probability of a single cell's commitment to defined terminal states, we were able to resolve cell fate probabilities towards nine haematopoietic lineages (erythroid, neutrophil, megakaryocyte, lymphoid, monocyte, dendritic cell (DC), plasmacytoid dendritic cell (pDC), basophil and eosinophil (Figure 2)). From these probabilities we defined nine trajectories from the earliest uncommitted progenitors to terminally differentiated populations, including a pDC trajectory involving both lymphoid and common DC progenitors. Analysis of trajectory-based gene expression trends provides a framework for discovery of lineage drivers as well as shared gene expression programs across multiple lineages. Leveraging cell surface proteome data permits the use of ‘in-silico-FACS’ to describe phenotypes of cell populations which exist at cell fate branch-points, for further in-vitro characterisation. To demonstrate the utility of our atlas as a reference, we projected perturbation datasets from 7 pre-leukaemic mouse models onto the atlas, allowing quantitation of mutation-associated cellular and tissue-scale alterations. Finally, we have leveraged the transcription-start site enrichment of 5' scRNA-seq to compute enhancer RNA (eRNA) expression in our atlas, allowing the identification of dynamic usage of specific enhancer elements during haematopoietic specification and thus providing a novel framework for correlation of eRNA expression (as a proxy for active distal chromatin regions) with gene expression trends along specific haematopoietic lineages.
Hematopoietic stem and progenitor cells (HSPCs) maintain the adult blood system, and their dysregulation is linked to multitude of disease. Jak2V617F and Tet2 mutations are known to collaboratively ...lead to the formation of myeloproliferative disorders. scRNA-Seq technologies have been successfully used to characterise the hematopoietic system of the mouse bone marrow. We have now applied these techniques to characterise the hematopoietic landscape of these two myeloproliferative mouse models carrying mutations in Jak2V617F and Tet2 and have developed and utilised new computational models/techniques to be able to shed insight into the molecular mechanisms at play during transformation. Using a previously published dataset as a reference, we can now compare in an unbiased way the abundance changes of different populations between different models and investigate how different trajectories are affected in different models. The most uncommitted progenitors from the different mutant models were compared to find the driver cells and processes that initiate the disease in each model.
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Background:
In the treatment of advanced Hodgkin lymphoma, it is increasingly common UK practice to modify escalated BEACOPP (eBPP) by removing oral procarbazine and replacing it with intravenous ...dacarbazine (250mg/m2 D2-3) to reduce haematopoietic stem cell and gonadal toxicity. However, published data of the “escalated BEACOPDac (eBPDac)” regimen are very limited.
Methods:
This is a retrospective study of 225 patients from 20 centres in the UK, Ireland and France who were treated with eBPDac first line for advanced stage Hodgkin Lymphoma. Toxicity outcomes were compared with 58 matched patients treated with eBPP at 4 UK centres and survival outcomes were compared with 2073 eBPP patients in the HD18 trial 1 and with 1088 patients aged 18-59y in the RATHL trial 2,3. Most eBPDac patients were treated as per HD18 protocol. The 34 patients treated in Paris followed the AHL2011 protocol with two courses of eBPDac given upfront and if iPET2 negative were deescalated to 4 cycles of ABVD.
Toxicity outcomes:
Toxicity was compared between eBPDac patients (n=225; median follow-up 22.1 months) and matched real-world UK eBPP patients (n=58; median follow-up 52.7 months) over the first 4 cycles. eBPP and eBPDac patients were well matched with no significant differences in age (median 23 y vs 26 y), sex, stage (stage 3/4 82% vs 83%) and international prognostic score (IPS 3+ 74% vs 65%). 55% of eBPDac patients received only 4 cycles (vs 12% of eBPP patients; p<0.001) reflecting publication of HD18 trial data. Mean day 8 (D8) ALT was similar between the two regimens. Mean D8 neutrophil count tended to be lower in eBPDac than eBPP patients (2.04 vs 2.45; p=0.072; G-CSF given D9), however it increased to 6.48 in eBPDac patients given GCSF from D4. There were fewer non-elective days of inpatient care for eBPDac compared with eBPP (mean 3.35 vs 5.84; p=0.022), and eBPDac patients received fewer red cell transfusions compared with eBPP patients (mean 1.79 units vs 4.16 units; p<0.001). Women aged <35, who completed ≥4 cycles of eBPDac/eBPP had a similar rate of return of menstrual cycles (eBPP 22/25; eBPDac 41/41), although eBPDac patients appeared to restart menstruation earlier post chemotherapy (mean 4.64 months vs 9.12 months, p=0.0026). However, this could also reflect the higher mean chemotherapy cycle number completed by the eBPP women (5.86 vs 4.60; p<0.001). The use of Goserelin to suppress ovulation varied between centres.
Disease outcomes:
The eBPDac patients (n=225) were younger than the HD18 patients (median age 26 y vs 35 y, p<0.001) and the RATHL patients (median age 26 y vs 31 y). However, they had higher risk disease than HD18 (IPS 4+ 36% vs 16%, p<0.001) and RATHL patients (IPS 3+ 65% vs 33%, p<0.001) and more stage 4 disease than HD18 (66% vs 36%, p<0.001) and RATHL (66% vs 28%, p<0.001). Of the 225 patients who started eBPDac, 77% achieved iPET2 Deauville score (DS) ≤3, similar to RATHL (DS ≤3: 83.7%) and HD18 (DS ≤3: 76%). Of the eBPDac patients, one patient had primary refractory disease, and ten have relapsed at 6 to 36 months. One 56-year-old eBPDac patient with high IPS died with bowel perforation during cycle 1 and one 34-year-old with alcoholic liver disease died 8 months after treatment while still in remission. There have been no lymphoma-related deaths to date.
Figure 1 shows Kaplan-Meier plots for progression-free survival (PFS) and overall survival (OS). The PFS at 22 months (median follow-up) of eBPDac patients was 94.9% (91.7-98.3%) which is similar to HD18 3 year PFS of 92.3% (91.1-93.5%) and appears superior to RATHL 5 year PFS 81.4% (78.9-83.7%). The difference in PFS between eBPDac and RATHL is most marked in IPS3+ patients. The OS rates with all 3 regimens are excellent, with 22 month eBPDac OS estimate of 98.9% (97.4-100%).
Summary/Conclusion:
Accepting the limitations of a retrospective study, we suggest that substituting dacarbazine for procarbazine is unlikely to compromise the efficacy of eBPP and may have some toxicity benefits. Despite the clear preference of clinicians to offer this regimen to high-risk advanced stage patients, with nearly 2 years median follow-up we have observed similar PFS and OS compared to HD18 but superior survival estimates compared with 18-59y RATHL patients, suggesting that eBPDac is highly efficacious for the treatment of Hodgkin lymphoma.
References:
1Borchmann P et al. Lancet 2017; 390:2790-2802
2Johnson P et al. NEJM. 2016; 374:2419-2429
3Russell J et al. Ann Hematol 2021; 100:1049-1058
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