Alteration of the immune environment in bone marrow from children with recurrent B cell precursor acute lymphoblastic leukemia

Abstract Due to the considerable success of cancer immunotherapy for leukemia, the tumor immune environment has become a focus of intense research; however, there are few reports on the dynamics of the tumor immune environment in leukemia. Here, we analyzed the tumor immune environment in pediatric B cell precursor acute lymphoblastic leukemia by analyzing serial bone marrow samples from nine patients with primary and recurrent disease by mass cytometry using 39 immunophenotype markers, and transcriptome analysis. High‐dimensional single‐cell mass cytometry analysis elucidated a dynamic shift of T cells from naïve to effector subsets, and clarified that, during relapse, the tumor immune environment comprised a T helper 1‐polarized immune profile, together with an increased number of effector regulatory T cells. These results were confirmed in a validation cohort using conventional flow cytometry. Furthermore, RNA transcriptome analysis identified the upregulation of immune‐related pathways in B cell precursor acute lymphoblastic leukemia cells during relapse, suggesting interaction with the surrounding environment. In conclusion, a tumor immune environment characterized by a T helper 1‐polarized immune profile, with an increased number of effector regulatory T cells, could contribute to the pathophysiology of recurrent B cell precursor acute lymphoblastic leukemia. This information could contribute to the development of effective immunotherapeutic approaches against B cell precursor acute lymphoblastic leukemia relapse.


| INTRODUC TI ON
B cell precursor acute lymphoblastic leukemia is a hematopoietic cell malignancy derived from B-lineage lymphoid precursors. 1 Despite intensive chemotherapy, including central nervous system prophylaxis, approximately 20% of children with BCP-ALL will relapse, 2 and the outcome of recurrent BCP-ALL remains unsatisfactory. In recent years, immunotherapy against leukemia has become a research focus, due to the success of chimeric antigen receptor T cell therapy 3 and other Ab-related drugs, such as bispecific T cell engagers, 4 which can dramatically improve treatment outcomes. 5 Bone marrow is a hematopoietic tissue composed of various cell types, including mesenchymal stem cells, endothelial cells, osteoprogenitors, and immune cells, and provides an essential niche for hematopoietic stem cells; however, leukemic cells also take advantage of BM to aid their survival. 6 Recently, the immune environment of BM, the primary site of leukemia, has been the subject of intense research, 7 leading to elucidation of its role in relapse 8,9 ; however, the immunological background of BCP-ALL in the BM remains unclear.
Understanding the dynamics of the BM TIE in BCP-ALL has potential to facilitate the identification of novel risk factors and the development of better treatment strategies, applicable to the different disease status of individual patients.
Immune responses involve concerted actions by several immune cell types, of which Th cells play a central, orchestrating role. Over recent decades, Th17, Th9, Th22, T follicular-helper, and Tregs have been recognized, in addition to the classical biphasic model of Th1 and Th2 cell differentiation. 10 Of Th cell subgroups, Tregs have an immunosuppressive effect, which is indispensable for the maintenance of immune homeostasis. 11 FOXP3 (a master regulator of Tregs) and CD45RA can be used to classify Tregs into three phenotypically and functionally distinct populations: FOXP3 lo CD45RA + (fraction I, naïve or resting Tregs), FOXP3 hi CD45RA -(fraction II, effector Tregs), and FOXP3 lo CD45RA -(fraction III, non-Tregs) cells. Of these Treg populations, only FOXP3 hi CD45RAcells truly have effective immunosuppressive capacity. [12][13][14] Regulatory T cells contribute to tumor development and progression by inhibiting antitumor immunity. 15,16 There have been several reports of increased Treg numbers and activity in BCP-ALL [17][18][19] ; however, detailed analysis has not been carried out.
In this study, we focused on the immune environment of pediatric BCP-ALL in the BM by analyzing serial samples of primary and recurrent phase leukemias, using high-dimensional, single-cell mass cytometry (also known as "cytometry by time-of-flight" or CyTOF) and transcriptome analysis. The use of mass cytometry with metal conjugated Abs for detecting cellular protein markers is a relatively new technology; however, the underlying principle is similar to that of conventional flow cytometry, and results from mass cytometry analyses are comparable with those from flow cytometry. 20 We also undertook a flow cytometry experiment to validate our mass cytometry findings. We comprehensively assessed the BM immune environment in BCP-ALL, and closely investigated T cells, revealing that a Th1-polarized inflammatory TIE, with an increase in effector Tregs, is characteristic of recurrent BCP-ALL.

| Flow cytometry
Thawed BM mononuclear cells were resuspended as described above, and then washed twice with PBS. After blocking with Fc receptor binding inhibitor, fluorescent-labeled Abs were added to yield 100 μl final reaction volumes in CSM. Samples were incubated at RT for 30 minutes and washed twice with PBS. Intracellular staining was carried out using the FOXP3/Transcription Factor Staining Buffer Set with an anti-FOXP3 Ab, according to the manufacturer's instructions. Before flow cytometry analysis, samples were resuspended in 500 μl CSM and measured using FACSVerse (Becton Dickinson). Acquired data were analyzed using Cytobank software (https://www.beckm an.jp/flow-cytom etry/softw are/cytob ankpremium). The Ab panel for flow cytometry is presented in Table S2. Differentially regulated MSigDB ontology gene sets with a false discovery rate q value of less than 0.10 were filtered and evaluated.

| Statistical analysis
Data analysis was undertaken using Cytobank software (https:// www.beckm an.jp/flow-cytom etry/softw are/cytob ank-premium), Prism 8 (GraphPad), and R statistical software (http://www.r-proje ct.org) within the CATALYST (Cytometry dATA anALYSis Tools) pipeline, referring to the CyTOF workflow (version 4). [27][28][29] To test the statistical significance of differences between two groups, a twotailed paired Student's test was applied in Prism 8. Using the CyTOF workflow, differential abundance analysis was assessed using a linear mixed model to calculate adjusted P values. 28

| Patient characteristics
A detailed list of patient characteristics is shown in Table 1. We collected serial samples from nine relapsed patients by BM aspiration at initial diagnosis and relapse. None of the subjects had ap- and R9 experienced early relapse; however, they had entered complete remission in response to induction therapy before they relapsed. For validation flow cytometric analysis, we used serial samples from another seven patients, whose characteristics are summarized in Table S3.

| Mononuclear cell landscape in BM samples from patients with primary and recurrent BCP-ALL
To comprehensively understand the cell profiles in BCP-ALL samples, data obtained from mass cytometric analysis were input into Cytobank software, and immune cell subsets were detected using viSNE. 30 First, a control sample was used to confirm the basic BM constitution; eight different cellular components were distinguished, as follows: CD4 T cells, CD3 + CD4 + CD8 -; CD8 T cells, (Figure 2A). Next, samples from patients with BCP-ALL at initial diagnosis and relapse were analyzed. An example of visualization of cellular components using viSNE is shown in Figure 2B

| High-dimensional single-cell analysis of T cells in serial BM samples reveals dynamic immunological alteration toward a Th1-dominant environment in recurrent BCP-ALL
Next, we focused on T cell subtypes and their status in primary and recurrent BCP-ALL. Samples from patient R9 did not contain sufficient T cells and were consequently excluded from this analysis. To evaluate T cells in detail, we adopted a two-step approach, consistent with the CyTOF workflow. 27 To evaluate the transition of T cell subgroups in relapsed patients, differential abundance analysis was carried out, and adjusted P values were calculated using a generalized linear mixed model ( Figure 3C). Interestingly, there was a tendency for naïve T cell subgroups to decrease, and effector T cell subgroups to increase, at relapse relative to onset. The significant increase of effector memory and terminally differentiated T cells indicated that the TIE of recurrent BCP-ALL is in an immunologically stimulated state. Second, we compared normalized expression patterns of state markers in each effector T cell subgroup using a differential state test ( Figure 3D). In this analysis, state marker-subgroup combina- As the above analysis included patients who had relapsed during ALL therapy, we undertook further analyses focused on the four patients who relapsed more than 6 months after completion of treatments (patients R1-R4), to rule out the influence of chemotherapeutic agents on the TIE. In this analysis, the upregulation of CXCR3 expression on effector T cells at relapse was more marked ( Figure 3E). In summary, the BM microenvironment in recurrent BCP-ALL was characterized by Th1 dominance.

| Recurrent BCP-ALL immune environment is enriched for effector Tregs
In the above analysis, effector memory Tregs showed a trend to-   In our study, PD-1, CTLA-4, and CCR4 were significantly upregulated in CD25 + CD127 -Tregs at relapse, supporting an augmentation of effector characteristics ( Figure 4C). An evaluation of Tregs in only To test the statistical significance of differences between two groups, a two-tailed Wilcoxon paired signed rank test was applied the four patients who relapsed more than 6 months after completing treatments (patients R1-R4) is shown in Figure S3, and the features were consistent with the results described above.

| Validation of results of mass cytometric analysis by conventional flow cytometry in samples from a new patient cohort
To validate our mass cytometry findings, we undertook flow cytometric analysis of samples from a validation cohort, including seven patients (Table S3). Both CXCR3 + CD4 T cells and effector Tregs were detected by flow cytometry with the same Ab clones as those used in mass cytometry analysis (Table S2). Our findings confirmed that CXCR3 + CD4 T cells and effector Tregs showed a tendency to increase at the time of relapse in this cohort ( Figure 5), consistent with the results of mass cytometry.

| Immune-related pathways are broadly upregulated in BCP-ALL cells at relapse
As described above, our data reveal that the immune environment in  and CTLs have the potential to eliminate ALL cells, threatening ALL progression and persistence. 48 The CTL suppression function is crucial in the context of tumor immune escape for relapse.

| D ISCUSS I ON
Factors reported to be involved in suppressing CTL function are PD-L1, 49 CD38, 50 and Tregs. 17 Expression of PD-L1 on tumor cells is associated with poor prognosis in some hematologic malignancies; however, there is little information regarding the involvement of PD-L1/L2 in BCP-ALL, and PD-L1/L2 expression is reported to be lower in BCP-ALL than in other hematological malignancies, 51 although PD-L1 expression has been detected in BCP-ALL cells in some studies. 52,53 In our research, we did not detect distinct PD-L1 expression (data not shown). Another possible mechanism by which BCP-ALL cells suppress CTL function is overexpression of CD38 on tumor cells, leading to increased extracellular adenosine levels, which might contribute to resistance to CTLs. 50 In our analysis, CD38 was highly expressed on BCP-ALL cells, regardless of the time at which BM was collected ( Figure S4). Regulatory T cells are suppressors of antileukemic immune functions, including those of CTLs. Several studies reported that patients with BCP-ALL had higher numbers of Tregs than healthy controls, 18,19,54 and that the immunosuppressive potential of Tregs increases with malignant progression in BCP-ALL. 17 In the present study, we observed an increase in effector Tregs, which are reported to mediate suppression of antitumor immune responses. 16 Although we cannot definitively conclude whether the observed Th1-polarized TIE and increase in the effector Treg population are causes or results of relapse, our GSEA results show that BCP-ALL cells at relapse had gene signatures predicted to attract lymphocytes and enhance immunological responses, relative to the onset phase, indicating a close relationship between BCP-ALL cells and the TIE.
There are some limitations to this study. We examined nine relapsed patients with mass cytometry and seven with flow cytometry; however, our sample size was insufficient for analysis of BCP-ALL subgroups, which could have undiscovered differences in TIE. In addition, functional assessment could not be carried out due to the small quantities of samples available. These aspects may warrant further study.
In conclusion, a Th1-polarized immune environment became dominant, and an increase of the effector Treg population was observed in BM samples from children with recurrent BCP-ALL.
Consistent with the context, immune-related pathways were broadly upregulated in BCP-ALL cells at relapse. Although the precise mechanisms involved in interactions between the TIE and BCP-ALL cells are poorly understood, our data indicate that a Th1polarized immune environment with an increase of effector Tregs might be associated with BCP-ALL relapse, and that targeted therapy modifying the TIE has potential to enhance the efficiency of existing immunotherapies.

ACK N OWLED G M ENTS
The mass cytometric data used in this research have been deposited