Transcriptomic and functional characterization of LSC-like cells

Cell line sensitivity and additional patient data

7 Tables showing: molecular and clinical patient data, cell line info, antibody panels and overview of assays used

MAC-Score predict response within mutational subgroups

Comparison of MAC-Score and BH-3 profiling based response prediction

Subpopulation specific Bcl-2 family levels and time on HMA/Venetoclax

Subpopulation specific Bcl-2 family levels and response to HMA/Venetoclax

Bcl-2 family gene expression in LSCs predicts in vitro response to HMA/VEN

(1) Background: The basophil activation test (BAT) is a functional whole blood-based ex vivo assay to quantify basophil activation after allergen exposure by flow cytometry. One of the most important prerequisites for the use of the BAT in the routine clinical diagnosis of allergies is a reliable, standardized and reproducible data analysis workflow. (2) Methods: We re-analyzed a public mass cytometry dataset from peanut (PN) allergic patients (n = 6) and healthy controls (n = 3) with our binning approach “pattern recognition of immune cells” (PRI). Our approach enabled a comprehensive analysis of the dataset, evaluating 30 markers to achieve optimal basophil identification and activation through multi-parametric analysis and visualization. (3) Results: We found FcεRIα/CD32 (FcγRII) as a new marker couple to identify basophils and kept CD63 as an activation marker to establish a modified BAT in combination with our PRI analysis approach. Based on this, we developed an algorithm for automated raw data processing, which enables direct data analysis and the intuitive visualization of the test results including controls and allergen stimulations. Furthermore, we discovered that the expression pattern of CD32 correlated with FcεRIα, anticorrelated with CD63 and was detectable in both the re-analyzed public dataset and our own flow cytometric results. (4) Conclusions: Our improved BAT, combined with our PRI procedure (bin-BAT), provides a reliable test with a fully reproducible analysis. The advanced bin-BAT enabled the development of an automated workflow with an intuitive visualization to discriminate allergic patients from non-allergic individuals.

Current treatment strategies for multiple myeloma (MM) are highly effective, but most patients develop relapsed/refractory disease (RRMM). The anti-CD38/CD3xCD28 trispecific antibody SAR442257 targets CD38 and CD28 on MM cells and co-stimulates CD3 and CD28 on T cells (TCs). We evaluated different key aspects such as MM cells and T cells avidity interaction, tumor killing, and biomarkers for drug potency in three distinct cohorts of RRMM patients. We found that a significantly higher proportion of RRMM patients (86%) exhibited aberrant co-expression of CD28 compared to newly diagnosed MM (NDMM) patients (19%). Furthermore, SAR442257 mediated significantly higher TC activation, resulting in enhanced MM killing compared to bispecific functional knockout controls for all relapse cohorts (Pearson’s r = 0.7). Finally, patients refractory to anti-CD38 therapy had higher levels of TGF-β (up to 20-fold) compared to other cohorts. This can limit the activity of SAR442257. Vactoserib, a TGF-β inhibitor, was able to mitigate this effect and restore sensitivity to SAR442257 in these experiments. In conclusion, SAR442257 has high potential for enhancing TC cytotoxicity by co-targeting CD38 and CD28 on MM and CD3/CD28 on T cells.

Abstract The BCL2 inhibitor venetoclax (VEN) in combination with azacitidine (5-AZA) is currently transforming acute myeloid leukemia (AML) therapy. However, there is a lack of clinically relevant biomarkers that predict response to 5-AZA/VEN. Here, we integrated transcriptomic, proteomic, functional, and clinical data to identify predictors of 5-AZA/VEN response. Although cultured monocytic AML cells displayed upfront resistance, monocytic differentiation was not clinically predictive in our patient cohort. We identified leukemic stem cells (LSC) as primary targets of 5-AZA/VEN whose elimination determined the therapy outcome. LSCs of 5-AZA/VEN-refractory patients displayed perturbed apoptotic dependencies. We developed and validated a flow cytometry-based “Mediators of apoptosis combinatorial score” (MAC-Score) linking the ratio of protein expression of BCL2, BCL-xL, and MCL1 in LSCs. MAC scoring predicts initial response with a positive predictive value of more than 97% associated with increased event-free survival. In summary, combinatorial levels of BCL2 family members in AML-LSCs are a key denominator of response, and MAC scoring reliably predicts patient response to 5-AZA/VEN. Significance: Venetoclax/azacitidine treatment has become an alternative to standard chemotherapy for patients with AML. However, prediction of response to treatment is hampered by the lack of clinically useful biomarkers. Here, we present easy-to-implement MAC scoring in LSCs as a novel strategy to predict treatment response and facilitate clinical decision-making. This article is highlighted in the In This Issue feature, p. 1275

Recently, the dogma representing effector CD4 T cell (Teff) diversity as discrete subsets has been challenged by unsupervised analyses of single-cell RNA sequences. In particular, two studies from Cano-Gamez et al. and Kiner et al. showed that in vivo differentiated Teffs do not cluster into discrete populations but rather form a transcriptional continuum following either a gradient of “effectorness” [1] or a temporal gradient reflecting the kinetics of response to infections [2]. Despite a technologic burst allowing for multiparametric protein expression analysis at single-cell level by flow or mass cytometry [3], gradients of protein expression remain hardly tangible because conventional analyses use successive gating that creates discrete cell subsets. Other methods, using dimensional reduction and clustering like t-SNE or UMAP, require down-sampling and render quantitative comparisons problematic. Here, we tested whether the CD4+ T cell continuum described at the transcriptomic level also existed at the protein level by analyzing flow cytometric data with our new semi-continuous bin-based algorithm for “pattern recognition of immune cells” (PRI) [4]. We took the example of the CD44+CD4+ T memory cells (Tmems) expressing IL-10 and those expressing other cytokines, such as IFN-γ-producing T helper type 1 cells (Th1), IL-21-producing T follicular helper cells (Tfh), or TNF-α+IFN-γ+IL-2+IL-21+ lupus-associated T super helper cells (Tsh) [4], in mouse autoimmunity and aging. First, we compared IL-10 expression in Tmems relatively to PD-1 and IFN-γ by conventional two-parameter analysis (Fig. 1A; Supporting Information Fig. S1A) and three-parameter bin-plot analysis with PRI (Fig. 1B). Despite lower frequencies of IL-10-producing cells (Fig. 1C) in young wild-type C57BL/6 and pre-lupus NZBxNZW F1 (NZBxW) mice, the IL-10 pattern was conserved independently of age or health status. This pattern characterized by largely overlapping IFN-γ and IL-10 patterns (60% of co-expressers), albeit IL-10+IFN-γ+ cells produced low IFN-γ level (Fig. 1A, B). The IL-10+ cells mainly clustered in the PD-1+/high/IFN-γ+ area, with minute proportions in the IFN-γhigh and PD-1– areas (Fig. 1B,D). Further analysis with PRI, allowing a more comprehensive analysis of IL-10 level in all (mean signal intensity [MSI]) and IL-10+ cells (positive mean signal intensity [MSI+]) than the “color mapping of dots” in FlowJo software (Supporting Information Fig. S1B), revealed a gradient of IL10 expression originating from the PD-1–/low area and culminating in the PD-1high area (Fig. 1D). The positive correlation between IL-10 and PD-1 expression was most striking in sick NZBxW mice but was also visible in old C57BL/6 mice. Next, comparing IL-10, TNF-α, IL-2, IL-21, and IFN-γ expression in relation with PD-1 and IFN-γ level showed distinct but partially overlapping patterns that formed a cloud gathering multiple cytokines producers in the PD-1–/lowIFN-γ+ area (Fig. 2A). In particular, the PD-1+IFN-γ+ quadrant contained overlapping IL-21+ and IL-10+ areas. TNF-α and IL-2 displayed similar patterns, modestly overlapping IL-10 pattern. We explored the actual combinatorial expression of IL-10 with the other cytokines by 4-parameter bin-plot analysis (quadru-plots) (Supporting Information Fig. S2A). The largest IL-10+ subset (37.23%) produced exclusively IL-10 (Fig. 2B), while a third of the IL-10+ cells co-expressed an additional cytokine (Fig. 2B and Supporting Information Fig. S2B). Most frequent double-expressers produced IFN-γ (18.44%) and IL-21 (10.47%), whereas IL-10+IL-2+ and IL10+TNF-α+ cells were scarce. Triple-expressers were mainly IL-10+IFN-γ+IL-21+ cells (8.1%). Finally, in double-, triple-, quadruple- and quintuple-producers, the highest IL-10 intensities were associated with IL-21 and IFN-γ expression (Fig. 2B, vignettes with yellow and orange bins), whereas the lowest intensities correlated with IL-2 and TNF-α expression. UMAP approach confirmed little co-expression of IL-10 with TNF-α and IL-2 (Supporting Information Fig. S1C). Further analysis revealed populations of high TNF-α and/or IL-2 producers in the IL-21high areas, excluded from the IL-10+ quadrants and likely representing Tsh cells. This distribution confirmed negative correlations between IL-10 and TNF-α or IL-2 expression (Fig. 2C). Altogether, this analysis revealed a continuum between IL-10-producing Tmems and cells with cytokine profiles reminiscent of Th1, Tfh and Tsh cells, with all possible cytokine combinations at single-cell level. The consistency of the cytokine patterns in mice of different age and health status suggests that these may represent conserved programs of CD4 T cell differentiation that vary quantitatively depending on the physiological context. Progressive accumulation of multifunctional Tmems evokes the “effectorness” gradient evidenced by single-cell transcriptomics [1]. Negative correlations between IL-10 and IFN-γ level, and TNF-α and IL-2 expression suggest a repression of these cytokines in IL-10-producing cells. Given the positive correlation between IL-10 and PD-1 expression, this down-regulation could involve PD-1 [5]. Supporting these hypotheses, IL-10 blockade increases TNF-α and IFN-γ production in vitro, and lupus manifestations in mice [6, 7]. The best characterized IL-10+IFN-γ+ cells are Foxp3– Type 1 regulatory CD4+ T cells with clear suppressive functions [8]. However, IL-10 has been linked to lupus progression [6], while IFN-γ-receptor signaling reduces disease development [9]. Thus, IFN-γ and IL-10 may synergize to achieve higher anti-inflammatory functions. Conversely, IL-21/IL-10 association may promote autoantibody production and aggravate autoimmunity [10]. Therefore, IL-10 could be protective or pathogenic, depending on its association with other cytokines at single-cell level. Further, the amount of cytokines per cell may be significant to disambiguate the biological activity of IL-10-producing Tmems. PRI, integrating the combinatorial and level of protein expression, may help extracting clinically relevant quantitative data. We thank Ines Hoppe for bioinformatics support. Work funded by the German Federal Ministry of Education and Research (e:Med MelAutim project #01ZX1905C to JV, RB). Open access funding enabled and organized by Projekt DEAL. The authors have declared no conflict of interests. The peer review history for this article is available at https://publons.com/publon/10.1002/eji.202249829. All cytometry files are openly available at http://flowrepository.org/: FR-FCM-Z5EQ; FR-FCM-Z5ER, FR-FCM-Z5ES. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Recently, mass cytometry has enabled quantification of up to 50 parameters for millions of cells per sample. It remains a challenge to analyze such high-dimensional data to exploit the richness of the inherent information, even though many valuable new analysis tools have already been developed. We propose a novel algorithm "pattern recognition of immune cells (PRI)" to tackle these high-dimensional protein combinations in the data. PRI is a tool for the analysis and visualization of cytometry data based on a three or more-parametric binning approach, feature engineering of bin properties of multivariate cell data, and a pseudo-multiparametric visualization. Using a publicly available mass cytometry dataset, we proved that reproducible feature engineering and intuitive understanding of the generated bin plots are helpful hallmarks for re-analysis with PRI. In the CD4+T cell population analyzed, PRI revealed two bin-plot patterns (CD90/CD44/CD86 and CD90/CD44/CD27) and 20 bin plot features for threshold-independent classification of mice concerning ineffective and effective tumor treatment. In addition, PRI mapped cell subsets regarding co-expression of the proliferation marker Ki67 with two major transcription factors and further delineated a specific Th1 cell subset. All these results demonstrate the added insights that can be obtained using the non-cluster-based tool PRI for re-analyses of high-dimensional cytometric data.

Dysregulated cytokine expression by T cells plays a pivotal role in the pathogenesis of autoimmune diseases. However, the identification of the corresponding pathogenic subpopulations is a challenge, since a distinction between physiological variation and a new quality in the expression of protein markers requires combinatorial evaluation. Here, we were able to identify a super-functional follicular helper T cell (Tfh)-like subpopulation in lupus-prone NZBxW mice with our binning approach "pattern recognition of immune cells (PRI)". PRI uncovered a subpopulation of IL-21+ IFN-γhigh PD-1low CD40Lhigh CXCR5- Bcl-6- T cells specifically expanded in diseased mice. In addition, these cells express high levels of TNF-α and IL-2, and provide B cell help for IgG production in an IL-21 and CD40L dependent manner. This super-functional T cell subset might be a superior driver of autoimmune processes due to a polyfunctional and high cytokine expression combined with Tfh-like properties.

Transcription factors of the nuclear factor of activated T cell (NFAT) family are essential for antigen-specific T cell activation and differentiation. Their cooperative DNA binding with other transcription factors, such as AP1 proteins (FOS, JUN, and JUNB), FOXP3, IRFs, and EGR1, dictates the gene regulatory action of NFATs. To identify as yet unknown interaction partners of NFAT, we purified biotin-tagged NFATc1/αA, NFATc1/βC, and NFATc2/C protein complexes and analyzed their components by stable isotope labeling by amino acids in cell culture-based mass spectrometry. We revealed more than 170 NFAT-associated proteins, half of which are involved in transcriptional regulation. Among them are many hitherto unknown interaction partners of NFATc1 and NFATc2 in T cells, such as Raptor, CHEK1, CREB1, RUNX1, SATB1, Ikaros, and Helios. The association of NFATc2 with several other transcription factors is DNA-dependent, indicating cooperative DNA binding. Moreover, our computational analysis discovered that binding motifs for RUNX and CREB1 are found preferentially in the direct vicinity of NFAT-binding motifs and in a distinct orientation to them. Furthermore, we provide evidence that mTOR and CHEK1 kinase activity influence NFAT's transcriptional potency. Finally, our dataset of NFAT-associated proteins provides a good basis to further study NFAT's diverse functions and how these are modulated due to the interplay of multiple interaction partners.

IL-31, predominantly produced by CD45RO + CLA + Th2 cells, plays an important pathogenetic role in pruritic skin diseases like atopic dermatitis. As tumor cells in Sézary syndrome (SS) and Mycosis fungoides (MF) possess similar immunophenotypes and the conditions mentioned are often associated with pruritus, the analysis of the IL-31 pathway in MF/SS patients is of interest. Serum samples from the peripheral blood of 23 patients and 17 controls were analyzed for IL-31 abundance and correlated with disease stage and pruritus. Furthermore IL-31-, IL-31 receptor alpha (IL-31Rα)- and Oncostatin M receptor beta (OSMRβ)-mRNA expression was measured in blood tumor cells from SS patients, memory T-cells from controls and lymphoma cell lines. Serum IL-31 levels were low but differed between groups with no or strong pruritus. Expression of IL-31 was detectable at low levels in cell lines, but not in the tumor cells of SS patients. Stimulation with PMA/ionomycin led to indiscriminate expression in peripheral blood tumor cells and control T-cells. IL-2-stimulation resulted in expression only in 9/11 patient samples. IL-31Rα-expression was detectable in 10/10 cell lines, 8/15 peripheral blood samples from SS patients, and 4/10 controls; whereas, OSMRβ mRNA was detectable in 4/10 cell lines, but only one patient and control sample. The results of our analyses regarding serum levels and receptor expression do not suggest a central role of IL-31 in MF/SS pathogenesis. However, the results of IL-2 stimulation as well as the increased IL-31 levels in patients with strong pruritus offer a rationale for therapeutic approach in this subset of patients.