GateMeClass: Gate mining and classification of cytometry data.

MOTIVATION: Cytometry comprises powerful techniques for analyzing the cell heterogeneity of a biological sample by examining the expression of protein markers. These technologies impact especially the field of oncoimmunology, where cell identification is essential to analyze the tumor microenvironment. Several classification tools have been developed for the annotation of cytometry datasets, which include supervised tools that require a training set as a reference (ie, reference-based) and semi-supervised tools based on the manual definition of a marker table. The latter are closer to the traditional annotation of cytometry data based on manual gating. However, they require the manual definition of a marker table that cannot be extracted automatically in a reference-based fashion. Therefore, we are lacking methods that allow both classification approaches while maintaining the high biological interpretability given by the marker table. RESULTS: We present a new tool called GateMeClass (Gate Mining and Classification) which overcomes the limitation of the current methods of classification of cytometry data allowing both semi-supervised and supervised annotation based on a marker table that can be defined manually or extracted from an external annotated dataset. We measured the accuracy of GateMeClass for annotating three well-established benchmark mass cytometry datasets and one flow cytometry dataset. The performance of GateMeClass is comparable to reference-based methods and marker table-based techniques, offering greater flexibility and rapid execution times. AVAILABILITY: GateMeClass is implemented in R language and is publicly available at https://github.com/simo1c/GateMeClass. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

B-cell receptor signaling activity identifies patients with mantle cell lymphoma at higher risk of progression.

Mantle cell lymphoma (MCL) is an incurable B-cell malignancy characterized by a high clinical variability. Therefore, there is a critical need to define parameters that identify high-risk patients for aggressive disease and therapy resistance. B-cell receptor (BCR) signaling is crucial for MCL initiation and progression and is a target for therapeutic intervention. We interrogated BCR signaling proteins (SYK, LCK, BTK, PLCγ2, p38, AKT, NF-κB p65, and STAT5) in 30 primary MCL samples using phospho-specific flow cytometry. Anti-IgM modulation induced heterogeneous BCR signaling responses among samples allowing the identification of two clusters with differential responses. The cluster with higher response was associated with shorter progression free survival (PFS) and overall survival (OS). Moreover, higher constitutive AKT activity was predictive of inferior response to the Bruton's tyrosine kinase inhibitor (BTKi) ibrutinib. Time-to-event analyses showed that MCL international prognostic index (MIPI) high-risk category and higher STAT5 response were predictors of shorter PFS and OS whilst MIPI high-risk category and high SYK response predicted shorter OS. In conclusion, we identified BCR signaling properties associated with poor clinical outcome and resistance to ibrutinib, thus highlighting the prognostic and predictive significance of BCR activity and advancing our understanding of signaling heterogeneity underlying clinical behavior of MCL.

Immune checkpoint blockade therapy mitigates systemic inflammation and affects cellular FLIP-expressing monocytic myeloid-derived suppressor cells in non-progressor non-small cell lung cancer patients.

Cancer cells favor the generation of myeloid cells with immunosuppressive and inflammatory features, including myeloid-derived suppressor cells (MDSCs), which support tumor progression. The anti-apoptotic molecule, cellular FLICE (FADD-like interleukin-1ß-converting enzyme)-inhibitory protein (c-FLIP), which acts as an important modulator of caspase-8, is required for the development and function of monocytic (M)-MDSCs. Here, we assessed the effect of immune checkpoint inhibitor (ICI) therapy on systemic immunological landscape, including FLIP-expressing MDSCs, in non-small cell lung cancer (NSCLC) patients. Longitudinal changes in peripheral immunological parameters were correlated with patients' outcome. In detail, 34 NSCLC patients were enrolled and classified as progressors (P) or non-progressors (NP), according to the RECIST evaluation. We demonstrated a reduction in pro-inflammatory cytokines such as IL-8, IL-6, and IL-1ß in only NP patients after ICI treatment. Moreover, using t-distributed stochastic neighbor embedding (t-SNE) and cluster analysis, we characterized in NP patients a significant increase in the amount of lymphocytes and a slight contraction of myeloid cells such as neutrophils and monocytes. Despite this moderate ICI-associated alteration in myeloid cells, we identified a distinctive reduction of c-FLIP expression in M-MDSCs from NP patients concurrently with the first clinical evaluation (T1), even though NP and P patients showed the same level of expression at baseline (T0). In agreement with the c-FLIP expression, monocytes isolated from both P and NP patients displayed similar immunosuppressive functions at T0; however, this pro-tumor activity was negatively influenced at T1 in the NP patient cohort exclusively. Hence, ICI therapy can mitigate systemic inflammation and impair MDSC-dependent immunosuppression.