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. 2022 Sep 28:13:965905.
doi: 10.3389/fimmu.2022.965905. eCollection 2022.

Unravelling soluble immune checkpoints in chronic lymphocytic leukemia: Physiological immunomodulators or immune dysfunction

Alicia Landeira-Viñuela  1 , Carlota Arias-Hidalgo  1 , Pablo Juanes-Velasco  1 , Miguel Alcoceba  2 , Almudena Navarro-Bailón  2 , Carlos Eduardo Pedreira  3 , Quentin Lecrevisse  1 , Laura Díaz-Muñoz  1 , José Manuel Sánchez-Santos  4 , Ángela-Patricia Hernández  1   5 , Marina L García-Vaquero  1 , Rafael Góngora  1 , Javier De Las Rivas  6 , Marcos González  2 , Alberto Orfao  1 , Manuel Fuentes  1   7
Affiliations

Unravelling soluble immune checkpoints in chronic lymphocytic leukemia: Physiological immunomodulators or immune dysfunction

Alicia Landeira-Viñuela et al. Front Immunol. .

Abstract

Chronic lymphocytic leukemia (CLL) is a lymphoid neoplasm characterized by the accumulation of mature B cells. The diagnosis is established by the detection of monoclonal B lymphocytes in peripheral blood, even in early stages [monoclonal B-cell lymphocytosis (MBLhi)], and its clinical course is highly heterogeneous. In fact, there are well-characterized multiple prognostic factors that are also related to the observed genetic heterogenicity, such as immunoglobulin heavy chain variable region (IGHV) mutational status, del17p, and TP53 mutations, among others. Moreover, a dysregulation of the immune system (innate and adaptive immunity) has been observed in CLL patients, with strong impact on immune surveillance and consequently on the onset, evolution, and therapy response. In addition, the tumor microenvironment is highly complex and heterogeneous (i.e., matrix, fibroblast, endothelial cells, and immune cells), playing a critical role in the evolution of CLL. In this study, a quantitative profile of 103 proteins (cytokines, chemokines, growth/regulatory factors, immune checkpoints, and soluble receptors) in 67 serum samples (57 CLL and 10 MBLhi) has been systematically evaluated. Also, differential profiles of soluble immune factors that discriminate between MBLhi and CLL (sCD47, sCD27, sTIMD-4, sIL-2R, and sULBP-1), disease progression (sCD48, sCD27, sArginase-1, sLAG-3, IL-4, and sIL-2R), or among profiles correlated with other prognostic factors, such as IGHV mutational status (CXCL11/I-TAC, CXCL10/IP-10, sHEVM, and sLAG-3), were deciphered. These results pave the way to explore the role of soluble immune checkpoints as a promising source of biomarkers in CLL, to provide novel insights into the immune suppression process and/or dysfunction, mostly on T cells, in combination with cellular balance disruption and microenvironment polarization leading to tumor escape.

Keywords: cellular microenvironment; chronic lymphocytic leukaemia (CLL); cytokines profiles; immune dysfunction; soluble immune checkpoints.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Clinical and biological info of the cohort. Schematic representation of cohort main features. (A) Diagnosis. (B) Distribution of diagnosis group. (C) Main prognostic factors.
Figure 2
Figure 2
Schematic representation of IL-4-associated differential profiles in TME. (A) Cytokine profile observed between MBLhi and c-CLL. (B) Cytokine profile between c-CLL and p-CLL. (Created in Biorender.com)
Figure 3
Figure 3
mRMR analysis: MBLhi vs. CLL. (A) Distribution and classification of MBLhi and CLL patients from quantitative soluble immune checkpoints and cytokines in serum. (B) Boxplot sCD47 in serum between MBLhi and CLL (*p – value< 0.05).
Figure 4
Figure 4
mRMR analysis: MBLhi vs. CLL stages. (A) Distribution and classification of MBLhi and c-CLL/p-CLL from quantitative soluble immune checkpoints and cytokines in serum. (B) Boxplot sCD27 in serum between MBLhi and CLL stages (*p – value< 0.05).
Figure 5
Figure 5
Schematic models of the TME from the role of the observed differential serum profiles. (A) MBLhi vs. c-CLL (stable and progression). (B) c-CLL vs. CLL-PFT. (C) CLL-PFT vs. CLL-TFT. (Created in Biorender.com)
Figure 6
Figure 6
mRMR analysis according to therapy. (A) Distribution and classification from differential profiles of soluble receptors and immune checkpoints. (B) Boxplot sIL-2R in serum across the analyzed cohort.
Figure 7
Figure 7
mRMR analysis according to IGHV status. (A) Distribution and classification from differential profiles of soluble immune checkpoints correlated with IGHV mutational status. (B) Boxplot CXCL10/IP-10 in serum between groups (*p – value< 0.05).
Figure 8
Figure 8
Model of the role of soluble immune checkpoints in the TME in the studied cohort. Inside circles, protein profiling decreased levels according to disease evolution. Inside squares, protein profile correlated with mutated IGHV. Underline, protein profiles with fluctuations over disease evolution and IGHV mutational status. (Created in Biorender.com)
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