BUSCADOR RECOLECTA

Supplemental Table S1. Complete overview of the inclusion and exclusion criteria for this study. Supplemental Table S2. Composition of the EuroFlow B-cell panel and technical information on the reagents for the IMI-2 PERISCOPE BERT study. Supplemental Table S3. Phenotypic descriptions used to define B-cell subsets stained with the EuroFlow B-cell panel by manual analysis. Supplemental Table S4. Baseline distribution of leukocytes, lymphocytes, T cells, and NK cells in donor groups. Supplemental Table S5. Spearman Ranking Correlation between IgG1+ plasma cell and memory B-cell kinetics and vaccine-component-specific serum IgG. Supplemental Table S6. Spearman Ranking Correlation between IgA1+ plasma cell and IgA memory B-cell kinetics and vaccine-component-specific serum IgA. Supplemental Figure S1. No clear over-time postvaccination changes in major populations in any of the donor groups. Supplemental Figure S2. Over-time maturation of total plasma cells. Supplemental Figure S3. No significant changes in IgG1+ memory B-cell subsets upon vaccination. Supplemental Figure S4. Correlation between cellular changes as measured by flow cytometry and ELISpot. Supplemental Figure S5. Correlation between cellular changes and the vaccine-specific serum IgG level postvaccination as determined by Spearman’s Ranking Correlation per age cohort. Supplemental Figure S6. Impact of sex on cellular responses after vaccination in the young adult cohort (all wP-primed). Supplemental Figure S7. IgG1+ and total plasma cell expansion is more prominent in non-age-matched donors after wP priming., Peer reviewed

The following are available online at https://www.mdpi.com/article/10.3390/cancers14020449/s1. Supplementary Figures. Figure S1: Distribution of pathological CSF samples (without healthy ones) among each phase of study and the different groups according to the incidence of the pathology, depending on the infiltration (CSF +/− LM) and the primary tumor (hematologic and solid tumor). Figure S2: Quality control images of the planar protein microarrays generated. Figure S3: A quantile normalization in planar protein microarrays. Figure S4: Coomasie gels which indicate protein distribution across samples. Figure S5: Venn diagrams of total identified proteins with LC-MS/MS. Figure S6: Plots showing the functional proteins using the Reactome for different conditions. Figure S7: Differential protein profiles within CSF + LM according to primary tumor (Lymphoma) by protein microarrays. Figure S8: Differential protein profiles within CSF + LM according to primary tumor (Leukemia) by protein microarrays. Figure S9: Differential protein profiles within CSF + LM according to primary tumor (Lymphoma) by affinity proteomics. Figure S10: Differential protein profiles within CSF + LM according to primary tumor (Leukemia) by affinity proteomics. Figure S11: Summary of the multipronged proteomics characterization among the different phases of study. Supplementary Tables. Table S1: Table of clinical-biological characteristics from the whole CSF samples used in the study. Table S2: Antibodies list used in Planar Protein Microarrays. Table S3: Antibodies list used in Beads Suspension Microarrays. Table S4: Protein identification with LC-MS/MS among the different strategies and their emPAI quantification. Table S5: Boxplots of the protein identified in validation and confirmation phases, respectively, comparing the different groups of study. Table S6: Intensity data results from Planar Protein Microarrays. Table S7: Intensity data results from Beads Suspension Microarrays. Table S8: ROC analysis list of potential biomarker panel on CSF +/− LM and the different comparisons by protein arrays and affinity proteomics., Peer reviewed

Innate myeloid cell (IMC) populations form an essential part of innate immunity. Flow cytometric (FCM) monitoring of IMCs in peripheral blood (PB) has great clinical potential for disease monitoring due to their role in maintenance of tissue homeostasis and ability to sense micro-environmental changes, such as inflammatory processes and tissue damage. However, the lack of standardized and validated approaches has hampered broad clinical implementation. For accurate identification and separation of IMC populations, 62 antibodies against 44 different proteins were evaluated. In multiple rounds of EuroFlow-based design-testing-evaluation-redesign, finally 16 antibodies were selected for their non-redundancy and separation power. Accordingly, two antibody combinations were designed for fast, sensitive, and reproducible FCM monitoring of IMC populations in PB in clinical settings (11-color; 13 antibodies) and translational research (14-color; 16 antibodies). Performance of pre-analytical and analytical variables among different instruments, together with optimized post-analytical data analysis and reference values were assessed. Overall, 265 blood samples were used for design and validation of the antibody combinations and in vitro functional assays, as well as for assessing the impact of sample preparation procedures and conditions. The two (11- and 14-color) antibody combinations allowed for robust and sensitive detection of 19 and 23 IMC populations, respectively. Highly reproducible identification and enumeration of IMC populations was achieved, independently of anticoagulant, type of FCM instrument and center, particularly when database/software-guided automated (vs. manual “expert-based”) gating was used. Whereas no significant changes were observed in identification of IMC populations for up to 24h delayed sample processing, a significant impact was observed in their absolute counts after >12h delay. Therefore, accurate identification and quantitation of IMC populations requires sample processing on the same day. Significantly different counts were observed in PB for multiple IMC populations according to age and sex. Consequently, PB samples from 116 healthy donors (8-69 years) were used for collecting age and sex related reference values for all IMC populations. In summary, the two antibody combinations and FCM approach allow for rapid, standardized, automated and reproducible identification of 19 and 23 IMC populations in PB, suited for monitoring of innate immune responses in clinical and translational research settings., Peer reviewed

Supplemental Table 1. Detailed biological and demographic features of patients. Supplemental Table 2: Composition of the EuroFlow B-CLPD panel Supplemental Table 3. Overview on the data analysis strategy within the scope of the main study Supplemental Table 4. Canonical coefficients for CA1 and CA2. Significance of contribution of individual parameters to the canonical axes CA1 and CA2 by differential diagnosis. V Supplemental Table 5. SD lines utilized as decision criterion per pair-wise differential diagnosis Supplemental Table 6. Medians (10th – 90th 309 percentile) of medFIs and of BT ratio, 3respectively, by parameter and entity (see Figure 3 for corresponding box plots) Supplemental Table 7. Monte Carlo cross-validation results Supplemental Table 8. Cases rejected prior to study inclusion Supplemental Table 9: Markers representing predominantly background signal (BS) by entity, Peer reviewed

S1: Combining sample aliquots stained with a CD45 antibody conjugated to 8 different fluorochromes into only two antibody combinations ready to be measured in the flow cytometer. Table S1: Panels of fluorochrome-conjugated antibody reagents used in this study. Table S2: Sources and specificities of the monoclonal antibody reagents used in this study. Table S3: Detailed immunophenotypic features of T-cell subsets showing extreme TRBC1+ percentages within the more mature polyclonal and monoclonal Tαβ-cell populations expressing a specific TCRVβ family. Figure S1: Distribution of T cells expressing different TCRVβ families among total Tαβ cells and their Tαβ CD8+ and Tαβ CD4+ cell subsets and their maturation-associated stages of CD28− effector memory and terminal effector cells as identified in the blood of healthy donors (n = 6)., Peer reviewed

S1. Sample preparation protocol for evaluation of OneFlow kits (BD). Figure S1. Impact of different BSA concentrations and different pH of the washing buffer on the relative percentage distribution of different cell populations identifiable in normal PB samples with LST. Figure S2. Impact of different BSA concentrations and different pH of the washing buffer on the expression levels of individual LST markers as assessed on different cell populations in normal PB samples. Table S1. Detailed information on the antibody reagents used in the current study, including the antibody titer used per test. Table S2. Absolute and relative percentage changes of particular cell populations, debris and doublets detectable in BM samples stained with ALOT tube at day 0 and after 24-h storage. Table S3. Ratios of percentage of debris, doublets, and pathological/clonal B-cells in particular BM and PB samples stained with LST tube at different time points. Table S4. Ratios of percentage of debris, doublets, and pathological/clonal B-cells in particular BM and PB samples stained with the first tube of B-CLPD panel at different time points. Table S5. Ratios of percentage of debris, doublets, and pathological/clonal plasma cells in particular BM and PB samples stained with PCD panel at different time points. Table S6. MFI values of LST markers evaluated on relevant cell populations obtained at different pH and BSA concentrations. Table S7. Expression of markers on relevant PB cell populations stained with LST tube at different pH on averaging the results for different BSA concentrations. Table S8. Selected additional markers that were not directly studied in the current study that may be influenced by the preparation-associated variables., Peer reviewed

Supplementary Table 1: Overview of the excluded patients (N=147. Supplementary Table 2: Study population demographics. Supplementary Table 3: Manual Gating strategy for the identification of lymphoid populations in blood according to the EuroFlow guidelines for analysis of blood samples stained with PIDOT. Supplementary Table 4: The clinical characteristics of the non-PID disease controls (DC) (n=116). Supplementary Table 5: Verification of the EuroFlow reference values using an independent healthy control group (N=68). Supplementary Figures Supplementary Figure 1: Box plots of serum immunoglobulin levels at time of PIDOT analysis. Supplementary Figure S2: Box plots of total memory and switched memory B-cells (% as expressed on the B-cells) measured by the PIDOT. Supplementary Figure 3: Box plots of frequency of total defective lymphoid populations (over the 22 FCM PIDOT variables). Supplementary Figure 4: Box plots of frequency of total increased cell counts (over the 22 FCM PIDOT variables). Supplementary Figure 5: Receiver Operating Characteristic (ROC) curve to assess the performance of the decision-tree algorithm in relation to the predicted probabilities for lymphoid-PID., [Introduction]: Multiparameter flow cytometry (FCM) immunophenotyping is an important tool in the diagnostic screening and classification of primary immunodeficiencies (PIDs). The EuroFlow Consortium recently developed the PID Orientation Tube (PIDOT) as a universal screening tool to identify lymphoid-PID in suspicious patients. Although PIDOT can identify different lymphoid-PIDs with high sensitivity, clinical validation in a broad spectrum of patients with suspicion of PID is missing. In this study, we investigated the diagnostic performance of PIDOT, as part of the EuroFlow diagnostic screening algorithm for lymphoid-PID, in a daily practice at a tertiary reference center for PID., [Methods]: PIDOT was tested in 887 consecutive patients suspicious of PID at the Ghent University Hospital, Belgium. Patients were classified into distinct subgroups of lymphoid-PID vs. non-PID disease controls (non-PID DCs), according to the IUIS and ESID criteria. For the clinical validation of PIDOT, comprehensive characterization of the lymphoid defects was performed, together with the identification of the most discriminative cell subsets to distinguish lymphoid-PID from non-PID DCs. Next, a decision-tree algorithm was designed to guide subsequent FCM analyses., [Results]: The mean number of lymphoid defects detected by PIDOT in blood was 2.87 times higher in lymphoid-PID patients vs. non-PID DCs (p < 0.001), resulting in an overall sensitivity and specificity of 87% and 62% to detect severe combined immunodeficiency (SCID), combined immunodeficiency with associated or syndromic features (CID), immune dysregulation disorder (ID), and common variable immunodeficiency (CVID). The most discriminative populations were total memory and switched memory B cells, total T cells, TCD4+cells, and naive TCD4+cells, together with serum immunoglobulin levels. Based on these findings, a decision-tree algorithm was designed to guide further FCM analyses, which resulted in an overall sensitivity and specificity for all lymphoid-PIDs of 86% and 82%, respectively., [Conclusion]: Altogether, our findings confirm that PIDOT is a powerful tool for the diagnostic screening of lymphoid-PID, particularly to discriminate (S)CID, ID, and CVID patients from other patients suspicious of PID. The combination of PIDOT and serum immunoglobulin levels provides an efficient guide for further immunophenotypic FCM analyses, complementary to functional and genetic assays, for accurate PID diagnostics., Peer reviewed

Supplementary Tables Supplementary Table 1: Clinical data of the study cohort. Supplementary Table 2: Functional enrichment by trend of soluble proteins for Diagnostic group study. Supplementary Table 3: Summary of significant soluble proteins for each study after classical statistical analysis. Supplementary Table 4: Functional enrichment of significant soluble proteins for comparison Monoclonal B-cell lymphocytosis (MBLhi) vs. Chronic Lymphocytic Leukemia (CLL) and CLL in progression (p-CLL) vs. Stable/constant CLL (c-CLL) vs. MBLhi and combinations. Supplementary Table 5: Functional enrichment of significant soluble proteins according to trend for Diagnostic group study. Supplementary Table 6: Protein correlation. Supplementary Table 7: Top 20/30 proteins after random forest analyses. Supplementary Table 8: Summary of significant soluble proteins for each study after linear model. Supplementary Table 9: Functional enrichment by trend of soluble proteins for Diagnostic group and treatment line. Supplementary Table 10: Functional enrichment of significant soluble proteins for all combination combinations between Monoclonal B-cell lymphocytosis (MBLhi), Stable/constant Chronic Lymphocytic Leukemia (c-CLL), CLL in progression previously to 1st line treatment (CLL-PFT) and CLL in progression to time from 1st line treatment (CLL-TFT). Supplementary Table 11: Functional enrichment by trend of soluble proteins for Immunoglobulin Heavy chain Variable (IGHV) gene status study. Supplementary Table 12: Functional enrichment of significant soluble proteins for Immunoglobulin Heavy chain Variable (IGHV) gene status study., 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., Peer reviewed

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., Peer reviewed

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., Peer reviewed