Immune biomarkers to predict SARS-CoV-2 vaccine effectiveness in patients with hematological malignancies

Abstract

There is evidence of reduced SARS-CoV-2 vaccine effectiveness in patients with hematological malignancies. We hypothesized that tumor and treatment-related immunosuppression can be depicted in peripheral blood, and that immune profiling prior to vaccination can help predict immunogenicity. We performed a comprehensive immunological characterization of 83 hematological patients before vaccination and measured IgM, IgG, and IgA antibody response to four viral antigens at day +7 after second-dose COVID-19 vaccination using multidimensional and computational flow cytometry. Health care practitioners of similar age were the control group (n = 102). Forty-four out of 59 immune cell types were significantly altered in patients; those with monoclonal gammopathies showed greater immunosuppression than patients with B-cell disorders and Hodgkin lymphoma. Immune dysregulation emerged before treatment, peaked while on-therapy, and did not return to normalcy after stopping treatment. We identified an immunotype that was significantly associated with poor antibody response and uncovered that the frequency of neutrophils, classical monocytes, CD4, and CD8 effector memory CD127low T cells, as well as naive CD21+ and IgM+D+ memory B cells, were independently associated with immunogenicity. Thus, we provide novel immune biomarkers to predict COVID-19 vaccine effectiveness in hematological patients, which are complementary to treatment-related factors and may help tailoring possible vaccine boosters.

Fig. 1. Study design.

A Peripheral blood and serum from 83 patients with hematological malignancies and 102 health care practitioners (HCP) were analyzed. B Antibody response at day 7 after the second-dose vaccination was measured using a CE-IVD serological SARS-CoV-2 multiplex bead-based flow cytometry immunoassay. It allows the simultaneous and quantitative detection of specific IgM, IgG, and IgA antibodies to four different antigens present in serum: (1) the receptor-binding domain (RBD) of the S-glycoprotein; (2) the stable trimer of the spicule (S) glycoprotein; (3) the nucleocapsid (N) protein; and (4) the main virus protease (Mpro). Detection of antibodies against the N and Mpro antigens allows the identification of individuals infected with SARS-CoV-2 prior or during vaccination. C Immune profiling of hematological patients and HCP prior vaccination was performed using multidimensional and computational flow cytometry. A total of 59 immune cell types were systematically measured in peripheral blood, including basophils, eosinophils, neutrophils, antigen-presenting cells (APC) and lymphocytes. D APC were sub-clustered into classical, intermediate, SLAN− and SLAN+ non-classical monocytes, as well as plasmacytoid and myeloid dendritic cells (pDC and mDC, respectively). E Sub-clustering of T cells into 32 subsets related to antigen-dependent differentiation, as well as activation and exhaustion phenotypes in helper and cytotoxic compartments. F Sub-clustering of B cells into 18 subsets related to antigen-dependent differentiation. CM, central memory; EM, effector memory; TEMRA, effector memory T cells re-expressing CD45RA; Tfh, follicular helper T cells; Treg, regulatory T cells; CPCs, circulating plasma cells.

Fig. 2. The immune landscape of hematological patients and HCP.

A Supervised clustering of 83 patients with hematological malignancies and 102 health care practitioners (HCP), based on the percentile distribution of 59 immune cell types in peripheral blood that were categorized into five groups: granulocytes, antigen-presenting cells (APC), CD4, and CD8 T cells and B cells. Patients were grouped based on the diagnosis of a B-cell lymphoproliferative disorder (B-cell dis.), monoclonal gammopathies (MG), and Hodgkin lymphoma (HL). B Statistical significance of differences across groups (with graphical representation of such differences in Supplemental Fig. 2). CM, central memory; EM, effector memory; Treg, T regulatory cells; Tfh, T follicular-like; TEMRA, terminally effector memory CD45RA+; PC, plasma cells. *P < 0.05; **P < 0.01; ***P < 0.001; n.s., not significant.

Fig. 3. Tumor-related immune dysregulation and therapy-related immunosuppression.

A Relative distribution of granulocytes, antigen-presenting cells (APC), CD4 and CD8 T cells, and B cells across health care practitioners (HCPs), hematological patients that had never received treatment (No tx, N = 17), those that were on (Active tx, N = 17), and patients that were off-treatment (Post-tx, N = 49) before vaccination. B Statistical significance of differences across groups (with graphical representation of such differences in Supplemental Fig. 3). APC, antigen-presenting cells; CM, central memory; EM, effector memory; Treg, T regulatory cells; Tfh, T follicular-like; TEMRA, terminally effector memory CD45RA+; PC, plasma cells. *P < 0.05; **P < 0.01; ***P < 0.001; n.s., not significant.

Fig. 4. Antibody response in hematological patients and HCP.

A Index of IgM, IgG, and IgA antibodies against the receptor-binding domain (RBD) of the S-glycoprotein in health care practitioners (HCP, N = 102) and hematological patients (N = 83). B Concentration of anti-RBD IgG in HCP, hematological patients with B-cell lymphoproliferative disorders (B-cell dis., N = 48), monoclonal gammopathies (MG, N = 28) and Hodgkin lymphoma (HL, N = 7). C Concentration of anti-RBD IgG in HCP, hematological patients that had never received treatment (No Tx, N = 17), those that were on (N = 17), and patients that were off-treatment (N = 49) before vaccination. D Concentration of anti-RBD IgG in HCP and hematological patients with or without previous SARS-CoV-2 infection, based on the detection of IgG antibodies against the nucleocapsid (N) protein and the main virus protease (Mpro). Among HCP, 85 were negative and 17 positive for both antigens. Among hematological patients, 70 were negative and 13 positive for both antigens. In all panels, horizontal lines represent the median. *P < 0.05; **P < 0.01; ***P < 0.001.

Fig. 5. Immunotypes associated with poor antibody response.

Unsupervised clustering of 83 hematological patients and 102 health care practitioners (HCPs) based on the relative percentile distribution of 59 immune cell types in peripheral blood before vaccination, categorized into four groups: granulocytes, antigen-presenting cell (APC) subsets, T-cell and B-cell subsets. For the columns to the left of the cell-percentage data, moving from left to right, rows are color-coded according to gender, age groups, type and subtype of hematological malignancy, treatment status, number of previous lines of therapy, immunoparesis, autologous transplant, treatment with anti-CD20 antibodies, anti-CD38 antibodies and immunomodulatory drugs (IMiDs), depth of response to treatment (complete remission, CR), previous SARS-CoV-2 infection, and vaccine type. For the columns to the right of the cell-percentage data, moving from left to right, rows are color-coded according to seroconversion and presence of ≥553.5 IU/mL IgG against the receptor-binding domain (RBD) of the S-glycoprotein. CM, central memory; EM, effector memory; Treg, T regulatory cells; Tfh, T follicular-like; TEMRA, terminally effector memory CD45RA+; PC, plasma cells.

Fig. 6. Immune biomarkers of immunogenicity.

A Odds ratio univariate analysis with 95% confidence intervals (CI) of variables included in the logistic regression model. B Logistic regression coefficients with treatment-related features as well as immune biomarkers associated with generation of ≥553.5 IU/mL of IgG antibodies against the receptor-binding domain (RBD) of the S-glycoprotein. C Area under the curve (AUC) of prediction probabilities of patients’ dataset. D Correlation matrix of immune and treatment-related features. E Fourfold cross-validation of AUC of prediction probabilities of patients’ dataset.