Long-term SARS-CoV-2-specific and cross-reactive cellular immune responses correlate with humoral responses, disease severity, and symptomatology.

BACKGROUND: Cellular immune memory responses post coronavirus disease 2019 (COVID-19) have been difficult to assess due to the risks of contaminating the immune response readout with memory responses stemming from previous exposure to endemic coronaviruses. The work herein presents a large-scale long-term follow-up study investigating the correlation between symptomology and cellular immune responses four to five months post seroconversion based on a unique severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific peptide pool that contains no overlapping peptides with endemic human coronaviruses. METHODS: Peptide stimulated memory T cell responses were assessed with dual interferon-gamma (IFNγ) and interleukin (IL)-2 Fluorospot. Serological analyses were performed using a multiplex antigen bead array. RESULTS: Our work demonstrates that long-term SARS-CoV-2-specific memory T cell responses feature dual IFNγ and IL-2 responses, whereas cross-reactive memory T cell responses primarily generate IFNγ in response to SARS-CoV-2 peptide stimulation. T cell responses correlated to long-term humoral immune responses. Disease severity as well as specific COVID-19 symptoms correlated with the magnitude of the SARS-CoV-2-specific memory T cell response four to five months post seroconversion. CONCLUSION: Using a large cohort and a SARS-CoV-2-specific peptide pool we were able to substantiate that initial disease severity and symptoms correlate with the magnitude of the SARS-CoV-2-specific memory T cell responses.

Infiltration of NK and plasma cells is associated with a distinct immune subset in non-small cell lung cancer.

Immune cells of the tumor microenvironment are central but erratic targets for immunotherapy. The aim of this study was to characterize novel patterns of immune cell infiltration in non-small cell lung cancer (NSCLC) in relation to its molecular and clinicopathologic characteristics. Lymphocytes (CD3+, CD4+, CD8+, CD20+, FOXP3+, CD45RO+), macrophages (CD163+), plasma cells (CD138+), NK cells (NKp46+), PD1+, and PD-L1+ were annotated on a tissue microarray including 357 NSCLC cases. Somatic mutations were analyzed by targeted sequencing for 82 genes and a tumor mutational load score was estimated. Transcriptomic immune patterns were established in 197 patients based on RNA sequencing data. The immune cell infiltration was variable and showed only poor association with specific mutations. The previously defined immune phenotypic patterns, desert, inflamed, and immune excluded, comprised 30, 13, and 57% of cases, respectively. Notably, mRNA immune activation and high estimated tumor mutational load were unique only for the inflamed pattern. However, in the unsupervised cluster analysis, including all immune cell markers, these conceptual patterns were only weakly reproduced. Instead, four immune classes were identified: (1) high immune cell infiltration, (2) high immune cell infiltration with abundance of CD20+ B cells, (3) low immune cell infiltration, and (4) a phenotype with an imprint of plasma cells and NK cells. This latter class was linked to better survival despite exhibiting low expression of immune response-related genes (e.g. CXCL9, GZMB, INFG, CTLA4). This compartment-specific immune cell analysis in the context of the molecular and clinical background of NSCLC reveals two previously unrecognized immune classes. A refined immune classification, including traits of the humoral and innate immune response, is important to define the immunogenic potency of NSCLC in the era of immunotherapy. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Plasma Proteomic Analysis in Non-Small Cell Lung Cancer Patients Treated with PD-1/PD-L1 Blockade.

Checkpoint inhibitors have been approved for the treatment of non-small cell lung cancer (NSCLC). However, only a minority of patients demonstrate a durable clinical response. PD-L1 scoring is currently the only biomarker measure routinely used to select patients for immunotherapy, but its predictive accuracy is modest. The aim of our study was to evaluate a proteomic assay for the analysis of patient plasma in the context of immunotherapy. Pretreatment plasma samples from 43 NSCLC patients who received anti-PD-(L)1 therapy were analyzed using a proximity extension assay (PEA) to quantify 92 different immune oncology-related proteins. The plasma protein levels were associated with clinical and histopathological parameters, as well as therapy response and survival. Unsupervised hierarchical cluster analysis revealed two patient groups with distinct protein profiles associated with high and low immune protein levels, designated as "hot" and "cold". Further supervised cluster analysis based on T-cell activation markers showed that higher levels of T-cell activation markers were associated with longer progression-free survival (PFS) (p < 0.01). The analysis of single proteins revealed that high plasma levels of CXCL9 and CXCL10 and low ADA levels were associated with better response and prolonged PFS (p < 0.05). Moreover, in an explorative response prediction model, the combination of protein markers (CXCL9, CXCL10, IL-15, CASP8, and ADA) resulted in higher accuracy in predicting response than tumor PD-L1 expression or each protein assayed individually. Our findings demonstrate a proof of concept for the use of multiplex plasma protein levels as a tool for anti-PD-(L)1 response prediction in NSCLC. Additionally, we identified protein signatures that could predict the response to anti-PD-(L)1 therapy.

WITHDRAWN: Characterization of Patterns of Immune Cell Infiltration in NSCLC.

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Extracorporeal human whole blood in motion, as a tool to predict first-infusion reactions and mechanism-of-action of immunotherapeutics.

First infusion reactions along with severe anaphylactic responses can occur as a result of systemic administration of therapeutic antibodies. The underlying mechanisms by which monoclonal antibodies induce cytokine release syndrome (CRS) can involve direct agonistic effects via the drug target, or a combination of target-engagement along with innate receptor interactions. Despite the wide variety of pathways and cells that can play a role in CRS, many currently used assays are devoid of one or more components that must be present for these responses to occur. One assay that has not been assessed for its capacity to predict CRS is the modified Chandler loop model. Herein we evaluate a plethora of commercially available monoclonal antibodies to evaluate the modified Chandler loop model's potential in CRS prediction. We demonstrate that in a 4-hour loop assay, both the superagonistic antibodies, anti-CD3 (OKT3) and anti-CD28 (ANC28.1), display a clear cytokine response with a mixed adaptive/innate cytokine source. OKT3 induce TNFα and IFN-γ release in 20 out of 23 donors tested, whereas ANC28.1 induce TNF-α, IL-2 and IFN-γ release in all donors tested (n=18-22). On the other hand, non-agonistic antibodies associated with no or low infusion reactions in the clinic, namely cetuximab and natalizumab, neither induce cytokine release nor cause false positive responses. A TGN1412-like antibody also display a clear cytokine release with an adaptive cytokine profile (IFN-γ and IL-2) and all donors (n=9) induce a distinct IL-2 response. Additionally, the value of an intact complement system in the assay is highlighted by the possibility to dissect out the mechanism-of-action of alemtuzumab and rituximab. The loop assay can either complement lymph node-like assays or stand-alone to investigate drug/blood interactions during preclinical development, or for individual safety screening prior to first-in-man clinical trial.