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BackgroundMost patients with advanced non-small-cell lung cancer (NSCLC) fail to derive significant benefit from programmed cell death protein-1 (PD-1) axis blockade, and new biomarkers of response are needed. In this study, we aimed to discover and validate spatially resolved protein markers associated with sensitivity to PD-1 axis inhibition in NSCLC.MethodsWe initially assessed a discovery cohort of 56 patients with NSCLC treated with PD-1 axis inhibitors at Yale Cancer Center. Using the GeoMx Digital Spatial Profiling (DSP) system, 71 proteins were measured in spatial context on each spot in a tissue microarray. We used the AQUA method of quantitative immunofluorescence (QIF) to orthogonally validate candidate biomarkers. For external independent validation, we assessed whole tissue sections derived from 128 patients with NSCLC treated with single-agent PD-1 axis inhibitors at the 12 de Octubre Hospital (Madrid) using DSP. We further analyzed two immunotherapy untreated cohorts to address prognostic significance (n=252 from Yale Cancer Center; n=124 from University Clinic of Navarra) using QIF and DSP, respectively.ResultsUsing continuous log-scaled data, we identified CD44 expression in the tumor compartment (pan-cytokeratin (CK)+) as a novel predictor of prolonged progression-free survival (PFS) (multivariate HR=0.68, p=0.043) in the discovery set. We validated by QIF that tumor CD44 levels assessed as continuous QIF scores were associated with longer PFS (multivariate HR=0.31, p=0.022) and overall survival (multivariate HR=0.29, p=0.038). Using DSP in an independent immunotherapy treated cohort, we validated that CD44 levels in the tumor compartment, but not in the immune compartment (panCK–/CD45+), were associated with clinical benefit (OR=1.22, p=0.018) and extended PFS under PD-1 axis inhibition using the highest tertile cutpoint (multivariate HR=0.62, p=0.03). The effect of tumor cell CD44 in predicting PFS remained significant after correcting for programmed death-ligand 1 (PD-L1) Tumor Proportion Score (TPS) in both cohorts. High tumor cell CD44 was not prognostic in the absence of immunotherapy. Using DSP data, intratumoral regions with elevated tumor cell CD44 expression showed prominent (fold change>1.5, adjusted pConclusionsThis work highlights CD44 as a novel indicative biomarker of sensitivity to PD-1 axis blockade that might help to improve immunotherapy strategies for NSCLC.

Introduction: Most patients with advanced NSCLC fail to develop durable responses from PD-1 axis blockade, and more robust predictive biomarkers are needed. In this study, we aimed to discover and validate spatially resolved protein markers associated with sensitivity to PD-1 axis inhibition across multiple NSCLC cohorts. Methods: We first used the GeoMx Digital Spatial Profiling (DSP) system in a discovery cohort of 56 NSCLC patients treated with PD-1 axis inhibitors at Yale (USA). Pre-treatment tumors were represented in a tissue microarray (YTMA471) and analyzed using a 71-plex primary antibody panel. Proteins were measured from three molecularly defined tissue compartments: tumor (CK+), leukocytes (CD45+/CD68-), and macrophages (CD68+). We used quantitative immunofluorescence (QIF) to orthogonally validate candidate biomarkers. For external validation of identified predictors, we assessed pre-treatment whole tissue sections from a cohort of 128 NSCLC patients treated with single-agent PD-1 axis inhibitors at the Hospital 12 de Octubre (Madrid, Spain) using DSP (39-plex protein panel, measured from CK+ and CD45+ compartments). In addition, we analyzed two immunotherapy untreated cohorts to address prognostic significance: YTMA423 (Yale, USA; n = 252) and CIMA-CUN (UNAV, Spain; n = 124), using QIF and DSP respectively. Results: Using continuous log-scaled data, we found 5 markers independently associated with progression-free survival (PFS) in the tumor compartment (including PD-L1, p = 0.002). Among the novel candidate predictors, tumor cell CD44, a marker of pluripotency and stemness, was associated with longer PFS (multivariate HR = 0.63, p= 0.002). Using QIF, we orthogonally validated that CD44 expression in the tumor compartment was associated with longer PFS (p 1.5, p Conclusion: This work highlights CD44 as a novel indicative biomarker of sensitivity to PD-1 axis blockade in NSCLC. NSCLCs with high CD44 expression in epithelial cells may be associated with an immune contexture primed for higher response to immune checkpoint blockade. Further studies are needed to understand the interplay between CD44+ cancer stem cell phenotype and mechanisms of immune evasion. Citation Format: Myrto Moutafi, Magdalena Molero, Sandra Martinez-Morilla, Javier Baena, Ioannis Vathiotis, Niki Gavrielatou, Laura Castro, Gorka Ruiz de Garibay, Vera Adradas, Daniel Orive, Karmele Valencia, Alfonso Galvo, Luis M. Montuenga, Santiago Ponce, Kurt Schalper, Luis Paz-Ares, David L. Rimm, Jon Zugazagoitia. Spatially resolved proteomic profiling identifies tumor cell CD44 as a novel indicative biomarker of sensitivity to PD-1 axis blockade in advanced non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2028.

The oral mucosa is a first line of defense against pathogenic organisms and yet tolerates food antigens and resident bacteria. Mucosal epithelial cells are emerging as important regulators of innate and adaptive immune responses. However, the contribution of oral epithelial cells (OECs) determining oral immunity is understudied. Here, we evaluated the ability of H413 and TR146 cells, two OEC lines derived from human oral squamous cell carcinomas, and primary OECs to modulate immune responses to a cocktail of Gram+ and Gram- bacteria known as MV130. OECs expressed CD40 constitutively and class II major histocompatibility complex (MHC II) molecules when stimulated with IFNγ, but not CD80 or CD86. Dendritic cells (DCs) treated with bacteria in co-culture with OECs did not fully mature, as judged by the expression of MHC II, CD80 and CD86, and barely released IL-12 and TNFα, compared to control DCs. Furthermore, in the presence of OECs, DCs were unable to stimulate allogenic naive CD4 T cells to produce IFNγ and TNFα. Similarly, OECs in culture with total CD4 T cells or Th1 cells stimulated with anti-CD3 and anti-CD28 antibodies abrogated CD25 and CD69 expression, T cell proliferation and the release of IFNγ and TNFα. The inhibition on T cell activation by OECs was cell-contact dependent, TGFβ independent and largely irreversible. Overall, this behavior of OECs is likely key to avoid immune system over-reaction against resident bacteria.

Background and aim To diagnose coeliac disease (CD) in individuals on a gluten free diet (GFD), we aimed to assess the utility of detecting activated γδ and CD8 T cells expressing gut-homing receptors after a short gluten challenge. Methods We studied 15 CD patients and 35 non-CD controls, all exposed to three days of gluten when following a GFD. Peripheral blood was collected before and six days after starting gluten consumption, and the expression of CD103, β7 and CD38 in γδ and CD8 T cells was assessed by flow cytometry. Determination of IFN-γ and IP-10 was performed by means of ELISPOT and/or Luminex technology. Results We observed both γδ and CD8 T cells coexpressing CD103, β7hi and CD38 in every patient with CD on day six, but only in one control. The studied CD8 T subpopulation was easier to detect than the γδ subpopulation. Increased IFN-γ and IP-10 levels after challenge were observed in patients with CD, but not in controls. Conclusion A short three-day gluten challenge elicits the activation of CD103+ β7hi CD8+ T cells in CD. These cells can be detected by flow cytometry in peripheral blood, opening new possibilities for CD diagnosis in individuals on a GFD.

Concerns that variola viruses might be used as bioweapons have renewed the interest in developing new and safer smallpox vaccines. Variola virus genomes are now widely available, allowing computational characterization of the entire T-cell epitome and the use of such information to develop safe and yet effective vaccines. To this end, we identified 124 proteins shared between various species of pathogenic orthopoxviruses including variola minor and major, monkeypox, cowpox, and vaccinia viruses, and we targeted them for T-cell epitope prediction. We recognized 8,106, and 8,483 unique class I and class II MHC-restricted T-cell epitopes that are shared by all mentioned orthopoxviruses. Subsequently, we developed an immunological resource, EPIPOX, upon the predicted T-cell epitome. EPIPOX is freely available online and it has been designed to facilitate reverse vaccinology. Thus, EPIPOX includes key epitope-focused protein annotations: time point expression, presence of leader and transmembrane signals, and known location on outer membrane structures of the infective viruses. These features can be used to select specific T-cell epitopes suitable for experimental validation restricted by single MHC alleles, as combinations thereof, or by MHC supertypes.

Peptide binding to major histocompatibility complex (MHC) molecules is the most selective requisite for T-cell recognition. Therefore, prediction of peptide–MHC binding is the main basis for anticipating T-cell epitopes. A very popular and accurate method to predict peptide–MHC binding is based on motif-profiles and here we show how to make them using EPIMHC (http://imed.med.ucm.es/epimhc/). EPIMHC is a database of T-cell epitopes and MHC-binding peptides that unlike any related resource provides a framework for computational vaccinology. In this chapter, we describe how to derive peptide–MHC binding motif-profiles in EPIMHC and use them to predict peptide–MHC binding and T-cell epitopes. Moreover, we show evidence that customization of peptide–MHC binding predictors can lead to enhanced epitope predictions.

Peptide binding to major histocompatibility complex (MHC) molecules is the most selective requisite for T-cell recognition. Therefore, prediction of peptide-MHC binding is the main basis for anticipating T-cell epitopes. A very popular and accurate method to predict peptide-MHC binding is based on motif-profiles and here we show how to make them using EPIMHC (http://imed.med.ucm.es/epimhc/). EPIMHC is a database of T-cell epitopes and MHC-binding peptides that unlike any related resource provides a framework for computational vaccinology. In this chapter, we describe how to derive peptide-MHC binding motif-profiles in EPIMHC and use them to predict peptide-MHC binding and T-cell epitopes. Moreover, we show evidence that customization of peptide-MHC binding predictors can lead to enhanced epitope predictions.