Project description:Immune checkpoint inhibitors such as anti-PD-1 antibodies (aPD1) can be effective in treating advanced cancers. However, many patients do not respond and the mechanisms underlying these differences remain incompletely understood. In this study, we profile a cohort of patients with locally-advanced or metastatic basal cell carcinoma undergoing aPD-1 therapy using single-cell RNA sequencing, high-definition spatial transcriptomics in tumors and draining lymph nodes, and spatial immunoreceptor profiling, with long-term clinical follow-up. We find that successful responses to PD-1 inhibition are characterized by an induction of B-cell receptor (BCR) clonal diversity after treatment initiation. These induced BCR clones spatially co-localize with T-cell clones, facilitate their activation, and traffic alongside them between tumor and draining lymph nodes to enhance tumor clearance. Furthermore, we validated aPD1-induced BCR diversity as a predictor of clinical response in a larger cohort of glioblastoma, melanoma, and head and neck squamous cell carcinoma patients, suggesting that this is a generalizable predictor of treatment response across many types of cancers. We discover that pre-treatment tumors harbor a characteristic gene expression signature that portends a higher probability of inducing BCR clonal diversity after aPD-1 therapy, and we develop a machine learning model that predicts PD-1-induced BCR clonal diversity from baseline tumor RNA sequencing. These findings underscore a dynamic role of B cell diversity during immunotherapy, highlighting its importance as a prognostic marker and a potential target for intervention in non-responders.
Project description:Immunotherapy has revolutionized cancer treatment, however, a reliable biomarker is missing. MGAT5-mediated PD-L1 glycosylation is critical for its interaction with PD-1. MGAT5 expression is a functional biomarker that predicts immunotherapy responses.
Project description:Immune checkpoint blockade has revolutionized cancer therapy. In particular, inhibition of programmed cell death protein 1 (PD-1) is effective for the treatment of metastatic melanoma and other cancers. Despite a dramatic increase in progression-free survival, a large proportion of patients do not show durable response. Therefore, predictive biomarkers of clinical response are urgently needed. Here, we employed high-dimensional single cell mass cytometry and a bioinformatics pipeline for the in-depth characterization of the immune cell subsets in the peripheral blood of metastatic melanoma patients before and after anti-PD-1 immunotherapy. During therapy, we observed a clear treatment response to immunotherapy in the T cell compartment. However, prior to commending therapy a strong predictor of progression free and overall survival in response to anti-PD-1 immunotherapy was the frequency of CD14+CD16-HLA-DRhi monocytes. We could confirm this by conventional flow cytometry in an independent validation cohort and propose this as a novel predictive biomarker for therapy decisions in the clinic. In order to determine whether there are cell intrinsic changes in the monocyte signature, we performed RNA sequencing on sorted CD14+CD16-HLA-DRhi cells from HD, NR and R at baseline. Representative samples (n=4, each) of responders/non responders/ and healthy donors were selected from archival samples stored in the dermatology biobank according to the same clinical criteria used in the discovery and validation cohorts for CyTOF and FACS analysis. CD14+CD16-HLA-DRhiLin- (CD3, CD4, CD19, CD45RO) monocytes were sorted from frozen PBMC form blood samples from HD, R and NR at baseline.
Project description:An improved understanding of the anti-tumor CD8+ T cell response after checkpoint blockade would enable more informed and effective therapeutic strategies. Here we examined the dynamics of the effector response of CD8+ tumor-infiltrating lymphocytes (TILs) after checkpoint blockade therapy. Bulk and single-cell RNA profiles of CD8+ TILs after combined Tim-3+PD-1 blockade in preclinical models revealed significant changes in the transcriptional profile of PD-1? TILs. These cells could be divided into subsets bearing characterstics of naive-, effector-, and memory-precursor-like cells. Effector- and memory-precursor-like TILs contained tumor-antigen-specific cells, exhibited proliferative and effector capacity, and expanded in response to different checkpoint blockade therapies across different tumor models. The memory-precursor-like subset shared features with CD8+ T cells associated with response to checkpoint blockade in patients and was compromised in the absence of Tcf7. Expression of Tcf7 was requisite for the efficacy of diverse immunotherapies, highlighting the importance of this transcriptional regulator in the development of effective CD8+ T cell responses upon immunotherapy.
Project description:As the anti-PD-1 monoclonal antibody (mAb) becomes to be used in many types of cancers, the needs for the predictive biomarker of PD-1 blockade are growing. T cell receptor (TCR) repertoire—which reflects the anti-tumor T-cell responses based on the antigen specificity—is a potential biomarker of cancer immunotherapy. Here, we analyzed the TCR repertoire of patients with advanced gastrointestinal cancer and explored its association with clinical responses.
Project description:As the anti-PD-1 monoclonal antibody (mAb) becomes to be used in many types of cancers, the needs for the predictive biomarker of PD-1 blockade are growing. T cell receptor (TCR) repertoire—which reflects the anti-tumor T-cell responses based on the antigen specificity—is a potential biomarker of cancer immunotherapy. Here, we analyzed the TCR repertoire of patients with advanced gastrointestinal cancer and explored its association with clinical responses.
Project description:Anti-PD-1 immunotherapies have transformed cancer treatment, but the determinants of clinical response are largely unknown. We performed CODEX multiplexed tissue imaging and RNA sequencing on 70 tumor regions of advanced cutaneous T cell lymphoma (CTCL) from 14 patients enrolled in a clinical trial of pembrolizumab therapy. Clinical response was not associated with the frequency of tumor-infiltrating T cell subsets, but rather with striking differences in the spatial organization and functional immune state of the tumor microenvironment (TME). After treatment, pembrolizumab responders had a localized enrichment of tumor and CD4+ T cells, which coincided with immune activation and cytotoxic PD-1+ CD4+ T cells. In contrast, non-responders had a localized enrichment of Tregs pre- and post-treatment, consistent with a persistently immunosuppressed TME and exhausted PD-1+ CD4+ T cells. Integrating these findings by computing the physical distances between PD-1+ CD4+ T cells, tumor cells, and Tregs revealed a spatial biomarker predictive of pembrolizumab response. Finally, the chemokine CXCL13 was upregulated in tumor cells in responders post-treatment, suggesting that chemoattraction of PD-1+ CD4+ T cells towards tumor cells facilitates a positive outcome. Together, these data show that T cell topography reflects the balance of effector and suppressive activity within the TME and predicts clinical response to PD-1 blockade in CTCL.
Project description:We report results of transcriptional profiling of ex vivo MC38 murine organotypic tumor spheroids following programmed death-1 (PD-1) blockade compared to isotype control IgG treatment by bulk and single-cell RNA-sequencing after 6 days of treatment. Additionally, we report results from single-cell RNA-sequencing of MC38 tumors treated in vivo with PD-1 blockade versus isotype control IgG to validate ex vivo workflow.
Project description:We report results of transcriptional profiling of ex vivo MC38 murine organotypic tumor spheroids following programmed death-1 (PD-1) blockade compared to isotype control IgG treatment by bulk and single-cell RNA-sequencing after 6 days of treatment. Additionally, we report results from single-cell RNA-sequencing of MC38 tumors treated in vivo with PD-1 blockade versus isotype control IgG to validate ex vivo workflow.