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      Tumor-infiltrating mast cells are associated with resistance to anti-PD-1 therapy

      , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
      Nature Communications
      Springer Science and Business Media LLC

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          Abstract

          Anti-PD-1 therapy is used as a front-line treatment for many cancers, but mechanistic insight into this therapy resistance is still lacking. Here we generate a humanized (Hu)-mouse melanoma model by injecting fetal liver-derived CD34 + cells and implanting autologous thymus in immune-deficient NOD- scid IL2Rγ null (NSG) mice. Reconstituted Hu-mice are challenged with HLA-matched melanomas and treated with anti-PD-1, which results in restricted tumor growth but not complete regression. Tumor RNA-seq, multiplexed imaging and immunohistology staining show high expression of chemokines, as well as recruitment of FOXP3 + Treg and mast cells, in selective tumor regions. Reduced HLA-class I expression and CD8 +/Granz B + T cells homeostasis are observed in tumor regions where FOXP3 + Treg and mast cells co-localize, with such features associated with resistance to anti-PD-1 treatment. Combining anti-PD-1 with sunitinib or imatinib results in the depletion of mast cells and complete regression of tumors. Our results thus implicate mast cell depletion for improving the efficacy of anti-PD-1 therapy.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Primary, Adaptive, and Acquired Resistance to Cancer Immunotherapy.

            Cancer immunotherapy can induce long lasting responses in patients with metastatic cancers of a wide range of histologies. Broadening the clinical applicability of these treatments requires an improved understanding of the mechanisms limiting cancer immunotherapy. The interactions between the immune system and cancer cells are continuous, dynamic, and evolving from the initial establishment of a cancer cell to the development of metastatic disease, which is dependent on immune evasion. As the molecular mechanisms of resistance to immunotherapy are elucidated, actionable strategies to prevent or treat them may be derived to improve clinical outcomes for patients.
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              B cells and tertiary lymphoid structures promote immunotherapy response

              Treatment with immune checkpoint blockade (ICB) has revolutionized cancer therapy. Until now, predictive biomarkers1-10 and strategies to augment clinical response have largely focused on the T cell compartment. However, other immune subsets may also contribute to anti-tumour immunity11-15, although these have been less well-studied in ICB treatment16. A previously conducted neoadjuvant ICB trial in patients with melanoma showed via targeted expression profiling17 that B cell signatures were enriched in the tumours of patients who respond to treatment versus non-responding patients. To build on this, here we performed bulk RNA sequencing and found that B cell markers were the most differentially expressed genes in the tumours of responders versus non-responders. Our findings were corroborated using a computational method (MCP-counter18) to estimate the immune and stromal composition in this and two other ICB-treated cohorts (patients with melanoma and renal cell carcinoma). Histological evaluation highlighted the localization of B cells within tertiary lymphoid structures. We assessed the potential functional contributions of B cells via bulk and single-cell RNA sequencing, which demonstrate clonal expansion and unique functional states of B cells in responders. Mass cytometry showed that switched memory B cells were enriched in the tumours of responders. Together, these data provide insights into the potential role of B cells and tertiary lymphoid structures in the response to ICB treatment, with implications for the development of biomarkers and therapeutic targets.
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                Journal
                Nature Communications
                Nat Commun
                Springer Science and Business Media LLC
                2041-1723
                December 2021
                January 12 2021
                December 2021
                : 12
                : 1
                Article
                10.1038/s41467-020-20600-7
                676ee912-f9ff-4ccc-a3e7-6be1aa2f75a8
                © 2021

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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