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T cell infiltration and proliferation in tumor tissues are the main factors that significantly affect the therapeutic outcomes of cancer immunotherapy. Emerging evidence has shown that interferon-gamma (IFNγ) could enhance CXCL9 secretion from macrophages to recruit T cells, but Siglec15 expressed on TAMs can attenuate T cell proliferation. Therefore, targeted regulation of macrophage function could be a promising strategy to enhance cancer immunotherapy via concurrently promoting the infiltration and proliferation of T cells in tumor tissues. We herein developed reduction-responsive nanoparticles (NPs) made with poly (disulfide amide) (PDSA) and lipid-poly (ethylene glycol) (lipid-PEG) for systemic delivery of Siglec15 siRNA (siSiglec15) and IFNγ for enhanced cancer immunotherapy. After intravenous administration, these cargo-loaded could highly accumulate in the tumor tissues and be efficiently internalized by tumor-associated macrophages (TAMs). With the highly concentrated glutathione (GSH) in the cytoplasm to destroy the nanostructure, the loaded IFNγ and siSiglec15 could be rapidly released, which could respectively repolarize macrophage phenotype to enhance CXCL9 secretion for T cell infiltration and silence Siglec15 expression to promote T cell proliferation, leading to significant inhibition of hepatocellular carcinoma (HCC) growth when combining with the immune checkpoint inhibitor. The strategy developed herein could be used as an effective tool to enhance cancer immunotherapy.

T cell infiltration and proliferation in tumor tissues are the main factors that significantly affect the therapeutic outcomes of cancer immunotherapy. Emerging evidence has shown that interferon-gamma (IFN γ) could enhance CXCL9 secretion from macrophages to recruit T cells, but Siglec15 expressed on TAMs can attenuate T cell proliferation. Therefore, targeted regulation of macrophage function could be a promising strategy to enhance cancer immunotherapy via concurrently promoting the infiltration and proliferation of T cells in tumor tissues. We herein developed reduction-responsive nanoparticles (NPs) made with poly (disulfide amide) (PDSA) and lipid-poly (ethylene glycol) (lipid-PEG) for systemic delivery of Siglec15 siRNA (siSiglec15) and IFN γ for enhanced cancer immunotherapy. After intravenous administration, these cargo-loaded could highly accumulate in the tumor tissues and be efficiently internalized by tumor-associated macrophages (TAMs). With the highly concentrated glutathione (GSH) in the cytoplasm to destroy the nanostructure, the loaded IFN γ and siSiglec15 could be rapidly released, which could respectively repolarize macrophage phenotype to enhance CXCL9 secretion for T cell infiltration and silence Siglec15 expression to promote T cell proliferation, leading to significant inhibition of hepatocellular carcinoma (HCC) growth when combining with the immune checkpoint inhibitor. The strategy developed herein could be used as an effective tool to enhance cancer immunotherapy. Interferon-gamma (IFNγ) recruits T cells, while Siglec15 on tumor-associated macrophages hampers the proliferation. Reduction-responsive nanoparticles delivering siSiglec15 and IFNγ combined with immune checkpoint inhibitors could inhibit hepatocellular carcinoma growth. [Display omitted] [ABSTRACT FROM AUTHOR]

Increasing evidence suggests that the presence and spatial localization and distribution pattern of tumor infiltrating lymphocytes (TILs) is associate with response to immunotherapies. Recent studies have identified TGFβ activity and signaling as a determinant of T cell exclusion in the tumor microenvironment and poor response to PD-1/PD-L1 blockade. Here we coupled the artificial intelligence (AI)-powered digital image analysis and gene expression profiling as an integrative approach to quantify distribution of TILs and characterize the associated TGFβ pathway activity. Analysis of T cell spatial distribution in the solid tumor biopsies revealed substantial differences in the distribution patterns. The digital image analysis approach achieves 74% concordance with the pathologist assessment for tumor-immune phenotypes. The transcriptomic profiling suggests that the TIL score was negatively correlated with TGFβ pathway activation, together with elevated TGFβ signaling activity observed in excluded and desert tumor phenotypes. The present results demonstrate that the automated digital pathology algorithm for quantitative analysis of CD8 immunohistochemistry image can successfully assign the tumor into one of three infiltration phenotypes: immune desert, immune excluded or immune inflamed. The association between “cold” tumor-immune phenotypes and TGFβ signature further demonstrates their potential as predictive biomarkers to identify appropriate patients that may benefit from TGFβ blockade.

Increasing evidence suggests that the presence and spatial localization and distribution pattern of tumor infiltrating lymphocytes (TILs) is associate with response to immunotherapies. Recent studies have identified TGF β activity and signaling as a determinant of T cell exclusion in the tumor microenvironment and poor response to PD-1/PD-L1 blockade. Here we coupled the artificial intelligence (AI)-powered digital image analysis and gene expression profiling as an integrative approach to quantify distribution of TILs and characterize the associated TGF β pathway activity. Analysis of T cell spatial distribution in the solid tumor biopsies revealed substantial differences in the distribution patterns. The digital image analysis approach achieves 74% concordance with the pathologist assessment for tumor-immune phenotypes. The transcriptomic profiling suggests that the TIL score was negatively correlated with TGF β pathway activation, together with elevated TGF β signaling activity observed in excluded and desert tumor phenotypes. The present results demonstrate that the automated digital pathology algorithm for quantitative analysis of CD8 immunohistochemistry image can successfully assign the tumor into one of three infiltration phenotypes: immune desert, immune excluded or immune inflamed. The association between "cold" tumor-immune phenotypes and TGF β signature further demonstrates their potential as predictive biomarkers to identify appropriate patients that may benefit from TGF β blockade. An artificial intelligence-powered digital image analysis approach has been established to characterize and classify the cancer immune phenotypes, which might be utilized as predictive biomarkers for TGF β blockade therapy. [Display omitted] [ABSTRACT FROM AUTHOR]

Increasing evidence suggests that the presence and spatial localization and distribution pattern of tumor infiltrating lymphocytes (TILs) is associate with response to immunotherapies. Recent studies have identified TGF β activity and signaling as a determinant of T cell exclusion in the tumor microenvironment and poor response to PD-1/PD-L1 blockade. Here we coupled the artificial intelligence (AI)-powered digital image analysis and gene expression profiling as an integrative approach to quantify distribution of TILs and characterize the associated TGF β pathway activity. Analysis of T cell spatial distribution in the solid tumor biopsies revealed substantial differences in the distribution patterns. The digital image analysis approach achieves 74% concordance with the pathologist assessment for tumor-immune phenotypes. The transcriptomic profiling suggests that the TIL score was negatively correlated with TGF β pathway activation, together with elevated TGF β signaling activity observed in excluded and desert tumor phenotypes. The present results demonstrate that the automated digital pathology algorithm for quantitative analysis of CD8 immunohistochemistry image can successfully assign the tumor into one of three infiltration phenotypes: immune desert, immune excluded or immune inflamed. The association between "cold" tumor-immune phenotypes and TGF β signature further demonstrates their potential as predictive biomarkers to identify appropriate patients that may benefit from TGF β blockade., (© 2022 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. Production and hosting by Elsevier B.V.)