Interferon gamma in cancer immunotherapy

Ni, Ling; Lu, Jian

doi:10.1002/cam4.1700

Cited by 285 publications

(205 citation statements)

References 50 publications

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“…INFG induces the expression of PD-L1 through increasing STAT1 signaling and decreasing STAT3 activation. In some studies, strong correlation between INFG and ICB response was reported [26] . In a study of NSCLC and UC, INFG signature is associated with TMB signature [27] .…”

Section: Discussionmentioning

confidence: 99%

Using deep learning to predict anti-PD-1 response in melanoma and lung cancer patients from histopathology images

Cui

Yang

et al. 2021

Translational Oncology

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Highlights Our deep neural network based model represents the first finding of making use of H&E images to predict immunotherapy response. Area under the curve (AUC) of 0.778(95% confidence interval, 0.638–0.905) was obtained on 54 melanoma H&E samples. Our model was also validated on a second set of 55 lung cancer samples, yielding an AUC of 0.645 (95% confidence interval, 0.494–0.784), confirming the robustness of this model.

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Section: Discussionmentioning

confidence: 99%

Using deep learning to predict anti-PD-1 response in melanoma and lung cancer patients from histopathology images

Cui

Yang

et al. 2021

Translational Oncology

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“…Additionally, these pathways are also extensively reported in other processes such as immunosurveillance 157 , stem cell self-renewal 158 and epithelial to mesenchymal transition 159 . Some of these processes have already been described in endometriosis pathogenesis and they include Kras signalling 160,161 , MYC targets 162,163 , mTORC1 signalling [164][165][166] , PI3K AKT mTOR signalling [167][168][169][170] , TGF beta signalling [171][172][173] , interferon gamma [174][175][176][177] , and interferon alpha response 178,179 . In accordance with our data regarding microenvironment heterogeneity, certain pathways that are enriched in the stage III-IV phenotype are directly associated to M1-M2 macrophage polarization, and these pathways include TGF beta sinalling 180,181 , PI3K AKT mTOR signalling 151,182 , interferon gamma response 79 , adipogenesis, glycolysis and other metabolic reprograming pathways 183 .…”

Section: Discussionmentioning

confidence: 99%

Transcriptome meta-analysis reveals differences of immune profile between eutopic endometrium from stage I-II and III-IV endometriosis independently of hormonal milieu

Poli-Neto

Meola

Silva

et al. 2020

Sci Rep

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Eutopic endometrium appears to be crucial for endometriosis development. Despite of the evident importance, data regarding the cellular microenvironment remain unclear. Our objective was to explore the tissue microenvironment heterogeneity, transcripts, and pathways that are enriched in all phases of the menstrual cycle by analysing publicly deposited data derived from whole transcriptome microarrays of eutopic endometria of women with and without endometriosis. A meta-analysis of the transcriptome microarrays was performed using raw data available from a public database. Eligibility criteria included eutopic endometrium samples from women with endometriosis and healthy controls without any pathological condition reported the presence of an adequately reported normal menstrual phase, and samples containing both glandular and stromal components. Raw data were processed using a robust multiarray average method to provide background correction, normalisation, and summarisation. The batch effect was estimated by principal variant component analysis and removed using an empirical Bayes method. Cellular tissue heterogeneity was inferred using the xCell package. Differentially expressed genes were identified based on a 5% adjusted p value and a 2.0-fold change. Pathways were identified by functional enrichment based on the Molecular Signatures Database, a p value of < 5%, and an FDR q value of ≤ 25%. Genes that were more frequently found in pathways were identified using leading edge analysis. In a manner independent of cycle phase, the subpopulations of activated dendritic cells, CD4 T effector memory phenotype cells, eosinophils, macrophages M1, and natural killer T cells (NKT) were all higher in stage I-II endometriosis compared to those in healthy controls. The subpopulations of M2 macrophages and natural killer T cells were elevated in eutopic endometriums from women with stage III-IV endometriosis, and smooth muscle cells were always more prevalent in healthy eutopic endometriums. Among the differently expressed genes, FOS, FOSB, JUNB, and EGR1 were the most frequently mapped within the interaction networks, and this was independent of stage and cycle phase. The enriched pathways were directly related to immune surveillance, stem cell self-renewal, and epithelial mesenchymal transition. PI3K AKT mTOR, TGF signalling, and interferon alpha/gamma responses were enriched exclusively in stage III-IV endometriosis. The cellular microenvironments and immune cell profiles were different between eutopic endometriums from women with stage I-II and stage III-IV endometriosis, and these differences were independent of the hormonal milieu. Specifically, a pro-inflammatory profile was predominant in stage I-II endometriosis, and M1-M2 polarization into eutopic endometrium may be crucial for the progression of the disease. The higher prevalence of NKT cells in eutopic endometriums from women with endometriosis that

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“…Although IFNγ secreted by immune cells promotes growth arrest of tumors by augmenting MHC class I expression, contributing to the recruitment of effector cells, mediating Treg fragility and coordinating innate and adaptive antitumor responses (33,34), IFNγ signaling can also compromise antitumor immunity by blocking these activities through the induction of immune checkpoint inhibitory molecules on T and tumor cells (35). The overall balance and timing of IFNγ expression over the course of tumor development and the downstream consequences likely critically determine an effective vs. suppressive immune response and an immunologic profile of consideration for immunotherapeutic approaches to treatment (36).…”

Section: Discussionmentioning

confidence: 99%

NGS Evaluation of Colorectal Cancer Reveals Interferon Gamma Dependent Expression of Immune Checkpoint Genes and Identification of Novel IFNγ Induced Genes

Pelosof

Wang

et al. 2020

Front. Immunol.

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To evaluate the expression of immune checkpoint genes, their concordance with expression of IFNγ, and to identify potential novel ICP related genes (ICPRG) in colorectal cancer (CRC), the biological connectivity of six well documented ("classical") ICPs (CTLA4, PD1, PDL1, Tim3, IDO1, and LAG3) with IFNγ and its co-expressed genes was examined by NGS in 79 CRC/healthy colon tissue pairs. Identification of novel IFNγ-induced molecules with potential ICP activity was also sought. In our study, the six classical ICPs were statistically upregulated and correlated with IFNγ, CD8A, CD8B, CD4, and 180 additional immunologically related genes in IFNγ positive (FPKM > 1) tumors. By ICP co-expression analysis, we also identified three IFNγ-induced genes [(IFNγ-inducible lysosomal thiol reductase (IFI30), guanylate binding protein1 (GBP1), and guanylate binding protein 4 (GBP4)] as potential novel ICPRGs. These three genes were upregulated in tumor compared to normal tissues in IFNγ positive tumors, co-expressed with CD8A and had relatively high abundance (average FPKM = 362, 51, and 25, respectively), compared to the abundance of the 5 well-defined ICPs (Tim3, LAG3, PDL1, CTLA4, PD1; average FPKM = 10, 9, 6, 6, and 2, respectively), although IDO1 is expressed at comparably high levels (FPKM = 39). We extended our evaluation by querying the TCGA database which revealed the commonality of IFNγ dependent expression of the three potential ICPRGs in 638 CRCs, 103 skin cutaneous melanomas (SKCM), 1105 breast cancers (BC), 184 esophageal cancers (ESC), 416 stomach cancers (STC), and 501 lung squamous carcinomas (LUSC). In terms of prognosis, based on Pathology Atlas data, correlation of GBP1 and GBP4, but not IFI30, with 5-year survival rate was favorable in CRC, BC, SKCM, and STC. Thus, further studies defining the role of IFI30, GBP1, and GBP4 in CRC are warranted.

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Interferon gamma in cancer immunotherapy

Cited by 285 publications

References 50 publications

Using deep learning to predict anti-PD-1 response in melanoma and lung cancer patients from histopathology images

Using deep learning to predict anti-PD-1 response in melanoma and lung cancer patients from histopathology images

Transcriptome meta-analysis reveals differences of immune profile between eutopic endometrium from stage I-II and III-IV endometriosis independently of hormonal milieu

NGS Evaluation of Colorectal Cancer Reveals Interferon Gamma Dependent Expression of Immune Checkpoint Genes and Identification of Novel IFNγ Induced Genes

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