Journal for ImmunoTherapy of Cancer

Journal for ImmunoTherapy of Cancer

Published by BMJ and Society for Immunotherapy of Cancer (SITC)

Online ISSN: 2051-1426

Disciplines: Oncology, Immunology

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273 Chimeric PGC1α expression in CAR-T cells improves metabolic function and anti-tumor efficacy in solid tumors

November 2024

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156 Reads

Samyabrata Bhaduri

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Emily Kuiper

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Kshitij Sharma

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Amy Jensen-Smith

Background The solid tumor microenvironment (TME) suppresses CAR T cell function through various mechanisms including competition for nutrients and chronic stimulation resulting in limited T cell effector functions or T cell exhaustion. Therefore, CAR T cells with enhanced metabolic fitness or more durable early memory phenotype could improve the clinical outcome against solid tumors. PPARγ coactivator 1α (PGC1α) is a transcriptional coactivator that influences many aspects of cellular metabolism. Previous studies have indicated that exogenous expression of PGC1α can enhance T cell anti-tumor activity, however the large size of PGC1α renders it difficult to co-express with a CAR. An N-terminal truncated PGC1α (NT-PGC1α) has previously been shown to improve co-expression with a CAR, however, despite imparting mitochondrial benefits, NT-PGC1α failed to increase anti-tumor activity. Methods We developed a novel chimeric PGC1α consisting of an N-terminal truncation with the addition of an exogenous DNA binding domain derived from PPARγ. The chimeric PGC1α was co-expressed with a ROR1-targeted CAR in human T cells and was compared to CAR alone in in vitro and in vivo studies. Results We observed a decrease in the number of dysfunctional mitochondria and a subsequent increase in glucose uptake in CAR T cells expressing the chimeric PGC1α compared to CAR alone. These cells also demonstrated enhanced resistance to chronic antigen stimulation and a less differentiated and less exhausted phenotype compared to CAR alone. Moreover, when tested in vivo, CAR T cells expressing the chimeric PGC1α had superior anti-tumor activity compared to CAR T alone in a solid tumor model of clear cell renal cell carcinoma. Conclusions These data suggest that incorporation of this novel chimeric PGC1α in CAR T cells may be a promising approach to enhance CAR T cell efficacy in patients with solid tumors.

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1058 Development of AZD9793 for GPC3 + solid tumor treatment: a next generation CD8-guided T cell engager with superior therapeutic index versus conventional formats

November 2024

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107 Reads

Background Hepatocellular carcinoma (HCC) is a significant cause of cancer-related deaths globally. Glypican-3 (GPC3) is an onco-fetal protein reactivated in various tumors, including 70–80% of HCC cases, and is linked to poor prognosis. Its tumor-restricted surface expression makes it an ideal target for T cell engagers (TCE). While current bispecific TCE-based treatments have been effective against hematological malignancies and certain solid tumors, they are also linked to substantial toxicity, including CRS and ICANS. This toxicity restricts their therapeutic window and potential for combination therapies in clinical settings. Methods Our goal was to develop a TCE with a favorable safety/efficacy profile through CD8 preferential engagement. This approach involves leveraging CD8+ T cell biological functions while minimizing CD4+ T cell activation, thereby limiting cytokine release as CD4+ T cells are the main driver of CRS-risk. AZD9793, a novel CD8-guided TCE, is an asymmetric trispecific IgG1 monoclonal antibody comprising 2 Fab binding domains to human GPC3, one VHH binding domain to human TCR, and one VHH binding domain to human CD8 co-receptor. AZD9793 biological activity were evaluated in vitro and in vivo, in comparison to a conventional GPC3xCD3 TCE. Results In preclinical in vitro experiments, AZD9793 demonstrated potent GPC3-specific T cell dependent cellular cytolysis activity across various HCC cell lines expressing different levels of GPC3 (HepG2, Hep3B, Huh7, and PLC/PRF/5). Compared to a conventional TCE that broadly activates CD3+ T cells, AZD9793 induced preferential engagement of CD8+ T cells through CD8/TCR binding and potent killing, with limited CD4+ T cell activation and lower cytokine secretion profiles. Pre-clinical in vivo assessment confirmed that AZD9793 exhibits dose-dependent anti-tumor effects in humanized mouse models with HCC xenografts. Notably, when comparable anti-tumor efficacy was achieved in vivo, AZD9793 induced significantly less systemic cytokine production than a conventional GPC3xCD3 TCE, with circulating levels of TNF-α, IL-6, IFN-g, IL-10, IL-2 and IL-17A similar to those in vehicle-treated animals. Furthermore, AZD9793 demonstrated bystander killing activity, leading to growth inhibition of heterogeneously expressing GPC3 tumor cells, in both in vitro and in vivo settings. Conclusions AZD9793 is a first-in-class TCE for the treatment of GPC3+ solid tumor with the potential of a significantly improved therapeutic index over conventional GPC3xCD3 engagers. Bivalent GPC3 binding, CD8-biased engagement, and low affinity TCR binding improve AZD9793 cytotoxic potency on tumor cells while decreasing the risk of cytokine release and associated toxicity. These attributes provide a rational for the evaluation of AZD9793 in clinic. Ethics Approval The in vivo studies were conducted according to Institutional Animal Care and Use Committee approved protocols in the Laboratory Animal Resources facility at AstraZeneca, an Association for Animal Accreditation of Laboratory Animal Care and United States Department of Agriculture-licensed facility. Human PBMCs were used in accordance with Informed Consent Form (ICF).

Aims and scope


The Journal for ImmunoTherapy of Cancer (JITC) is an open access, peer-reviewed journal that aims to enrich communication and advance scientific understanding in the rapidly evolving fields of tumor immunology and cancer immunotherapy. Topics of interest range across the basic science-translational-clinical spectrum and include tumor-host interactions, the tumor microenvironment, animal models, predictive and prognostic immune biomarkers, novel pharmaceutical and cellular therapies, vaccines, combination immune-based therapies, and immune-related toxicity.

JITC is the official journal of the Society for Immunotherapy of Cancer (SITC).

Recent articles


Figure 1 Relationship between sympathetic nerve gene set expression, cancer prognosis, and immune function. (A) Expression of the sympathetic nerve gene set in the tumor and corresponding normal tissues in the GEPIA2 database. (B) The relationship between the expression of the sympathetic nerve gene set and cancer prognosis is presented on the Kaplan-Meier Plotter website. (C-E) The association of the sympathetic nerve gene set expression in KIRC tumor tissues with (C) immune cell infiltration, (D) immune checkpoint expression, and (E) immunosuppressive factors expression. (F) Gene Set Enrichment Analysis of T-cell exhaustion characteristics with high and low expression of the sympathetic nerve gene set. HNSC, head and neck squamous cell carcinoma; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; PAAD, pancreatic adenocarcinoma; PCPG, pheochromocytoma and paraganglioma; SARC, sarcoma; THYM, thymoma.
Figure 5 Assessment of immune activity in tumor-infiltrating T cells. (A) Uniform Manifold Approximation and Projection (UMAP) representation of scRNA-seq analysis of whole cells. (B) UMAP analysis of T-cell clusters. (C) UMAP plots show the expression of Cd4 and Cd8a. (D) The relative proportion of T-cell subtypes per cluster. (E) Dotplot illustrating the relative expression of signature genes in CD8 T-cell clusters. (F-H) Gene signature analysis of (F) effector memory T cells, (G) exhausted T cells, and (H) tissue-resident T cells in CD8 T cells. (I) Flow cytometry assessment of the proportion of exhausted phenotypes in tumor-infiltrating CD8 T cells. Tex prog (Tim3 − CD101 − ), Tex int (Tim3 + CD101 − ), Tex term (Tim3 + CD101 + ). (J) Flow cytometry analysis of Ki67 expression in tumor-infiltrating CD8 T cells. Mean±SD, *p<0.05, **p<0.01, and ****p<0.0001. NGF, nerve growth factor; scRNA-seq, single-cell RNA sequencing; Tex, exhausted T cells; UT, untransduced T.
Figure 6 Evaluation of immune activity in tumor-infiltrating myeloid cells. (A) UMAP analysis of macrophage cell clusters. (B) The relative proportion of macrophage subtypes per cluster. (C) Dotplot showing the relative expression of signature genes in macrophage clusters. (D) GSEA of different macrophage clusters for relevant pathways, with significant terms indicated by a black circle. (E) Flow cytometry analysis of the phenotyping of tumor-infiltrating macrophages. (F) UMAP analysis of neutrophil clusters. (G) The relative proportion of neutrophil subtypes per cluster. (H) StackedViolin plot displaying the relative expression of PMN-MDSC signature genes in neutrophil clusters. (I) Flow cytometry assessment of the proportion of PMN-MDSCs in tumor tissues. Mean±SD, **p<0.01. GSEA, Gene Set Enrichment Analysis; NGF, nerve growth factor; UMAP, Uniform Manifold Approximation and Projection; UT, untransduced T. on December 9, 2024 by guest. Protected by copyright.
Figure 7 Analysis of immune activity in splenic T cells. (A) Volcano plot displaying differentially expressed genes, comparing V28z/αNGF versus V28z. (B) Heatmap analysis of immunosuppressive genes in splenic T cells. (C) Heatmap analysis of immune effector genes in splenic T cells. (D) Gene Set Enrichment Analysis of differentially expressed T-cell genes enriched in signaling pathways, comparing V28z/αNGF versus V28z. (E) Flow cytometry analysis of IL-2 and Ki67 expression in CD8 T cells after restimulation of splenic lymphocytes. (F) Schematic illustration of the antitumor action of V28z/αNGF in solid tumors. Mean±SD, *p<0.05, **p<0.01. CAR, chimeric antigen receptor; IL, interleukin; NGF, nerve growth factor; scFv, single chain fragment variable; UT, untransduced T; vascular endothelial growth factor receptor 2. on December 9, 2024 by guest. Protected by copyright.
CAR T cells secreting NGF-neutralizing scFv enhance efficacy in clear cell renal cell carcinoma by relieving immunosuppression through immunosympathectomy
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December 2024

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3 Reads

Peiwei Yang

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Xi Chen

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Fan Yu

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Hanmei Xu

Background Chimeric antigen receptor (CAR) T cells have demonstrated remarkable breakthroughs in treating hematologic malignancies, yet their efficacy in solid tumors is limited by the immunosuppressive microenvironment. Sympathetic nerves significantly contribute to this immunosuppressive milieu in solid tumors. However, the impact of tumor sympathetic denervation on enhancing CAR T-cell antitumor efficacy remains unclear. Methods We screened for sympathetic gene sets in various types of cancers and investigated the association of sympathetic nerves with immunosuppression in renal clear cell carcinoma. Using antibodies to block the nerve growth factor (NGF) pathway, we explored sympathetic nerve distribution in tumor tissues and tumor progression. Additionally, we engineered CAR T cells to secrete NGF single chain fragment variable (scFv) to achieve tumor immunosympathectomy and assessed their antitumor efficacy. Bulk RNA sequencing and single-cell RNA sequencing analyses were conducted to evaluate changes in immune cell phenotypes within the tumor microenvironment. Results Blocking the NGF pathway with antibodies effectively reduced sympathetic nerve distribution in tumor tissues and delayed tumor progression. CAR T cells engineered to secrete NGF scFv achieved a similar tumor immunosympathectomy and exhibited enhanced tumor suppression. RNA sequencing analyses revealed that this augmented effect was primarily due to the inhibition of the terminal exhaustion phenotype in tumor-infiltrating CD8 T cells and the prevention of macrophage polarization from M1 to M2. This approach maintained a stronger antitumor immune state at the tumor site. Additionally, splenic T cells also exhibited a more potent immune effector phenotype following the infusion of NGF scFv-secreting CAR T cells. Conclusions Our results suggest that immunosympathectomy is a novel approach to weaken tumor microenvironment immunosuppression and synergistically enhance CAR T-cell efficacy against solid tumors.


Figure 1 Patients initially amplify Ad-specific T cells after CAdVEC treatment, but HDAd-infected cells escape Ad-specific T cells recognition. (A) PBMCs were isolated from patients' blood pre and 1-week post CAdVEC treatment in our ongoing Phase I clinical trial (NCT03740256). We performed an IFN-γ ELISpot assay with adenoviral peptides or peptides of cancer/testis antigens. (B) Matched pretreatment and 1-week post-treatment tumor biopsies from patient #7 were stained with human CD8 IgG for immunohistochemistry (IHC). Representative images presented. (C) HLA-A2 + cancer cells were infected with total 10 viral particles (vp)/cell of OAdRFP and HDAdEGFP (OAd:HD=1:1). Infected cancer cells were co-cultured with HLA-A2 + AdVSTs 48 hours post-infection at effector to target (E:T) ratio of 1:10 for SUM-159 or SCC-90, and 1:4 for PANC-1 or HCT-116. Live infected cells were measured as a total area of RFP or GFP signals using Incucyte live cell imaging and analysis system. Data are presented as means±SD (n=6). P values were determined by two-tailed t-test. (D) PBMCs were isolated from patients' blood pre and 2-4-week post CAdVEC treatment in our ongoing Phase I clinical trial (NCT03740256). We performed an IFN-γ ELISpot assay with peptides of cancer/testis antigens. (E) EGFP-expressing HLA-A2 + cancer cells were infected with 100 vp/cell of HDAd0 (no transgene) or HDAdTrio. Infected cancer cells were co-cultured with multi-tumor associated antigen-specific T cells (mTAA-T cell) from an HLA-A2 + donor 48 hours post-infection at an E:T ratio of 5:1. Residual live cancer cells were measured as a total area of GFP signals using an Incucyte. Data are presented as means±SD (n=6). P values were determined by one-way analysis of variance. Ad, adenovirus; AdVST, adenovirus specific T cell; CAdVEC, binary oncolytic/helper-dependent adenovirus system; CR, complete response; DL, clinical trial dose level; EGFP, enhanced green fluorescent protein, ELISpot, enzyme linked immunosorbent spot; GFP, green fluorescent protein; HDAd, helper-dependent Ad; IFN, interferon; OAds, oncolytic adenoviruses; PBMC, peripheral blood mononuclear cell; PDAC, pancreatic ductal adenocarcinoma; PR, partial response; RFP, red fluorescent protein; SD, stable disease. on December 9, 2024 by guest. Protected by copyright.
Figure 2 HDTetra additionally expressing CD44v6.BiTE leads to Ad-specific T-cell killing of cancer cells in vitro. (A) CD44v6 immunohistochemistry of tumor biopsies from patients in our ongoing Phase I clinical trial (NCT03740256). Representative images presented. (B) Schematic structure of HDAdTrio and HDAdTetra. A549 cells were infected with 500 viral particles (vp)/ cell of HDAdTrio or HDAdTetra. Media were collected 48 hours post-infection. Medium samples were subjected to western blotting for PD-L1 mini-antibody, which is detected by anti-HA antibody, and assessed for human IL-12p70 by ELISA assay. Data are presented as means±SD (n=4). A549 cells were infected with 500 vp/cell of HDAdTrio or HDAdTetra. Media containing ganciclovir (GCV) were added to denoted wells 24 hours post-infection and refreshed daily. After 5 days viable cells were fixed and stained with crystal violet. Representative image presented. (C) (Left) EGFP expressing HLA-A2 + cancer cells were infected with 100 vp/cell of HDAd0, HDAdTrio or HDAdTetra. Infected cancer cells were co-cultured with HLA-A2 + AdVSTs 48 hours post-infection at an E:T ratio of 1:10. Residual live cancer cells were measured as a total area of GFP signals using an Incucyte. Data are presented as means±SD (n=6). P values were determined by one-way ANOVA. (Right) HLA-A2 + cancer cells were infected with 100 vp/cell of HDAd0, HDAdTrio or HDAdTetra. Infected cancer cells were co-cultured with EGFP expressing HLA-A2 + AdVSTs 48 hours post-infection at an E:T ratio of 1:10. Live AdVSTs were measured as a total area of GFP signals using an Incucyte. Data are presented as means±SD (n=6). P values were determined by one-way ANOVA. AdVST, adenovirus specific T cell; ANOVA, analysis of variance; BiTE, bi-specific T-cell engager molecule; Colon, colorectal carcinoma; EGFP, enhanced green fluorescent protein; ET, effector to target; ER/PR, estrogen/progesterone positive breast cancer; HDAd, helperdependent Ad; HNC, head and neck cancer, HSVtk, HSV thymidine kinase; IL, interleukin; PD-L1, programmed death-ligand 1; PDAC, pancreatic ductal adenocarcinoma; TNBC, triple-negative breast cancer. on December 9, 2024 by guest. Protected by copyright.
Figure 4 CAdTetra shows better antitumor effects and tumor-specific T-cell development than CAdTrio in Ad-immunized humanized mouse model with triple-negative breast cancer. (A) FfLuc expressing SUM-159 cells were orthotopically transplanted into the mammary fat pad of Ad-preimmunized humanized female mice (n=5 per group). After tumor volume reached >100 mm 3 , a total of 1×10 6 viral particles of CAdVEC (OAd:HD=1:1) were injected intratumorally. Tumor volume and bioluminescence of cancer cells were monitored at the indicated time points. Data are presented as means±SD (n=5). P values were determined by one-way ANOVA. Tumor samples were collected at 4 weeks post CAdVEC. Freshly isolated tumor-infiltrating immune cells were phenotyped using different immune cell markers (B) or T-cell markers (C). Representative FlowSOM data are presented from each group. %Totals are presented as means±SD (n=5). P values were determined by oneway ANOVA. (D) We isolated the tumor-infiltrating human CD8 + T cells from the SUM-159 tumors at 4 weeks post CAdVEC. We co-cultured them with ffLuc expressing SUM-159, A549, or FaDu cells at an effector-to-target ratio of 1:1. After 24 hours co-culture, we measured residual live cancer cells using a luciferase assay system. % Lysis are presented as means±SD (n=5). P values were determined by one-way ANOVA. ANOVA, analysis of variance; CAdVEC, binary oncolytic/helper-dependent adenovirus system; NK, natural killer; OAd, oncolytic adenovirus.
Figure 5 CAdVEC treatment changes gene signatures in triple-negative breast cancer tumors of adenovirus-immunized humanized mouse model. We performed Visium spatial gene expression with the SUM-159 tumors collected 4 weeks post CAdVEC. Spots on the cytassist slides are colored by their cluster labels (top panels). The contribution of spots to each cluster for all samples is shown in the bar chart and the percentage of spots shared between clusters in each treatment is shown in the Venn diagram. The top five pathways from Gene Set Enrichment Analysis of cluster 5, that is, shared between CAdTrio and CAdTetra are shown. Only receptor ligand activity and cytokine activity were significant. CAdVEC, binary oncolytic/helperdependent adenovirus system. on December 9, 2024 by guest. Protected by copyright.
Figure 6 CAdTetra shows better antitumor effects and tumor-specific T-cell development than CAdTrio in Ad-immunized humanized mice model with PDAC. (A) PANC-1 cells were transplanted into the right flank of Ad-preimmunized humanized mice (n=5 per group). After tumor volume reached >100 mm 3 , a total of 1×10 7 vp of CAdVEC (OAd:HD=1:1) were injected intratumorally. Tumor volumes were monitored at the indicated time points. Data are presented as means±SD (n=5). P values were determined by one-way ANOVA. Kaplan-Meier survival curve after CAdTrio or CAdTetra administration in mice (n=5). P values were determined using the log-rank Mantel-Cox test (df=3). (B) FfLuc expressing PANC-1 cells were injected into the left flank of the mice, which had controlled initial PANC-1 tumor growth and survived over 10 weeks, as a rechallenge model. Tumor volume and bioluminescence of cancer cells were monitored at the indicated time points. Representative images presented. P values were determined by one-way ANOVA. (C) Tumor samples were collected 35 days post-rechallenge. Due to the limited number of tumor-infiltrating immune cells in this "cold" tumor model, samples were combined per treatment to have sufficient cell numbers and phenotyped using different immune cell markers. ANOVA, analysis of variance; CAdVEC, binary oncolytic/helper-dependent adenovirus; HD, helper-dependent adenovirus; NK, natural killer; OAd, oncolytic adenovirus; PDAC, pancreatic ductal adenocarcinoma. on December 9, 2024 by guest. Protected by copyright.
Additional expression of T-cell engager in clinically tested oncolytic adeno-immunotherapy redirects tumor-infiltrated, irrelevant T cells against cancer cells to enhance antitumor immunity

Daisuke Morita

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Amanda Rosewell Shaw

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Greyson Biegert

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Masataka Suzuki

Background Oncolytic adenoviruses (OAds) are the most clinically tested viral vectors for solid tumors. However, most clinically tested “Armed” OAds show limited antitumor effects in patients with various solid tumors even with increased dosages and multiple injections. We developed a binary oncolytic/helper-dependent adenovirus system (CAdVEC), in which tumors are coinfected with an OAd and a non-replicating helper-dependent Ad (HDAd). We recently demonstrated that a single low-dose CAdVEC expressing interleukin-12, programmed death-ligand 1 blocker, and HSV thymidine kinase safety switch (CAdTrio) induces significant antitumor effects in patients, including complete response. Similar to previous OAd studies, all patients primarily amplified Ad-specific T cells after treatment however, CAdVEC was still able to induce clinical responses even given at a 100-fold lower dose. Methods To address the mechanisms of CAdTrio-mediated antitumor effect in patients, we analyzed patients’ samples using Enzyme-linked immunosorbent spot (ELISpot) to measure T-cell specificity and quantitative polymerase chain reaction (qPCR) to measure CAdVEC viral genome copies at tumor sites. We then evaluated potential mechanisms of CAdVEC efficacy in vitro using live-cell imaging. Based on those results, we developed a new CAdVEC additionally expressing a T-cell engager molecule targeting CD44v6 to redirect tumor-infiltrating irrelevant T cells against cancer stem cell populations (CAdTetra) for further improvement of local CAdVEC treatment. We tested its efficacy against different cancer types both in vitro and in vivo including Ad pre-immunized humanized mice. Results We found that HDAd-infected cells escape Ad-specific T-cell recognition with enhanced tumor-specific T-cell activity through immunomodulatory transgenes. Since CAdVEC treatment initially amplified Ad-specific T cells in patients, we re-direct these virus-specific T cells to target tumor cells by additionally expressing CD44v6.BiTE from CAdTetra. CAdTetra significantly controlled tumor growth, repolarizing local and systemic responses against cancer cells in both immunologically “hot” and “cold” tumor models and also induced immunologic memory against rechallenged tumors. Conclusions Our results indicate that CAdTetra effectively induces adaptive T-cell responses against cancer cells by using tumor-infiltrating irrelevant T cells.


Figure 1 APC mutations promote an immunosuppressive microenvironment in CRC progression. (A) The correlation between APC, TP53, KRAS mutation and the accumulation of CD8+T cells and MDSCs in CRC was analyzed by the TIMER database. (B) Heatmap of the expression of B7/CD28 family gene expression in WT-APC and truncated APC SW480 cells (GSE76307). (C) Schematic diagram of the AOM/DSS model administration method. Tumors in situ in the intestine of Apc WT and Apc Min/+ mice are shown, and the number of tumor nodules was analyzed by Student's t-test. Scale bar represents 1 cm. (D) Morphological features in the indicated colon tumors of Apc WT and Apc Min/+ mice. Scale bar represents 100µm. (E) MICSSS of the infiltration of MDSCs (Gr1+) and CD8+T cells and the expression of VISTA in CRC tissues from Apc WT and Apc Min/+ mice. The statistical methods were Student's t-tests, and the charts are shown on the right. (F) Flow cytometry sorting was performed to analyze the percentage of CD8+T cells (CD3+CD8+) and MDSCs (CD11b+Ly6G/Ly6C+) in CRC tissues from Apc WT and Apc Min/+ mice. The statistical methods were Student's t-tests, and the charts are shown on the right. The asterisk (*) indicates p<0.05; the asterisk (**) indicates p<0.01; the asterisk (***) indicates p<0.001; the hash (#) indicates p>0.05. AOM, azoxymethane; APC, adenomatous polyposis coli; CRC, colorectal cancer; CTLA4, cytotoxic T-lymphocytes-associated protein 4; DSS, dextran sodium sulfate; FITC, fluorescein isothiocyanate; MDSC, myeloid-derived suppressor cell; MICSSS, multiplexed immunohistochemical consecutive staining on single slide; PD-1, programmed cell death protein-1; PD-L1, programmed deathligand 1; TIMER, tumor immune estimation resource; VISTA, V-domain immunoglobulin suppressor of T-cell activation. on December 9, 2024 by guest. Protected by copyright.
Figure 4 The APC978∆-METTL3-HIF1α axis upregulates the expression of VISTA in CRC cells. (A) A m6A enzyme-linked immunoassay kit was used to determine the m6A level in control and APC978∆-overexpressing HCT116 cells. The statistical methods were Student's t-tests. (B) Expression of m6A regulatory factors based on CRC tissues from Apc Min/+ and Apc WT mice (GSE65461). (C) Heatmap of the expression of m6A regulatory factors in WT-APC SW480 cells and truncated APC SW480 cells (GSE76307). (D) The relationship between METTL3 expression and MDSC infiltration was detected from TIMER data. The statistical method was Pearson χ² test. (E) IF staining was performed to assess the colocalization of METTL3-HA (green) and APC978∆-Flag (red) in HCT116-METTL3-HA-APC978∆-Flag cells. The scale bar represents 50 µm. (F) CoIP assays were conducted to ascertain protein interactions between APC978∆, APC1309, APC331 and METTL3 in HCT116-APC978∆-Flag, HCT116-APC1309-Flag and HCT116-APC331-Flag cells. (G) Alterations in the protein levels of METTL3, HIF1α and VISTA in NC cells, APC978∆ cells and METTL3-silenced HCT116 cells were detected by WB assays. (H) The m6A modification sites of HIF1α mRNA were predicted by the SRAMP database. MeRIP-qPCR assays were performed to detect the enrichment of METTL3 in the m6A region of HIF1α mRNA. The statistical methods were Student's t-tests. (I) The expression and correlation of METTL3, HIF1α, VISTA, and CD8 in human CRC tissues were detected by IHC analysis. Two representative cases are shown. The statistical methods were Pearson χ² tests. The scale bar represents 100 µm. (J) Percentage of patients with CRC with high or low expression of indicated factors are shown below. (K) The protein levels of METTL3, HIF1α, and VISTA in CRC tissues and normal tissues from clinical patients were analyzed by WB. APC, adenomatous polyposis coli; CoIP, co-immunoprecipitation; CRC, colorectal cancer; HIF1α, hypoxia-inducible factor-1 alpha; IF, immunofluorescence; IHC, immunohistochemistry; m6A, N6-methyladenosine; MDSC, myeloid-derived suppressor cell; MeRIP, methylated RNA immunoprecipitation; METTL3, methyltransferase-like protein 3; mRNA, messenger RNA; NC, negative control; qPCR, quantitative PCR; SRAMP, sequencebased RNA adenosine methylation site predictor; TIMER, tumor immune estimation resource; VISTA, V-domain immunoglobulin suppressor of T-cell activation; WB, western blot. on December 9, 2024 by guest. Protected by copyright.
Figure 5 The APC978∆-HIF1α axis upregulated downstream genes by promoting transcriptional activity. (A) The downstream cytokines and chemokines of HIF1α were detected by protein microarray. (B) GO analysis was performed to reveal the functions of differentially expressed genes. (C) The mRNA levels of HIF1α and downstream factors in NC and HIF1α-sh or HIF1α-OE SW620 cells were assessed by qRT-PCR. The statistical methods were Student's t-tests. (D) Flow cytometry sorting was used to detect the percentages of CD8+T cells (CD3+CD8+) and MDSCs (CD11b+HLA-DR-) in PBMCs from patients with CRC stimulated with the chemokines and co-cultured with SW480 cells. The statistical methods were Student's t-tests. (E) The release of the cytokines and chemokines was detected by ELISA. The statistical methods were Student's t-tests. (F) The transcriptional regulation of downstream factors by HIF1α was determined by Ch-IP. (G) A dual luciferase reporter system was used to verify the binding sites between HIF1α and the downstream factor promoter. The statistical methods were Student's t-tests. APC, adenomatous polyposis coli; Ch-IP, chromatin immuno-precipitation; CRC, colorectal cancer; FITC, fluorescein isothiocyanate; GO, gene ontology; HIF1α, hypoxia-inducible factor-1 alpha; MDSC, myeloid-derived suppressor cell; mRNA, messenger RNA; NC, negative control; PBMCs, peripheral blood mononuclear cells; qRT-PCR, quantitative reverse transcription PCR. on December 9, 2024 by guest. Protected by copyright. http://jitc.bmj.com/
METTL3-VISTA axis-based combination immunotherapy for APC truncation colorectal cancer

Ling Wu

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Rui Bai

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Yujie Zhang

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[...]

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Liang Zhao

Objective Although immune checkpoint blockade (ICB) therapy represents a bright spot in antitumor immunotherapy, its clinical benefits in colorectal cancer (CRC) are limited. Therefore, a new target for mediating CRC immunosuppression is urgently needed. Adenomatous polyposis coli (APC) mutations have been reported as early-stage characteristic events in CRC, but the role of truncated APC in the CRC immune microenvironment remains unclear and its clinical significance has yet to be explored. Design Adenocarcinoma formation in the colon of the APC Min/+ mouse model, which displays features associated with the translation of truncated APC proteins, was induced by azoxymethane/dextran sodium sulfate. Multiplexed immunohistochemical consecutive staining on single slides and flow cytometry were used to explore the activation of immune cells and the expression of the immune checkpoint V-domain immunoglobulin suppressor of T-cell activation (VISTA) in the CRC tissues of APC WT and APC Min/+ mice. The construction of truncated APC vectors and an initial subserosal graft tumor mouse model was employed to mimic the tumor microenvironment (TME) during APC mutation. Methylated RNA immunoprecipitation-quantitative PCR assays were performed to investigate the N6-methyladenosine (m6A)-dependent transcriptional regulation of hypoxia-inducible factor-1 alpha (HIF1α) by methyltransferase-like protein 3 (METTL3). Mettl3 fl/fl vil1-cre +/− mice were used to demonstrate that targeting METTL3 is a mediator that mitigates the deleterious effects of the APC978∆-HIF1α axis on antitumor immunity. A chimeric VISTA humanized mouse model was used to evaluate the drug efficacy of the VISTA-targeted compound onvatilimab. Results We showed that APC978∆, a truncated APC protein, mediated overexpression of METTL3, resulting in m6A methylation of HIF1α messenger RNA and high expression of HIF1α. Furthermore, HIF1α promotes the migration of myeloid-derived suppressor cells to the TME by binding to the promoters of MCP-1 and MIF. In addition, HIF1α enhances the expression of the immune checkpoint VISTA on CRC cells, weakening tumor immune monitoring. Conclusions We elucidate that an underappreciated function of truncated APC in CRC is its ability to drive an immunosuppressive program that boosts tumor progression. Our work could provide a new perspective for the clinical application of immunotherapy in patients with CRC resistant to ICB therapy.


Figure 1 The study design. AUC, area under curve; FOXP3, forkheadbox protein 3; MPR, major pathological response; NK, natural killer; PD-1, programmed cell death 1; Tac, activated T cells; Tcm, central memory T cells; Tem, effector memory T cells; Tex, exhausted T cells; TMB, tumor mutation burden; TPM, transcripts per million; Treg, regulatory T cell; Trm, resting memory cells; UMAP, Uniform Manifold Approximation and Projection. on December 9, 2024 by guest. Protected by copyright.
Figure 2 Differential tumor microenvironment before treatment. (A) Box plots showing the differences in the immune cells' positive ratio between the MPR and non-MPR groups as quantified by mIHC before treatment. The top panel represents the tumor parenchyma, and the bottom panel represents the tumor stroma. (B) Box plots showing the differences in the immune cells' density between the MPR and non-MPR groups as quantified by mIHC before treatment. The left panel represents the tumor parenchyma, and the right panel represents the tumor stroma. (C) Volcano plot showing DEGs between the MPR and non-MPR groups. Red dots indicate upregulated genes in MPR patients, while blue dots indicate upregulated genes in non-MPR patients. (D) KEGG enrichment analysis for upregulated genes in the MPR group. (E) KEGG enrichment analysis of upregulated genes in the non-MPR group. (F) ROC curve for the prediction of major pathologic responses using the TMB, the positive ratio (mIHC) of PD-1+cells, the positive ratio (mIHC) of CD3+cells, and LASSO model features (DEGs).AUC, area under curve; cAMP,cyclic adenosine monophosphate; DEG, differentially expressed gene; IL, interleukin; KEGG, Kyoto Encyclopedia of Genes and Genomes; LASSO, least absolute shrinkage and selection operator; mIHC, multiplex immunohistochemistry; MPR, major pathological response; NK, natural killer; PD-1, programmed cell death 1; PD-L1, programmed cell death ligand 1; ROC, receiver operating characteristic; TMB, tumor mutation burden; TNF, tumor necrosis factor. on December 9, 2024 by guest. Protected by copyright.
Figure 5 Single-cell analysis of T-cell subtypes and their distribution in the MPR and non-MPR groups. (A) UMAP plot showing the categorization of 1,390 cells into seven major T-cell subtypes: CD8_Trm, CD8_Tem, CD8_Tac, CD4_Trm, CD4_Tcm, CD4_ Tex, and Treg. (B) UMAP plot depicting the distribution of cells categorized into the MPR and non-MPR groups. (C) Bar plot illustrating the fraction of each T-cell subtype within the MPR and non-MPR groups. Significant differences between groups are indicated. (D) Proportion of T-cell subtypes in the tumor microenvironment, comparing the MPR and non-MPR groups. (E) Pseudotime trajectory analysis of T cells showing the progression and differentiation states of various T-cell subtypes. (F) Differentiation pathway of T cells using PAGA (partition-based graph abstraction), highlighting the transitions between different T-cell states from CD8_Tac (0) through various intermediates to Tregs (6). (G) Box plots showing the expression levels of the immune checkpoint molecules PDCD1 (PD-1), CTLA-4, and TIGIT in the MPR and non-MPR groups. (H) Box plots illustrating the expression levels of the cytotoxic and activation markers CD8A, NKG7, GZMA, and GZMK in the MPR and non-MPR groups. CTLA-4, cytotoxic T lymphocyte associate protein 4; GZMA, granzyme A; GZMK, granzyme K; MPR, major pathological response; NKG7, natural killer cell granule protein 7; PD-1, programmed cell death protein 1; PDCD1, programmed cell death 1; Tac, activated T cells; Tcm, central memory T cells; Tem, effector memory T cells; Tex, exhausted T cells; TIGIT, T cell immunoreceptor with Ig and ITIM domains; Treg, regulatory T cell; Trm, resting memory cells; UMAP, Uniform Manifold Approximation and Projection. on December 9, 2024 by guest. Protected by copyright.
Multiomics reveals tumor microenvironment remodeling in locally advanced gastric and gastroesophageal junction cancer following neoadjuvant immunotherapy and chemotherapy

December 2024

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3 Reads

Zhi Ji

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Xia Wang

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Jiaqi Xin

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[...]

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Rui Liu

Background Perioperative chemotherapy is the standard of care for patients with locally advanced gastric and gastroesophageal junction cancer. Recent evidence demonstrated the addition of programmed cell death protein 1 (PD-1) inhibitors enhanced therapeutic efficacy. However, the mechanisms of response and resistance remain largely undefined. A detailed multiomic investigation is essential to elucidate these mechanisms. Methods We performed whole-exome sequencing, whole-transcriptome sequencing, multiplex immunofluorescence and single-cell RNA sequencing on matched pretreatment and post-treatment samples from 30 patients enrolled in an investigator-initiated Phase 2 clinical trial ( NCT04908566 ). All patients received neoadjuvant PD-1 inhibitors in combination with chemotherapy. A major pathologic response (MPR) was defined as the presence of no more than 10% residual viable tumor cells following treatment. Results Before treatment, the positive ratio of CD3+T cells in both the tumor parenchyma and stroma was significantly higher in the non-MPR group compared with the MPR group (p=0.042 and p=0.013, respectively). Least absolute shrinkage and selection operator regression was employed for feature gene selection and 13 genes were ultimately used to construct a predictive model for identifying MPR after surgery. The model exhibited a perfect area under curve (AUC) of 1.000 (95% CI: 1.000 to 1.000, p<0.001). Post-treatment analysis revealed a significant increase in CD3+T cells, CD8+T cells and NK cells in the tumor stroma of MPR patients. In the tumor parenchyma, aside from a marked increase in CD8+T cells and NK cells, a notable reduction in macrophage was also observed (all p<0.05). Importantly, forkheadbox protein 3 (FOXP3), the principal marker for regulatory T cells (Treg) cells, showed a significant decrease during treatment in MPR patients. FOXP3 expression in the non-MPR group was significantly higher than in the MPR group (p=0.0056) after treatment. Furthermore, single-cell RNA sequencing analysis confirmed that nearly all Treg cells were derived from the non-MPR group. Conclusions Our study highlights the critical role of dynamic changes within the tumor immune microenvironment in predicting the efficacy of neoadjuvant combined immunochemotherapy. We examined the disparities between MPR/non-MPR groups, shedding light on potential mechanisms of immune response and suppression. In addition to bolstering cytotoxic immune responses, specifically targeting Treg cells may be crucial for enhancing treatment outcomes.


Figure 5 Kinetics of MiHA-specific T cells in 16 patients who responded to DLI after HLA-matched alloSCT. (A) Estimated frequencies of MiHA-specific T cells by pMHC-multimer staining are shown for 16 patients who responded to DLI after HLAmatched alloSCT. Of the 16 patients, 3 patients responded to DLI without GvHD (patients #9465, #9953, #4739), 3 patients developed limited GvHD (patients #7010, #6711, #8353) and 10 patients had severe GvHD (#6091, #8490, #5528, #8905, #5596, #10605, #8334, #9528, #6061, #7956) after DLI. Each stack represents the estimated T-cell frequency for a MiHA that is mismatched (dark blue) or not mismatched (light blue) in the respective patient. For two samples (gray), frequencies of pMHC-multimer + T cells measured during FACS sorting are displayed, but barcode sequence data were not determined due to PCR failure. Letters indicate MiHAs for which estimated T-cell frequencies of ≥1% of CD8 + T cells were detected. (B) Tissue expression of MiHAs targeted by T cells for the sample with the highest measured T-cell frequencies of each patient. Hashtags indicate two samples in which T-cell frequencies were measured by conventional pMHC-tetramer staining as barcode sequence data were missing. Colors indicate relative expression of the MiHA encoding gene in hematopoietic compared with nonhematopoietic cells using single cell RNA-Seq data of the Human Protein Atlas. alloSCT, allogeneic stem cell transplantation; DLI, donor lymphocyte infusion; GvHD, graft-versus-host disease; GvL, graft-versus-leukemia; MiHAs, minor histocompatibility antigens; pMHC, peptide-major histocompatibility complex. on December 9, 2024 by guest. Protected by copyright.
DNA barcoded peptide-MHC multimers to measure and monitor minor histocompatibility antigen-specific T cells after allogeneic stem cell transplantation

Kyra J Fuchs

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Marcus Göransson

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Michel G D Kester

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[...]

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Marieke Griffioen

Allogeneic stem cell transplantation (alloSCT) provides a curative treatment option for hematological malignancies. After HLA-matched alloSCT, donor-derived T cells recognize minor histocompatibility antigens (MiHAs), which are polymorphic peptides presented by HLA on patient cells. MiHAs are absent on donor cells due to genetic differences between patient and donor. T cells targeting broadly expressed MiHAs induce graft-versus-leukemia (GvL) reactivity as well as graft-versus-host disease (GvHD), while T cells for MiHAs with restricted or preferential expression on hematopoietic or non-hematopoietic cells may skew responses toward GvL or GvHD, respectively. Besides tissue expression, overall strength of GvL and GvHD is also determined by T-cell frequencies against MiHAs. Here, we explored the use of DNA barcode-labeled peptide-MHC multimers to detect and monitor antigen-specific T cells for the recently expanded repertoire of HLA-I-restricted MiHAs. In 16 patients who experienced an immune response after donor lymphocyte infusion, variable T-cell frequencies up to 30.5% of CD8 ⁺ T cells were measured for 49 MiHAs. High T-cell frequencies above 1% were measured in 12 patients for 19 MiHAs, with the majority directed against mismatched MiHAs, typically 6–8 weeks after donor lymphocyte infusion and at the onset of GvHD. The 12 patients included 9 of 10 patients with severe GvHD, 2 of 3 patients with limited GvHD and 1 of 3 patients without GvHD. In conclusion, we demonstrated that barcoded peptide-MHC multimers reliably detect and allow monitoring for MiHA-specific T cells during treatment to investigate the kinetics of immune responses and their impact on development of GvL and GvHD after HLA-matched alloSCT.


Figure 1 Hypothesis regarding redox modulation for efficient cancer therapy. This simplified scheme illustrates how reactive oxygen species (ROS; red stars) can play different roles at different levels in cancer, and how we propose that directed redox modulation may be used to achieve more efficient therapy outcome. Typically, cancer cells and stroma of the tumor microenvironment (right half of the figure, blue background) promote the accumulation of higher ROS levels that hamper the functionality of immune effector cells (left half of the figure, orange background), including dendritic cells, natural killer cells (NK cells), B cells, and cytotoxic T cells. Additionally, immunosuppressive cells, including myeloid-derived suppressor cells (MDSCs) and regulatory T cells, exert negative control over immune effector cells through ROS-dependent mechanisms. In the context of tumor therapy, further increased oxidation in cancer cells can trigger intolerable levels of ROS, leading to either immunogenic cell death (ICD) or non-ICD. ICD, in particular, can give release of damage-associated molecular patterns (DAMPs) further promoting adaptive immune responses. A therapeutic use of antioxidants or the deliberate increase of endogenous antioxidant systems in immune effector cells rendering them more resistant to ROS can improve their efficacy. A combination of approaches resulting in an increase of ROS levels in cancer cells and the improved resistance to ROS in immune effector cells may have the potential to synergistically enhance the efficacy of cancer therapy. Green arrows: redox modulatory processes supporting cancer therapy. Red arrows: redox modulatory processes counteracting efficient therapeutic outcome in cancer therapy. Figure created with BioRender. See table 1 and the main text for further details.
Figure 2 (A) ROS are essential for normal inflammation and the immune response. The interaction between the MHC-antigen complex and the TCR generates ROS facilitating T-cell activation and expansion. High levels of environmental ROS favor Th2 cellular differentiation. ROS contributes to activation-induced cell death via induction of FasL expression facilitating Tcell contraction. ROS are essential for antimicrobial killing by phagocytes via the oxidative burst and NLRP3 inflammasome activation. ROS are implicated in both the M1 and M2 macrophage polarization. ROS participate in neutrophil extracellular traps construction, release and NETosis. ROS partake in dendritic cell (DC) differentiation, maturation, activation and DC secretory function. DC-derived ROS is indispensable in antigen presentation and cross-presentation. (B). Oxidative stress in the TME facilitates immunosuppression. ROS contributes to the maintenance of MDSCs in an undifferentiated state. 51 MDSCs are a source of various ROS including peroxynitrite (ONOO − ), myeloperoxidase (MPO) and hydrogen peroxide (H 2 O 2 ). Ferroptotic PMN-MDSCs release immunosuppressive factors, including peroxidized lipids and PGE2. ROS facilitate the acquisition of an immunosuppressive tumor promoting "M2-like" TAM phenotype. TME oxidative stress promotes dendritic cell dysfunction and impairs intratumoral DCs. ROS can facilitate Treg stability and immunosuppression. ROS can induce Treg apoptosis. Apoptotic tumor-associated Tregs release ATP that is metabolized to adenosine via CD39 and CD73 limiting antitumor immunity via the A2A pathway. ROS can reduce T-cell expression of CD3ζ and IFN-γ. ROS induce nitration of the TCR-CD8 complex and impair trafficking of antigen-specific T-cells via CCL2 nitration and alteration of the MHC class I peptidome presented by tumors diminished immunogenicity. TME elevated intrinsic ROS drives the induction of terminally exhausted T cells. TME ROS promote apoptosis of NK cells. IFN, interferon; IL, interleukin; CCL2 chemokine ligand 2, MDSC, myeloid-derived suppressor cell; MHC, major histocompatibility complex; NK, natural killer; NLRP3 NOD-, LRR-and pyrin domain-containing protein 3, PG2 prostaglandin E2, PMN-MDSC, polymorphonuclear MDSC; ROS, reactive oxygen species; TAM, tumor associated macrophage; TCR T-cell receptor, TME, tumor microenvironment; Treg, regulatory T cells. Figure created with BioRender.
Reactive oxygen species: Janus-faced molecules in the era of modern cancer therapy

Aine O’Reilly

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Wenchao Zhao

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Stina Wickström

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[...]

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Rolf Kiessling

Oxidative stress, that is, an unbalanced increase in reactive oxygen species (ROS), contributes to tumor-induced immune suppression and limits the efficacy of immunotherapy. Cancer cells have inherently increased ROS production, intracellularly through metabolic perturbations and extracellularly through activation of NADPH oxidases, which promotes cancer progression. Further increased ROS production or impaired antioxidant systems, induced, for example, by chemotherapy or radiotherapy, can preferentially kill cancer cells over healthy cells. Inflammatory cell-derived ROS mediate immunosuppressive effects of myeloid-derived suppressor cells and activated granulocytes, hampering antitumor effector cells such as T cells and natural killer (NK) cells. Cancer therapies modulating ROS levels in tumors may thus have entirely different consequences when targeting cancer cells versus immune cells. Here we discuss the possibility of developing more efficient cancer therapies based on reduction-oxidation modulation, as either monotherapies or in combination with immunotherapy. Short-term, systemic administration of antioxidants or drugs blocking ROS production can boost the immune system and act in synergy with immunotherapy. However, prolonged use of antioxidants can instead enhance tumor progression. Alternatives to systemic antioxidant administration are under development where gene-modified or activated T cells and NK cells are shielded ex vivo against the harmful effects of ROS before the infusion to patients with cancer.


Baseline characteristics of patients by plasma ARG level
Predictive analysis for OS with baseline ARG levels dichotomized by median
Multivariable analysis for OS for patients treated with D+T
Plasma arginine as a predictive biomarker for outcomes with immune checkpoint inhibition in metastatic colorectal cancer: a correlative analysis of the CCTG CO.26 trial

December 2024

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4 Reads

Background Nutritional stress is a mechanism that allows tumor cells to evade the immune system. Arginine (ARG), an amino acid involved in immunomodulation, aids in regulating T-lymphocyte cell activity and the antitumor response. ARG deficiency in the tumor microenvironment can impair T-cell response while ARG supplementation may promote antitumor immune activity. In this exploratory post hoc analysis of the randomized phase II CO.26 trial, we investigated the role of plasma ARG in predicting response to immune checkpoint inhibitors (ICI) in patients with microsatellite stable refractory metastatic colorectal cancer (mCRC). Methods CO.26 randomized patients with refractory mCRC to durvalumab plus tremelimumab (D+T) versus best supportive care (BSC). Plasma ARG concentrations were determined from pretreatment blood samples using high-performance liquid chromatography-tandem mass spectrometry. The median plasma ARG value was used as a cut-off stratifying patients into ARG-high (≥10 700 ng/mL) versus ARG-low (<10 700 ng/mL) groups. Overall survival (OS) was estimated using the Kaplan-Meier method and compared using the log-rank test. Cox proportional hazard models were used to analyze the prognostic and predictive impacts of ARG on OS. Results Of 180 patients enrolled in CO.26, 161 (N=114 treated with D+T and 47 BSC) had pretreatment blood samples for ARG analysis. There were no significant differences in baseline characteristics between patients included in this analysis and the total study patients, or between ARG-high and ARG-low patients. In the BSC arm, the median OS was 3.09 months for ARG-high versus 4.27 months for ARG-low patients (univariable HR 0.89 (0.49–1.65), p=0.72). In the D+T arm, the median OS was 7.62 months for ARG-high versus 5.27 months for ARG-low patients (univariable HR 0.68, (0.48–1.0], p=0.048). In ARG-high patients, D+T significantly improved OS (median OS 7.62 months with D+T vs 3.09 months BSC; HR 0.61 (0.37–0.99), p=0.047; adjusted p=0.042 for interaction). In ARG-low patients there was no OS benefit with D+T (median OS 5.27 months D+T vs 4.27 months BSC; HR 0.87 (0.52–1.46), p=0.61). Conclusion High baseline plasma ARG was predictive of improved OS in patients with mCRC treated with D+T. Further investigations are needed to validate ARG as a biomarker. Therapeutic approaches targeting the ARG pathway may augment ICI activity. Trial registration number NCT02870920 .


Figure 1 Generation and characterization of oncolytic adenoviruses (A, B) Kaplan-Meier survival curves for breast cancer (A) and colon cancer (B) are shown. Patient cohorts are segregated by the ratio of IL-18 to IL-18BP transcripts (red-high, black-low). The numbers underneath the figures are individuals at each time point. (C) Schematic illustration of oAdDR18. The virus has a 24 bp deletion in the CR2 of the E1A gene, corresponding to the region responsible for Rb protein binding, and is conditionally oncolytic in cells defective in the Rb pathway. The fiber was modified by incorporating the Ad3 knob to the Ad5 shaft and tail for increased tumor targeting (Ad5/3). A complementary DNA for wild-type IL-18 or IL-18BP-binding inactive variant DR18 mCS2 (DR18) was used as transgene for this study. For protein secretion, a signal peptide of human albumin was inserted. (D) DR18 production in oAdDR18-infected A549 cells were determined at 72 hours post infection by quantitative ELISA. Cells in 24-well plates (1×10 5 cells/well) were infected with oAd or oAdDR18 at MOIs as indicated. Data are mean±SD of triplicate samples. (E) IL-18 and IFN-γ production in a tumor setting. After tumor establishment, CT26 xenografts were intratumorally injected with rDR18, oAdDR18, or PBS (n=3). Mouse blood was collected prior to or on days 1 and 3 post injection for measurement of IL-18 and IFN-γ production by ELISA. IL-18 transgene in tumor tissues was further analyzed. DR18, decoy-resistant IL-18; IFN, interferon; IL, interleukin; IL-18BP, IL-18 binding protein; mRNA, messenger RNA; ND, not detectable; oAd, oncolytic adenovirus; oAdDR18, oAd harboring DR18; PBS, phosphate-buffered saline; rDR18, recombinant DR18 protein. on December 4, 2024 by guest. Protected by copyright.
Figure 4 oAdDR18 activated systemic antitumor effect in blood and spleen. (A) Quantification of percentage of CD45 + CD3 + T cells by flow cytometry in blood in CT26 tumor model. (B) Representative flow plots and quantification of CD4 + T cells and CD8 + T cells in the blood. (C) Representative flow plots and quantification of CD8 + CD69 + T cells in the blood. (D) Representative flow plots and quantification of CD4 + T cells and CD8 + T cells in the spleen. (E) Representative flow plots and quantification of CD8 + CD69 + T cells in the spleen. (F) The cytotoxicity of PBMCs. PBMCs from treated tumor-bearing mice were co-cultured with CT26 cells. The ratios between effector PBMCs and target CT26 were at 10:1. Results are expressed as the mean±SD indicated by error bars. (G) ELISpot assay for interferon-γ. The number of spots counted at a concentration of 5×10 5 splenocytes. Each value represents the average±SD of representative of triplicate samples. IL, interleukin; oAd, oncolytic adenovirus; oAdDR18, oAd harboring decoy-resistant IL-18; oAdwtIL-18, wild-type IL-18 delivered by oAd; PBS, phosphate-buffered saline. on December 4, 2024 by guest. Protected by copyright.
Figure 5 oAdDR18 inhibited non-injected distant tumors and 4T1 tumor metastasis. (A) Diagram illustrating the experimental setups. In the bilateral subcutaneous CT26 model, PBS, oAd, oAdwtIL-18, rDR18 or oAdDR18 was administrated intratumorally on the right tumors twice a week. Tumor growth of treated side (B), untreated side (C), and tumor weight (D) of treated side (solid bar) and untreated side (empty box). (E) Mouse body weight. (F) Diagram of 4T1-Luc lung metastasis model. When the 4T1 tumor inoculated subcutaneously grew up to 300 mm 3 , lung metastasis was established by was tail vein injection of 5×10 5 cells 4T1-Luc cells. (G) Live animal bioluminescent images. The images showed entrapped 4T1-Luc cells after 2 hours and 10 days of tail vein injection in the individual group. (H) H&E staining of lung tissue. Representative flow plots and quantification of CD8 + T cells (I), and CD8 + CD69 + T cells (J). IL, interleukin; oAd, oncolytic adenovirus; oAdDR18, oAd harboring decoy-resistant IL-18; oAdwtIL-18, wild-type IL-18 delivered by oAd; PBS, phosphate-buffered saline; rDR18, recombinant decoy-resistant IL-18 protein. on December 4, 2024 by guest. Protected by copyright.
Figure 6 Intratumoral administration of oncolytic adenovirus harboring decoy-resistant interleukin-18induces the establishment of antitumor memory. (A) Mice that achieved complete regression against CT26 tumor and age-matched treatment-naive mice were subcutaneously inoculated with CT26 and 4T1. Images at 10 days after tumor inoculation. (B) CT26 tumor growth. (C) 4T1 tumor growth. (D) CT26 tumor weight and photographs. (E) 4T1 tumor weight and photographs.
Durable antitumor response via an oncolytic virus encoding decoy-resistant IL-18

December 2024

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4 Reads

Background Interleukin-18 (IL-18), or interferon (IFN)-γ-inducing factor, potentiates T helper 1 and natural killer cell activation as well as CD8 ⁺ T-cell proliferation. Recombinant IL-18 has displayed limited clinical efficacy in part due to the expression of the decoy receptor, IL-18 binding protein (IL-18BP). A series of IL-18 variants that are devoid of IL-18BP binding, termed DR18 (decoy-resistant IL-18), was developed via directed evolution. We tested DR18 using oncolytic adenovirus (oAd) as a platform for delivery in syngeneic mouse tumor models. Methods oAd harboring wild-type IL-18 or DR18 (oAdDR18) was constructed by inserting IL-18 mutant into modified oAd backbone with Ad5/3 chimeric fiber. The delivery effect and IFN-γ induction were determined by ELISA. The antitumor efficiency of oAdDR18 was tested in CT26, B16BL6 and 4T1 tumor-bearing mice, or athymic nude mice and compared with recombinant DR18 protein (rDR18). 4T1 lung metastasis model was used to evaluate the antitumor efficiency of local and distant tumors. Antitumor memory and synergistic effect with an anti-programmed cell death protein-1 (PD-1) antibody was evaluated. The phenotypes of the immune cells in tumor microenvironment were analyzed by flow cytometry and immunohistochemistry. Results Mice received oAdDR18 maintained stable production of IL-18 and IFN-γ compared with those received rDR18. Intratumoral delivery of oAdDR18 significantly reduced tumor growth across several tumor models, but not in the athymic nude mouse model. Mice that had tumor remission showed antitumor memory. The antitumor effect was associated with intratumor infiltration of CD4 ⁺ and CD8 ⁺ T cells. DR18 delivered by oAd demonstrated long-lasting and enhanced antitumor activities against local and distant tumors compared with that received rDR18 or wild-type IL-18 delivered by oAd (oAdwtIL-18). oAdDR18 treatment also reduced 4T1 lung metastasis. In addition, combination of this virotherapy with immune checkpoint inhibitors (ICIs) like the anti-PD-1 antibody further enhanced the antitumor activity as compared with respective monotherapy. Conclusions oAdDR18 demonstrates enhanced antitumor activities through the induction of stronger local and system immunities and modulation of the tumor microenvironment compared with those of oAdwtIL-18 and rDR18. A combination of oncolytic virotherapy with cytokine engineering would lead to cytokine-based therapeutics for cancer and other diseases.


Figure 2 NaBi reprograms the metabolism of T cells and macrophages in an LA environment. Analysis of mitochondrial content (A) and glucose uptake (B) in CD8 + T cells on days 4 and 14 after treatment in tumor-bearing mice. Representative flow cytogram of MitoTracker for 2NBDG staining in CD8 + T cells and spleen-T cells and tabulated flow cytometric data are shown (n=5). (C) Representative cytometry and statistical plots of mitochondrial content in TAMs and spleen-MФ at indicated time points (n=5). (D) Statistics of mitochondrial content in M1.TAMs at indicated time points (n=5). (E and F) The concentration of LA in the supernatant of T cells (E) and BMDMs (F) cultured in fresh RPMI 1640 medium or MC38 cell culture supernatant for 48 hours (n=3). (G and H) Representative flow histograms and statistics of changes in mitochondrial content of T cells (G) and BMDM (H) cultured in RPMI 1640 medium containing different compositions for 48 hours in vitro (n=3). (I) The release of IFN-γ and TNF-α of T cells cultured in RPMI 1640 medium with different compositions for 48 hours in vitro (n=3). (J) BMDM were cultured in RPMI 1640 with different components for 48 hours, then mixed with oncolytic adenovirus-αCD47-infected tumor cell culture supernatant with or without CD47 antibody for 1 hour. This was followed by incubation with MC38 cells stained with the cell membrane red dye-DiD for 4 hours. Fluorescence and flow images of MC38 cells phagocytosed by BMDM were measured by confocal microscopy and fluorescence-activated cell sorting (MC38: BMDM=1:5; n=3). Data represent the mean±SEM. P values were measured using one-way analysis of variance. BMDM, bone marrow-derived macrophage; IFNγ, γ-interferon; LA, lactate; M1.TAM, M1-type macrophages TAM; MFI, mean fluorescence intensity; NaBi, sodium bicarbonate; PBS, phosphatebuffered saline; RPMI, Roswell Park Memorial Institute; TAM, tumor-associated macrophage; TIL, tumor-infiltrating T cells; TNF, tumor necrosis factor. on December 4, 2024 by guest. Protected by copyright.
Mitochondrial metabolic reprogramming of macrophages and T cells enhances CD47 antibody-engineered oncolytic virus antitumor immunity

December 2024

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1 Read

Background Although immunotherapy can reinvigorate immune cells to clear tumors, the response rates are poor in some patients. Here, CD47 antibody-engineered oncolytic viruses (oAd-αCD47) were employed to lyse tumors and activate immunity. The oAd-αCD47 induced comprehensive remodeling of the tumor microenvironment (TME). However, whether the acidic TME affects the antitumor immunotherapeutic effects of oncolytic viruses-αCD47 has not been clarified. Methods To assess the impact of oAd-αCD47 treatment on the TME, we employed multicolor flow cytometry. Glucose uptake was quantified using 2NBDG, while mitochondrial content was evaluated with MitoTracker FM dye. pH imaging of tumors was performed using the pH-sensitive fluorophore SNARF-4F. Moreover, changes in the calmodulin-dependent protein kinase II (CaMKII)/cyclic AMP activates-responsive element-binding proteins (CREB) and peroxisome proliferator-activated receptor gamma coactivator-1α (PGC1α) signaling pathway were confirmed through western blotting and flow cytometry. Results Here, we identified sodium bicarbonate (NaBi) as the potent metabolic reprogramming agent that enhanced antitumor responses in the acidic TME. The combination of NaBi and oAd-αCD47 therapy significantly inhibited tumor growth and produced complete immune control in various tumor-bearing mouse models. Mechanistically, combination therapy mainly reduced the number of regulatory T cells and enriched the ratio of M1-type macrophages TAMs (M1.TAMs) to M2-type macrophages TAMs (M2.TAMs), while decreasing the abundance of PD-1 ⁺ TIM3 ⁺ expression and increasing the expression of CD107a in the CD8 ⁺ T cells. Furthermore, the combination therapy enhanced the metabolic function of T cells and macrophages by upregulating PGC1α, a key regulator of mitochondrial biogenesis. This metabolic improvement contributed to a robust antitumor response. Notably, the combination therapy also promoted the generation of memory T cells, suggesting its potential as an effective neoadjuvant treatment for preventing postoperative tumor recurrence and metastasis. Conclusions Tumor acidic microenvironment impairs mitochondrial energy metabolism in macrophages and T cells inducing oAd-αCD47 immunotherapeutic resistance. NaBi improves the acidity of the TME and activates the CaMKII/CREB/PGC1α mitochondrial biosynthesis signaling pathway, which reprograms the energy metabolism of macrophages and T cells in the TME, and oral NaBi enhances the antitumor effect of oAd-αCD47.


Figure 1 T H 9 cells exhibited superior antitumor effects in established lung metastasis as compared with T H 1 and T H 17 cells. (A) Balb/c mice were inoculated with 1×10 5 4T1-OVA-luci or K7M2-OVA-luci in 100 µl PBS intravenously on day 0. Tumorbearing mice were transferred with 3×10 6 OVA-specific T H 1, T H 9, or T H 17 cells in 100 µl PBS intravenously on day 5, day 12 and day 19. (B and C) In vivo bioluminescence images (B) and quantification (C) of the tumor burden in lungs on day 7, day 14 and day 21. (D) Survival of K7M2-OVA or 4T1-OVA tumor-bearing mice with lung metastasis treated with PBS, tumorspecific T H 1, T H 9, and T H 17 cells. (E) H&E staining of lungs bearing with K7M2-OVA or 4T1-OVA tumor on day 14. Scale bar, 2.5 mm. (F) Tumor area and lung weight correspond to (E). Data were analyzed by one-way ANOVA test or unpaired t-test. Representative results from three independent experiments are shown (mean±SEM); n=5 in (B, C and F), n=8 in (D), n=3 in (E and F). ns denote no significant difference,*indicates p<0.05, **indicates p<0.01, ***indicates p<0.001, ****indicates p<0.0001. ANOVA, analysis of variance; OVA, ovalbumin; PBS, phosphate-buffered saline; T H , T helper. on December 4, 2024 by guest. Protected by copyright.
Figure 4 ITCH regulates the ubiquitination of CXCR4 in T H 9 cells. (A) Real-time PCR analysis of Cxcr4 mRNA level in T H 0, T H 1, T H 9 and T H 17 cells. (B and C) Western blotting analysis of CXCR4 protein level in T H 9 cells stimulated with or without 10 µM CQ or 20 µM MG132 for 4 hours. (D) Immunoprecipitation analysis of ubiquitinated CXCR4 level in T H 0, T H 1, T H 9 and T H 17 cells. (E) Western blotting analysis of CXCR4 and Flag expression in 3T3 and 3T3 overexpressed with CXCR4. (F) Mass spectrum identified the E3 enzymes from proteins pulled down by CXCR4 among vector-3T3 and cxcr4-overexpression-3T3 samples. (G) Immunoprecipitation analysis of ITCH protein in T H 9 cells. (H) Western blotting analysis of CXCR4 protein level in T H 9 cells treated by negative control or Itch-siRNAs. (I) Western blotting analysis of CXCR4 protein level in T H 9 cells treated by negative control or Itch-OE retroviral. (J) Statistical analysis of flow cytometry was conducted on CD45.1-T H 9 cells in the lung 48 hours after intravenous injection of CD45.1-derived T H 9 cells treated with either negative control or Itch-siRNAs into healthy CD45.2 mice. (K) In vivo bioluminescence images of 4T1 or K7M2 tumor burden in the lungs on day 7, day 14 and day 21. The mice were treated with PBS or tumor-specific T H 9 treated with either negative control or Itch-siRNAs. (L and M) Statistical analysis of total flux in lungs bearing with 4T1 (L) or K7M2 (M) tumor. Data were analyzed by one-way ANOVA test or unpaired t-test. Representative results from three independent experiments are shown (mean±SEM); n=6 in (A), n=4 in (J), n=3 in (K). ns denote no significant difference, *indicates p<0.05, **indicates p<0.01, ***indicates p<0.001, ****indicates p<0.0001 (unpaired two-tailed Student's t-test). ANOVA, analysis of variance; CQ, chloroquine; DMSO, dimethyl sulfoxide; IgG, immunoglobulin G; mRNA, messenger RNA; NC, negative control; OE, overexpression; PBS, phosphate-buffered saline; siRNA, small interference RNA; T H , T helper. on December 4, 2024 by guest. Protected by copyright. http://jitc.bmj.com/
Natural lung-tropic T H 9 cells: a sharp weapon for established lung metastases

December 2024

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10 Reads

Background Lung metastasis remains the primary cause of tumor-related mortality, with limited treatment options and unsatisfactory efficacy. In preclinical studies, T helper 9 (T H 9) cells have shown promise in treating solid tumors. However, it is unclear whether T H 9 cells can tackle more challenging situations, such as established lung metastases. Moreover, comprehensive exploration into the nuanced biological attributes of T H 9 cells is imperative to further unravel their therapeutic potential. Methods We adoptively transferred T H 1, T H 9, and T H 17 cells into subcutaneous, in situ , and established lung metastases models of osteosarcoma and triple-negative breast cancer, respectively, comparing their therapeutic efficacy within each distinct model. We employed flow cytometry and an in vivo imaging system to evaluate the accumulation patterns of T H 1, T H 9, and T H 17 cells in the lungs after transfusion. We conducted bulk RNA sequencing on in vitro differentiated T H 9 cells to elucidate the chemokine receptor CXCR4, which governs their heightened pulmonary tropism relative to T H 1 and T H 17 cell counterparts. Using Cd4 cre Cxcr4 flox/flox mice, we investigate the effects of CXCR4 on the lung tropism of T H 9 cells. We performed mass spectrometry to identify the E3 ligase responsible for CXCR4 ubiquitination and elucidated the mechanism governing CXCR4 expression within T H 9 cellular milieu. Ultimately, we analyzed the tumor immune composition after T H 9 cell transfusion and evaluated the therapeutic efficacy of adjunctive anti-programmed cell death protein-1 (PD-1) therapy in conjunction with T H 9 cells. Results In this study, we provide evidence that T H 9 cells exhibit higher lung tropism than T H 1 and T H 17 cells, thereby exhibiting exceptional efficacy in combating established lung metastases. CXCR4-CXCL12 axis is responsible for lung tropism of T H 9 cells as ablating CXCR4 in CD4 ⁺ T cells reverses their lung accumulation. Mechanistically, tumor necrosis factor receptor-associated factor 6 (TRAF6)-driven hyperactivation of NF-κB signaling in T H 9 cells inhibited ITCH-mediated ubiquitination of CXCR4, resulting in increased CXCR4 accumulation and enhanced lung tropism of T H 9 cells. Besides, T H 9 cells’ transfusion significantly improved the immunosuppressed microenvironment. T H 9 cells and anti-PD-1 exhibit synergistic effects in tumor control. Conclusions Our findings emphasized the innate lung tropism of T H 9 cells driven by the activation of TRAF6, which supports the potential of T H 9 cells as a promising therapy for established lung metastases.



Impact of immunological aging on T cell-mediated therapies in older adults with multiple myeloma and lymphoma

The treatment landscape for lymphoma and multiple myeloma, which disproportionally affect older adults, has been transformed by the advent of T cell-mediated immunotherapies, including immune checkpoint inhibition, T cell-engaging bispecific antibodies, and chimeric antigen receptor (CAR) T cell therapy, during the last decade. These treatment modalities re-enable the patient’s own immune system to combat malignant cells and offer the potential for sustained remissions and cure for various diseases. Age profoundly affects the physiological function of the immune system. The process of biological aging is largely driven by inflammatory signaling, which is reciprocally fueled by aging-related alterations of physiology and metabolism. In the T cell compartment, aging contributes to T cell senescence and exhaustion, increased abundance of terminally differentiated cells, a corresponding attrition in naïve T cell numbers, and a decrease in the breadth of the receptor repertoire. Furthermore, inflammatory signaling drives aging-related pathologies and contributes to frailty in older individuals. Thus, there is growing evidence of biological aging modulating the efficacy and toxicity of T cell-mediated immunotherapies. Here, we review the available evidence from biological and clinical studies focusing on the relationship between T cell-mediated treatment of hematologic malignancies and age. We discuss biological features potentially impacting clinical outcomes in various scenarios, and potential strategies to improve the safety and efficacy of immune checkpoint inhibitors, T cell-engaging bispecific antibodies, and CAR-T cell therapy in older patients.


Figure 1 Primary functional capacities of C6.5 derived CAR T cells. (A) Affinities of Her2-specific C6.5 scFv mutants as previously determined by surface plasmon resonance (SPR) using BIAcore. 28 (B) scFvs were integrated into a second generation CAR harboring a human IgG1 hinge, CD28 transmembrane and costimulatory, and CD3 signaling domain. CAR expression level on human peripheral blood T cells was assessed by flow cytometry; data represent the geometric mean of the mean fluorescent intensity (MFI) for n=3 donors. CAR graphics: Created in BioRender. Abken, H. (2024) https://BioRender.com/ y88w452. 1×10 5 T cells were co-incubated with 2×10 4 Her2 medium LS174T cells (C, E, G, I, K) or 2×10 4 Her2 high SKOV3 cells (D, F, H, J, L). (C, D) CAR-mediated T cell activation strength was evaluated by the Jurkat-Lucia NFAT reporter cell line engineered with the respective Her2 CAR at similar CAR expression levels. Bioluminescence was recorded as relative light units (RLU) after 20 hours of co-incubation with the respective Her2 + target cells. Data represent means±SD of n=4 (C) or n=3 (D) independent experiments. (E, F) CD8 + CAR + T cell proliferation was monitored by recording proliferation dye eFluor TM 450 dilution after 5 days of co-culture with target cells. (G, H) CD8 + CAR + T cell subsets after 72 hours of co-culture were determined by flow cytometry (naïve: CD45RO − CD62L + ; CM: CD45RO + CD62L + ; EM: CD45RO + CD62L − ; E: CD45RO − CD62L − ). (I, J) FASL expression after 72 hours of co-culture of CD8 + CAR high CD45RO + CD62L + CAR + T cells with target cells. (K, L) Annexin V staining after 72 hours of co-culture of CD8 + CAR + T cells with target cells. Annexin V MFI for each CAR T cell was normalized to the mean MFI of each individual donor. Data represent means±SD of n≥3 donors. One-way analysis of variance was performed with Tukey's multiple comparison correction. ns (not significant); *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001. CM, central memory; E, effector; EM, effector memory. on December 2, 2024 by guest. Protected by copyright. http://jitc.bmj.com/
Figure 3 Non-linear correlation between scFv affinity and CAR T cell avidity. (A) The expression levels of Her2 CARs and of the CD30 CAR of irrelevant specificity on engineered T cells; data represent the geometric mean of the mean fluorescence intensity (MFI), n=3 donors. CAR graphics (A,G): Created in BioRender. Abken, H. (2024) https://BioRender.com/y88w452. (B) CAR T cell binding force on a Her2 high SKOV3 cell monolayer as determined by z-Movi avidity analysis. Graphics: Created in BioRender. Abken, H. (2024) https://BioRender.com/b81y849. (C) Force ramp applied after 5 min CAR T cell co-incubation with SKOV3 cells. Detachment curve of CAR T cells displayed as mean values of n=3 technical replicates (3 runs on 3 different chips per experimental group), data for one representative donor are shown. (D) Collated data of n=3 technical replicates of n=3 donors (n=9 runs on n=9 chips total per experimental group); data represent the percentage of T cells bound at plateau force. (E) Soluble Her2 protein at increasing concentration was added to Her2 CAR (C6MH3-B1) and CD30 CAR T cells; data represent the percentage of CAR T cells at the end of force ramp±SD, n=3 technical replicates (n=1 donor). Graphics: Created in BioRender. Barden, M. (2024) https://BioRender.com/q55q213. (F) Percentage of CAR T cells bound after 2, 5, 20, and 40 min; representative data for 1 out of 3 donors are displayed. (G-I) T cells were engineered with the CAR-GFP CAR, incubated on Her2 coated plates, and imaged by TIRF microscopy. (I) Data show the mean fluorescence signal per single cell in the contact region with Her2 after 40 min, n≥30 cells for one donor. Wilcoxon-Mann-Whitney test was performed. (J) Mean CAR T cell motility measured by timelapse-live video (TLLV) microscopy over a time-course of 12.5 hours, n=3 donors. Box plots display IQR and median. One-way ANOVA with post hoc Tukey's HSD test was performed to identify significant group differences. (A, D, J). ns (not significant); *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001. ANOVA, analysis of variance; HSD, honestly significant difference; TIRF, total internal reflection fluorescence. on December 2, 2024 by guest. Protected by copyright.
Figure 4 High affinity Her2 CAR T cells provide a prolonged antitumor response in vitro and in vivo. CAR graphics: Created in BioRender. Abken, H. (2024) https://BioRender.com/y88w452. (A) Her2 CAR T cells (1×10 5 ) and control CAR T cells of irrelevant specificity (CD19 CAR) were repetitively stimulated with Her2 medium LS174T or Her2 high SKOV3 cells (2×10 4 ) until cancer cell outgrowth (R6 and R5). After each round of co-culture (ie, after 3 or 4 days), 10% of culture volume was removed for flow cytometric quantitation of cell numbers and CAR surface expression. (B) Frequencies of LS174T cells, (C) CAR T cells co-cultured with LS174T cells, (E) SKOV3 cells, (F) CAR T cells co-cultured with SKOV3 cells were determined by flow cytometry using counting beads. Data represent mean values of n=3 healthy T cell donors. (D, G) CAR molecules per cell were estimated by flow cytometry using PE quantitation beads. Data represent mean±SD of n=3 healthy donors. (H) NSG mice were s.c. injected with Her2 high N87.ffLuc (3×10 6 ) cells. On day 14 mice received one intravenous dose of non-modified T cells (NT) (5×10 5 , n=3, beige line), or C6.5 scFv CAR T cells (5×10 5 , n=5, pink line), or C6-B1D2 scFv CAR T cells (5×10 5 , n=5, grey-blue line). Bioluminescence imaging was performed weekly. (I) Tumor growth was monitored by luminescence intensity recording (photons/s/cm 2 /sr). Graphs represent mean values±SD. (J) Percent survival of mice is shown as Kaplan-Meyer curve; Log-rank (Mantel-Cox) test was applied for analysis. Two-way ANOVA (D, G) or one-way ANOVA (I) was performed with Tukey's (D, G) or Bonferroni's (I) multiple comparison correction. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001. ANOVA, analysis of variance. on December 2, 2024 by guest. Protected by copyright. http://jitc.bmj.com/
Integrating binding affinity and tonic signaling enables a rational CAR design for augmented T cell function

December 2024

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24 Reads

Background The success of chimeric antigen receptor (CAR) T cell therapy for hematological malignancies has not yet translated into long-term elimination of solid tumors indicating the need for adequately tuning CAR T cell functionality. Methods We leveraged a translational pipeline including biophysical characterization and structural prediction of the CAR binding moiety, evaluation of cellular avidity, synapse formation, T cell motility, and functional capacities under repetitive target challenge and in sustained tumor control. Results As an example of clinical relevance, we derived a panel of anti-Her2 CARs covering a 4-log affinity range, all expected to target the same Her2 epitope. The same scFv mutations increased both antigen-specific affinity, cellular avidity, and antigen-independent “tonic” signaling; above a minimum threshold, raise in affinity translated into functional avidity in a non-linear fashion. In this case, replacement by amino acids of higher hydrophobicity within the scFv coincidentally augmented affinity, non-specific binding, spontaneous CAR clustering, and tonic signaling, all together relating to T cell functionality in an integrated fashion. Conclusions Data emphasize that tonic signaling is not always due to the positive charge but can be driven by hydrophobic interactions of the scFv. CAR binding affinity above the threshold and tonic signaling are required for sustained T cell functionality in antigen rechallenge and long-term tumor control.


Figure 2 DTriTE-induced T cell-Mediated cytotoxicity against glioblastoma multiforme cells. Cell viability of (A) U87vIII, (B) U251, (C) DK, (D) A172, (E) U373, and (F) LN18 were monitored using xCELLigence real-time cell analysis, followed by addition of DTriTE-containing supernatants (10 ng/mL) and primary human T cells (effector to target cell ratio of 10:1). Viability was normalized at the time of antibody addition to assess relative viability. The microscopic images of U87vII and U251 cells show the morphological changes in target cells at 0 and 24 hours post T-cell addition, with cell clusters indicating areas of T cell-induced cytotoxicity. DBTE, DNA-encoded bispecific T-cell engagers; DTriTE, DNA-encoded tri-specific T-cell engagers.
Figure 6 RNA sequencing analysis of T-cell activation by DT2035. (A) Heatmap displaying significant differential gene expression (p<0.05) in CD3+T cells isolated post tumor-killing assay (24 hours) with DT2035 against U87vIII/ U251 heterogeneous tumors. (B) Ingenuity pathway analysis identifying activated (red bars) and inhibited (blue bars) regulatory pathways in the T cells from the assay in (A).
Novel tri-specific T-cell engager targeting IL-13Rα2 and EGFRvIII provides long-term survival in heterogeneous GBM challenge and promotes antitumor cytotoxicity with patient immune cells

December 2024

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9 Reads

Background Glioblastoma multiforme (GBM) is known for its high antigenic heterogeneity, which undermines the effectiveness of monospecific immunotherapies. Multivalent immunotherapeutic strategies that target multiple tumor antigens simultaneously could enhance clinical outcomes by preventing antigen-driven tumor escape mechanisms. Methods We describe novel trivalent antibodies, DNA-encoded tri-specific T-cell engagers (DTriTEs), targeting two GBM antigens, epidermal growth factor receptor variant III (EGFRvIII) and IL-13Rα2, and engaging T cells through CD3. We engineered three DTriTE constructs, each with a unique arrangement of the antigen-binding fragments within a single-chain sequence. We assessed the binding efficiency and cytotoxic activity of these DTriTEs in vitro on target cells expressing relevant antigens. In vivo efficacy was tested in immunocompromised mice, including a longitudinal expression study post-administration and a survival analysis in an NOD scid gamma (NSG)-K mouse model under a heterogeneous tumor burden. RNA sequencing of DTriTE-activated T cells was employed to identify the molecular pathways influenced by the treatment. The antitumor cytotoxicity of patient-derived immune cells was evaluated following stimulation by DTriTE to assess its potential effectiveness in a clinical setting. Results All DTriTE constructs demonstrated strong binding to EGFRvIII and IL-13Rα2-expressing cells, induced significant T cell-mediated cytotoxicity, and enhanced cytokine production (interferon-γ, tumor necrosis factor (TNF)-α, and interleukin(IL)-2). The lead construct, DT2035, sustained expression for over 105 days in vivo and exhibited elimination of tumor burden in a heterogeneous intracranial GBM model, outperforming monospecific antibody controls. In extended survival studies using the NSG-K model, DT2035 achieved a 67% survival rate over 120 days. RNA sequencing of DTriTE-activated T cells showed that DT2035 enhances genes linked to cytotoxicity, proliferation, and immunomodulation, reflecting potent immune activation. Finally, DT2035 effectively induced target-specific cytotoxicity in post-treatment peripheral blood mononuclear cells from patients with GBM, highlighting its potential for clinical effectiveness. Conclusions DTriTEs exhibit potent anti-tumor effects and durable in vivo activity, offering promising therapeutic potential against GBM. These findings support further development of such multivalent therapeutic strategies to improve treatment outcomes in GBM and potentially other antigenically heterogeneous tumors. The opportunity to advance such important therapies either through biologic delivery or direct in vivo nucleic acid production is compelling.


Treatment-related adverse events
Camrelizumab plus carboplatin and pemetrexed as first-line therapy for advanced non-squamous non-small-cell lung cancer: 5-year outcomes of the CameL randomized phase 3 study

November 2024

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7 Reads

Background CameL phase 3 study demonstrated the superiority of camrelizumab plus chemotherapy over chemotherapy alone for progression-free survival in patients with previously untreated advanced non-squamous non-small-cell lung cancer (NSCLC) without EGFR / ALK alterations. Here, we present the 5-year outcomes. Methods Patients were randomized (1:1) and received 4–6 cycles of camrelizumab plus carboplatin and pemetrexed (n=205) or carboplatin and pemetrexed (n=207) every 3 weeks, followed by maintenance camrelizumab plus pemetrexed or pemetrexed only. Crossover from chemotherapy group to camrelizumab monotherapy was permitted after disease progression. Results Median time from randomization to data cut-off was 65.2 months (range, 59.7–72.2). HR for overall survival (OS) was 0.74 (95% CI 0.58 to 0.93; one-sided p=0.0043), and was 0.62 (95% CI 0.49 to 0.79; one-sided p<0.0001) after adjustment for crossover. Five-year OS rates were 31.2% (95% CI 24.7% to 37.9%) with camrelizumab plus chemotherapy versus 19.3% (95% CI 13.9% to 25.3%) with chemotherapy alone. Among the 33 patients who completed 2 years of camrelizumab, 5-year OS rate was 84.3% (95% CI 66.4% to 93.2%), and 5-year duration of response rate was 46.5% (95% CI 24.9% to 65.6%) in the 32 responders. No new safety signals were noted. Conclusions Camrelizumab plus carboplatin and pemetrexed as first-line therapy continued to demonstrate long-term OS benefit over carboplatin and pemetrexed, with manageable toxicity. Patients who completed 2 years of camrelizumab had enduring response and impressive OS. Current 5-year updated analysis further supports camrelizumab plus carboplatin and pemetrexed as a standard-of-care for previously untreated advanced non-squamous NSCLC without EGFR / ALK alterations. Trial registration number NCT03134872 .


Figure 4 B cell c-Maf deficiency inhibits B16.F10 melanoma tumor progression. (A) Percentages of CD19 + B cells within CD45 + leukocytes in TDLN of tumor-bearing mice. (B) Percentages of IL-10-producing B cells in TDLN of tumor-bearing mice compared with that in NDLN. (C) Tumor progression in control and c-Maf KO mice (n=9-10). (D) viSNE analysis of CyTOF immunophenotyping of TDLN from tumor-bearing control and c-Maf KO mice, all samples combined (n=10, left). FlowSOM clustering into 20 final immune cell types showing as a normalized expression heatmap (right). (E) viSNE analysis of CyTOF immunophenotyping in TDLN of control and c-Maf KO mice (n=5). Percentages of IL-10-producing B cells, TNF-α + CD4 + and CD8 + T cells were summarized. (F) T cell proliferation of OT-II CD4 + T cells after co-culture with isolated B cells (G) IFN-γ production by anti-CD3 mAb-activated CD4 + and CD8 + T cells after co-culture with IL-10 − and IL-10 + B cells. **p<0.01; ****p<0.0001; ns, not significant. CyTOF, mass cytometry; IL-10, interleukin 10; KO, knockout; NDLN, non-draining lymph nodes; TDLN, tumor-draining lymph nodes. on November 28, 2024 by guest. Protected by copyright.
B cell c-Maf signaling promotes tumor progression in animal models of pancreatic cancer and melanoma

November 2024

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9 Reads

Background The role of B cells in antitumor immunity remains controversial, with studies suggesting the protumor and antitumor activity. This controversy may be due to the heterogeneity in B cell populations, as the balance among the subtypes may impact tumor progression. The immunosuppressive regulatory B cells (Breg) release interleukin 10 (IL-10) but only represent a minor population. Additionally, tumor-specific antibodies (Abs) also exhibit antitumor and protumor functions dependent on the Ab isotype. Transcription factor c-Maf has been suggested to contribute to the regulation of IL-10 in Breg, but the role of B cell c-Maf signaling in antitumor immunity and regulating Ab responses remains unknown. Methods Conditional B cell c-Maf knockout (KO) and control mice were used to establish a KPC pancreatic cancer model and B16.F10 melanoma model. Tumor progression was evaluated. B cell and T cell phenotypes were determined by flow cytometry, mass cytometry, and cytokine/chemokine profiling. Differentially expressed genes in B cells were examined by using RNA sequencing (RNA-seq). Peripheral blood samples were collected from healthy donors and patients with melanoma for B cell phenotyping. Results Compared with B cells from the spleen and lymph nodes (LN), B cells in the pancreas exhibited significantly less follicular phenotype and higher IL-10 production in naïve mice. c-Maf deficiency resulted in a significant reduction of CD9 ⁺ IL-10-producing Breg in the pancreas. Pancreatic ductal adenocarcinoma (PDAC) progression resulted in the accumulation of circulating B cells with the follicular phenotype and less IL-10 production in the pancreas. Notably, B cell c-Maf deficiency delayed PDAC tumor progression and resulted in proinflammatory B cells. Further, tumor volume reduction and increased effective T cells in the tumor-draining LN were observed in B cell c-Maf KO mice in the B16.F10 melanoma model. RNA-seq analysis of isolated B cells revealed that B cell c-Maf signaling modulates immunoglobulin-associated genes and tumor-specific Ab production. We furthermore demonstrated c-Maf-positive B cell subsets and an increase of IL-10-producing B cells after incubation with IL-4 and CD40L in the peripheral blood of patients with melanoma. Conclusion Our study highlights that B cell c-Maf signaling drives tumor progression through the modulation of Breg, inflammatory responses, and tumor-specific Ab responses.


Figure 1 Flowchart of patients
Baseline characteristics of patients
Efficacy endpoints
Sintilimab plus HPV vaccine for recurrent or metastatic cervical cancer

November 2024

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8 Reads

Purpose Recurrent or metastatic cervical cancer (r/m CC) presents limited treatment options for patients failed or progressed quickly following first-line therapy. This study investigated the potential of sintilimab with a prophylactic human papillomavirus (HPV) quadrivalent vaccine as a second-line treatment for r/m CC. Methods In this phase 2 clinical trial, patients with r/m CC previously unresponsive or intolerant to standard treatments for metastatic or recurrent lesions were enrolled. Participants received sintilimab (3 mg/kg for body weight <60 kg; 200 mg for ≥60 kg) every 3 weeks until 24 months or 35 cycles and 3 doses of the HPV quadrivalent vaccine (initial dose prior to sintilimab initiation, with subsequent doses at 2 and 6 months). The primary endpoint was the objective response rate (ORR). A Simon two-stage optimal design was used. Results From October 2019 to October 2022, 13 patients with r/m CC were enrolled. ORR achieved 53.8% (95% CI 25.1% to 80.8%), and the disease control rate was 76.9% (95% CI 46.2% to 95.0%). Median follow-up duration was 16.07 months (range: 3.64–48.2 months), and median progressive free survival was 7.16 months (95% CI 1.91 –not applicable (NA)). The median overall survival (OS) was not reached (95% CI 9.89 –NA). Hypothyroidism (15.6%) was the most common treatment-related adverse event (AE). No grade 3 or above AEs were observed. Conclusions This study suggests the combination of sintilimab plus prophylactic HPV vaccine offers a potentially promising therapeutic strategy for patients with r/m CC unresponsive or intolerant to standard therapies. Trial registration number NCT04096911 .


Figure 1 MARCHF3 affects the patient response to ICIs and is correlated with better outcomes. (A) Heatmap showing the differentially expressed genes between responders and non-responders to ICIs. (B) Volcano plot of the differentially expressed genes between ICI responders and non-responders. (C) GO analyses of the DEGs. (D) Scheme of the experimental procedure. (E) Tumor burdens in C57BL/6 mice subcutaneously injected with control vector or March3-overexpressing Hepa1-6 cells and treated with anti-PD-1 antibodies. (F) In vivo imaging systems were used to measure the fluorescence intensity in HCC tumors. (G) The mRNA expression of MARCHF3 in HCC tissues, presented as log(T/N) values. (H-I) Representative image of IHC staining showing MARCHF3 expression in HCC tumors and matched adjacent tissues. Scale bar, 40µm. (J) Protein imprinting analysis revealed the expression of the MARCHF3 protein in HCC tumors and matched adjacent tissues. (K) Kaplan-Meier OS and (L) DFS curves for patients with HCC in the high and low MARCHF3 expression tissue microarray cohorts. (M) Univariate and (N) multivariate analyses of factors associated with OS and DFS. Scale bar: 100 mm (top panel). **p<0.01, Student's ttest. DFS, disease-free survival; GO, Gene Ontology; HCC, hepatocellular carcinoma; ICI, immune checkpoint inhibitor; IHC, immunohistochemistry; MARCHF3, membrane-associated ring-CH-type finger 3; MHC, major histocompatibility complex; mRNA, messenger RNA; OS, overall survival; PD-1, programmed cell death protein-1. on November 29, 2024 by guest. Protected by copyright. http://jitc.bmj.com/
Figure 5 MARCHF3 interacted with PARP1. (A) Silver staining of eluates resolved by SDS-PAGE. (B-C) Coimmunoprecipitation assays showing the interaction of MARCHF3 with PARP1 in (B) Huh7 and (C) PLC cells. (D) Immunofluorescence assays showing the interaction and nuclear colocalization of MARCHF3 with PARP1 in hepatocellular carcinoma cells. Scale bar, 20 µm. (E-F) The interaction between MARCHF3 and PARP1 was assayed via GST precipitation, and purified GST was used as the control. (G) Domain structures of MARCHF3 and the MARCHF3 deletion mutants used in the study. (H) The domain of MARCHF3 that interacts with PARP1. (I) Domain structures of the PARP1 and deletion mutants used in the study. (J) The domain of PARP1 that interacts with MARCHF3.GST, Glutathione-S-transferase; IP, immunoprecipitation; MARCHF3, membrane-associated ring-CH-type finger 3; PARP1, Poly [ADP-ribose] polymerase 1; SDS-PAGE, Sodium dodecyl sulfate polyacrylamide gel electrophoresis. on November 29, 2024 by guest. Protected by copyright.
Figure 7 Blockade of PARP1 potentiated the efficacy of anti-PD-1 therapy in HCC. (A) Schematic showing the schedule of treatment with the anti-PD-1 antibody and the PARP1 inhibitor olaparib in the HCC model C57BL/6 mice. (B) Subcutaneous tumor xenografts in mice. (C) In vivo imaging systems were used to measure the fluorescence intensity in HCC tumors. (D) Representative images of multiplex immunohistochemistry staining for CD11c and CD8 in orthotopic HCC tissues. Scale bars, 30 µm. (E-F) Flow cytometry analysis of the proportions of infiltrating (E) CD86 + CD11c + DCs and (F) INF-γ + CD8 + T cells in xenograft HCC tissues from each group. (G) ATF4 induced MARCHF3 expression on ER stress. Increased MARCHF3 expression inhibits PARP1-mediated DNA repair that induces DNA damage and cytoplasmic dsDNA release, leading to DC cGAS/STING-dependent activation of type I IFN signaling and thereby reprogramming of the tumor microenvironment. Targeting the DNA damage pathway, such as with the PARP1 inhibitor olaparib. Immune checkpoint inhibitor efficacy against HCC is potentiated. DC, dendritic cell; dsDNA, double-strand DNA; ER, endoplasmic reticulum; GZMB, Granzyme B; HCC, hepatocellular carcinoma; IFN, interferon; MARCHF3, membrane-associated ring-CH-type finger 3; PARP1, Poly [ADP-ribose] polymerase 1; PARPi, PARP inhibitors; PD-1, programmed cell death protein-1. on November 29, 2024 by guest. Protected by copyright.
Degradation of PARP1 by MARCHF3 in tumor cells triggers cCAS-STING activation in dendritic cells to regulate antitumor immunity in hepatocellular carcinoma

November 2024

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2 Reads

Background Resistance to immune checkpoint inhibitors (ICIs) significantly limits the efficacy of immunotherapy in patients with hepatocellular carcinoma (HCC). However, the mechanisms underlying immunotherapy resistance remain poorly understood. Our aim was to clarify the role of membrane-associated ring-CH-type finger 3 (MARCHF3) in HCC within the framework of anti-programmed cell death protein-1 (PD-1) therapy. Methods MARCHF3 was identified in the transcriptomic profiles of HCC tumors exhibiting different responses to ICIs. In humans, the correlation between MARCHF3 expression and the tumor microenvironment (TME) was assessed via multiplex immunohistochemistry. In addition, MARCHF3 expression in tumor cells and immune cell infiltration were assessed by flow cytometry. Results MARCHF3 was significantly upregulated in tumors from patients who responded to ICIs. Increased MARCHF3 expression in HCC cells promoted dendritic cell (DC) maturation and stimulated CD8 ⁺ T-cell activation, thereby augmenting tumor control. Mechanistically, we identified MARCHF3 as a pivotal regulator of the DNA damage response. It directly interacted with Poly(ADP-Ribose) Polymerase 1 (PARP1) via K48-linked ubiquitination, leading to PARP1 degradation. This process promoted the release of double-strand DNA and activated cCAS-STING in DCs, thereby initiating DC-mediated antigen cross-presentation and CD8 ⁺ T-cell activation. Additionally, ATF4 transcriptionally regulated MARCHF3 expression. Notably, the PARP1 inhibitor olaparib augmented the efficacy of anti-PD-1 immunotherapy in both subcutaneous and orthotopic HCC mouse models. Conclusions MARCHF3 has emerged as a pivotal regulator of the immune landscape in the HCC TME and is a potent predictive biomarker for HCC. Combining interventions targeting the DNA damage response with ICIs is a promising treatment strategy for HCC.


Figure 1 Single-cell multidimensional dissection of OCCC. (A) Study design: five fresh OCCC samples were collected during surgery and underwent single-cell RNA sequencing (scRNA-seq) and single-cell T-cell receptor sequencing (scTCR-seq). Major cell compositions and functional subsets were identified and characterized through integrated analysis. Eight formalinfixed paraffin-embedded (FFPE) samples with follow-up data were retrospectively collected for scFFPE-seq. Additionally, 31 OCCC samples were used for IHC staining. Analysis on spatial transcriptomics and bulk RNA sequencing datasets were also conducted. (B) Integrative UMAP plot of 49 228 single cell transcriptomics from five harmonized OCCC fresh tumors visualizing main cell compartments (left) and refined cell subsets (right) in OCCC. (C) Radar plot comparing cell proportions in newly diagnosed (blue, n=2) and recurrent OCCC (red, n=3). (D) Flowchart of sample collection for scFFPE-seq and UMAP plot. (E) UMAP plots and bar plot showing the difference in cell composition between ARID1A-mutant (MUT, n=2) and wildtype (WT, n=2) treatment-naive newly diagnosed OCCCs. (F) Bar plot showing the distribution of immune ecotypes and corresponding clinical relevance of ARID1A WT (n=17) and MUT tumors (n=14) in GSE226870 dataset suggested by Ecotyper. FFPE, formalin-fixed paraffin-embedded; IHC, immunohistochemistry; OCCC, ovarian clear cell carcinoma; UMAP, Uniform Manifold Approximation and Projection. on November 29, 2024 by guest. Protected by copyright.
Figure 2 Characteristics and MPs of epithelial cells in OCCC. (A) Heatmap displaying AUCell score of Hallmark pathways across different pathologies (OCCC scRNA-seq cohort: n=5, this article; HGSC scRNA-seq cohort, n=7, GSE184880; normal ovary scRNA-seq cohort, n=5, GSE184880). (B) Heatmap and feature plot showing 10 MPs of OCCC epithelial cells extracted by NMF method. (C) Pathway activity (left), evolutionary trajectory (middle) and trajectory-related gene expression (left) of ARID1A MUT OCCC epithelial cells (two patients) compared with ARID1A WT cells (two patients) in FFPE cohort. (D) UMAP plot showing five subclusters of 11 448 epithelial cells in OCCC scRNA-seq cohort, and differential distribution in newly diagnosed (n=2) and recurrent OCCC (n=3). (E) Pseudo-time analysis by monocle2 and cell stemness scoring by CytoTRACE in OCCC scRNA-seq cohort uncovered an epithelial cell evolution trajectory from EC1 to EC2. Heatmap showing transcriptional factor and metabolic pathway activity between the two subpopulations. (F) Left: differential gene expression between EC2 and EC1. Right: upregulation of CD36 and CD47 while downregulation of CDH1 and HLA-A throughout the epithelial evolutionary trajectory. (G) Representative IHC staining of CD36 and CD47 in OCCC tumors (left: whole slide, right: high power field, 400×), and correlations (Pearson χ 2 analysis) between CD36 and CD47 expression levels in OCCC IHC cohort (n=31) and presampling NACT. (H) urvival analysis of CD36 and CD47 expression level in OCCC IHC cohort (n=31, outcome: PFS, logrank test). FFPE, formalin-fixed paraffin-embedded; HGSC, high-grade serous cancer; IHC, immunohistochemistry; NACT, neoadjuvant chemotherapy; NMF, non-negative matrix factorization; OCCC, ovarian clear cell carcinoma; UMAP, Uniform Manifold Approximation and Projection. on November 29, 2024 by guest. Protected by copyright.
Figure 3 T cell phenotypes and neoantigen-reactive subsets in OCCC. (A) UMAP plot of 12 215 T cells (6554 CD8 + T cells, 4746 CD4 + T cells, and 915 NK cells) in OCCC scRNA-seq cohort (n=5). (B) Subclustering of CD8 + T cells in OCCC scRNA-seq cohort, and differential distribution in newly diagnosed (n=2) and recurrent OCCC (n=3). (C) Comparisons of the proportion of marker gene positive cells of CD8 + T cluster between OCCC and normal ovary (OCCC scRNA-seq cohort: n=5, this article; normal ovary scRNA-seq cohort, n=5, GSE184880). (D) Three of the five fresh OCCC tumors (OCCC-3, OCCC-4, OCCC-5) underwent scTCR-seq. Dimplot featuring TCR clonotypes (determined by scRepertoire R package) of CD8 + T subclusters. Categories of TCR clonotype were determined by the frequency of a certain TCR clonotype among whole T cell population. Hyperexpanded: beyond 10%; large: 1%-10%; medium: 0.1%-1%; small: 0.001%-0.01%. (E) FeaturePlot marking CTLA4, CXCL13, and PDCD1 expression in CD8 + T cells. (F) Left: comparison of CD8_C1_CXCL13_CTLA4 signature UCell score of T cells between ARID1A MUT and WT OCCCs (Wilcoxon test). Right: survival curves showing correlation between CD8_ C1_CXCL13_CTLA4 signature score and overall survival of pan-cancer patients receiving immunotherapy (log-rank test). (G) Ligand-receptor activity inferred by Nichenet predicted potential regulatory networks of CD8 + T cells. (H) Subclustering of CD4 + T cells in OCCC scRNA-seq cohort (n=5). (I) Comparisons of essential gene expression frequency in CD4 + T cells between OCCC and normal ovary (OCCC scRNA-seq cohort: n=5, this article; normal ovary scRNA-seq cohort, n=5, GSE184880). (J) Hallmark pathway activity of CD4 + T cells subclusters in OCCC scRNA-seq cohort (n=5). MUT, mutant; OCCC, ovarian clear cell carcinoma; UMAP, Uniform Manifold Approximation and Projection; WT, wildtype. on November 29, 2024 by guest. Protected by copyright. http://jitc.bmj.com/
Figure 5 VEGF inhibition reinvigorates T cell function. (A) Spatial characterization of OCCC sample. Feature plots and violin plots showing distribution of lineage signature score (calculated by UCell method) in different niches (epithelial-rich, stromalrich, endothelial-rich and immune-rich). (B) UMAP plot and bar plot showing cell composition of recurrent OCCC with or without adjuvant bevacizumab treatment. (C) Dot plot of T cell phenotype score between patients receiving TC (Taxol plus carboplatin) chemotherapy and TC+BEVA (bevacizumab) adjuvant regimen. (D) Barplot of relative cell-cell communication network activity between TC and TC+BEVA subgroup patients with OCCC. OCCC, ovarian clear cell carcinoma; UMAP, Uniform Manifold Approximation and Projection. on November 29, 2024 by guest. Protected by copyright.
Figure 6 VEGFi plus anti-PD-1 exerts clinical benefit in refractory OCCC. (A) Line chart showing dynamic changes of cancer antigens of persistent OCCC case 1 and representative CT images who received VEGF inhibition plus anti-PD1 during the course of disease. (B) Line chart showing dynamic changes of cancer antigens of recurrent OCCC case 2 and representative PET-CT and CT images who received VEGF inhibition plus anti-PD1 during the course of disease. (C) OCCC case 3: metastatic patients with OCCC received anti-PD1/CTLA4 treatment. (D) Graphical abstract. OCCC, ovarian clear cell carcinoma. on November 29, 2024 by guest. Protected by copyright.
Genetic and therapeutic heterogeneity shape the baseline and longitudinal immune ecosystem of ovarian clear cell carcinoma

November 2024

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18 Reads

Background Ovarian clear cell carcinoma (OCCC) is a rare and chemo-resistant subtype of ovarian cancer. While immunotherapy has demonstrated effectiveness in some OCCC cases, the mechanisms for heterogeneous immunoreactivity and potential combinatory strategies remain unclear. Methods Tumor samples from 13 patients with OCCC underwent single-cell mRNA-seq and TCR-seq to generate 1 40 683 cells transcriptome, while additionally 31 formalin-fixed paraffin-embedded samples were used for immunohistochemistry. Spatial transcriptomics of two OCCC samples and bulk RNA-seq of 58 patients were incorporated for spatial and interpatient level explorations. Serum tumor markers and radiologic images of three patients with OCCC who received combinatory VEGF and PD-1 inhibition were retrospectively analyzed. Results OCCC exhibited a dynamic immune architecture shaped by genetic and therapeutic pressure. ARID1A mutation linked to baseline immune activation, correlated with an enrichment of neoantigen-reactive CXCL13 ⁺ CTLA4 ⁺ CD8 ⁺ T cells (p<0.001) and enhanced FASLG–FAS interactions. Recurrent OCCC was fibrotic, angiogenic, and immunosuppressive, exhibiting metabolic reprogramming towards activated activity in fatty acid metabolism. High CD36 (log-rank p=0.012, HR: 4.515) and CD47 expression (log-rank p=0.037, HR: 3.246) indicated worse progression-free survival. Treatment with bevacizumab increased intratumoral T cell infiltration and activated T cell interferon-γ signaling. Retrospective analysis of clinical cases revealed that combination therapy with anti-VEGF (vascular endothelial growth factor) and anti-PD-1 agents exerted clinical benefits in patients with OCCC with persistent, recurrent, and metastatic disease. Conclusions ARID1A mutation correlated with OCCC baseline immune activation. Stromal reconstruction and tumor metabolic reprogramming functioned as key processes of OCCC dynamic progression. VEGF inhibition remodeled OCCC stroma, restored T cell function and potentiated immunotherapy. CD36 and CD47 might be potential therapeutic targets for recurrent OCCC.


Figure 2 Diverse phenotypes of immune cell composition and function between first and second CAR-T expansion peaks: (A) t-SNE visualization, FlowSOM distribution of clusters and projection of selected markers in CD45+lymphocytes; (B) divergence region analysis of cluster distributions between the first and second CAR-T expansion peaks (per cent reflects the degree of divergence; 0-5 and 95-100 are statistically significant); (C, F) t-SNE visualization and FlowSOM cluster distributions and comparisons between first and second peaks; (D, G) heatmap showing hierarchical clustering and expression (normalized across markers) in FlowSOM clusters; (E, H) barplots showing the proportions of FlowSOM clusters at two expansion peaks; (C-E) CAR+CD8+T cells; (F-H) endogenous CD8+T cells. CAR, chimeric antigen receptor; NK, natural killer; t-SNE, t-distributed stochastic neighbor embedding; Teff, T effector cells; TCM, T central memory cells. on November 29, 2024 by guest. Protected by copyright.
Uncommon biphasic CAR-T expansion induces hemophagocytic lymphohistiocytosis-like syndrome and fatal multiple infections following BCMA CAR-T cell therapy: a case report

November 2024

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10 Reads

B-cell maturation antigen(BCMA)-directed chimeric antigen receptor (CAR)-T-cell therapy has significantly improved the treatment of relapsed or refractory multiple myeloma (MM). Nevertheless, the uncommon phenomenon of biphasic CAR-T cell expansion in vivo and its related severe toxicities have not been methodically described and studied. Herein, we report a case of patients with MM who experienced two CAR-T cell expansion peaks and subsequently developed multiple severe toxicities following BCMA CAR-T cell infusion. The first expansion peak occurred on Day 7, accompanied by grade 3 cytokine release syndrome. The second peak occurred on Day 28, associated with severe immune effector cell-associated hematotoxicity (ICAHT), immune effector-cell associated hemophagocytic lymphohistiocytosis-like syndrome (IEC-HS), and polymicrobial infections. Both ICAHT and IEC-HS were refractory to our standard treatments; however, human umbilical cord mesenchymal stem cell infusion exhibited some efficacy in improving cytopenia. Despite the administration of a broad-spectrum anti-infective regimen, cytomegalovirus viremia remained uncontrollable, resulting in the development of central nervous system infection, neurological symptoms, and ultimately death. Additionally, we also employed high-dimensional 33-color spectral flow cytometry to describe the dynamic changes in immune cell components and functions between the two expansion peaks. Collectively, this case provides novel insights into the biphasic CAR-T expansion and related immune effector cell-associated toxicities.


Figure 1 Neoadjuvant immunotherapy in sarcomas. (A) The phase II clinical trial conducted by Roland et al, in which 17 patients with dedifferentiated liposarcoma (DDLPS) and 10 patients with undifferentiated pleomorphic sarcoma (UPS) were categorized by subtypes and randomly assigned to nivolumab group or nivolumab+ipilimumab group (UPS patients received radiation therapy concurrently). The primary endpoint is per cent hyalinization at surgery, observed in 17.6% of DDLPS patients (3/17) and 90% in UPS patients (9/10). (B) The trial timeline of SU2C-SARC032. Patients were randomly assigned to SOC arm (neoadjuvant RT) or EXP arm (neoadjuvant RT+pembrolizumab and adjuvant pembrolizumab). Pembrolizumab was given 200 mg once every 3 weeks for 3 doses during neoadjuvant therapy and up to 14 cycles during adjuvant therapy. (C) The changes in tumor microenvironment of sarcomas after neoadjuvant therapy, including increased CD4+T cells and M2 macrophage infiltration, increased B cells infiltration with suggestion of tertiary lymphatic structures, and enhanced antigen presentation, were associated with survival or treatment response. (D) The potential mechanism of hyper progressive diseases after adjuvant immunotherapy was reported by Li et al. T cell-derived IFNγ could stimulate the secretion of FGF2 in an autocrine way in tumor cells, followed by the phosphorylation of PKM2 that inhibited glycolysis in tumor so that NAD+levels was decreased, causing the acetylation of β-catenin and the activation of β-catenin signal pathway to promote tumor cells proliferation and stemness. Created with BioRender.com. EXP, experimental; FGF2, fibroblast growth factor 2; FGFR, fibroblast growth factor receptor; NAD, nicotinamide adenine dinucleotide; PKM2, pyruvate kinase isozyme type M2; RT, radiation therapy; SOC, standard of care. on November 24, 2024 by guest. Protected by copyright.
Neoadjuvant immunotherapy in the evolving landscape of sarcoma treatment

Soft tissue sarcoma is characterized by its rarity and complexity, making it more difficult to conduct large clinical trials compared with other solid tumors. Also known as ‘cold tumors,’ sarcomas, especially advanced sarcomas, have poor responses to immunotherapy. Based on that, the results of two groundbreaking phase 2 clinical trials about neoadjuvant immunotherapy in patients with liposarcoma or undifferentiated pleomorphic sarcoma are encouraging. In this paper, we discuss the results of these clinical trials and the challenges we are facing to conduct neoadjuvant immunotherapy in sarcomas and call for further research to promote the development of it.


Figure 1 Single-cell profiles of cells derived from patients with HCC responding and nonresponding to ICI-based treatment. (A) Schematic diagram of the research design, visualized by BioRender (https://biorender.com/). 14 ascites samples from 7 patients with HCC were collected before and after anti-PD-1 combined with anti-VEGF treatment, followed by scRNA and scTCR sequencing. (B) Uniform Manifold Approximation and Projection (UMAP) plot depicting the distribution of major cell subtypes. (C) Heatmap showing the expression patterns of canonical marker genes identifying distinct cell subgroups. (D) UMAP embeddings colored by cell types, showing the distribution across four sample groups (upper panels), with kernel density estimation (lower panels). (E) Heatmap illustrating the treatment response status of individual cell clusters before and after treatment, based on R o/e estimates generated using the Startrac R package (V.0.1.0). The bar plot on the right showing the cell subgroups composition of sample types. (F) UMAP plot visualizing the number of DEGs between responders and nonresponders across various cell types. (G) Volcano plots illustrating the DEGs between responders and non-responders across various immune cells. (H) Estimates for the dependence of all-time risk of death on the responder-likeness score in the TCGA LIHC cohort across various immune cells. The solid curve was generated using the Cox proportional hazards model, while the dotted curves represent the 95% CI of the log HR. (I) Kaplan-Meier curves showing PFS probability differences in patients with TCGA LIHC with high and low responder-likeness scores across various immune cells. on November 24, 2024 by guest. Protected by copyright.
Figure 2 Characteristics of the T cell lineage in patients with HCC responding to ICI-based treatment. (A) Box plots illustrating the disparity in proportions of CD8 + T cells (left) and CD4 + T cells (right) among immune cell populations in responders and nonresponders. Significant p values were determined using the Wilcoxon rank-sum test. (B) UMAP plot showing the distribution of T cell subset clusters. (C) UMAP embeddings displaying the number of DEGs between responders and non-responders in T cell subgroups. (D) UMAP plots visualizing T cells annotated with TCRαβ clone sizes, stratified into responders (top) and nonresponders (bottom). (E) Bar plot exhibiting enriched BP terms associated with treatment response samples derived CD8 + T cells via over-representation analysis. (F) Scatter plots of CD8A + and IFIT3 + expression levels in each of 28 911 CD8 + T cells were compared between responders (left) and non-responders (right). Percentages represent the proportion of IFIT3 + and CD8A + cells within the CD8 + T cell population. The inserts present contingency tables derived from dichotomized expression data. (G) Pie charts illustrating the proportion of IFIT3 + T cells in the total T cell population across various clone sizes in responders and nonresponders. (H) The inferred developmental trajectories of CD8 + CTLs, illustrated and colored by pseudotime, response status, cytotoxicity, CytoTRACE score, ISG score, and neoantigen-reactivity. (I) Loess smoothed curves depicting the expression changes of representative genes along the pseudotime. on November 24, 2024 by guest. Protected by copyright.
Figure 4 Transcriptomic features of myeloid cells relevant to ICI-based treatment response. (A) UMAP plot showing the distribution of myeloid cell clusters. (B) UMAP embeddings illustrating the number of DEGs between responders and nonresponders in myeloid cell subgroups. (C) Bar plot exhibiting enriched BP terms associated with treatment response samples derived macrophage via over-representation analysis. (D) Scatter plot illustrating the fold change in gene expression (log2) between M1-and M2-like macrophages (y-axis) plotted against the respective values for responders and non-responders (xaxis). Each data point represents a gene and is color-coded based on its fold change in expression between the ISG high and ISG low groups. (E) UMAP plots showing the M1 likeness score across various myeloid cell subtypes in responders and nonresponders (left panels), along with the ISG score in responders and non-responders (right panels). (F) Chord diagram depicting cell communication from macrophage cell subsets to T cell subtypes in the CXCL signaling pathway, as inferred by CellChat. (G) Heatmaps displaying the average expression levels of ligand-receptor genes. (H) Box plots showing the average expression level of ligand-receptor genes in (G) various treatment responses in GO30140 and IMbrave150 cohorts. Significant p values were determined using the Wilcoxon rank-sum test. on November 24, 2024 by guest. Protected by copyright.
Figure 6 Independent validations confirm the convergence on IFN signaling and its prognostic significance in HCC. (A) Box plots illustrating CD8 + T cell fraction (left panel) and M1 to M2 macrophage ratio (right panel) in ISG high and ISG low samples from the TCGA LIHC cohort. (B) Box plots illustrating the ISG scores across different immune cell types, between ISG high and ISG low samples from the TCGA LIHC cohort. (C) Box plots depicting cytotoxicity levels of CD8 + T and NK cells, B cell activation, as well as antigen processing and presentation ability in DC, comparing ISG high and ISG low samples from the TCGA LIHC cohort. P values in (A-C) were determined by the Wilcoxon test. (D) Forest plot visualizing the results of univariate Cox proportional hazards model assessing the impact of ISG score on PFS across major cancer types in TCGA and OS in ICGC LIRI-JP cohort. (E) Box plots illustrating the distribution of ISG scores across different treatment responses in the GO30140 and IMbrave150 cohorts, grouped by various deconvoluted immune expression profiles. P values were calculated by the ANOVA test. (F) KaplanMeier curves illustrating the difference in PFS probability between ISG high and ISG low groups in GO30140 and IMbrave150 cohorts. on November 24, 2024 by guest. Protected by copyright.
Integrated single-cell transcriptome and TCR profiles of hepatocellular carcinoma highlight the convergence on interferon signaling during immunotherapy

November 2024

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14 Reads

Background Despite the success of immune checkpoint inhibitor (ICI)-based combination therapies in hepatocellular carcinoma (HCC), its effectiveness remains confined to a subset of patients. The development of reliable, predictive markers is important for accurate patient stratification and further mechanistic understanding of therapy response. Methods We comprehensively analyzed paired single-cell RNA transcriptome and T-cell repertoire profiles from 14 HCC ascites samples, collected from 7 patients before and after treatment with the combination of sintilimab (anti-PD-1) and bevacizumab (anti-VEGF). Results We identify a widespread convergence on interferon (IFN) signaling across various immune cell lineages in treatment-responsive patients with HCC, indicating a common transcriptional state transition in the immune microenvironment linked to immunotherapy response in HCC. Strong IFN signaling marks CD8 ⁺ T cells with larger clonal expansion and enhanced cytotoxicity, macrophages toward M1-like polarization and strong T-cell recruitment ability, dendritic cells with increased antigen presentation capacity, as well as highly cytotoxic natural killer cells and activated B cells. By translating our finding to cohorts of patients with HCC, we demonstrate the specificity of IFN-signaling in the prognosis of patients with HCC and its ability to predict immunotherapy response. Conclusions This study provides a unique single-cell resource with clonal and longitudinal resolution during ICI therapy and reveals IFN signaling as a biomarker of immunotherapy response in HCC, suggesting a beneficial effect by combining IFN inducers with ICIs for patients with HCC.


Figure 1 Identification and validation of propafenone to synergize with ferroptosis inducers in melanoma. (A) Schematic of the identification of clinically applicable cardiovascular drugs that sensitize melanoma to RSL3. (B) Scatter plot shows the viability ratio of A375 and SK-MEL-28 cells treated with single-drug versus dual-drug treatment. The red plot shows propafenone (PPF). (C) Percentage of cell viability rate was presented in a series of 6×6 screening experiments in A375 and SK-MEL-28 cells. Synergy was evaluated by the Chou-Talalay Combination Index (CI) for PPF and RSL3 across the indicated cell lines. The x-axis of CI plots represents the fraction affected. (D-E) Cell morphological features (D) and cell viability (E) of A375 and SK-MEL-28 cells at different time points of treatment with DMSO, PPF, RSL3, or the combination of PPF and RSL3 (COM). PPF, 5 µM; RSL3, 2.5 µM. Images were taken at 200× magnification. (F) Clonogenic assay of A375 and SK-MEL-28 cells treated with PPF, RSL3 or a combination as indicated. (G) GPX4 protein levels were quantified by western blotting in control (sgCtrl) and GPX4 deficient (sgGPX4) cells. (H) Cell viability of sgCtrl or sgGPX4 cells treated with different concentrations of PPF for 12 hours. (I) Heatmap shows the cell viability of WM35, A375, A2058, SK-MEL-2, SK-MEL-5 and SK-MEL-28 cell lines treated with PPF, various ferroptosis inducers IKE, RSL3, ML210, ML162, and FINO2, or a combination as indicated. COM, combination; CCK-8, Cell Counting Kit-8; DMSO, dimethyl sulfoxide; FDA, Food and Drug Administration; GPX4, glutathione peroxidase 4. on November 24, 2024 by guest. Protected by copyright.
Figure 3 Propafenone combination with RSL3 collaboratively triggers mitochondrial-associated ferroptosis. (A) Transmission electron microscopy images of A375 cells after the indicated treatment for 3 hours. PPF, 5 µM; RSL3, 2.5 µM. Scale bar, upper, 2 µm; lower, 500 nm. (B-C) Mitochondrial membrane potential (B) or mitochondrial ROS (C) was determined in A375 and SK-MEL-28 cells after indicated treatment for 3 hours by flow cytometry using JC-1 staining or Mito-SOX red fluorescence probe, respectively. (D) Dose response of RSL3-induced death of PPF-treated A375 and SK-MEL-28 cells in the presence of TEMPO (20 µM) or Mito-TEMPO (10 µM) for 6 hours. (E-F) Mito-lipid peroxidation (E) and PTGS2 mRNA (F) were measured in A375 and SK-MEL-28 cells after the indicated treatment for 3 hours. PPF, 5 µM; RSL3, 2.5 µM. TEMPO, 20 µM; Mito-TEMPO, 10 µM. (G) Western blotting showing GPX4 protein levels in sgGPX4 A375 cells that overexpress the cytosolic or mitochondrial GPX4. (H) Cell viability measurement in sgGPX4 A375 cells that express the indicated GPX4 constructs treated with different doses of PPF for 6 hours. (I) Relative levels of overall cellular ferrous iron were assessed in A375 and SK-MEL-28 cells after the indicated treatment for 3 hours. (J) Mitochondrial ferrous iron levels were assessed by fluorescence microscopy using Mito-FerroGreen (green) in A375 cells after the indicated treatment for 1 hour. PPF, 5 µM; RSL3, 2.5 µM; DFO, 100 µM. Mitochondria stained with Mito-Tracker (red). Nuclei counterstained by Hoechst (blue). Scale bar, 100 µm. (K) Mitochondrial ferrous iron levels were assessed by flow cytometry using Mito-FerroGreen in A375 and SK-MEL-28 cells after the indicated treatment for 1 hour. Then the mean Mito-FerroGreen fluorescence intensity of each cell was quantified. P values were calculated using one-way analysis of variance analysis. ns, p>0.05; ***p<0.001. COM, combination; DFO, deferoxamine; DMSO, dimethyl sulfoxide; GPX4, glutathione peroxidase 4; mRNA, messenger RNA; PPF, propafenone; PTGS2, prostaglandin-endoperoxide synthase 2; ROS, reactive oxygen species; TEMPO, 2,2,6,6-tetramethylpiperidin-1-oxyl. on November 24, 2024 by guest. Protected by copyright.
Figure 4 Propafenone facilitates mitochondria-associated ferroptosis through synergistic upregulation of HMOX1 when combined with RSL3. (A) Volcano plots of RNA sequencing data showing the most differentially expressed genes in combination versus DMSO (left), combination versus PPF (median), and combination versus RSL3 (right). Significantly expressed genes above and below 1.5-fold are shown in red and blue, respectively; PPF, 5 µM; RSL3, 2.5 µM. (B) Venn diagram showing the overlap of common differentially expressed upregulated genes and mitochondrial iron metabolism-related genes identified from GeneCards. (C) Heatmap represents the normalized expression of five genes in each group. (D) Real-time PCR analysis of HMOX1 mRNA levels in A375 and SK-MEL-28 cells after the indicated treatment for 3 hours. (E-F) Western blotting showing HMOX1 protein levels in whole cell and mitochondrial fractions from A375 (E) and SK-MEL-28 cells (F) after indicated treatment for 3 hours. PPF, 5 µM; RSL3, 2.5 µM. (G) Gene Set Enrichment Analysis showing the enrichment of response to endoplasmic reticulum stress pathway (blue) and heme metabolism pathway (red) between co-treatment group and RSL3 group in RNA sequencing. (H) HMOX1 was positively correlated with WP_ferroptosis score using Spearman's correlation in cell lines from the Cancer Cell Line Encyclopedia database. (I) Relative cell viability of indicated cells treated with DMSO, 20 µM or 30 µM hemin in the presence of RSL3 for 6 hours. (J) Mitochondrial ferrous iron levels were assessed by fluorescence microscopy using MitoFerroGreen (green) in A375 cells after the indicated treatment for 1 hour. PPF, 5 µM; RSL3, 2.5 µM; ZnPP, 10 µM. Mitochondria stained with Mito-Tracker (red). Nuclei counterstained by Hoechst (blue). Scale bar, 100 µm. (K) Mitochondrial ferrous iron levels were assessed by flow cytometry using Mito-FerroGreen in A375 and SK-MEL-28 cells after the indicated treatment for 1 hour. (L-M) Mito-lipid peroxidation (L) and lipid peroxidation (M) were measured by flow cytometry in A375 and SK-MEL-28 cells after the indicated treatment for 3 hours. (N) Dose-response of RSL3-induced death of DMSO or PPF-treated A375 and SK-MEL-28 cells in the absence or presence of ZnPP for 6 hours. (O) Cell viability of shCtrl and shHMOX1 A375 and SK-MEL-28 cells following PPF and RSL3 co-treatment for 3 hours. P values were calculated using one-way ANOVA analysis in D,I, k, L and M or two-way ANOVA analysis in O. *p<0.05; ***p<0.001. ANOVA, analysis of variance; COM, combination; DMSO, dimethyl sulfoxide; HMOX1, heme oxygenase 1; PPF, propafenone; TPM, transcripts per million; ZnPP, zinc protoporphyrin IX. on November 24, 2024 by guest. Protected by copyright. http://jitc.bmj.com/
Figure 6 Enhanced efficacy of immunotherapy combined with propafenone in vivo. (A) Heatmap showing the WP_ferroptosis score and normalized ferroptosis-related pathway enrichment scores of each sample calculated by single-sample Gene Set Enrichment Analysis (Ribas's cohort, n=437). (B) VlnPlot showing the differences of WP_ferroptosis, JUN and HMOX1 between responses to the immunotherapy group and non-responses to the immunotherapy group. (C) The proportion of patients with different responses to immunotherapy in the melanoma immune checkpoint inhibitor (ICI) cohort (PRJEB23709). (D) KaplanMeier curves compare progression-free survival (PRJEB23709; ICI cohorts) between the high WP_ferroptosis/JUN/HMOX1 and low WP_ferroptosis/JUN/ HMOX1 groups. (E) The differences in immunotherapy outcome-related scores between high WP_ferroptosis/JUN/HMOX1 and low WP_ferroptosis/JUN/ HMOX1 groups. (F) Schedule for administration of PPF (10 mg/ kg) or anti-PD-1 (100 µg/per mouse) in YUMM1.7 tumor-bearing C57BL/6 mice. (G-I) Tumor volume (G), body weight (H), and survival curves (I) in the indicated groups. n=5 per group. Statistical significance was assessed using the Fisher's exact test/χ 2 test in C, or log-rank test in D and I, or mixed-effects models in G. Wilcoxon rank-sum test was used in E. ns, p>0.05; *p<0.05, **p<0.01, and ***p<0.001. BCR, B-cell receptor; COM, combination; CTL, cytotoxic T lymphocytes; CYT, cytolytic activity; GEP, gene expression profile; HMOX1, heme oxygenase 1; IFN, interferon; i.p, intraperitoneally; JUN, Jun proto-oncogene; mAb, monoclonal antibody; PD-1, programmed cell death 1; PPF, propafenone; MDSC, myeloid-derived suppressor cells; TAM, tumor-associated macrophages; TCR, T-cell receptor. on November 24, 2024 by guest. Protected by copyright.
Propafenone facilitates mitochondrial-associated ferroptosis and synergizes with immunotherapy in melanoma

November 2024

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16 Reads

Background Despite the successful application of immunotherapy, both innate and acquired resistance are typical in melanoma. Ferroptosis induction appears to be a potential strategy to enhance the effectiveness of immunotherapy. However, the relationship between the status of ferroptosis and the effectiveness of immunotherapy, as well as viable strategies to augment ferroptosis, remains unclear. Methods A screening of 200 cardiovascular drugs obtained from the Food and Drug Administration-approved drug library was conducted to identify the potential ferroptosis sensitizer. In vitro and in vivo experiments explored the effects of propafenone on ferroptosis in melanoma. Animal models and transcriptomic analyses evaluated the therapeutic effects and survival benefits of propafenone combined with immune checkpoint blockades (ICBs). The relationship between propafenone targets and the efficacy of ICBs was validated using the Xiangya melanoma data set and publicly available clinical data sets. Results Through large-scale drug screening of cardiovascular drugs, we identified propafenone, an anti-arrhythmia medication, as capable of synergizing with ferroptosis inducers in melanoma. Furthermore, we observed that propafenone, in combination with glutathione peroxidase 4 inhibitor RSL3, collaboratively induces mitochondrial-associated ferroptosis. Mechanistically, propafenone transcriptionally upregulates mitochondrial heme oxygenase 1 through the activation of the Jun N-terminal kinase (JNK)/JUN signaling pathway under RSL3 treatment, leading to overloaded ferrous iron and reactive oxygen species within the mitochondria. In xenograft models, the combination of propafenone and ferroptosis induction led to nearly complete tumor regression and prolonged survival. Consistently, propafenone enhances immunotherapy-induced tumorous ferroptosis and antitumor immunity in tumor-bearing mice. Significantly, patients exhibiting high levels of ferroptosis/JUN/HMOX1 exhibited improved efficacy of immunotherapy and prolonged progression-free survival. Conclusions Taken together, our findings suggest that propafenone holds promise as a candidate drug for enhancing the efficacy of immunotherapy and other ferroptosis-targeted therapies in the treatment of melanoma.


Figure 2 Identification of 226 CRC blood biomarkers. (A-C) Volcano plot representing the −log10 adjusted p value (padj) as a function of the log2 fold change for the DEA between CRC and CON samples, for the entire discovery set (Swiss and Korean) (A), only the Swiss samples (B) and the Korean samples (C). Dash lines represent the significance threshold (padj<0.01). Downregulated genes are marked in blue and upregulated genes in red. (D) Venn diagram depicting the overlap between the first three DEA analyses in panels A to C. (E) Scatter plots comparing −log10 padj from the CRC versus CON DEAs of Swiss versus Korean cohorts. Red dots represent selected genes (n=430) from CRC versus CON DEA for validation. Dotted line indicates the significance threshold (padj<0.01). (F) Scatter plots comparing the log2 fold change from the CRC versus CON DEA of Swiss versus Korean cohorts. Red dots represent selected genes for validation. (G-H) Venn diagram representing the overlap between the BMKs identified by the different univariate (G) and multivariate (H) analyses performed on the discovery set. (I) Volcano plot representing the −log10 adjusted p value (padj) as a function of the log2 fold change for the DEA between CRC I-IV and CON samples for the entire validation set. (J) Scatter plots of the −log10 padj from the CRC I-IV versus CON DEA of the discovery versus validation set. (K) Scatter plots of the log2 fold change from the CRC I-IV versus CON DEA of the discovery versus validation set. Black dash lines represent the significance threshold (padj<0.01). Black dots represent the 524 BMKs identified in the discovery set, orange dots the 226 validated BMKs and gray dots the entire DEA results. AA, advanced adenoma; BMK, biomarker; CON, control subjects; CRC, colorectal cancer; DEA, differential expression analysis; DEG, differential expressed genes; HG, high grade; NSC, nearest shrunken centroids. on November 22, 2024 by guest. Protected by copyright.
Figure 4 CRC development and progression is characterized by stage-specific biomarkers and associated biological processes. (A) Graphical representation of BMK cluster identification. (B) Heatmap representing the mean log2 fold change per cluster and DEA. (C) Graphical representation of the biological processes from GO BP and reactome where each gene cluster participates. In clusters A and D, significantly enriched pathways are in dark colors and not significantly enriched pathways in light colors. In clusters B, E and F, significantly enriched pathways are represented whereas in clusters C and G, no significantly enriched pathways are indicated, due to the low number of genes per cluster. (D) Graphical representation of the enriched biological processes from GO BP in tumor DEG clusters (padj <0.01). Pathways commonly found in patients with CRC PBMCs are marked in bold. (E) Heatmap representing the mean log2 fold change between indicated groups and control colorectum or PBMCs (mean log2FC of discovery and validation sets). (F) Scatter plots comparing the log2 fold change from the CRC versus CON DEA of the entire discovery set and CRC tumor versus normal tissue DEA of indicated public data sets. Red dots indicate DEGs (padj<0.05) of each tumor data set, while the blue line represents the linear regression fit. A paired DEA was performed for GSE196006 (n=21) and GSE89393 (n=5) data sets, whereas an unpaired DEA was performed for GSE156451 (n_tumor=72, n_normal=72), GSE109203 (n_tumor=12 and n_normal=3), GSE136630 (n_tumor=5, n_normal=5), GSE76987 (n_tumor=4 and n_normal=12) and GSE164541 (n_tumor=5, n_normal=5). GSE156451, GSE109203 and GSE164541 include samples from China, GSE196006, GSE136630 and GSE76987 from USA and GSE89393 from Poland. Pearson correlation coefficients and p values are displayed on the top. AA, advanced adenoma; BMK, biomarker; CON, control subjects; CRC, colorectal cancer; DEA, differential expression analysis; padj, adjusted p value; PBMC, peripheral blood mononuclear cell. on November 22, 2024 by guest. Protected by copyright.
Data sets compositions
Human blood cell transcriptomics unveils dynamic systemic immune modulation along colorectal cancer progression

November 2024

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8 Reads

Background Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide. CRC deaths can be reduced with prevention and early diagnosis. Circulating tumor DNA-based liquid biopsies, are emerging tools for cancer detection. However, the tumor-signal-dependent nature of this approach results in low sensitivity in precancerous and early CRC stages. Here we propose the host immune response to the onset of cancer as an alternative approach for early detection of CRC. Methods We perform whole transcriptome analysis of peripheral blood mononuclear cells (PBMCs) isolated from individuals with CRC, precancerous lesions or negative colonoscopy in two independent cohorts using next-generation sequencing. Results We discover and validate novel early CRC RNA biomarkers. Taking into account, and adjusting for, the sensitivity of PBMCs transcriptomes to processing times, we report distinct transcriptomic changes in the periphery related to specific CRC stages. Activation of innate immunity is already detectable in the peripheral blood of individuals with pre-malignant advanced adenomas. This immune response is followed by signs of transient B-cell activation and sustained inhibition of T-cell responses along CRC progression, whereby at late stages, protumoral myeloid cells, wound healing and coagulation processes prevail. Moreover, some biomarkers show similar dysregulation in tumors and are implicated in known pathways of CRC pathophysiology. Conclusions The strong systemic immune modulation triggered during CRC progression leads to previously unnoticed alterations detectable in PBMCs, paving the way for the development of an early CRC screening blood test, incorporating 226 validated biomarkers identified through immunotranscriptomics.


Baseline characteristics of patients
Efficacy endpoints among evaluable patients
Subgroup analysis of ORR, EFS and OS
Sintilimab plus decitabine for higher-risk treatment-naïve myelodysplastic syndromes: efficacy, safety, and biomarker analysis of a phase II, single-arm trial

November 2024

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7 Reads

Background Immunotherapy combined with azacitidine was feasible in higher-risk myelodysplastic syndromes (MDSs) with limited sample size of treatment-naïve patients, while the optimization of treatment strategies, including the optimal immune checkpoint inhibitor and hypomethylating agent and possible benefiting population, remained undefined. This study first evaluates the efficacy and safety of sintilimab, a PD-1 blockade, plus decitabine in treatment-naïve higher-risk MDS patients and investigates biomarkers for predicting treatment response. Methods In this phase II, single-arm trial (ChiCTR2100044393), treatment-naïve higher-risk MDS patients with an International Prognostic Scoring System-Revised score >3.5 received sintilimab (200 mg, days 1 and 22) and decitabine (20 mg/m ² , day 1–5) over 6-week cycles. The primary endpoint was the overall response rate (ORR), including complete remission (CR), partial remission (PR) or marrow CR. Results A total of 54 eligible patients were enrolled and treated, with 25 (46.3%) having very high-risk MDS. Among 53 evaluable patients, the ORR was 77.4% (n=41), including 26.4% CR (n=14). The overall clinical improvement rate (CR, PR, marrow CR or hematological improvement) reached 81.1%. With a median follow-up of 20.0 months, the median event-free survival was 23 months with 12 progressing to acute myeloid leukemia. Median overall survival was not reached. Treatment was generally well tolerated, with hematologic toxicities being the most common adverse events. Biomarker analysis highlighted a negative correlation between T cell exhaustion markers, particularly TIM-3 and PD-1, with ORR. Conclusions The combination of sintilimab and decitabine shows promise efficacy for higher-risk MDS, with a favorable safety profile. The potential predictive value of T cell exhaustion biomarkers might help screen the possible benefiting population. Trial registration number ChiCTR210044393.


Journal metrics


10.3 (2023)

Journal Impact Factor™


23%

Acceptance rate


17.7 (2023)

CiteScore™


5 days

Submission to first decision


31 days

Acceptance to publication


GBP 2,163 / EUR 2,459 / USD 2,972

Article processing charge

Editors