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[This corrects the article DOI: 10.3389/fimmu.2024.1368749.].

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Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

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Antígeno B7-H1 , Neoplasias , Humanos , Antígeno B7-H1/metabolismo , Neoplasias/tratamiento farmacológico , Neoplasias/genética , Biomarcadores de Tumor/genética , Inmunoterapia/métodos , Inhibidores de Puntos de Control Inmunológico/farmacología , Inhibidores de Puntos de Control Inmunológico/uso terapéutico , Microambiente Tumoral

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BACKGROUND: Macrophage receptor with collagenous structure (MARCO), a novel immune checkpoint expressed on tumor-associated macrophages, has antitumor therapeutic properties. However, the association between MARCO and patient prognosis, immune infiltration, and ICI immunotherapy needs to be studied urgently. METHODS: MARCO distribution in cancer tissues was investigated using the TCGA and GTEx databases. The PrognoScan and KM Plotter databases was used to assess the MARCO prognosis. TIMER2.0, GEPIA, cBioPortal, and GSEA all confirmed the link between MARCO and immune infiltration, mutation profile, and enrichment pathway analysis. Data visualization was implemented by R language. RESULTS: In general, MARCO had a substantial impact on the prognosis of cancer patients and was expressed differently in cancer and adjacent normal tissues. High expression of MARCO was associated with poorer OS in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), lung squamous cell carcinoma (LUSC), colon adenocarcinoma (COAD), and prostate adenocarcinoma (PRAD). However, high expression of MARCO had a better PFI in brain lower-grade glioma (LGG) and skin cutaneous melanoma (SKCM). We discovered that MARCO expression was lowest in pancreatic adenocarcinoma (PAAD) and rectum adenocarcinoma (READ) stage 1, BLCA stage 2, LUSC and stomach adenocarcinoma (STAD) stage 3, and liver hepatocellular carcinoma (LIHC) stage 4. Subsequently, we analyzed the correlation between MARCO and 47 immune checkpoints and observed that MARCO was positively connected with CD80, CD86, and leukocyte-associated immunoglobulin-like receptor 1(LAIR1) in most cancers. In COAD, MARCO has the most microsatellite instability (MSI). In addition, we discovered that high expression of MARCO patients had a better prognosis after immune checkpoint inhibitor (ICI) treatment in SKCM. Finally, GSEA revealed a significant correlation between MARCO and TNF/NFκB signaling, KRAS signaling, PI3K/AKT/mTOR pathway, IL-6-STAT3 signaling, TGFß pathway, and p53 pathway. CONCLUSION: This study comprehensively investigated the relationship between MARCO and clinical prognosis, immune infiltration, and ICI immunotherapy in various cancers. We demonstrated the potential of MARCO as an emerging biomarker, exploring new avenues for future tumor immunotherapy.

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Neoplasias , Receptores Inmunológicos , Humanos , Biomarcadores , Inmunoterapia , Neoplasias/diagnóstico , Neoplasias/tratamiento farmacológico , Pronóstico

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Approximately 80% of patients with advanced bladder cancer do not respond to immune checkpoint inhibitor (ICI) immunotherapy. Therefore, there is an urgent unmet need to develop clinically relevant preclinical models so that factors governing immunotherapy responses can be studied in immunocompetent mice. We developed a line of mouse triple knockout (TKO: Trp53, Pten, Rb1) urothelial carcinoma organoids transplanted into immunocompetent mice. These bladder tumors recapitulate the molecular phenotypes and heterogeneous immunotherapy responses observed in human bladder cancers. The TKO organoids were characterized in vivo and in vitro and compared to the widely used MB49 murine bladder cancer model. RNAseq analysis of the TKO tumors demonstrated a basal subtype. The TKO xenografts demonstrated the expression of urothelial markers (CK5, CK7, GATA3, and p63), whereas MB49 subcutaneous xenografts did not express urothelial markers. Anti-PD-1 immunotherapy resulted in a mixed pattern of treatment responses for individual tumors. Eight immune cell types were identified (basophils, B cells, dendritic cells, macrophages, monocytes, neutrophils, NK cells, and T cells) in ICI-treated xenografts. Responder xenografts displayed significantly increased immune cell infiltration (15.3%, 742 immune cells/4861 total cells) compared to the non-responder tumors (10.1%, 452 immune cells/4459 total cells, Fisher Exact Test p < 0.0001). Specifically, there were more T cells (1.0% vs. 0.4%, p = 0.002) and macrophages (8.6% vs. 6.4%, p = 0.0002) in responder xenografts than in non-responder xenografts. In conclusion, we have developed a novel preclinical model that exhibits a mixed pattern of response to anti-PD-1 immunotherapy. The higher percentage of macrophage tumor infiltration in responders suggests a potential role for the innate immune microenvironment in regulating ICI treatment responses.