Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies.

The efficacy of cancer immunotherapy largely depends on the tumor microenvironment, especially the tumor immune microenvironment. Emerging studies have claimed that microbes reside within tumor cells and immune cells, suggesting that these microbes can impact the state of the tumor immune microenvironment. For the first time, this review delineates the landscape of intra-tumoral microbes and their products, herein defined as the tumor microbe microenvironment. The role of the tumor microbe microenvironment in the tumor immune microenvironment is multifaceted: either as an immune activator, inhibitor, or bystander. The underlying mechanisms include: (I) the presentation of microbial antigens by cancer cells and immune cells, (II) microbial antigens mimicry shared with tumor antigens, (III) microbe-induced immunogenic cell death, (IV) microbial adjuvanticity mediated by pattern recognition receptors, (V) microbe-derived metabolites, and (VI) microbial stimulation of inhibitory checkpoints. The review further suggests the use of potential modulation strategies of the tumor microbe microenvironment to enhance the efficacy and reduce the adverse effects of checkpoint inhibitors. Lastly, the review highlights some critical questions awaiting to be answered in this field and provides possible solutions. Overall, the tumor microbe microenvironment modulates the tumor immune microenvironment, making it a potential target for improving immunotherapy. It is a novel field facing major challenges and deserves further exploration.

The numbers of cases and deaths from coronavirus disease 2019 (COVID-19) are continuously increasing. Many people are concerned about the efficacy and safety of the COVID-19 vaccines. We performed a comprehensive analysis of the published trials of COVID-19 vaccines and the real-world data from the Vaccine Adverse Event Reporting System. Globally, our research found that the efficacy of all vaccines exceeded 70%, and RNA-based vaccines had the highest efficacy of 94.29%; moreover, Black or African American people, young people, and males may experience greater vaccine efficacy. The spectrum of vaccine-related adverse drug reactions (ADRs) is extremely broad, and the most frequent ADRs are pain, fatigue, and headache. Most ADRs are tolerable and are mainly grade 1 or 2 in severity. Some severe ADRs have been identified (thromboembolic events, 21–75 cases per million doses; myocarditis/pericarditis, 2–3 cases per million doses). In summary, vaccines are a powerful tool that can be used to control the COVID-19 pandemic, with high efficacy and tolerable ADRs. In addition, the spectrum of ADRs associated with the vaccines is broad, and most of the reactions appear within a week, although some may be delayed. Therefore, ADRs after vaccination need to be identified and addressed in a timely manner.

In this era of precision medicine, with the help of biomarkers, immunotherapy has significantly improved prognosis of many patients with malignant tumor. Deficient mismatch repair (dMMR)/microsatellite instability (MSI) status is used as a biomarker in clinical practice to predict favorable response to immunotherapy and prognosis. MSI is an important characteristic which facilitates mutation and improves the likelihood of a favorable response to immunotherapy. However, many patients with dMMR/MSI still respond poorly to immunotherapies, which partly results from intratumor heterogeneity propelled by dMMR/MSI. In this review, we discuss how dMMR/MSI facilitates mutations in tumor cells and generates intratumor heterogeneity, especially through type II interferon (IFN-γ) signaling and tumor-infiltrating lymphocytes (TILs). We discuss the mechanism of immunotherapy from the perspective of dMMR/MSI, molecular pathways and TILs, and we discuss how intratumor heterogeneity hinders the therapeutic effect of immunotherapy. Finally, we summarize present techniques and strategies to look at the tumor as a whole to design personalized regimes and achieve favorable prognosis.

Background and Aims: Tumor microenvironment (TME) heterogeneity leads to a discrepancy in survival prognosis and clinical treatment response for hepatocellular carcinoma (HCC) patients. The clinical applications of documented molecular subtypes are constrained by several issues. Approach and Results: We integrated three single-cell datasets to describe the TME landscape and identified six prognosis-related cell subclusters. Unsupervised clustering of subcluster-specific markers was performed to generate transcriptomic subtypes. The predictive value of these molecular subtypes for prognosis and treatment response was explored in multiple external HCC cohorts and the Xiangya HCC cohort. TME features were estimated using single-cell immune repertoire sequencing, mass cytometry and multiplex immunofluorescence. The prognosis-related score (PRS) was constructed based on machine learning algorithm. Comprehensive single-cell analysis described TME heterogeneity in HCC. The five transcriptomic subtypes possessed different clinical prognoses, stemness characteristics, immune landscapes and therapeutic responses. Class 1 exhibited an inflamed phenotype with better clinical outcomes, while Classes 2 and 4 were characterized by a lack of T cell infiltration. Classes 5 and 3 indicated an inhibitory tumor immune microenvironment. Analysis of multiple therapeutic cohorts suggested that Classes 5 and 3 were sensitive to ICB and targeted therapy, whereas Classes 1 and 2 were more responsive to transcatheter arterial chemoembolization treatment. Class 4 displayed resistant to all conventional HCC therapies. Three potential therapeutic agents and four targets were further identified for high-PRS HCC patients. Conclusion: Our study generated a clinically valid molecular classification to guide precision medicine in patients with HCC. Export

Background: Centromere protein L (CENPL) is an important member of the centromere protein (CENP) family. However, the correlation between CENPL expression and cancer development and immune infiltration has rarely been studied. Here, we studied the role of CENPL in pan-cancer and further verified the results in lung adenocarcinoma (LUAD) through in vitro experiments.Methods: The CENPL expression level was studied with TIMER 2.0 and Oncomine databases. The potential value of CENPL as a diagnostic and prognostic biomarker in pan-cancer was evaluated with the TCGA database and GEPIA. The CENPL mutation character was analyzed using the cBioPortal database. The LinkedOmics and CancerSEA databases were used to carry out the function analysis of CENPL. The role of CENPL in immune infiltration was studied using the TIMER and TISIDB websites. Moreover, the expression of CENPL was detected through RT-qPCR and Western blotting. Immunohistochemistry was used to evaluate the infiltration level of CD8⁺ T cells. Cell proliferation was detected by EdU and CCK8. A flow cytometer was used to analyze the influence of CENPL in cell cycle and apoptosis.Results: CENPL was increased in most of the cancers. The upregulation and mutation of CENPL were associated with a poorer prognosis in many cancers. The results showed a significant positive correlation between CENPL and myeloid-derived suppressor cell (MDSC) infiltration and a negative correlation between CENPL and T-cell NK infiltration in most of the cancers. CENPL regulated cell proliferation and cell cycle, and was negatively correlated with the inflammation level of LUAD. The in vitro experiments suggested that CENPL was increased in LUAD tissue and cell lines. There was a negative correlation between CENPL expression and CD8⁺ T-cell infiltration. The knockdown of CENPL significantly suppressed the expression of CDK2 and CCNE2, and induced G0/G1 arrest and apoptosis of LUAD.Conclusions: CENPL may function as a potential biomarker and oncogene in pan-cancer, especially LUAD. Furthermore, CENPL was associated with immune cell infiltration in pan-cancer, providing a potential immune therapy target for tumor treatment.