Intratumoral T-cell and B-cell receptor architecture associates with distinct immune tumor microenvironment features and clinical outcomes of anti-PD-1/L1 immunotherapy

Cyclin E1 overexpression triggers interferon signaling and is associated with antitumor immunity in breast cancer

BACKGROUND

Cyclin E1 overexpression drives oncogenesis in several cancers through deregulation of DNA replication and induction of genomic instability, which may potentially trigger immune signaling via cytoplasmic DNA. However, the effects of cyclin E1 overexpression on tumor immunity and its effects on the response to immune checkpoint inhibitors remain largely unclear.

METHODS

Tissue microarrays and clinical outcomes of 398 patients with breast cancer were analyzed to explore the correlation between cyclin E1 expression, patient survival, and immune cell infiltration using immunohistochemistry. Genomic data from publicly available data sets and three clinical trials evaluating immunotherapy were assessed to measure the impact of cyclin E1 expression on the immune cells in the tumor microenvironment and response to immunotherapy in patients with breast cancer. In addition, breast cancer cell lines with inducible cyclin E1 overexpression were employed to analyze the effects of cyclin E1 on inflammatory signaling.

RESULTS

Increased cyclin E1 expression in breast cancer was positively correlated with immune cell infiltration, including T cells, B cells, and natural killer cells, and activation of interferon-related pathways. Importantly, higher cyclin E1 expression or CCNE1 amplification was associated with better response to immunotherapy in three clinical trials. Mechanistically, cyclin E1 overexpression resulted in micronuclei formation and activation of innate immune signaling, resulting in increased immune cell migration.

CONCLUSIONS

Our data show that cyclin E1 overexpression associate with antitumor immunity through activation of innate inflammatory signaling and warrants investigation into amplification or overexpression of cyclin E1 in identifying patients with breast cancer eligible for immunotherapy.

AI Model for Predicting Anti-PD1 Response in Melanoma Using Multi-Omics Biomarkers

BACKGROUND

Immune checkpoint inhibitors (ICIs) have demonstrated significantly improved clinical efficacy in a minority of patients with advanced melanoma, whereas non-responders potentially suffer from severe side effects and delays in other treatment options. Predicting the response to anti-PD1 treatment in melanoma remains a challenge because the current FDA-approved gold standard, the nonsynonymous tumor mutation burden (nsTMB), offers limited accuracy.

METHODS

In this study, we developed a multi-omics-based machine learning model that integrates genomic and transcriptomic biomarkers to predict the response to anti-PD1 treatment in patients with advanced melanoma. We employed least absolute shrinkage and selection operator (LASSO) regression with 49 biomarkers extracted from tumor-normal whole-exome and RNA sequencing as input features. The performance of the multi-omics AI model was thoroughly compared to that of nsTMB alone and to models that use only genomic or transcriptomic biomarkers.

RESULTS

We used publicly available DNA and RNA-seq datasets of melanoma patients for the training and validation of our model, forming a meta-cohort of 449 patients for which the outcome was recorded as a RECIST score. The model substantially improved the prediction of anti-PD1 outcomes compared to nsTMB alone, with an ROC AUC of 0.7 in the training set and an ROC AUC of 0.64 in the test set. Using SHAP values, we demonstrated the explainability of the model's predictions on a per-sample basis.

CONCLUSIONS

We demonstrated that models using only RNA-seq or multi-omics biomarkers outperformed nsTMB in predicting the response of melanoma patients to ICI. Furthermore, our machine learning approach improves clinical usability by providing explanations of its predictions on a per-patient basis. Our findings underscore the utility of multi-omics data for selecting patients for treatment with anti-PD1 drugs. However, to train clinical-grade AI models for routine applications, prospective studies collecting larger melanoma cohorts with consistent application of exome and RNA sequencing are required.

Characterization of TCRβ and IGH Repertoires in the Spleen of Two Chicken Lines with Differential ALV-J Susceptibility Under Normal and Infection Conditions

This study investigates the immunological factors underlying the differential susceptibility of two chicken strains, E- and M-lines, to avian leukosis virus subgroup J (ALV-J). During the eradication of avian leukosis at a chicken breeder farm in Guangdong, we observed strain-specific differences in susceptibility to ALV-J. Moreover, E-line chickens exhibited a slower antibody response to ALV-J compared to M-line chickens. As the T cell receptor (TCR) and B cell receptor (BCR) are critical for antigen recognition, their activation triggers specific immune responses, including antibody production. Using high-throughput sequencing, we characterized the T cell receptor beta (TCRβ) and immunoglobulin heavy chain (IGH) repertoires in spleen tissues from both chicken strains. The M-line demonstrated higher clonal diversity in both TCRβ and IGH repertoires under normal conditions compared to the E-line, suggesting a broader baseline antigen recognition capacity. Following ALV-J infection, the TCRβ repertoire diversity remained unchanged, while the IGH repertoire displayed distinct clonal expansion patterns and complementarity-determining region 3 (CDR3) length distributions between the two lines, potentially affecting their ability to recognize ALV-J antigens. Our study provides the first comprehensive comparison of TCRβ and IGH repertoire dynamics in chickens with different ALV-J susceptibilities, offering new insights into the molecular and immunological mechanisms underlying resistance to ALV-J.

The intratumoral landscape of T cell receptor repertoire in esophageal squamous cell carcinoma

BACKGROUND

Esophageal squamous cell carcinoma (ESCC) is a malignant neoplasm with detrimental implications for human health. The landscape of ESCC therapy has been revolutionized by the introduction of immunotherapy, specifically involving immune checkpoint inhibitors (ICIs). A number of studies have documented the prognostic significance of T-cell receptor (TCR) repertoire and its association with many tumors. Nevertheless, the TCR repertoire landscape and its significance in ESCC still need to be explored.

METHODS

In this study, we conducted RNA-Seq analysis to investigate the characteristics of the TCR repertoire in 90 patients. Moreover, high-throughput TCR sequencing was performed on tumor tissues from 41 patients who received immunotherapy. Additionally, a comprehensive analysis of the T-cell receptor repertoire landscape within ESCC tumors was carried out through immunohistochemical staining on all patient samples.

RESULTS

We noticed a diminished diversity of TCR repertoire within the tumor compared to its adjacent normal tissue. In terms of immunotherapy responses, non-responsive patients exhibited higher TCR repertoire diversity indices and an increased frequency of common V and J genes. Additionally, elevated TCR repertoire diversity correlated with improved overall survival rates. Lastly, immunohistochemical staining results indicated a correlation between TCR repertoire diversity and the tumor immune microenvironment (TIME).

CONCLUSIONS

Our study primarily describes the landscape of TCR repertoires in ESCC through three aspects: differences in tumor tissues, immune response to immunotherapy, and survival prognosis of patients. These results emphasize the importance of TCR repertoire characteristics as unique and relevant biomarkers for ESCC immunotherapy.

Exploring the impact of body mass index on tumor biology and cancer development

PURPOSE

Cancer continues to be a major global health challenge, affecting millions of individuals and placing substantial burdens on healthcare systems worldwide. Recent research suggests a complex relationship between obesity and cancer, with obesity increasing the risk of various cancers while potentially improving outcomes for diagnosed patients, a phenomenon termed the "obesity paradox". In this study, we used a cohort of 1781 patients to investigate the impact of obesity on tumor characteristics, including gene expression, pathway dysfunction, genetic alterations and immune infiltration.

METHODS

Patient samples spanned 10 different cancer types, and were obtained from the Cancer Genome Atlas, with annotations for body mass index (BMI), age, sex, tumor size and tumor gene expression data.

RESULTS

When we compared the proportion of large (T3-T4) to small tumors (T1-T2) between obese and non-obese patients, we found that obese patients tended to present with smaller, less invasive tumors and exhibited distinct gene expression profiles, particularly in metabolic and proliferative pathways. Moreover, smaller tumors in obese patients show higher immune cell infiltration and increased T cell diversity, suggesting enhanced immune activity.

CONCLUSION

Taken together, these findings highlight the influence of obesity on tumor biology, with implications for personalized treatment strategies that consider patient physiology alongside tumor characteristics.

Identification of prognostic risk model based on plasma cell markers in hepatocellular carcinoma through single-cell sequencing analysis

Hepatocellular carcinoma (HCC) represents a substantial global health burden. Tumorinfiltrating B lymphocytes (TIL-Bs) contribute to tumor progression and significantly impact the efficacy of tumor therapy. However, the characteristics of TIL-Bs in HCC and their effect on HCC therapy remain elusive. Single-cell RNA sequencing (scRNAseq) was applied to investigate the heterogeneity, cellular differentiation and cell-cell communication of TIL-Bs in HCC. Further, the Cancer Genome Atlas-liver hepatocellular carcinoma (TCGA-LIHC) and liver cancer institutes (LCI) cohorts were applied to construct and validate the plasma cell marker-based prognostic risk model. The relationship between the prognostic risk model and the responsiveness of immunotherapy and chemotherapy in patients with HCC were estimated by OncoPredict and tumor immune dysfunction and exclusion (TIDE) algorithm. Finally, we established nomogram and calibration curves to evaluate the precision of the risk score in predicating survival probability. Our data identified five subtypes of TIL-Bs in HCC, each exhibiting varying levels of infiltration in tumor tissues. The interactions between TIL-Bs and other cell types contributed to shaping distinct tumor microenvironments (TME). Moreover, we found that TIL-Bs subtypes had disparate prognostic values in HCC patients. The prognostic risk model demonstrated exceptional predictive accuracy for overall survival and exhibited varying sensitivities to immunotherapy and chemotherapy among patients with HCC. Our data demonstrated that the risk score stood as an independent prognostic predictor and the nomogram results further affirmed its strong prognostic capability. This study reveals the heterogeneity of TIL-Bs and provides a prognostic risk model based on plasma cell markers in HCC, which could prove valuable in predicting prognosis and guiding the choice of suitable therapies for patients with HCC.

Single-cell aggrephagy-related patterns facilitate tumor microenvironment intercellular communication, influencing osteosarcoma progression and prognosis

Osteosarcoma, a common malignant tumor in children, has emerged as a major threat to the life and health of pediatric patients. Presently, there are certain limitations in the diagnosis and treatment methods for this disease, resulting in inferior therapeutic outcomes. Therefore, it is of great importance to study its pathogenesis and explore innovative approaches to diagnosis and treatment. In this study, a non-negative matrix decomposition method was employed to conduct a comprehensive investigation and analysis of aggregated autophagy-related genes within 331,394 single-cell samples of osteosarcoma. Through this study, we have elucidated the intricate communication patterns among various cells within the tumor microenvironment. Based on the classification of aggregated autophagy-related genes, we are not only able to more accurately predict patients' prognosis but also offer robust guidance for treatment strategies. The findings of this study hold promise for breakthroughs in the diagnosis and treatment of osteosarcoma, intervention of aggrephagy is expected to improve the survival rate and quality of life of osteosarcoma patients.