Pan-Cancer HLA Gene-Mediated Tumor Immunogenicity and Immune Evasion

Fatty acid synthase (FASN) is a tumor-cell-intrinsic metabolic checkpoint restricting T-cell immunity

Fatty acid synthase (FASN)-catalyzed endogenous lipogenesis is a hallmark of cancer metabolism. However, whether FASN is an intrinsic mechanism of tumor cell defense against T cell immunity remains unexplored. To test this hypothesis, here we combined bioinformatic analysis of the FASN-related immune cell landscape, real-time assessment of cell-based immunotherapy efficacy in CRISPR/Cas9-based FASN gene knockout (FASN KO) cell models, and mathematical and mechanistic evaluation of FASN-driven immunoresistance. FASN expression negatively correlates with infiltrating immune cells associated with cancer suppression, cytolytic activity signatures, and HLA-I expression. Cancer cells engineered to carry a loss-of-function mutation in FASN exhibit an enhanced cytolytic response and an accelerated extinction kinetics upon interaction with cytokine-activated T cells. Depletion of FASN results in reduced carrying capacity, accompanied by the suppression of mitochondrial OXPHOS and strong downregulation of electron transport chain complexes. Targeted FASN depletion primes cancer cells for mitochondrial apoptosis as it synergizes with BCL-2/BCL-XL-targeting BH3 mimetics to render cancer cells more susceptible to T-cell-mediated killing. FASN depletion prevents adaptive induction of PD-L1 in response to interferon-gamma and reduces constitutive overexpression of PD-L1 by abolishing PD-L1 post-translational palmitoylation. FASN is a novel tumor cell-intrinsic metabolic checkpoint that restricts T cell immunity and may be exploited to improve the efficacy of T cell-based immunotherapy.

Identification of immune subtypes associated with neutrophils in tuberculosis infection based on weighted gene co-expression network analysis

Tuberculosis (TB) is caused by Mycobacterium tuberculosis and is a major global health concern. Neutrophils play a significant role in TB infection and patient outcomes. This study aimed to identify gene modules associated with neutrophil infiltration in TB samples using WGCNA. Gene ontology and enrichment analyses were performed, and a random forest model was constructed to identify differentially expressed genes. K-means clustering was used to classify samples into subtypes, and immune-related scores, PD-L1 expression, HLA expression, and gene enrichment analysis were evaluated. The blue module showed significant correlation with neutrophils and enrichment in immune-related processes. The model exhibited good classification performance, and subtype 1 demonstrated higher immune-related scores, PD-L1 expression, HLA class I molecule expression, and immune-related pathway enrichment. These findings enhance our understanding of TB pathogenesis and provide potential targets for diagnosis and treatment strategies.

Complex immune microenvironment of chordoma: a road map for future treatment

BACKGROUND

Chordoma, a rare bone tumor, presents limited treatment options and patients typically exhibit poor survival outcomes. While immunotherapy has shown promising results in treating various tumors, research on the immune microenvironment of chordomas is still in its early stages. Therefore, understanding how the immune microenvironment of chordomas influences the outcomes of immunotherapy is crucial.

METHODS

We employed single-cell RNA sequencing (scRNA-seq), bulk RNA-seq, CellChat, gene set variation analysis, as well as calculation of immune features to further dissect the complex immune microenvironment of chordoma.

RESULTS

Previous research by van Oost et al argued that compared with other sarcomas, chordomas typically exhibit an immunologically "hot" microenvironment, a conclusion with which we concur based on their research findings. Additionally, the authors suggest that T cell-mediated immunotherapy is feasible for the majority of chordomas. However, we are inclined to categorize them as an immune-excluded phenotype according to the latest classification methods, rather than persisting with the concepts of "cold" and "hot". Unlike them, we explored immune infiltration scores (IS), T lymphocyte scoring (TLS), and human leucocyte antigen class I (HLA-I) using Bulk RNA-seq data from 126 chordoma patients and found that higher IS, TLS, and higher HLA-I expression were associated with poorer patient prognosis. Additionally, CellChat analysis of scRNA-seq results from six chordoma patients revealed no direct interaction between T cells and tumor cells.

CONCLUSIONS

These findings suggested that the efficacy of T cell-based immunotherapy may be limited or even ineffective for patients with chordoma.

Pan-cancer analyses of immunogenic cell death-derived gene signatures: Potential biomarkers for prognosis and immunotherapy

BACKGROUND

Immunogenic cell death (ICD) is a type of regulated cell death that is capable of initiating an adaptive immune response. Induction of ICD may be a potential treatment strategy, as it has been demonstrated to activate the tumor-specific immune response.

AIMS

The biomarkers of ICD and their relationships with the tumor microenvironment, clinical features, and immunotherapy response are not fully understood in a clinical context. Therefore, we conducted pan-cancer analyses of ICD gene signatures across 33 cancer types from The Cancer Genome Atlas database.

METHODS AND RESULTS

We identified key genes that had strong relationships with survival and the tumor microenvironment, contributing to a better understanding of the role of ICD genes in cancer therapy. In addition, we predicted therapeutic agents that target ICD genes and explored the potential mechanisms by which gemcitabine induce ICD. Moreover, we developed an ICD score based on the ICD genes and found it to be associated with patient prognosis, clinical features, tumor microenvironment, radiotherapy access, and immunotherapy response. A high ICD score was linked to the immune-hot phenotype, while a low ICD score was linked to the immune-cold phenotype.

CONCLUSION

We uncovered the potential of ICD gene signatures as comprehensive biomarkers for ICD in pan-cancer. Our research provides novel insights into immuno-phenotypic assessment and cancer therapeutic strategies, which could help to broaden the application of immunotherapy to benefit more patients.

HLA-class II restricted TCR targeting human papillomavirus type 18 E7 induces solid tumor remission in mice

T cell receptor (TCR)-engineered T cell therapy is a promising potential treatment for solid tumors, with preliminary efficacy demonstrated in clinical trials. However, obtaining clinically effective TCR molecules remains a major challenge. We have developed a strategy for cloning tumor-specific TCRs from long-term surviving patients who have responded to immunotherapy. Here, we report the identification of a TCR (10F04), which is human leukocyte antigen (HLA)-DRA/DRB1*09:01 restricted and human papillomavirus type 18 (HPV18) E784-98 specific, from a multiple antigens stimulating cellular therapy (MASCT) benefited metastatic cervical cancer patient. Upon transduction into human T cells, the 10F04 TCR demonstrated robust antitumor activity in both in vitro and in vivo models. Notably, the TCR effectively redirected both CD4+ and CD8+ T cells to specifically recognize tumor cells and induced multiple cytokine secretion along with durable antitumor activity and outstanding safety profiles. As a result, this TCR is currently being investigated in a phase I clinical trial for treating HPV18-positive cancers. This study provides an approach for developing safe and effective TCR-T therapies, while underscoring the potential of HLA class II-restricted TCR-T therapy as a cancer treatment.

Construction of a Liver Cancer Prognostic Model Based on Interferon-Gamma-Related Genes for Revealing the Immune Landscape

Inferferon-gamma (LFN-γ) exerts anti-tumor effects, but there is currently no reliable and comprehensive study on prognostic function of IFN-γ-related genes in liver cancer. In this study, IFN-γ-related differentially expressed genes (DEGs) in liver cancer were identified through GO/KEGG databases and open-access literature. Based on these genes, individuals with liver cancer were clustered. A prognostic model was built based on the intersection genes between differential genes in clusters and in liver cancer. Then, model predictive performance was analyzed and validated in GEO dataset. Regression analysis was fulfilled on the model, and a nomogram was utilized to evaluate model ability as an independent prognostic factor and its clinical application value. An immune-related analysis was conducted on both the H- and L-groups, with an additional investigation into link of model genes to drug sensitivity. Significant differential expression of IFN-γ-related genes was observed between the liver cancer and control groups. Subsequently, individuals with liver cancer were classified into two subtypes based on these genes, which displayed a notable difference in survival between the two subtypes. A 10-gene liver cancer prognostic model was constructed, with good prognostic performance and was an independent prognosticator for patient analysis. L-group patients possessed higher immune infiltration levels, immune checkpoint expression levels, and immunophenoscore, as well as lower TIDE scores. Drugs that had high correlations with the feature genes included SPANXB1: PF-04217903, SGX-523, MMP1: PF-04217903, DUSP13: Imatinib, TFF1: KHK-Indazole, and Fulvestrant. We built a 10-gene liver cancer prognostic model. It was found that L-group patients were more suitable for immunotherapy. This study provided valuable information on the prognosis of liver cancer.

Loss of Human Leukocyte Antigen and Immune Escape in Head and Neck Cancer

OBJECTIVES/HYPOTHESIS

Cancer cells evade recognition by the immune system to survive. Head and neck squamous cell carcinoma (HNSCC) is characterized by high levels of immune infiltration and mutation-associated neoantigens; therefore, immune evasion is likely to be an important mechanism in HNSCC tumorigenesis and progression. A commonly employed mechanism of immune evasion is downregulation of human leukocyte antigen (HLA) or loss of heterozygosity (LOH) in tumor cells. The objective of this study was to integrate multi-dimensional genomic and transcriptomic data from HNSCC tumors to better understand the clinical and immunologic implications of HLA LOH.

STUDY TYPE/DESIGN

Cross-sectional integrated clinical and genomic analysis.

METHODS

Whole-exome sequencing and RNA-sequencing data from 522 tumors profiled in The Cancer Genome Atlas HNSCC cohort were analyzed and integrated with secondary analyses including immune cell deconvolution data. Associations were analyzed with categorical hypothesis testing and multivariable logistic and Cox regression.

RESULTS

HLA LOH was a prevalent event that was identified in 53% of HNSCC tumors; in many cases, more than one class I HLA gene was targeted for LOH. HLA LOH was more common in advanced-stage tumors. Tumors with somatic HLA LOH had tumor microenvironments defined by decreased lymphocyte and T cell infiltration.

CONCLUSIONS

HLA LOH is one of the most prevalent genetic alterations in HNSCC, and is associated with a cold immune microenvironment, suggesting that HLA LOH is a means of immune evasion. It may have value as a predictive biomarker or potential as a cancer cell-specific therapeutic target.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:160-165, 2024.

Causal associations of Sjögren's syndrome with cancers: a two-sample Mendelian randomization study

BACKGROUND

Several observational studies have explored the associations between Sjögren's syndrome (SS) and certain cancers. Nevertheless, the causal relationships remain unclear. Mendelian randomization (MR) method was used to investigate the causality between SS and different types of cancers.

METHODS

We conducted the two-sample Mendelian randomization with the public genome-wide association studies (GWASs) summary statistics in European population to evaluate the causality between SS and nine types of cancers. The sample size varies from 1080 to 372,373. The inverse variance weighted (IVW) method was used to estimate the causal effects. A Bonferroni-corrected threshold of P < 0.0031 was considered significant, and P value between 0.0031 and 0.05 was considered to be suggestive of an association. Sensitivity analysis was performed to validate the causality. Moreover, additional analysis was used to assess the associations between SS and well-accepted risk factors of cancers.

RESULTS

After correcting the heterogeneity and horizontal pleiotropy, the results indicated that patients with SS were significantly associated with an increased risk of lymphomas (odds ratio [OR] = 1.0010, 95% confidence interval [CI]: 1.0005-1.0015, P = 0.0002) and reduced risks of prostate cancer (OR = 0.9972, 95% CI: 0.9960-0.9985, P = 2.45 × 10-5) and endometrial cancer (OR = 0.9414, 95% CI: 0.9158-0.9676, P = 1.65 × 10-5). Suggestive associations were found in liver and bile duct cancer (OR = 0.9999, 95% CI: 0.9997-1.0000, P = 0.0291) and cancer of urinary tract (OR = 0.9996, 95% CI: 0.9992-1.0000, P = 0.0281). No causal effect of SS on other cancer types was detected. Additional MR analysis indicated that causal effects between SS and cancers were not mediated by the well-accepted risk factors of cancers. No evidence of the causal relationship was observed for cancers on SS.

CONCLUSIONS

SS had significant causal relationships with lymphomas, prostate cancer, and endometrial cancer, and suggestive evidence of association was found in liver and bile duct cancer and cancer of urinary tract, indicating that SS may play a vital role in the incidence of these malignancies.

Antigen presentation in cancer - mechanisms and clinical implications for immunotherapy

Over the past decade, the emergence of effective immunotherapies has revolutionized the clinical management of many types of cancers. However, long-term durable tumour control is only achieved in a fraction of patients who receive these therapies. Understanding the mechanisms underlying clinical response and resistance to treatment is therefore essential to expanding the level of clinical benefit obtained from immunotherapies. In this Review, we describe the molecular mechanisms of antigen processing and presentation in tumours and their clinical consequences. We examine how various aspects of the antigen-presentation machinery (APM) shape tumour immunity. In particular, we discuss genomic variants in HLA alleles and other APM components, highlighting their influence on the immunopeptidomes of both malignant cells and immune cells. Understanding the APM, how it is regulated and how it changes in tumour cells is crucial for determining which patients will respond to immunotherapy and why some patients develop resistance. We focus on recently discovered molecular and genomic alterations that drive the clinical outcomes of patients receiving immune-checkpoint inhibitors. An improved understanding of how these variables mediate tumour-immune interactions is expected to guide the more precise administration of immunotherapies and reveal potentially promising directions for the development of new immunotherapeutic approaches.

The screening, identification, design and clinical application of tumor-specific neoantigens for TCR-T cells

Recent advances in neoantigen research have accelerated the development of tumor immunotherapies, including adoptive cell therapies (ACTs), cancer vaccines and antibody-based therapies, particularly for solid tumors. With the development of next-generation sequencing and bioinformatics technology, the rapid identification and prediction of tumor-specific antigens (TSAs) has become possible. Compared with tumor-associated antigens (TAAs), highly immunogenic TSAs provide new targets for personalized tumor immunotherapy and can be used as prospective indicators for predicting tumor patient survival, prognosis, and immune checkpoint blockade response. Here, the identification and characterization of neoantigens and the clinical application of neoantigen-based TCR-T immunotherapy strategies are summarized, and the current status, inherent challenges, and clinical translational potential of these strategies are discussed.

Identification of A novel anoikis-related genes-based signature for non-small cell lung cancer

The prognostic value of anoikis in NSCLC and its mechanism in tumorigenesis and progress have not been fully elucidated. This study aimed to reveal the correlation between anoikis-related genes (ARGs) and tumor prognosis, to reveal molecular and immune features, and to evaluate the anticancer drug sensitivity and the efficacy of immunotherapy of NSCLC. ARGs were selected from both the GeneCards and Harmonizome databases and then were intersected with the Cancer Genome Atlas (TCGA) database by differential expression analysis, followed by functional analysis of the target ARGs. An ARGs-based prognostic signature was constructed using LASSO (least absolute shrinkage and selection operator) Cox regression analysis; Kaplan-Meier analysis, univariant and multivariant Cox analysis were used to validate the value of this model in NSCLC prognosis. Differential analyses on molecular and immune landscapes were applied in the model. Anticancer drug sensitivity and efficacy in immune-checkpoint inhibitors (ICI) therapy were analyzed. A total of 509 ARGs and 168 differentially expressed ARGs in NSCLC were generated. Functional analysis revealed enrichment in extracolonic apoptotic signaling pathway, collagen-containing ECM, and integrin binding, and indicated an association with the PI3K-Akt signaling pathway. Subsequently, a 14-genes signature was generated. The high-risk group had a worse prognosis, with higherM0 and M2 macrophage infiltration, and fewer CD8 T-cells and T follicular helper (TFH) cells. The high-risk group had higher expression of immune checkpoint genes, HLA-I genes, and higher TIDE scores than the low-risk group, leading to less benefit of ICI therapy. Additionally, an Immunohistochemical staining comparison revealed that FADD was highly expressed in tumor tissue, compared to normal tissue, consistent with the previous results.

Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis

BACKGROUND

The TP53 tumor suppressor gene is one of the most mutated genes in lung adenocarcinoma (LUAD) and plays a vital role in regulating the occurrence and progression of cancer. We aimed to elucidate the association between TP53 mutations, response to immunotherapies and the prognosis of LUAD.

METHODS

Genomic, transcriptomic, and clinical data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) dataset. Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, gene set enrichment analysis (GSEA). Gene set variation analysis (GSVA) were performed to determine the differences in biological pathways. A merged protein-protein interaction (PPI) network was constructed and analyzed. MSIpred was used to analyze the correlation between the expression of the TP53 gene, tumor mutation burden (TMB) and tumor microsatellite instability (MSI). CIBERSORT was used to calculate the abundance of immune cells. Univariate and multivariate Cox regression analyses were used to determine the prognostic value of TP53 mutations in LUAD.

RESULTS

TP53 was the most frequently mutated in LUAD, with a mutational frequency of 48%. GO and KEGG enrichment analysis, GSEA, and GSVA results showed a significant upregulation of several signaling pathways, including PI3K-AKT mTOR (P < 0.05), Notch (P < 0.05), E2F target (NES = 1.8, P < 0.05), and G2M checkpoint (NES = 1.7, P < 0.05). Moreover, we found a significant correlation between T cells, plasma cells, and TP53 mutations (R² < 0.01, P = 0.040). Univariate and multivariate Cox regression analyses revealed that the survival prognosis of LUAD patients was related to TP53 mutations (Hazard Ratio (HR) = 0.72 [95% CI, 0.53 to 0.98], P < 0.05), cancer status (P < 0.05), and treatment outcomes (P < 0.05). Lastly, the Cox regression models showed that TP53 exhibited good power in predicting three- and five-year survival rates.

CONCLUSIONS

TP53 may be an independent predictor of response to immunotherapy in LUAD, and patients with TP53 mutations have higher immunogenicity and immune cell infiltration.

Clustering by antigen-presenting genes reveals immune landscapes and predicts response to checkpoint immunotherapy

Immune checkpoint blockade (ICB) has demonstrated efficacy by reinvigorating immune cytotoxicity against tumors. However, the mechanisms underlying how ICB induces responses in a subset of patients remain unclear. Using bulk and single-cell transcriptomic cohorts of melanoma patients receiving ICB, we proposed a clustering model based on the expression of an antigen-presenting machinery (APM) signature consisting of 23 genes in a forward-selection manner. We characterized four APM clusters associated with distinct immune characteristics, cancer hallmarks, and patient prognosis in melanoma. The model predicts differential regulation of APM genes during ICB, which shaped ICB responsiveness. Surprisingly, while immunogenically hot tumors with high baseline APM expression prior to treatment are correlated with a better response to ICB than cold tumors with low APM expression, a subset of hot tumors with the highest pre-ICB APM expression fail to upregulate APM expression during treatment. In addition, they undergo immunoediting and display infiltration of exhausted T cells. In comparison, tumors associated with the best patient prognosis demonstrate significant APM upregulation and immune infiltration following ICB. They also show infiltration of tissue-resident memory T cells, shaping prolonged antitumor immunity. Using only pre-treatment transcriptomic data, our model predicts the dynamic APM-mediated tumor-immune interactions in response to ICB and provides insights into the immune escape mechanisms in hot tumors that compromise the ICB efficacy. We highlight the prognostic value of APM expression in predicting immune response in chronic diseases.