The Putative Effects of Neoadjuvant Chemotherapy on the Immune System of Advanced Epithelial Ovarian Carcinoma

The epithelial ovarian carcinoma (EOC) is one of leading causes of cancer-related mortality in females. For some patients, complete resection cannot be achieved, thus neoadjuvant chemotherapy (NACT) following interval debulking surgery (IDS) could be an alternative choice. In general-held belief, cytotoxic chemotherapy is assumed to be immunosuppressive, because of its toxicity to dividing cells in the bone marrow and peripheral lymphoid tissues. However, increasing evidence highlighted that the anticancer activity of chemotherapy may also be related to its ability to act as an immune modulator. NACT not only changed the morphology of cancer cells, but also changed the transcriptomic and genomic profile of EOC, induced proliferation of cancer stem-like cells, gene mutation, and tumor-related adaptive immune response. This review will provide a comprehensive overview of recent studies evaluating the impact of NACT on cancer cells and immune system of advanced EOC and their relationship to clinical outcome. This information could help us understand the change of immune system during NACT, which might provide new strategies in future investigation of immuno-therapy for maintenance treatment of EOC.

Genetic alterations in the neuronal development genes are associated with changes of the tumor immune microenvironment in pancreatic cancer

Background

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis and is highly metastatic. Our prior studies have demonstrated the critical role of axon guidance pathway genes in PDAC and the connection between neuronal development and the tumor microenvironment. A recent study newly identified 20 neuronal development genes [disks large homolog 2 (DLG2), neuron-glial-related cell adhesion molecule (NRCAM), neurexin3 (NRXN3), mitogen-activated protein kinase 10 (MAPK10), platelet-derived growth factor D (PDGFD), protein kinase C epsilon (PRKCE), potassium calcium-activated channel subfamily M alpha 1 (KCNMA1), polycystic kidney and hepatic disease 1 (PKHD1), neural cell adhesion molecule 1 (NCAM1), neuregulin-1 (NRG1), zinc finger protein 667 (ZNF667), cystic fibrosis transmembrane conductance regulator (CFTR), acyl-CoA medium-chain synthetase-3 (ACSM3), complement 6 (C6), protein tyrosine phosphatase receptor type M (PTPRM), hypoxia-inducible factor 1 alpha (HIF1A), adenylyl cyclase 5 (ADCY5), adherens junctions-associated protein 1 (AJAP1), neurobeachin (NBEA), sodium voltage-gated channel alpha subunit 9 (SCN9A)] that are associated with perineural invasion and poor prognosis of PDAC. The relationship between genetic alterations in these 20 genes and tumor immune microenvironment (TME) has not previously been investigated.

Methods

We hence applied the sequential multiplex immunohistochemistry results of biopsy specimens from 63 PDAC patients to investigate this relationship.

Results

We found that, except for PTPRM and NBEA, genetic alterations involving these 20 genes are associated with significant changes in the densities of major immune cell subtypes. Except for AJAP1, the copy number loss involving this panel of neuronal development genes is significantly associated with changes in immune cell infiltrates. In contrast, the copy number gain in fewer genes, including NRXN3, ZNF667, ACSM3, C6, ADCY5, SCN9A, and PRKCE, is significantly associated with changes in immune cell infiltrates.

Conclusions

Our study suggested that neuronal development genes play a role in modulating TME in a pancreatic cancer setting.

A Novel Oncogenic Role of FDX1 in Human Melanoma Related to PD-L1 Immune Checkpoint

The aim of this study was to evaluate the association between Ferredoxin 1 (FDX1) expression and the prognostic survival of tumor patients and predict the efficacy of immunotherapy response to antitumor drug sensitivity. FDX1 plays an oncogenic role in thirty-three types of tumors, based on TCGA and GEO databases, and further experimental validation in vitro was provided through multiple cell lines. FDX1 was expressed highly in multiple types of cancer and differently linked to the survival prognosis of tumorous patients. A high phosphorylation level was correlated with the FDX1 site of S177 in lung cancer. FDX1 exhibited a significant association with infiltrated cancer-associated fibroblasts and CD8⁺ T cells. Moreover, FDX1 demonstrated correlations with immune and molecular subtypes, as well as functional enrichments in GO/KEGG pathways. Additionally, FDX1 displayed relationships with the tumor mutational burden (TMB), microsatellite instability (MSI), DNA methylation, and RNA and DNA synthesis (RNAss/DNAss) within the tumor microenvironment. Notably, FDX1 exhibited a strong connection with immune checkpoint genes in the co-expression network. The validity of these findings was further confirmed through Western blotting, RT-qPCR, and flow cytometry experiments conducted on WM115 and A375 tumor cells. Elevated FDX1 expression has been linked to the enhanced effectiveness of PD-L1 blockade immunotherapy in melanoma, as observed in the GSE22155 and GSE172320 cohorts. Autodocking simulations have suggested that FDX1 may influence drug resistance by affecting the binding sites of antitumor drugs. Collectively, these findings propose that FDX1 could serve as a novel and valuable biomarker and represent an immunotherapeutic target for augmenting immune responses in various human cancers when used in combination with immune checkpoint inhibitors.

Establishment of a prognostic model for ovarian cancer based on mitochondrial metabolism-related genes

Background

Mitochondrial metabolism and mitochondrial structure were found to be altered in high-grade serous ovarian cancer (HGSOC). The intent of this exploration was to systematically depict the relevance between mitochondrial metabolism-related genes (MMRGs) and the prognosis of HGSOC patients by bioinformatics analysis and establish a prognostic model for HGSOC.

Methods

First of all, screened differentially expressed genes (DEGs) between TCGA-HGSOC and GTEx-normal by limma, with RNA-seq related HGSOC sourced from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database. Subsequently, expressed MMRGs (DE-MMRGs) were acquired by overlapping DEGs with MMRGs, and an enrichment analysis of DE-MMRGs was performed. Kaplan-Meier (K-M) survival analysis and Cox regression analysis were conducted to validate the genes' prognostic value, Gene Set Enrichment Analysis (GSEA) to elucidate the molecular mechanisms of the risk score, and CIBERSORT algorithm to explore the immuno landscape of HGSOC patients. Finally, a drug sensitivity analysis was made via the Drug Sensitivity in Cancer (GDSC) database.

Results

436 HGSOC-related DE-MMRGs (222 up-regulated and 214 down-regulated) were observed to participate in multiple metabolic pathways. The study structured a MMRGs-related prognostic signature on the basis of IDO1, TNFAIP8L3, GPAT4, SLC27A1, ACSM3, ECI2, PPT2, and PMVK. Risk score was the independent prognostic element for HGSOC. Highly dangerous population was characterized by significant association with mitochondria-related biological processes, lower immune cell abundance, lower expression of immune checkpoint and antigenic molecules. Besides, 54 drugs associated with eight prognostic genes were obtained. Furthermore, copy number variation was bound up with the 8 prognostic genes in expression levels.

Conclusion

We have preliminarily determined the prognostic value of MMRGs in HGSOC as well as relationship between MMRGs and the tumor immune microenvironment.

m6A regulators featured by tumor immune microenvironment landscapes and correlated with immunotherapy in non-small cell lung cancer (NSCLC)

Introduction

Recent research has confirmed the critical role that epigenetic factors play in regulating the immune response. Nonetheless, what role m⁶A methylation modification might play in the immune response of non-small cell lung cancer (NSCLC) remains vague.

Methods

Herein, the gene expression, copy number variations (CNVs), and somatic mutations of 31 m⁶A regulators in NSCLC and adjacent control samples from the GEO and TCGA databases were comprehensively explored. Using consensus clustering, m⁶A modification patterns were identified. Correlations between m⁶A modification patterns and immune cell infiltration traits in the tumor immune microenvironment (TME) were systematically analyzed. Differentially expressed genes were verified and screened by random forest and cox regression analysis by comparing different m⁶A modification patterns. Based on the retained gene panel, a risk model was built, and m⁶Ascore for each sample was calculated. The function of m⁶Ascore in NSCLC prognosis, tumor somatic mutations, and chemotherapy/immunotherapy response prediction were evaluated.

Results

Consensus clustering classified all NSCLC samples into two m⁶A clusters (m⁶A_clusterA and m⁶A_clusterB) according to the expression levels of 25 m⁶A regulator genes. Hierarchical clustering further divides the NSCLC samples into two m⁶A gene clusters: m⁶AgeneclusterA and m⁶AgeneclusterB. A panel of 83 genes was screened from the 194 differentially expressed genes between m⁶A gene clusters. Based on this, a risk score model was established. m⁶A modification clusters, m⁶A gene clusters, and m⁶Ascore calculated from the risk model were able to predict tumor stages, immune cell infiltration, clinical prognosis, and tumor somatic mutations. NSCLC patients with high m⁶Ascore have poor drug resistance to chemotherapy drugs (Cisplatin and Gemcitabine) and exhibit considerable therapeutic benefits and favorable clinical responses to anti-PD1 or anti-CTLA4 immunotherapy.

Discussion

In conclusion, methylation modification patterns mediated by the m⁶A regulators in individuals play a non-negligible role in prognosis prediction and immunotherapy response, which will facilitate personalized treatment and immunotherapeutic strategies for NSCLC patients in the future.

Upregulated YTHDF1 associates with tumor immune microenvironment in head and neck squamous cell carcinomas

BACKGROUND

The nature of the tumor immune microenvironment (TME) is essential for the head and neck squamous cell carcinomas (HNSCC) initiation, prognosis, and response to immunotherapy. However, its gene regulatory network remains to be elucidated.

METHODS

To identify N6-methyladenosine (m6A) regulators that are involved in regulating the HNSCC TME, a computational screen was applied to The Cancer Genome Atlas (TCGA) HNSCC patient samples. The effects of mutation, copy number variation (CNV), and transcriptional regulation on YTHDF1 expression were analyzed. We analyzed the TME infiltration, cancer-immunity cycle activities, and YTHDF1-related Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.

RESULTS

Among the 24 m6A regulators, 3 factors (YTHDF1, ELAVL1, and METTL3) were highly correlated with TME infiltration. As the top candidate, YTHDF1 was up-regulated and amplified in HNSCC. YTHDF1 promoter gains active histone marks and high chromatin accessibility, which might be transcriptionally activated by SOX2 and TP63. Moreover, YTHDF1 expression significantly associates with tumor malignant phenotype in HNSCC, which has a positive correlation with CD4+ T cells and a negative correlation with CD8+ T cells infiltration. Specifically, YTHDF1 expression is negatively associated with the cancer-immunity cycle and immune checkpoint inhibitors. In terms of the underlying biological mechanisms, YTHDF1 may interact with YTHDF2/3 to regulate several vital immune-related pathways.

CONCLUSIONS

We identify YTHDF1 associated with TME and elucidate an underlying mechanism of immune escape in HNSCC, which might be used as a predictive marker in guiding immunotherapy.

Identification and Validation of Immune- and Stemness-Related Prognostic Signature of Melanoma

Purpose: Our aim was to construct a signature that accurately predicted the prognostic and immune response of melanoma. Methods: First, the weighted co-expression network analysis (WGCNA) algorithm was used to identify the hub genes related to clinical phenotypes of melanoma in the cancer genome atlas (TCGA) database. Nest, the least absolute shrinkage and selection operator (LASSO) analysis was used to dimensionality reduction of these hub genes and constructed a prognostic signature to predict the prognosis and immunosuppressive response of melanoma. Result: Through in-depth analysis, we constructed a 5-mRNA prognostic signature and verified its prognostic value in internal (TCGA-SKCM, n = 452) and external independent datasets (GSE53118, n = 79). Based on this signature, the tumor immune microenvironment (TME) of melanoma was characterized, and the result was found that patients in the high-risk group had lower CD8 T cell infiltration and immune checkpoint expression (PD-1, PD-L1, CTLA4), as well as higher M0/M2 macrophage infiltration. Our results also found the risk score based on a 5-mRNA signature was significantly associated with tumor mutational burden (TMB) and tumor stem cell markers (CD20, CD38, ABCB5, CD44, etc.). Lastly, we built a nomogram for clinician prediction for the prognosis of patients with melanoma. Conclusion: Our findings indicated that the 5-mRNA signature has an important predictive value for the overall survival of melanoma. By analyzing the tumor immune microenvironment and tumor stem cell marker between different groups, a new method is provided for the stratified diagnosis and treatment of melanoma.

Identification and validation of a metabolism-related model and associated with tumor-infiltrating lymphocytes in p53 mutant lung adenocarcinoma patients

Background

The immunosuppressive tumor microenvironment produced by cancer cells is a key mechanisms of cancer immune escape. In this study, we investigated the relationship between the metabolic patterns and tumor immune environment in the TME of lung adenocarcinoma (LUAD) with the p53 mutation.

Methods

The clinical data of 495 LUAD patients was obtained from The Cancer Genome Atlas as transcriptomic and somatic mutation data. Using differential analysis, survival analysis, and a LASSO regression model based on metabolic unigenes from KEGG pathways, a tumor metabolic model was constructed to predict the prognosis of LUAD patients. Subsequently, nomogram, receiver operating characteristic, and decision curve analyses were conducted to assess the predictive ability of the model. In addition, the ESTIMATE and CIBERSORT algorithms were used to detect tumor purity and estimate the fractions of 22 immune cell types in each patient, respectively. We found a correlation between the composition of immune cells and the tumor metabolic model. The results were validated using an independent GSE72094 dataset with 442 patients, as well as an immunohistochemistry assay, RT-qPCR, and western blot.

Results

The tumor metabolic model reassigned the risk score of every patient, and a tumor metabolic risk score (TMRS) was generated to show the predictive ability for patient prognoses (hazard ratio =0.39; 95% confidence interval: 0.18–0.85). Using a combination of TMRS and clinical features, a nomogram was produced with a predictive accuracy of 0.72. Further analysis showed that CD4 memory resting T cells and M1 macrophages may by correlated with the TMRS, which corresponded to immunoediting in p53 mutant patients. Additionally, the similar expression of ALDH3A1 and MGAT5B were also verified by wetlab experiments.

Conclusions

Based on the identified tumor metabolism-immune landscape, we were able to predict a metabolism risk score for patient prognosis and identify a correlation with two types of infiltrating lymphocytes in the TME of p53-mutated LUAD. This landscape provides insights that will help identify the molecular mechanisms of immune-editing tumor metabolism.

Prognostic value of immune-related genes and immune cell infiltration analysis in the tumor microenvironment of head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is one of the few malignant tumors that respond well to immunotherapy. We aimed to investigate the immune-related genes and immune cell infiltration of HNSCC and construct a predictive model for its prognosis.

METHODS

We calculated the stromal/immune scores of patients with HNSCC from The Cancer Genome Atlas using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm and investigated the relationship between the scores and patients' prognosis. Three machine learning algorithms (LASSO, Random Forest, and Rbsurv) were performed to screen key immune-related genes and constructed a predictive model. The immune cell infiltrating was calculated by the Tumor Immune Estimation Resource algorithm.

RESULTS

The stromal and immune scores significantly correlated with prognosis. A 6-gene signature was selected and displayed a robust predictive effect. The expressions of key genes were associated with immune infiltrating. GSE65858 validated the results.

CONCLUSION

Our study comprehensively analyzed the tumor microenvironment of HNSCC and constructed a robust predictive model, providing a basis for further investigation of therapy.