Expressions of HLA Class II Genes in Cutaneous Melanoma Were Associated with Clinical Outcome: Bioinformatics Approaches and Systematic Analysis of Public Microarray and RNA-Seq Datasets

Assessment of the biological activity of Marrubium friwaldskyanum Boiss. (Lamiaceae)

Present scientific evidences about the biological activity and potential medical application of extracts derived from Marrubium friwaldskyanum Boiss. are limited. Therefore, our study was undertaken to define several main characteristics in this regard - in vitro cytotoxicity and antitumor properties, antibacterial activity and immunomodulatory potential. Extracts were obtained from different aerial parts of Marrubium friwaldskyanum - stems, leaves and flowers. The in vitro cytotoxicity and antitumor activity of the samples were evaluated by tetrazolium salt reduction tests and Neutral red uptake assays using four human cell lines (a normal fibroblastic and three adenocarcinoma cell lines/A549, HeLa, HT-29/) and by experiments with HT-29 tumor spheroids. Antibacterial activity toward Gram-negative (Escherichia coli) and Gram-positive (Bacillus cereus) species was assessed based on estimation of minimal inhibitory and minimal bactericidal concentrations as well as longitudinal studies on bacterial viability. Ex vivo assays with normal leukocytes were performed to define potential immunomodulatory activity of the extracts. Our results demonstrated selective antitumor activity of the extracts directed against colon adenocarcinoma HT-29 cells and cervical adenocarcinoma HeLa cell line. Metabolic activity of A549 lung adenocarcinoma cells was affected only by the sample derived from flowers. M. friwaldskyanum leaf and flower extracts showed the highest activity, which included reduction of HT-29 tumor spheroid growth and viability. The studied samples exhibited antibacterial activity against both bacterial species tested. Treatment with M. friwaldskyanum extracts affected specific leukocyte populations (HLA+, CD19+, CD11b+, CD25+ cells). These results demonstrate for the first time complex biological effects of extracts derived from M. friwaldskyanum and their potential to serve as a source of valuable compounds for the pharmaceutical industry.

A deep learning approach based on multi-omics data integration to construct a risk stratification prediction model for skin cutaneous melanoma

PURPOSE

Skin cutaneous melanoma (SKCM) is a highly aggressive melanocytic carcinoma whose high heterogeneity and complex etiology make its prognosis difficult to predict. This study aimed to construct a risk subtype typing model for SKCM.

METHODS

The study proposes a deep learning framework combining early fusion feature autoencoder (AE) and late fusion feature AE for risk subtype prediction of SKCM. The deep learning framework integrates mRNA, miRNA, and DNA methylation data of SKCM patients from The Cancer Genome Atlas (TCGA), and clusters the screened multi-omics features associated with survival prognosis to identify risk subtypes. Differential expression analysis and functional enrichment analysis were performed between risk subtypes, while SVM classifiers were constructed between differentially expressed genes (DEGs) obtained by Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression screening and risk subtype labels inferred from multi-omics data, and the predictive robustness of risk subtypes inferred from the risk subtype classification prediction model was validated using two independent datasets.

RESULTS

The deep learning framework that combined early fusion feature AE with late fusion feature AE distinguished the two best risk subtypes compared to the multi-omics integration approach with single strategy AE or PCA. A promising C-index (C-index = 0.748) and a significant difference in survival (log-rank P value = 4.61 × 10-9) were found between the identified risk subtypes. The DEGs with the top significance values together with differentially expressed miRNAs provided the biological interpretation of risk subtypes on SKCM. Finally, the framework was applied to predict risk subtypes in two independent test datasets of SKCM patients, all of which showed good predictive power (C-index > 0.680) and significant survival differences (log-rank P value < 0.01).

CONCLUSION

The SKCM risk subtypes identified by integrating multi-omics data based on deep learning can not only improve the understanding of the molecular mechanisms of SKCM, but also provide clinicians with assistance in treatment decisions.

[Tumor-infiltrating natural killer and T cells in melanoma]

Melanoma is a highly immunogenic cancer with an increased infiltration of lymphocytes (TIL). TIL are a very heterogeneous population which consists among others of CD8+ T cells, CD4+ T cells, regulatory T cells, B cells, and natural killer (NK) cells and may differ highly between melanoma patients. Distribution, concentration, phenotype, and activation status of the infiltrating lymphocytes vary greatly and impact the prognosis. Different subpopulations of CD8+ T cells, CD4+ T cells, and NK cells have been identified and have been associated with both the course of the disease and the therapeutic response to different therapies. Increased knowledge of the different functions, interactions, activation, and possibilities of actively influencing relevant subgroups may lead to novel, innovative, and promising therapeutic options.

Bioinformatics analysis of the clinical significance of HLA class II in breast cancer

Human leukocyte antigen (HLA) class II plays critical roles in antigen presentation and the initiation of immune responses. However, the correlation between the HLA class II gene expression level and the survival of patients with breast cancer is still under investigation. We analyzed microarray and RNA-Seq data of breast cancer from the cancer genome atlas (TCGA), genotype-tissue expression (GTEx) and Oncomine databases by using bioinformatics tools. The expression of the HLA-DQA1, HLA-DQA2, and HLA-DQB2 genes was significantly upregulated in breast cancer. Higher expression levels of HLA class II genes in breast cancer, especially HLA-DOB and HLA-DQB2, were significantly associated with better overall survival. Furthermore, the expression of HLA class II genes was more closely associated with survival in breast cancer than in other cancer types. CD48 coexpressed with both HLA-DOB and HLA-DQB2 was also positively associated with the overall survival of breast cancer patients. The results indicated that HLA class II and CD48 may enhance antitumor immunity, and their expression patterns may serve as potential prognostic biomarkers and therapeutic targets in breast cancer.

Identification and validation of ferroptosis-related lncRNA signature as a prognostic model for skin cutaneous melanoma

Background

Melanoma is a type of skin cancer, which originates from the malignant transformation of epidermal melanocytes, with extremely high lethality. Ferroptosis has been documented to be highly related to cancer pathogenesis and the effect of immunotherapy. In addition, the dysregulation of lncRNAs is greatly implicated in melanoma progression and ferroptosis regulation. However, the significance of ferroptosis-related lncRNA in melanoma treatment and the prognosis of melanoma patients remains elusive.

Methods

Via Least Absolute Shrinkage Selection Operator (LASSO) regression analysis in the TCGA SKCM database, a cutaneous melanoma risk model was established based on differentially-expressed ferroptosis-related lncRNAs (DEfrlncRNAs). The nomogram, receiver operating characteristic (ROC) curves, and calibration plots were conducted to examine the predictive performance of this model. Sequentially, we continued to analyze the differences between the high- and low-risk groups, in terms of clinical characteristics, immune cell infiltration, immune-related functions, and chemotherapy drug sensitivity. Moreover, the expressions of DEfrlncRNAs, PD-L1, and CD8 were also examined by qRT-PCR and immunohistochemical staining in melanoma tissues to further confirm the potential clinical implication of DEfrlncRNAs in melanoma immunotherapy.

Results

16 DEfrlncRNAs were identified, and a representative risk score for patient survival was constructed based on these 16 genes. The risk score was found to be an independent prognostic factor for the survival of melanoma patients. In addition, the low-risk group of patients had higher immune cell infiltration in the melanoma lesions, higher sensitivity to chemotherapeutic agents, and a better survival prognosis. Besides, the high expression of the identified 5 DEfrlncRNA in the low-risk group might suggest a higher possibility to benefit from immune checkpoint blockade therapy in the treatment of melanoma.

Conclusion

The DEfrlncRNA risk prediction model related to ferroptosis genes can independently predict the prognosis of patients with melanoma and provide a basis for evaluating the response of clinical treatment in melanoma.

HLA-DR Presentation of the Tumor Antigen MSLN Associates with Clinical Outcome of Ovarian Cancer Patients

Simple Summary

The immunopeptidome represents the entirety of peptides that are presented on the surface of cells on human leukocyte antigen (HLA) molecules and are recognized by the T-cell receptors of CD4⁺ and CD8⁺ T-cells. Malignant cells present tumor-associated antigens essential for tumor immune surveillance, which can be targeted by T-cell-based immunotherapy approaches. For ovarian carcinomas, various tumor-associated antigens, such as Mucin-16 and Mesothelin, have been described. The aim of our study is to analyze immunopeptidome-defined tumor antigen presentation in ovarian carcinoma patients in relation to clinical characteristics and disease outcomes to define potential biomarkers. Our work demonstrates the central role of HLA-DR-restricted peptide presentation of the tumor antigen Mesothelin and of CD4⁺ T-cell responses for tumor immune surveillance, and underlines Mesothelin as a prime target antigen for novel immunotherapeutic approaches for ovarian carcinoma patients.

Abstract

T-cell recognition of HLA-presented antigens is central for the immunological surveillance of malignant disease and key for the development of novel T-cell-based immunotherapy approaches. In recent years, large-scale immunopeptidome studies identified naturally presented tumor-associated antigens for several malignancies. Regarding ovarian carcinoma (OvCa), Mucin-16 (MUC16) and Mesothelin (MSLN) were recently described as the top HLA class I- and HLA class II-presented tumor antigens, respectively. Here, we investigate the role and impact of immunopeptidome-presented tumor antigens on the clinical outcomes of 39 OvCa patients with a follow-up time of up to 50 months after surgery. Patients with a HLA-restricted presentation of high numbers of different MSLN-derived peptides on their tumors exhibited significantly prolonged progression-free (PFS) and overall survival (OS), whereas the presentation of MUC16-derived HLA class I-restricted peptides had no impact. Furthermore, a high HLA-DRB gene expression was associated with increased PFS and OS. In line, in silico prediction revealed that MSLN-derived HLA class II-presented peptides are predominantly presented on HLA-DR allotypes. In conclusion, the correlation of MSLN tumor antigen presentation and HLA-DRB gene expression with prolonged survival indicates a central role of CD4⁺ T-cell responses for tumor immune surveillance in OvCa, and highlights the importance of immunopeptidome-guided tumor antigen discovery.

Investigation of the Molecular Profile of Granular Cell Tumours and Schwannomas of the Oral Cavity

Granular cell tumours (GCTs) are rare submucosal lesions, thought to develop from Schwann cells, characterised by large polygonal cells with abundant lysosomes. The objectives of this study are to investigate whether GCTs have an antigen-presenting cell (APC) phenotype or a neural crest phenotype using immunohistochemistry and to compare expression profiles with Schwannomas. Immunoreactivity to CD68, HLA-DR, CD163, CD40 and CD11c (APC phenotype) and markers of neural crest cell (NCC) origin S100, SOX10, NSE and GAP43 in 23 cases of GCTs and 10 cases of Schwannomas were evaluated. RT-qPCR was used to identify a possible NCC developmental phenotype in 6 cases of GCTs. GAP43 was identified as a new NCC marker for GCTs, and some evidence was found for an APC phenotype from CD68 and HLA-DR immunoreactivity. RT-qPCR failed to identify an NCC developmental phenotype of GCTs, likely due to technical issues.

Profile of Basal Cell Carcinoma Mutations and Copy Number Alterations - Focus on Gene-Associated Noncoding Variants

Basal cell carcinoma (BCC) of the skin is the most common cancer in humans, characterized by the highest mutation rate among cancers, and is mostly driven by mutations in genes involved in the hedgehog pathway. To date, almost all BCC genetic studies have focused exclusively on protein-coding sequences; therefore, the impact of noncoding variants on the BCC genome is unrecognized. In this study, with the use of whole-exome sequencing of 27 tumor/normal pairs of BCC samples, we performed an analysis of somatic mutations in both protein-coding sequences and gene-associated noncoding regions, including 5'UTRs, 3'UTRs, and exon-adjacent intron sequences. Separately, in each region, we performed hotspot identification, mutation enrichment analysis, and cancer driver identification with OncodriveFML. Additionally, we performed a whole-genome copy number alteration analysis with GISTIC2. Of the >80,000 identified mutations, ~50% were localized in noncoding regions. The results of the analysis generally corroborated the previous findings regarding genes mutated in coding sequences, including PTCH1, TP53, and MYCN, but more importantly showed that mutations were also clustered in specific noncoding regions, including hotspots. Some of the genes specifically mutated in noncoding regions were identified as highly potent cancer drivers, of which BAD had a mutation hotspot in the 3'UTR, DHODH had a mutation hotspot in the Kozak sequence in the 5'UTR, and CHCHD2 frequently showed mutations in the 5'UTR. All of these genes are functionally implicated in cancer-related processes (e.g., apoptosis, mitochondrial metabolism, and de novo pyrimidine synthesis) or the pathogenesis of UV radiation-induced cancers. We also found that the identified BAD and CHCHD2 mutations frequently occur in melanoma but not in other cancers via The Cancer Genome Atlas analysis. Finally, we identified a frequent deletion of chr9q, encompassing PTCH1, and unreported frequent copy number gain of chr9p, encompassing the genes encoding the immune checkpoint ligands PD-L1 and PD-L2. In conclusion, this study is the first systematic analysis of coding and noncoding mutations in BCC and provides a strong basis for further analyses of the variants in BCC and cancer in general.

A Ferroptosis-Related Gene Model Predicts Prognosis and Immune Microenvironment for Cutaneous Melanoma

Background

Cutaneous melanoma is a common but aggressive tumor. Ferroptosis is a recently discovered cell death with important roles in tumor biology. Nevertheless, the prognostic power of ferroptosis-linked genes remained unclear in cutaneous melanoma.

Methods

Cutaneous melanoma patients of TCGA (The Cancer Genome Atlas) were taken as the training cohort while GSE65904 and GSE22153 as the validation cohorts. Multifactor Cox regression model was used to build a prognostic model, and the performance of the model was assessed. Functional enrichment and immune infiltration analysis were used to clarify the mechanisms.

Results

A five ferroptosis-linked gene predictive model was developed. ALOX5 and GCH1 were illustrated as independent predictive factors. Functional assessment showed enriched immune-linked cascades. Immune infiltrating analysis exhibited the distinct immune microenvironment.

Conclusion

Herein, a novel ferroptosis-related gene prognostic model was built in cutaneous melanoma. This model could be used for prognostic prediction, and maybe helpful for the targeted and immunotherapies.

Distinct Molecular Mechanisms of Altered HLA Class II Expression in Malignant Melanoma

BACKGROUND

The human leukocyte antigen (HLA) class II molecules are constitutively expressed in some melanoma, but the underlying molecular mechanisms have not yet been characterized.

METHODS

The expression of HLA class II antigen processing machinery (APM) components was determined in melanoma samples by qPCR, Western blot, flow cytometry and immunohistochemistry. Immunohistochemical and TCGA datasets were used for correlation of HLA class II expression to tumor grading, T-cell infiltration and patients' survival.

RESULTS

The heterogeneous HLA class II expression in melanoma samples allowed us to characterize four distinct phenotypes. Phenotype I totally lacks constitutive HLA class II surface expression, which is inducible by interferon-gamma (IFN-γ); phenotype II expresses low basal surface HLA class II that is further upregulated by IFN-γ; phenotype III lacks constitutive and IFN-γ controlled HLA class II expression, but could be induced by epigenetic drugs; and in phenotype IV, lack of HLA class II expression is not recovered by any drug tested. High levels of HLA class II APM component expression were associated with an increased intra-tumoral CD4+ T-cell density and increased patients' survival.

CONCLUSIONS

The heterogeneous basal expression of HLA class II antigens and/or APM components in melanoma cells is caused by distinct molecular mechanisms and has clinical relevance.

A Multi-Omics Analysis of Metastatic Melanoma Identifies a Germinal Center-Like Tumor Microenvironment in HLA-DR-Positive Tumor Areas

The emergence of immune checkpoint inhibitors has dramatically changed the therapeutic landscape for patients with advanced melanoma. However, relatively low response rates and a high incidence of severe immune-related adverse events have prompted the search for predictive biomarkers. A positive predictive value has been attributed to the aberrant expression of Human Leukocyte Antigen-DR (HLA-DR) by melanoma cells, but it remains unknown why this is the case. In this study, we have examined the microenvironment of HLA-DR positive metastatic melanoma samples using a multi-omics approach. First, using spatial, single-cell mapping by multiplexed immunohistochemistry, we found that the microenvironment of HLA-DR positive melanoma regions was enriched by professional antigen presenting cells, including classical dendritic cells and macrophages, while a more general cytotoxic T cell exhaustion phenotype was present in these regions. In parallel, transcriptomic analysis on micro dissected tissue from HLA-DR positive and HLA-DR negative areas showed increased IFNγ signaling, enhanced leukocyte adhesion and mononuclear cell proliferation in HLA-DR positive areas. Finally, multiplexed cytokine profiling identified an increased expression of germinal center cytokines CXCL12, CXCL13 and CCL19 in HLA-DR positive metastatic lesions, which, together with IFNγ and IL4 could serve as biomarkers to discriminate tumor samples containing HLA-DR overexpressing tumor cells from HLA-DR negative samples. Overall, this suggests that HLA-DR positive areas in melanoma attract the anti-tumor immune cell infiltration by creating a dystrophic germinal center-like microenvironment where an enhanced antigen presentation leads to an exhausted microenvironment, nevertheless representing a fertile ground for a better efficacy of anti-PD-1 inhibitors due to simultaneous higher levels of PD-1 in the immune cells and PD-L1 in the HLA-DR positive melanoma cells.

Computing Skin Cutaneous Melanoma Outcome From the HLA-Alleles and Clinical Characteristics

Human leukocyte antigen (HLA) are essential components of the immune system that stimulate immune cells to provide protection and defense against cancer. Thousands of HLA alleles have been reported in the literature, but only a specific set of HLA alleles are present in an individual. The capability of the immune system to recognize cancer-associated mutations depends on the presence of a particular set of alleles, which elicit an immune response to fight against cancer. Therefore, the occurrence of specific HLA alleles affects the survival outcome of cancer patients. In the current study, prediction models were developed, using 401 cutaneous melanoma patients, to predict the overall survival (OS) of patients using their clinical data and HLA alleles. We observed that the presence of certain favorable superalleles like HLA-B∗55 (HR = 0.15, 95% CI 0.034-0.67), HLA-A∗01 (HR = 0.5, 95% CI 0.3-0.8), is responsible for the improved OS. In contrast, the presence of certain unfavorable superalleles such as HLA-B∗50 (HR = 2.76, 95% CI 1.284-5.941), HLA-DRB1∗12 (HR = 3.44, 95% CI 1.64-7.2) is responsible for the poor survival. We developed prediction models using key 14 HLA superalleles, demographic, and clinical characteristics for predicting high-risk cutaneous melanoma patients and achieved HR = 4.52 (95% CI 3.088-6.609, p-value = 8.01E-15). Eventually, we also provide a web-based service to the community for predicting the risk status in cutaneous melanoma patients (https://webs.iiitd.edu.in/raghava/skcmhrp/).