The Landscape of the Tumor Microenvironment in Skin Cutaneous Melanoma Reveals a Prognostic and Immunotherapeutically Relevant Gene Signature

Exploring the Common Mutational Landscape in Cutaneous Melanoma and Pancreatic Cancer

Cutaneous melanoma (CM) and pancreatic cancer are aggressive tumors whose incidences are rapidly increasing in the last years. This review aims to provide a complete and update description about mutational landscape in CM and pancreatic cancer, focusing on similarities of these two apparently so different tumors in terms of site, type of cell involved, and embryonic origin. The familial forms of CM and pancreatic cancers are often characterized by a common mutated gene, namely CDKN2A. In fact, a germline mutation in CDKN2A gene can be responsible for the development of the familial atypical multiple mole and melanoma syndrome (FAMMM), which is characterized by melanomas and pancreatic cancer development. Sporadic melanoma and pancreatic cancer showed different key-driven genes. The open-access resource cBioPortal has been explored to deepen and investigate the common mutational landscape of these two tumors. We investigated the common mutated genes found in both melanoma and pancreatic cancer with a frequency of at least 5% of tested patients and copy number alterations with a frequency of at least of 3%. Data showed that 18 mutated genes and 3 copy number alterations are present in both melanoma and pancreatic cancers types. Since we found two patients that developed both melanoma and pancreatic cancer, we compared mutation landscape between the two tumors and identified a pathogenic variant in BRCA2 gene. This review gives valuable insights into the genetic underpinnings of melanoma and pancreatic cancer, urging the continued exploration and research of new genetic biomarkers able to identify patients and families at high risk of developing both cancers and to address to screening and to an effective clinical management of the patient.

Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis

Background

The emergence of immunotherapy has changed the treatment modality for melanoma and prolonged the survival of many patients. However, a handful of patients remain unresponsive to immunotherapy and effective tools for early identification of this patient population are still lacking. Researchers have developed machine learning algorithms for predicting immunotherapy response in melanoma, but their predictive accuracy has been inconsistent. Therefore, the present systematic review and meta-analysis was performed to comprehensively evaluate the predictive accuracy of machine learning in melanoma response to immunotherapy.

Methods

Relevant studies were searched in PubMed, Web of Sciences, Cochrane Library, and Embase from their inception to July 30, 2022. The risk of bias and applicability of the included studies were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed on R4.2.0.

Results

A total of 36 studies consisting of 30 cohort studies and 6 case-control studies were included. These studies were mainly published between 2019 and 2022 and encompassed 75 models. The outcome measures of this study were progression-free survival (PFS), overall survival (OS), and treatment response. The pooled c-index was 0.728 (95%CI: 0.629-0.828) for PFS in the training set, 0.760 (95%CI: 0.728-0.792) and 0.819 (95%CI: 0.757-0.880) for treatment response in the training and validation sets, respectively, and 0.746 (95%CI: 0.721-0.771) and 0.700 (95%CI: 0.677-0.724) for OS in the training and validation sets, respectively.

Conclusion

Machine learning has considerable predictive accuracy in melanoma immunotherapy response and prognosis, especially in the former. However, due to the lack of external validation and the scarcity of certain types of models, further studies are warranted.

Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies

Background

Immunotherapy is an effective "precision medicine" treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the advances in bioinformatics and machine learning make such developments plausible. We performed a systematic review to determine the extent of development and validation of immune-related radiogenomic biomarkers for glioblastoma.

Methods

A systematic review was performed following PRISMA guidelines using the PubMed, Medline, and Embase databases. Qualitative analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist. PROSPERO registered: CRD42022340968. Extracted data were insufficiently homogenous to perform a meta-analysis.

Results

Nine studies, all retrospective, were included. Biomarkers extracted from magnetic resonance imaging volumes of interest included apparent diffusion coefficient values, relative cerebral blood volume values, and image-derived features. These biomarkers correlated with genomic markers from tumor cells or immune cells or with patient survival. The majority of studies had a high risk of bias and applicability concerns regarding the index test performed.

Conclusions

Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma. Targeted immunotherapy, stratified by these biomarkers, has the potential to allow individualized neo-adjuvant precision treatment options in clinical trials. However, there are no prospective studies validating these biomarkers, and interpretation is limited due to study bias with little evidence of generalizability.

Identification and validation of TME-related signatures to predict prognosis and response to anti-tumor therapies in skin cutaneous melanoma

The tumor microenvironment (TME) dynamically regulates cancer progression and affects clinical outcomes. This study aimed to identify molecular subtypes and construct a prognostic risk model based on TME-related signatures in skin cutaneous melanoma (SKCM) patients. We categorized SKCM patients based on transcriptome data of SKCM from The Cancer Genome Atlas (TCGA) database and 29 TME-related gene signatures. Differentially expressed genes were identified using univariate Cox regression and Lasso regression analysis, which were used for risk model construction. The robustness of this model was validated in independent external cohorts. Genetic landscape alterations, immune characteristics, and responsiveness to immunotherapy/chemotherapy were evaluated. Three TME-related subtypes were identified, and subtype C3 exhibited the most favorable prognosis, had enriched immune-related pathways, and possessed more infiltration of T_cells_CD8, T_cells_CD4_memory_activated, and Macrophages_M1 but a lower TumorPurity, whereas Macrophages_M2 were increased in subtype C1 and subtype C2. Subtype C1 was more sensitive to Cisplatin, subtype C2 was more sensitive to Temozolomide, and subtype C3 was more sensitive to Paclitaxel; 8 TME-related genes (NOTCH3, HEYL, ZNF703, ABCC2, PAEP, CCL8, HAPLN3, and HPDL) were screened for risk model construction. High-risk patients had dismal prognosis with good prediction performance. Moreover, low-risk patients were more sensitive to Paclitaxel and Temozolomide, whereas high-risk patients were more sensitive to Cisplatin. This risk model had robustness in predicting prognosis in SKCM patients. The results facilitate the understanding of TME-related genes in SKCM and provide a TME-related genes-based predictive model in prognosis and direction of personalized options for SKCM patients.

A novel risk model based on cuproptosis-related lncRNAs predicted prognosis and indicated immune microenvironment landscape of patients with cutaneous melanoma

Cutaneous melanoma (CM) is an aggressive form of malignancy with poor prognostic value. Cuproptosis is a novel type of cell death regulatory mechanism in tumors. However, the role of cuproptosis-related long noncoding RNAs (lncRNAs) in CM remains elusive. The cuproptosis-related lncRNAs were identified using the Pearson correlation algorithm. Through the univariate and multivariate Cox regression analysis, the prognosis of seven lncRNAs associated with cuproptosis was established and a new risk model was constructed. ESTIMATE, CIBERSORT, and single sample gene set enrichment analyses (ssGSEA) were applied to evaluate the immune microenvironment landscape. The Kaplan–Meier survival analysis revealed that the overall survival (OS) of CM patients in the high-risk group was remarkably lower than that of the low-risk group. The result of the validated cohort and the training cohort indicated that the risk model could produce an accurate prediction of the prognosis of CM. The nomogram result demonstrated that the risk score based on the seven prognostic cuproptosis-related lncRNAs was an independent prognostic indicator feature that distinguished it from other clinical features. The result of the immune microenvironment landscape indicated that the low-risk group showed better immunity than high-risk group. The immunophenoscore (IPS) and immune checkpoints results conveyed a better benefit potential for immunotherapy clinical application in the low-risk groups. The enrichment analysis and the gene set variation analysis (GSVA) were adopted to reveal the role of cuproptosis-related lncRNAs mediated by the immune-related signaling pathways in the development of CM. Altogether, the construction of the risk model based on cuproptosis-related lncRNAs can accurately predict the prognosis of CM and indicate the immune microenvironment of CM, providing a new perspective for the future clinical treatment of CM.

Comprehensive Analysis of Pyroptosis-Related Genes and Tumor Microenvironment Infiltration Characterization in Papillary Renal Cell Carcinoma

Background: Immunotherapy has emerged as an important technique for treating a variety of cancers. The dynamic interplay between tumor cells and invading lymphocytes in the tumor microenvironment is responsible for the good response to immunotherapy (TME). Pyroptosis, or inflammation-induced cell death, is closely linked to a number of cancers. However, in papillary renal cell carcinoma (KIRP), the association between pyroptosis and clinical prognosis, immune cell infiltration, and immunotherapy impact remains unknown.

Methods: We carefully investigated the link between pyroptosis and tumor growth, prognosis, and immune cell infiltration by evaluating 52 pyroptosis-related genes. The PRG score was utilized to measure a single tumor patient’s pyroptosis pattern. After that, we looked at how well these values predicted prognoses and therapy responses in KIRP.

Results: We discovered that PRG differences between subgroups were linked to clinical and pathological aspects, prognosis, and TME in two separate genetic subtypes. After that, a PRG score for estimating overall survival (OS) was developed, and its predictive potential in KIRP patients was confirmed. As a result, we developed a very precise nomogram to improve the PRG score’s clinical usefulness. A low PRG score, which is determined by mutation load and immune activation, suggests a good chance of survival. Furthermore, the PRG score was linked to chemotherapeutic drug sensitivity in a substantial way.

Conclusions: The possible functions of PRGs in the TME, clinical and pathological characteristics, and prognosis were established in our thorough investigation of PRGs in KIRP. These results might help us better understand PRGs in KIRP and offer a new avenue for prognostic evaluation and the development of more effective immunotherapy treatments.

Identification of Novel Molecular Therapeutic Targets and Their Potential Prognostic Biomarkers Based on Cytolytic Activity in Skin Cutaneous Melanoma

Skin cutaneous melanoma (SKCM) attracts attention worldwide for its extremely high malignancy. A novel term cytolytic activity (CYT) has been introduced as a potential immunotherapy biomarker associated with counter-regulatory immune responses and enhanced prognosis in tumors. In this study, we extracted all datasets of SKCM patients, namely, RNA sequencing data and clinical information from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database, conducted differential expression analysis to yield 864 differentially expressed genes (DEGs) characteristic of CYT and used non-negative matrix factorization (NMF) method to classify molecular subtypes of SKCM patients. Among all genes, 14 hub genes closely related to prognosis for SKCM were finally screen out. Based on these genes, we constructed a 14-gene prognostic risk model and its robustness and strong predictive performance were further validated. Subsequently, the underlying mechanisms in tumor pathogenesis and prognosis have been defined from a number of perspectives, namely, tumor mutation burden (TMB), copy number variation (CNV), tumor microenvironment (TME), infiltrating immune cells, gene set enrichment analysis (GSEA) and immune checkpoint inhibitors (ICIs). Furthermore, combined with GTEx database and HPA database, the expression of genes in the model was verified at the transcriptional level and protein level, and the relative importance of genes in the model was described by random forest algorithm. In addition, the model was used to predict the difference in sensitivity of SKCM patients to chemotherapy and immunotherapy. Finally, a nomogram was constructed to better aid clinical diagnosis.

Ferroptosis-related genes are candidate diagnostic and prognostic biomarkers for skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) is a disease with the highest mortality rate among skin cancers. As a new type of programmed cell death, ferroptosis has been confirmed to be related to the occurrence and development of a variety of cancers. At present, the expression and prognostic value of ferroptosis-related genes (FRGs) in SKCM are still unclear. In this study, we selected seven FRGs that were differentially expressed in SKCM and related to the patient’s prognosis through the databases. Further studies have shown that these genes are closely related to immune cell infiltration and immune checkpoints. All in all, these seven FRGs may be potential targets for clinical diagnosis, prognosis and treatment of SKCM patients.