Dataset Information

Prognostic and predictive value of PD-L2 DNA methylation and mRNA expression in melanoma.

ABSTRACT: BACKGROUND:PD-L1 (programmed cell death 1 ligand 1) expression in melanoma has been associated with a better response to anti-PD-1 (programmed cell death 1) therapy. However, patients with PD-L1-negative melanomas can respond to anti-PD-1 blockade, suggesting that the other PD-1 ligand, PD-L2 (programmed cell death 1 ligand 2), might also be relevant for efficacy of PD-1 inhibition. We investigated PD-L2 expression and methylation as a prognostic and predictive biomarker in melanoma. METHODS:DNA methylation at five CpG loci and gene expression of PD-L2 were evaluated with regard to survival in 470 melanomas from The Cancer Genome Atlas. PD-L2 promoter methylation in correlation with PD-L2 mRNA and protein expression was analyzed in human melanoma cell lines. Prognostic and predictive value of PD-L2 methylation was validated using quantitative methylation-specific PCR in a multicenter cohort of 129 melanoma patients receiving anti-PD-1 therapy. mRNA sequencing data of 121 melanoma patients receiving anti-PD-1 therapy provided by Liu et al. were analyzed for PD-L2 mRNA expression. RESULTS:We found significant correlations between PD-L2 methylation and mRNA expression levels in melanoma tissues and cell lines. Interferon-? inducible PD-L2 protein expression correlated with PD-L2 promoter methylation in melanoma cells. PD-L2 DNA promoter hypomethylation and high mRNA expression were found to be strong predictors of prolonged overall survival. In pre-treatment melanoma samples from patients receiving anti-PD-1 therapy, low PD-L2 DNA methylation and high PD-L2 mRNA expression predicted longer progression-free survival. CONCLUSION:PD-L2 expression seems to be regulated via DNA promoter methylation. PD-L2 DNA methylation and mRNA expression may predict progression-free survival in melanoma patients receiving anti-PD-1 immunotherapy. Assessment of PD-L2 should be included in further clinical trials with anti-PD-1 antibodies.

SUBMITTER: Hoffmann F

PROVIDER: S-EPMC7318478 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Prognostic and predictive value of PD-L2 DNA methylation and mRNA expression in melanoma.

Hoffmann Friederike F Zarbl Romina R Niebel Dennis D Sirokay Judith J Fröhlich Anne A Posch Christian C Holderried Tobias A W TAW Brossart Peter P Saavedra Gonzalo G Kuster Pia P Strieth Sebastian S Gielen Gerrit H GH Ring Sandra S SS Dietrich Jörn J Pietsch Torsten T Flatz Lukas L Kristiansen Glen G Landsberg Jennifer J Dietrich Dimo D

Clinical epigenetics 20200626 1

<h4>Background</h4>PD-L1 (programmed cell death 1 ligand 1) expression in melanoma has been associated with a better response to anti-PD-1 (programmed cell death 1) therapy. However, patients with PD-L1-negative melanomas can respond to anti-PD-1 blockade, suggesting that the other PD-1 ligand, PD-L2 (programmed cell death 1 ligand 2), might also be relevant for efficacy of PD-1 inhibition. We investigated PD-L2 expression and methylation as a prognostic and predictive biomarker in melanoma.<h4> ...[more]

PMID: 32586358

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Project description:Introduction: Despite the significant progress in understanding cancer biology, the deduction of metastasis is still a challenge in the clinic. Transcriptional regulation is one of the critical mechanisms underlying cancer development. Even though mRNA, microRNA, and DNA methylation mechanisms have a crucial impact on the metastatic outcome, there are no comprehensive data mining models that combine all transcriptional regulation aspects for metastasis prediction. This study focused on identifying the regulatory impact of genetic biomarkers for monitoring metastatic molecular signatures of melanoma by investigating the consolidated effect of miRNA, mRNA, and DNA methylation. Method: We developed multiple machine learning models to distinguish the metastasis by integrating miRNA, mRNA, and DNA methylation markers. We used the TCGA melanoma dataset to differentiate between metastatic melanoma samples by assessing a set of predictive models. For this purpose, machine learning models using a support vector machine with different kernels, artificial neural networks, random forests, AdaBoost, and Naïve Bayes are compared. An iterative combination of differentially expressed miRNA, mRNA, and methylation signatures is used as a candidate marker to reveal each new biomarker category's impact. In each iteration, the performances of the combined models are calculated. During all comparisons, the choice of the feature selection method and under and oversampling approaches are analyzed. Selected biomarkers of the highest performing models are further analyzed for the biological interpretation of functional enrichment. Results: In the initial model, miRNA biomarkers can identify metastatic melanoma with an 81% F-score. The addition of mRNA markers upon miRNA increased the F-score to 92%. In the final integrated model, the addition of the methylation data resulted in a similar F-score of 92% but produced a stable model with low variance across multiple trials. Conclusion: Our results support the role of miRNA regulation in metastatic melanoma as miRNA markers model metastasis outcomes with high accuracy. Moreover, the integrated evaluation of miRNA with mRNA and methylation biomarkers increases the model's power. It populates selected biomarkers on the metastasis-associated pathways of melanoma, such as the "osteoclast", "Rap1 signaling", and "chemokine signaling" pathways. Source Code: https://github.com/aysegul-kt/MelonomaMetastasisPrediction/.

Project description:Ovarian cancer (OC) is one of the deadliest gynecological cancers. Recent studies suggest a crucial role of inflammatory immune system cells in the progression and metastasis of OC. The understanding of inflammatory mechanisms is pivotal for the selection of a biomarker that allows the differentiation between malignant and benign tumors, monitoring the progression of the disease, and identification of patients that will respond to implemented treatment. Our study is aimed at evaluating the profile of IL-6 in the plasma and peritoneal fluid (PF) of patients with various clinical manifestations of OC (n = 78). We also examined the relationship between IL-6 and PD-L1/PD-L2 positive CD45+CD14+ inflammatory cell (MO/MA) levels in three OC environments (TME): peripheral blood (PB), PF, and tumor (TT) and their clinical and prognostic relevance in OC patients. The expression of PD-L1/PD-L2 molecules was analyzed by flow cytometry. The IL-6 levels were determined by ELISA. We found an elevated level of PD-L1/PD-L2 positive MO/MA in TT compared to PB (p < 0.0001). Significantly higher (p < 0.0001) levels of IL-6 were observed in PF of the OC patients than in the benign ovarian tumor group (n = 31). Additionally, we found higher IL-6 levels in PF than in the plasma of the OC patients. Interestingly, accumulation of IL-6 was observed in PF of patients with low-differentiated OC and correlated with worse prognosis. Moreover, we observed correlations between the level of IL-6 and CD45+CD14+ cells and between CD45+CD14+PD-L1+ cells and the IL-6 level in PF. For the first time, we discovered that the higher percentage of CD45+CD14+PD-L2+ cells in PF predicts better survival of OC patients. Our study suggests that CD45+CD14+PD-L2+ cells and IL-6 may be predictive biomarkers for OC patients. Understanding how the composition of TME changes during OC development and progression is a prerequisite for projecting new therapeutic strategies. Overall, further validation research is warranted.

Dataset Information

Prognostic and predictive value of PD-L2 DNA methylation and mRNA expression in melanoma.

Publications

Prognostic and predictive value of PD-L2 DNA methylation and mRNA expression in melanoma.

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