Characterization of the CpG Island Hypermethylated Phenotype Subclass in Primary Melanomas

Methylation of nonessential genes in cutaneous melanoma - Rule Out hypothesis

Differential methylation plays an important role in melanoma development and is associated with survival, progression and response to treatment. However, the mechanisms by which methylation promotes melanoma development are poorly understood. The traditional explanation of selective advantage provided by differential methylation postulates that hypermethylation of regulatory 5'-cytosine-phosphate-guanine-3' dinucleotides (CpGs) downregulates the expression of tumor suppressor genes and therefore promotes tumorigenesis. We believe that other (not necessarily alternative) explanations of the selective advantages of methylation are also possible. Here, we hypothesize that melanoma cells use methylation to shut down transcription of nonessential genes - those not required for cell survival and proliferation. Suppression of nonessential genes allows tumor cells to be more efficient in terms of energy and resource usage, providing them with a selective advantage over the tumor cells that transcribe and subsequently translate genes they do not need. We named the hypothesis the Rule Out (RO) hypothesis. The RO hypothesis predicts higher methylation of CpGs located in regulatory regions (CpG islands) of nonessential genes. It also predicts the higher methylation of regulatory CpGs linked to nonessential genes in melanomas compared to nevi and lower expression of nonessential genes in malignant (derived from melanoma) versus normal (derived from nonaffected skin) melanocytes. The analyses conducted using in-house and publicly available data found that all predictions derived from the RO hypothesis hold, providing observational support for the hypothesis.

Examination of the Functional Relationship between PD-L1 DNA Methylation and mRNA Expression in Non-Small-Cell Lung Cancer

Immunotherapy targeting the interaction between programmed cell death protein 1 (PD-1) and programmed death-ligand 1 (PD-L1) is a treatment option for patients with non-small-cell lung cancer (NSCLC). The expression of PD-L1 by the NSCLC cells determines treatment effectiveness, but the relationship between PD-L1 DNA methylation and expression has not been clearly described. We investigated PD-L1 DNA methylation, mRNA expression, and protein expression in NSCLC cell lines and tumor biopsies. We used clustered regularly interspaced short palindromic repeats-associated protein 9 (CRISPR-Cas9) to modify PD-L1 genetic contexts and endonuclease deficient Cas9 (dCas9) fusions with ten-eleven translocation methylcytosine dioxygenase 1 (TET1) and DNA (cytosine-5)-methyltransferase 3A (DNMT3A) to manipulate PD-L1 DNA methylation. In NSCLC cell lines, we identified specific PD-L1 CpG sites with methylation levels inversely correlated with PD-L1 mRNA expression. However, inducing PD-L1 mRNA expression with interferon-γ did not decrease the methylation level for these CpG sites, and using CRISPR-Cas9, we found that the CpG sites did not directly confer a negative regulation. dCas9-TET1 and dCas9-DNMT3A could induce PD-L1 hypo- and hyper-methylation, respectively, with the latter conferring a decrease in expression showing the functional impact of methylation. In NSCLC biopsies, the inverse correlation between the methylation and expression of PD-L1 was weak. We conclude that there is a regulatory link between PD-L1 DNA methylation and expression. However, since these measures are weakly associated, this study highlights the need for further research before PD-L1 DNA methylation can be implemented as a biomarker and drug target for measures to improve the effectiveness of PD-1/PD-L1 immunotherapy in NSCLC.

Evaluation and Comparison of Multi-Omics Data Integration Methods for Subtyping of Cutaneous Melanoma

There is a growing number of multi-domain genomic datasets for human tumors. Multi-domain data are usually interpreted after separately analyzing single-domain data and integrating the results post hoc. Data fusion techniques allow for the real integration of multi-domain data to ideally improve the tumor classification results for the prognosis and prediction of response to therapy. We have previously described the joint singular value decomposition (jSVD) technique as a means of data fusion. Here, we report on the development of these methods in open source code based on R and Python and on the application of these data fusion methods. The Cancer Genome Atlas (TCGA) Skin Cutaneous Melanoma (SKCM) dataset was used as a benchmark to evaluate the potential of the data fusion approaches to improve molecular classification of cancers in a clinically relevant manner. Our data show that the data fusion approach does not generate classification results superior to those obtained using single-domain data. Data from different domains are not entirely independent from each other, and molecular classes are characterized by features that penetrate different domains. Data fusion techniques might be better suited for response prediction, where they could contribute to the identification of predictive features in a domain-independent manner to be used as biomarkers.