DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma

A review on trends in development and translation of omics signatures in cancer

The field of cancer genomics and transcriptomics has evolved from targeted profiling to swift sequencing of individual tumor genome and transcriptome. The steady growth in genome, epigenome, and transcriptome datasets on a genome-wide scale has significantly increased our capability in capturing signatures that represent both the intrinsic and extrinsic biological features of tumors. These biological differences can help in precise molecular subtyping of cancer, predicting tumor progression, metastatic potential, and resistance to therapeutic agents. In this review, we summarized the current development of genomic, methylomic, transcriptomic, proteomic and metabolic signatures in the field of cancer research and highlighted their potentials in clinical applications to improve diagnosis, prognosis, and treatment decision in cancer patients.

MetDecode: methylation-based deconvolution of cell-free DNA for noninvasive multi-cancer typing

MOTIVATION

Circulating-cell free DNA (cfDNA) is widely explored as a noninvasive biomarker for cancer screening and diagnosis. The ability to decode the cells of origin in cfDNA would provide biological insights into pathophysiological mechanisms, aiding in cancer characterization and directing clinical management and follow-up.

RESULTS

We developed a DNA methylation signature-based deconvolution algorithm, MetDecode, for cancer tissue origin identification. We built a reference atlas exploiting de novo and published whole-genome methylation sequencing data for colorectal, breast, ovarian, and cervical cancer, and blood-cell-derived entities. MetDecode models the contributors absent in the atlas with methylation patterns learnt on-the-fly from the input cfDNA methylation profiles. In addition, our model accounts for the coverage of each marker region to alleviate potential sources of noise. In-silico experiments showed a limit of detection down to 2.88% of tumor tissue contribution in cfDNA. MetDecode produced Pearson correlation coefficients above 0.95 and outperformed other methods in simulations (P < 0.001; T-test; one-sided). In plasma cfDNA profiles from cancer patients, MetDecode assigned the correct tissue-of-origin in 84.2% of cases. In conclusion, MetDecode can unravel alterations in the cfDNA pool components by accurately estimating the contribution of multiple tissues, while supplied with an imperfect reference atlas.

AVAILABILITY AND IMPLEMENTATION

MetDecode is available at https://github.com/JorisVermeeschLab/MetDecode.

DNA methylation variations of DNA damage response correlate survival and local immune status in melanomas

AIM

We aimed to explore the impact of DNA methylation alterations on the DNA damage response (DDR) in melanoma prognosis and immunity. MATERIAL & METHODS: Different melanoma cohorts with molecular and clinical data were included.

RESULTS

Hierarchical clustering utilizing different combinations of DDR-relevant CpGs yielded distinct melanoma subtypes, which were characteristic of different prognoses, transcriptional function profiles of DDR, and immunity and immunotherapy responses but were associated with similar tumor mutation burdens. We then constructed and validated a clinically applicable 4-CpG risk-score signature for predicting survival and immunotherapy response.

CONCLUSION

Our study describes the close interrelationship among DNA methylation, DDR machinery, local tumor immune status, melanoma prognosis, and immunotherapy response.

Epigenetic changes associated with Bacillus Calmette-Guerin (BCG) treatment in bladder cancer

Bacillus Calmette-Guérin (BCG) treatment for non-muscle invasive bladder cancer (NMIBC) is an established immunotherapeutic, however, a significant portion of patients do not respond to treatment. Despite extensive research into the therapeutic mechanism of BCG, gaps remain in our understanding. This review specifically focuses on the epigenomic contributions in the immune microenvironment, in the context of BCG treatment for NMIBC. We also summarise the current understanding of NMIBC epigenetic characteristics, and discuss how future targeted strategies for BCG therapy should incorporate epigenomic biomarkers in conjunction with genomic biomarkers.

Genomic and Epigenomic Biomarkers of Immune Checkpoint Immunotherapy Response in Melanoma: Current and Future Perspectives

Immune checkpoint inhibitors (ICIs) demonstrate durable responses, long-term survival benefits, and improved outcomes in cancer patients compared to chemotherapy. However, the majority of cancer patients do not respond to ICIs, and a high proportion of those patients who do respond to ICI therapy develop innate or acquired resistance to ICIs, limiting their clinical utility. The most studied predictive tissue biomarkers for ICI response are PD-L1 immunohistochemical expression, DNA mismatch repair deficiency, and tumour mutation burden, although these are weak predictors of ICI response. The identification of better predictive biomarkers remains an important goal to improve the identification of patients who would benefit from ICIs. Here, we review established and emerging biomarkers of ICI response, focusing on epigenomic and genomic alterations in cancer patients, which have the potential to help guide single-agent ICI immunotherapy or ICI immunotherapy in combination with other ICI immunotherapies or agents. We briefly review the current status of ICI response biomarkers, including investigational biomarkers, and we present insights into several emerging and promising epigenomic biomarker candidates, including current knowledge gaps in the context of ICI immunotherapy response in melanoma patients.

Artificial neural network identified a 20-gene panel in predicting immunotherapy response and survival benefits after anti-PD1/PD-L1 treatment in glioblastoma patients

BACKGROUND

Immune checkpoint inhibitors (ICIs) are a promising immunotherapy approach, but glioblastoma clinical trials have not yielded satisfactory results.

OBJECTIVE

To screen glioblastoma patients who may benefit from immunotherapy.

METHODS

Eighty-one patients receiving anti-PD1/PD-L1 treatment from a large-scale clinical trial and 364 patients without immunotherapy from The Cancer Genome Atlas (TCGA) were included. Patients in the ICI-treated cohort were divided into responders and nonresponders according to overall survival (OS), and the most critical responder-relevant features were screened using random forest (RF). We constructed an artificial neural network (ANN) model and verified its predictive value with immunotherapy response and OS.

RESULTS

We defined two groups of ICI-treated glioblastoma patients with large differences in survival benefits as nonresponders (OS ≤6 months, n = 18) and responders (OS ≥17 months, n = 8). No differentially mutated genes were observed between responders and nonresponders. We performed RF analysis to select the most critical responder-relevant features and developed an ANN with 20 input variables, five hidden neurons and one output neuron. Receiver operating characteristic analysis and the DeLong test demonstrated that the ANN had the best performance in predicting responders, with an AUC of 0.97. Survival analysis indicated that ANN-predicted responders had significantly better OS rates than nonresponders.

CONCLUSION

The 20-gene panel developed by the ANN could be a promising biomarker for predicting immunotherapy response and prognostic benefits in ICI-treated GBM patients and may guide oncologists to accurately select potential responders for the preferential use of ICIs.

Conserved methylation signatures associate with the tumor immune microenvironment and immunotherapy response

BACKGROUND

Aberrant DNA methylation is a major characteristic of cancer genomes. It remains unclear which biological processes determine epigenetic reprogramming and how these processes influence the variants in the cancer methylome, which can further impact cancer phenotypes.

METHODS

We performed pairwise permutations of 381,900 loci in 569 paired DNA methylation profiles of cancer tissue and matched normal tissue from The Cancer Genome Atlas (TCGA) and defined conserved differentially methylated positions (DMPs) based on the resulting null distribution. Then, we derived independent methylation signatures from 2,465 cancer-only methylation profiles from the TCGA and 241 cell line-based methylation profiles from the Genomics of Drug Sensitivity in Cancer (GDSC) cohort using nonnegative matrix factorization (NMF). We correlated DNA methylation signatures with various clinical and biological features, including age, survival, cancer stage, tumor immune microenvironment factors, and immunotherapy response. We inferred the determinant genes of these methylation signatures by integrating genomic and transcriptomic data and evaluated the impact of these signatures on cancer phenotypes in independent bulk and single-cell RNA/methylome cohorts.

RESULTS

We identified 7,364 differentially methylated positions (2,969 Hyper-DMPs and 4,395 Hypo-DMPs) in nine cancer types from the TCGA. We subsequently retrieved three highly conserved, independent methylation signatures (Hyper-MS1, Hypo-MS1, and Hypo-MS4) from cancer tissues and cell lines based on these Hyper and Hypo-DMPs. Our data suggested that Hypo-MS4 activity predicts poor survival and is associated with immunotherapy response and distant tumor metastasis, and Hypo-MS4 activity is related to TP53 mutation and FOXA1 binding specificity. In addition, we demonstrated a correlation between the activities of Hypo-MS4 in cancer cells and the fractions of regulatory CD4 + T cells with the expression levels of immunological genes in the tumor immune microenvironment.

CONCLUSIONS

Our findings demonstrated that the methylation signatures of distinct biological processes are associated with immune activity in the cancer microenvironment and predict immunotherapy response.

Informing immunotherapy with multi-omics driven machine learning

Progress in sequencing technologies and clinical experiments has revolutionized immunotherapy on solid and hematologic malignancies. However, the benefits of immunotherapy are limited to specific patient subsets, posing challenges for broader application. To improve its effectiveness, identifying biomarkers that can predict patient response is crucial. Machine learning (ML) play a pivotal role in harnessing multi-omic cancer datasets and unlocking new insights into immunotherapy. This review provides an overview of cutting-edge ML models applied in omics data for immunotherapy analysis, including immunotherapy response prediction and immunotherapy-relevant tumor microenvironment identification. We elucidate how ML leverages diverse data types to identify significant biomarkers, enhance our understanding of immunotherapy mechanisms, and optimize decision-making process. Additionally, we discuss current limitations and challenges of ML in this rapidly evolving field. Finally, we outline future directions aimed at overcoming these barriers and improving the efficiency of ML in immunotherapy research.

DNA Methylation Signatures Correlate with Response to Immune Checkpoint Inhibitors in Metastatic Melanoma

BACKGROUND

DNA methylation profiles have emerged as potential predictors of therapeutic response in various solid tumors.

OBJECTIVE

This study aimed to analyze the DNA methylation profiles of patients with stage IV metastatic melanoma undergoing first-line immune checkpoint inhibitor treatment and evaluate their correlation with a radiological response according to immune-related Response Evaluation Criteria in Solid Tumors (iRECIST).

METHODS

A total of 81 tissue samples from 71 patients with metastatic melanoma (27 female, 44 male) were included in this study. We utilized Illumina Methylation EPIC Beadchips to retrieve their genome-wide methylation profile by interrogating >850,000 CpG sites. Clustering based on the 500 most differentially methylated genes was conducted to identify distinct methylation patterns associated with immune checkpoint inhibitor response. Results were further aligned with an independent, previously published data set.

RESULTS

The median progression-free survival was 8.5 months (range: 0-104.1 months), and the median overall survival was 30.6 months (range: 0-104.1 months). Objective responses were observed in 29 patients (40.8%). DNA methylation profiling revealed specific signatures that correlated with radiological response to immune checkpoint inhibitors. Three distinct clusters were identified based on the methylation patterns of the 500 most differentially methylated genes. Cluster 1 (12/12) and cluster 2 (12/24) exhibited a higher proportion of responders, while cluster 3 (39/45) predominantly consisted of non-responders. In the validation data set, responders also showed more frequent hypomethylation although differences in the data sets limit the interpretation.

CONCLUSIONS

These findings suggest that DNA methylation profiling of tumor tissues might serve as a predictive biomarker for immune checkpoint inhibitor response in patients with metastatic melanoma. Further validation studies are warranted to confirm the efficiency of DNA methylation profiling as a predictive tool in the context of immunotherapy for metastatic melanoma.

DNMT3L inhibits hepatocellular carcinoma progression through DNA methylation of CDO1: insights from big data to basic research

BACKGROUND

DNMT3L is a crucial DNA methylation regulatory factor, yet its function and mechanism in hepatocellular carcinoma (HCC) remain poorly understood. Bioinformatics-based big data analysis has increasingly gained significance in cancer research. Therefore, this study aims to elucidate the role of DNMT3L in HCC by integrating big data analysis with experimental validation.

METHODS

Dozens of HCC datasets were collected to analyze the expression of DNMT3L and its relationship with prognostic indicators, and were used for molecular regulatory relationship evaluation. The effects of DNMT3L on the malignant phenotypes of hepatoma cells were confirmed in vitro and in vivo. The regulatory mechanisms of DNMT3L were explored through MSP, western blot, and dual-luciferase assays.

RESULTS

DNMT3L was found to be downregulated in HCC tissues and associated with better prognosis. Overexpression of DNMT3L inhibits cell proliferation and metastasis. Additionally, CDO1 was identified as a target gene of DNMT3L and also exhibits anti-cancer effects. DNMT3L upregulates CDO1 expression by competitively inhibiting DNMT3A-mediated methylation of CDO1 promoter.

CONCLUSIONS

Our study revealed the role and epi-transcriptomic regulatory mechanism of DNMT3L in HCC, and underscored the essential role and applicability of big data analysis in elucidating complex biological processes.

Plasma Cell-Free Tumor Methylome as a Biomarker in Solid Tumors: Biology and Applications

DNA methylation is a fundamental mechanism of epigenetic control in cells and its dysregulation is strongly implicated in cancer development. Cancers possess an extensively hypomethylated genome with focal regions of hypermethylation at CPG islands. Due to the highly conserved nature of cancer-specific methylation, its detection in cell-free DNA in plasma using liquid biopsies constitutes an area of interest in biomarker research. The advent of next-generation sequencing and newer computational technologies have allowed for the development of diagnostic and prognostic biomarkers that utilize methylation profiling to diagnose disease and stratify risk. Methylome-based predictive biomarkers can determine the response to anti-cancer therapy. An additional emerging application of these biomarkers is in minimal residual disease monitoring. Several key challenges need to be addressed before cfDNA-based methylation biomarkers become fully integrated into practice. The first relates to the biology and stability of cfDNA. The second concerns the clinical validity and generalizability of methylation-based assays, many of which are cancer type-specific. The third involves their practicability, which is a stumbling block for translating technologies from bench to clinic. Future work on developing pan-cancer assays with their respective validities confirmed using well-designed, prospective clinical trials is crucial in pushing for the greater use of these tools in oncology.

Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review

BACKGROUND

The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology.

MATERIALS AND METHODS

We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data.

RESULTS

A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data.

CONCLUSION

AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.

DNA hypomethylation mediates immune response in pan-cancer

Abnormal DNA methylation is a fundamental characterization of epigenetics in cancer. Here we demonstrate that aberrant DNA methylating can modulate the tumour immune microenvironment in 16 cancer types. Differential DNA methylation in promoter region can regulate the transcriptomic pattern of immune-related genes and DNA hypomethylation mainly participated in the processes of immunity, carcinogenesis and immune infiltration. Moreover, many cancer types shared immune-related functions, like activation of innate immune response, interferon gamma response and NOD-like receptor signalling pathway. DNA methylation can further help identify molecular subtypes of kidney renal clear cell carcinoma. These subtypes are characterized by DNA methylation pattern, major histocompatibility complex, cytolytic activity and cytotoxic t lymphocyte and tumour mutation burden, and subtype with hypomethylation pattern shows unstable immune status. Then, we investigate the DNA methylation pattern of exhaustion-related marker genes and further demonstrate the role of hypomethylation in tumour immune microenvironment. In summary, our findings support the use of hypomethylation as a biomarker to understand the mechanism of tumour immune environment.

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.

Reshaping the tumour immune microenvironment in solid tumours via tumour cell and immune cell DNA methylation: from mechanisms to therapeutics

In recent years, the tumour microenvironment (TME) of solid tumours has attracted more and more attention from researchers, especially those non-tumour components such as immune cells. Infiltration of various immune cells causes tumour immune microenvironment (TIME) heterogeneity, and results in different therapeutic effects. Accumulating evidence showed that DNA methylation plays a crucial role in remodelling TIME and is associated with the response towards immune checkpoint inhibitors (ICIs). During carcinogenesis, DNA methylation profoundly changes, specifically, there is a global loss of DNA methylation and increased DNA methylation at the promoters of suppressor genes. Immune cell differentiation is disturbed, and exclusion of immune cells from the TME occurs at least in part due to DNA methylation reprogramming. Therefore, pharmaceutical interventions targeting DNA methylation are promising. DNA methyltransferase inhibitors (DNMTis) enhance antitumor immunity by inducing transcription of transposable elements and consequent viral mimicry. DNMTis upregulate the expression of tumour antigens, mediate immune cells recruitment and reactivate exhausted immune cells. In preclinical studies, DNMTis have shown synergistic effect when combined with immunotherapies, suggesting new strategies to treat refractory solid tumours.

Epigenetic control of CD1D expression as a mechanism of resistance to immune checkpoint therapy in poorly immunogenic melanomas

Immune Checkpoint Therapies (ICT) have revolutionized the treatment of metastatic melanoma. However, only a subset of patients reaches complete responses. Deficient β2-microglobulin (β2M) expression impacts antigen presentation to T cells, leading to ICT resistance. Here, we investigate alternative β2M-correlated biomarkers that associate with ICT resistance. We shortlisted immune biomarkers interacting with human β2M using the STRING database. Next, we profiled the transcriptomic expression of these biomarkers in association with clinical and survival outcomes in the melanoma GDC-TCGA-SKCM dataset and a collection of publicly available metastatic melanoma cohorts treated with ICT (anti-PD1). Epigenetic control of identified biomarkers was interrogated using the Illumina Human Methylation 450 dataset from the melanoma GDC-TCGA-SKCM study. We show that β2M associates with CD1d, CD1b, and FCGRT at the protein level. Co-expression and correlation profile of B2M with CD1D, CD1B, and FCGRT dissociates in melanoma patients following B2M expression loss. Lower CD1D expression is typically found in patients with poor survival outcomes from the GDC-TCGA-SKCM dataset, in patients not responding to anti-PD1 immunotherapies, and in a resistant anti-PD1 pre-clinical model. Immune cell abundance study reveals that B2M and CD1D are both enriched in tumor cells and dendritic cells from patients responding to anti-PD1 immunotherapies. These patients also show increased levels of natural killer T (NKT) cell signatures in the tumor microenvironment (TME). Methylation reactions in the TME of melanoma impact the expression of B2M and SPI1, which controls CD1D expression. These findings suggest that epigenetic changes in the TME of melanoma may impact β2M and CD1d-mediated functions, such as antigen presentation for T cells and NKT cells. Our hypothesis is grounded in comprehensive bioinformatic analyses of a large transcriptomic dataset from four clinical cohorts and mouse models. It will benefit from further development using well-established functional immune assays to support understanding the molecular processes leading to epigenetic control of β2M and CD1d. This research line may lead to the rational development of new combinatorial treatments for metastatic melanoma patients that poorly respond to ICT.

The Crossroads of Cancer Epigenetics and Immune Checkpoint Therapy

Immune checkpoint inhibitors (ICI) have significantly improved treatment outcomes for several types of cancer over the past decade, but significant challenges that limit wider effectiveness of current immunotherapies remain to be addressed. Certain "cold" tumor types, such as pancreatic cancer, exhibit very low response rates to ICI due to intrinsically low immunogenicity. In addition, many patients who initially respond to ICI lack a sustained response due to T-cell exhaustion. Several recent studies show that epigenetic modifiers, such as SETDB1 and LSD1, can play critical roles in regulating both tumor cell-intrinsic immunity and T-cell exhaustion. Here, we review the evidence showing that multiple epigenetic regulators silence the expression of endogenous antigens, and their loss induces viral mimicry responses bolstering the response of "cold" tumors to ICI in preclinical models. Similarly, a previously unappreciated role for epigenetic enzymes is emerging in the establishment and maintenance of stem-like T-cell populations that are critical mediators of response to ICI. Targeting the crossroads of epigenetics and immune checkpoint therapy has tremendous potential to improve antitumor immune responses and herald the next generation of sustained responses in immuno-oncology.

Multimodal integration of image, epigenetic and clinical data to predict BRAF mutation status in melanoma

BACKGROUND

In machine learning, multimodal classifiers can provide more generalised performance than unimodal classifiers. In clinical practice, physicians usually also rely on a range of information from different examinations for diagnosis. In this study, we used BRAF mutation status prediction in melanoma as a model system to analyse the contribution of different data types in a combined classifier because BRAF status can be determined accurately by sequencing as the current gold standard, thus nearly eliminating label noise.

METHODS

We trained a deep learning-based classifier by combining individually trained random forests of image, clinical and methylation data to predict BRAF-V600 mutation status in primary and metastatic melanomas of The Cancer Genome Atlas cohort.

RESULTS

With our multimodal approach, we achieved an area under the receiver operating characteristic curve of 0.80, whereas the individual classifiers yielded areas under the receiver operating characteristic curve of 0.63 (histopathologic image data), 0.66 (clinical data) and 0.66 (methylation data) on an independent data set.

CONCLUSIONS

Our combined approach can predict BRAF status to some extent by identifying BRAF-V600 specific patterns at the histologic, clinical and epigenetic levels. The multimodal classifiers have improved generalisability in predicting BRAF mutation status.

DNA methylation-based patterns for early diagnostic prediction and prognostic evaluation in colorectal cancer patients with high tumor mutation burden

Background

Immune checkpoint inhibitor (ICI) therapy has proven to be a promising treatment for colorectal cancer (CRC). We aim to investigate the relationship between DNA methylation and tumor mutation burden (TMB) by integrating genomic and epigenetic profiles to precisely identify clinical benefit populations and to evaluate the effect of ICI therapy.

Methods

A total of 536 CRC tissues from the Cancer Genome Atlas (TCGA) with mutation data were collected and subjected to calculate TMB. 80 CRC patients with high TMB and paired normal tissues were selected as training sets and developed the diagnostic and prognostic methylation models, respectively. In the validation set, the diagnostic model was validated in our in-house 47 CRC tissues and 122 CRC tissues from the Gene Expression Omnibus (GEO) datasets, respectively. And a total of 38 CRC tissues with high TMB from the COLONOMICS dataset verified the prognostic model.

Results

A positive correlation between differential methylation positions and TMB level was observed in TCGA CRC cohort (r=0.45). The diagnostic score that consisted of methylation levels of four genes (ADHFE1, DOK6, GPR75, and MAP3K14-AS1) showed high diagnostic performance in the discovery (AUC=1.000) and two independent validation (AUC=0.946, AUC=0.857) datasets. Additionally, these four genes showed significant positive correlations with NK cells. The prognostic score containing three genes (POU3F3, SYN2, and TMEM178A) had significantly poorer survival in the high-risk TMB samples than those in the low-risk TMB samples (P=0.016). CRC patients with low-risk scores combined with TMB levels represent a favorable survival.

Conclusions

By integrating analyses of methylation and mutation data, it is suggested that DNA methylation patterns combined with TMB serve as a novel potential biomarker for early screening in more high-TMB populations and for evaluating the prognostic effect of CRC patients with ICI therapy.

Epigenetic Mechanisms Underlying Melanoma Resistance to Immune and Targeted Therapies

Melanoma is an aggressive skin cancer reliant on early detection for high likelihood of successful treatment. Solar UV exposure transforms melanocytes into highly mutated tumor cells that metastasize to the liver, lungs, and brain. Even upon resection of the primary tumor, almost thirty percent of patients succumb to melanoma within twenty years. Identification of key melanoma genetic drivers led to the development of pharmacological BRAF^V600E and MEK inhibitors, significantly improving metastatic patient outcomes over traditional cytotoxic chemotherapy or pioneering IFN-α and IL-2 immune therapies. Checkpoint blockade inhibitors releasing the immunosuppressive effects of CTLA-4 or PD-1 proved to be even more effective and are the standard first-line treatment. Despite these major improvements, durable responses to immunotherapy and targeted therapy have been hindered by intrinsic or acquired resistance. In addition to gained or selected genetic alterations, cellular plasticity conferred by epigenetic reprogramming is emerging as a driver of therapy resistance. Epigenetic regulation of chromatin accessibility drives gene expression and establishes distinct transcriptional cell states. Here we review how aberrant chromatin, transcriptional, and epigenetic regulation contribute to therapy resistance and discuss how targeting these programs sensitizes melanoma cells to immune and targeted therapies.

Genetic and methylation profiles distinguish benign, malignant and spitzoid melanocytic tumors

Accurate classification of melanocytic tumors is important for prognostic evaluation, treatment and follow-up protocols of patients. The majority of melanocytic proliferations can be classified solely based on clinical and pathological criteria, however in select cases a definitive diagnostic assessment remains challenging and additional diagnostic biomarkers would be advantageous. We analyzed melanomas, nevi, Spitz nevi and atypical spitzoid tumors using parallel sequencing (exons of 611 genes and 507 gene translocation analysis) and methylation arrays (850k Illumina EPIC). By combining detailed genetic and epigenetic analysis with reference-based and reference-free DNA methylome deconvolution we compared Spitz nevi to nevi and melanoma and assessed the potential for these methods in classifying challenging spitzoid tumors. Results were correlated with clinical and histologic features. Spitz nevi were found to cluster independently of nevi and melanoma and demonstrated a different mutation profile. Multiple copy number alterations and TERT promoter mutations were identified only in melanomas. Genome-wide methylation in Spitz nevi was comparable to benign nevi while the Leukocytes UnMethylation for Purity (LUMP) algorithm in Spitz nevi was comparable to melanoma. Histologically difficult to classify Spitz tumor cases were assessed which, based on methylation arrays, clustered between Spitz nevi and melanoma and in terms of genetic profile or copy number variations demonstrated worrisome features suggesting a malignant neoplasm. Comprehensive sequencing and methylation analysis verify Spitz nevi as an independent melanocytic entity distinct from both nevi and melanoma. Combined genetic and methylation assays can offer additional insights in diagnosing difficult to classify Spitzoid tumors.

A machine learning approach utilizing DNA methylation as an accurate classifier of COVID-19 disease severity

Since the onset of the COVID-19 pandemic, increasing cases with variable outcomes continue globally because of variants and despite vaccines and therapies. There is a need to identify at-risk individuals early that would benefit from timely medical interventions. DNA methylation provides an opportunity to identify an epigenetic signature of individuals at increased risk. We utilized machine learning to identify DNA methylation signatures of COVID-19 disease from data available through NCBI Gene Expression Omnibus. A training cohort of 460 individuals (164 COVID-19-infected and 296 non-infected) and an external validation dataset of 128 individuals (102 COVID-19-infected and 26 non-COVID-associated pneumonia) were reanalyzed. Data was processed using ChAMP and beta values were logit transformed. The JADBio AutoML platform was leveraged to identify a methylation signature associated with severe COVID-19 disease. We identified a random forest classification model from 4 unique methylation sites with the power to discern individuals with severe COVID-19 disease. The average area under the curve of receiver operator characteristic (AUC-ROC) of the model was 0.933 and the average area under the precision-recall curve (AUC-PRC) was 0.965. When applied to our external validation, this model produced an AUC-ROC of 0.898 and an AUC-PRC of 0.864. These results further our understanding of the utility of DNA methylation in COVID-19 disease pathology and serve as a platform to inform future COVID-19 related studies.

Visual Clustering of Transcriptomic Data from Primary and Metastatic Tumors-Dependencies and Novel Pitfalls

Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP)) on three datasets of metastases (n = 682 samples) with three different data transformations (unprocessed, log10 as well as log10 + 1 transformed values), we visualized potential underlying clusters. Additionally, we analyzed two datasets (n = 616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters.

Epigenetics Regulates Antitumor Immunity in Melanoma

Melanoma is the most malignant skin cancer, which originates from epidermal melanocytes, with increasing worldwide incidence. The escape of immune surveillance is a hallmark of the tumor, which is manifested by the imbalance between the enhanced immune evasion of tumor cells and the impaired antitumor capacity of infiltrating immune cells. According to this notion, the invigoration of the exhausted immune cells by immune checkpoint blockades has gained encouraging outcomes in eliminating tumor cells and significantly prolonged the survival of patients, particularly in melanoma. Epigenetics is a pivotal non-genomic modulatory paradigm referring to heritable changes in gene expression without altering genome sequence, including DNA methylation, histone modification, non-coding RNAs, and m⁶A RNA methylation. Accumulating evidence has demonstrated how the dysregulation of epigenetics regulates multiple biological behaviors of tumor cells and contributes to carcinogenesis and tumor progression in melanoma. Nevertheless, the linkage between epigenetics and antitumor immunity, as well as its implication in melanoma immunotherapy, remains elusive. In this review, we first introduce the epidemiology, clinical characteristics, and therapeutic innovations of melanoma. Then, the tumor microenvironment and the functions of different types of infiltrating immune cells are discussed, with an emphasis on their involvement in antitumor immunity in melanoma. Subsequently, we systemically summarize the linkage between epigenetics and antitumor immunity in melanoma, from the perspective of distinct paradigms of epigenetics. Ultimately, the progression of the clinical trials regarding epigenetics-based melanoma immunotherapy is introduced.

DNA methylation profiling of central nervous system hemangioblastomas identifies two distinct subgroups

Hemangioblastomas (HBs) of the central nervous system are highly vascular neoplasms that occur sporadically or as a manifestation of von Hippel–Lindau (VHL) disease. Despite their benign nature, HBs are clinically heterogeneous and can be associated with significant morbidity due to mass effects of peritumoral cysts or tumor progression. Underlying molecular factors involved in HB tumor biology remain elusive. We investigated genome‐wide DNA methylation profiles and clinical and histopathological features in a series of 47 HBs from 42 patients, including 28 individuals with VHL disease. Thirty tumors occurred in the cerebellum, 8 in the brainstem and 8 HBs were of spinal location, while 1 HB was located in the cerebrum. Histologically, 12 HBs (26%) belonged to the cellular subtype and exclusively occurred in the cerebellum, whereas 35 HBs were reticular (74%). Unsupervised clustering and dimensionality reduction of DNA methylation profiles revealed two distinct subgroups. Methylation cluster 1 comprised 30 HBs of mainly cerebellar location (29/30, 97%), whereas methylation cluster 2 contained 17 HBs predominantly located in non‐cerebellar compartments (16/17, 94%). The sum of chromosomal regions being affected by copy‐number alterations was significantly higher in methylation cluster 1 compared to cluster 2 (mean 262 vs. 109 Mb, p = 0.001). Of note, loss of chromosome 6 occurred in 9/30 tumors (30%) of methylation cluster 1 and was not observed in cluster 2 tumors (p = 0.01). No relevant methylation differences between sporadic and VHL‐related HBs or cystic and non‐cystic HBs could be detected. Deconvolution of the bulk DNA methylation profiles revealed four methylation components that were associated with the two methylation clusters suggesting cluster‐specific cell‐type compositions. In conclusion, methylation profiling of HBs reveals 2 distinct subgroups that mainly associate with anatomical location, cytogenetic profiles and differences in cell type composition, potentially reflecting different cells of origin.

Immune Checkpoint Inhibitor-Induced Cerebral Pseudoprogression: Patterns and Categorization

Background

The inclusion of immune checkpoint inhibitors (ICIs) in therapeutic algorithms has led to significant survival benefits in patients with various metastatic cancers. Concurrently, an increasing number of neurological immune related adverse events (IRAE) has been observed. In this retrospective analysis, we examine the ICI-induced incidence of cerebral pseudoprogression and propose a classification system.

Methods

We screened our hospital information system to identify patients with any in-house ICI treatment for any tumor disease during the years 2007-2019. All patients with cerebral MR imaging (cMRI) of sufficient diagnostic quality were included. cMRIs were retrospectively analyzed according to immunotherapy response assessment for neuro-oncology (iRANO) criteria.

Results

We identified 12 cases of cerebral pseudoprogression in 123 patients treated with ICIs and sufficient MRI. These patients were receiving ICI therapy for lung cancer (n=5), malignant melanoma (n=4), glioblastoma (n=1), hepatocellular carcinoma (n=1) or lymphoma (n=1) when cerebral pseudoprogression was detected. Median time from the start of ICI treatment to pseudoprogression was 5 months. All but one patient developed neurological symptoms. Three different patterns of cerebral pseudoprogression could be distinguished: new or increasing contrast-enhancing lesions, new or increasing T2 predominant lesions and cerebral vasculitis type pattern.

Conclusion

Cerebral pseudoprogression followed three distinct patterns and was detectable in 3.2% of all patients during ICI treatment and in 9.75% of the patients with sufficient brain imaging follow up. The fact that all but one of the affected patients developed neurological symptoms, which would be classified as progressive disease according to iRANO criteria, mandates vigilance in the diagnosis and treatment of ICI-induced cerebral lesions.

Transcriptional determinants of cancer immunotherapy response and resistance

The host immune response is a potent defense mechanism against cancer development and progression. To survive, cancer cells must develop mechanisms to evade the immune response. Based on this knowledge, a series of new therapies collectively referred to as immunotherapies have been developed and translated to the clinic for treating cancer patients. Although some cancer subtypes have shown strong clinical responses, including curative outcomes in some patients, immunotherapies have not worked as desired for some subtypes and forms of cancers. We provide an overview of the transcriptional mechanisms that drive the response and resistance to immunotherapies. We also discuss possible interventions to enhance the outcomes of immunotherapies by targeting dysregulated transcriptional networks in cancer cells.

Methylation and mutation of the inhibin‑α gene in human melanoma cells and regulation of PTEN expression and AKT/PI3K signaling by a demethylating agent

Inhibin suppresses the pituitary secretion of follicle‑stimulating hormone and has been reported to act as a tumor suppressor gene in the gonad in mice. Epigenetic modifications, mutations, changes in the loss of heterozygosity (LOH) of the inhibin‑α gene and regulation of gene expression in response to a demethylating agent [5‑aza‑2'‑deoxycytidine (5‑Aza‑dC)] in human melanoma cells were assessed. In addition, the association between a mutation in the 5'‑untranslated region (5'‑UTR) of the inhibin‑α subunit and the expression of phosphatidylinositol 3,4,5‑trisphosphate‑dependent Rac exchanger 2 (PREX2) and phosphatase and tensin homolog (PTEN) as well as AKT/PI3K signaling was determined. The methylation status of the CpG sites of the inhibin‑α promoter was analyzed by methylation‑specific PCR in bisulfite‑treated DNA. Cell viability was counted using the trypan blue assay, mRNA expression was examined via reverse transcription‑quantitative PCR, and protein expression was examined via western blot analysis. The inhibin‑α promoter was hypermethylated in G361, SK‑MEL‑3, SK‑MEL‑24 and SK‑MEL‑28 cells and moderately methylated in SK‑MEL‑5 cells. Inhibin‑α gene mutations were observed in the 5'‑UTR exon 1 of G361, SK‑MEL‑5, SK‑MEL‑24 and SK‑MEL‑28 cells as well as in exon 2 of SK‑MEL‑3 cells. Allelic imbalance, including LOH, in the inhibin‑α gene was detected in human melanoma cells. Treatment with 5‑Aza‑dC increased inhibin‑α mRNA and protein levels, inhibited cell proliferation, and delayed the doubling times of surviving melanoma cells. In 5‑Aza‑dC‑treated cells, PREX2 protein expression was slightly increased in G361 and SK‑MEL‑24 cells and decreased in SK‑MEL3, SK‑MEL‑5 and SK‑MEL‑28 cells. However, the protein expression of PTEN was decreased in melanoma cells. In addition, AKT and PI3K protein phosphorylation levels increased in all melanoma cells, except of G361 cells, demonstrating decreased PI3K protein phosphorylation. These data provided evidence that methylation, mutation and LOH are observed in the inhibin α‑subunit gene and gene locus in human melanoma cells. Furthermore, the demethylating agent reactivated inhibin‑α gene expression and regulated PREX2 expression. AKT/PI3K signaling increased as PTEN expression decreased. In addition, mutations in the tumor suppressor inhibin‑α, PTEN and p53 genes were not associated with transcriptional silencing, gene expression and cell growth as analyzed through experiments and literature reviews. These data demonstrated that methylation and mutations were associated with the inhibin‑α gene in human melanoma cells and indicated the regulation of PTEN expression and AKT/PI3K signaling by a demethylating agent.