DNA methylation-based immune response signature improves patient diagnosis in multiple cancers

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.

Construction and Analysis of a Mitochondrial Metabolism-Related Prognostic Model for Breast Cancer to Evaluate Survival and Immunotherapy

As one of the most prevalent malignancies among women, breast cancer (BC) is tightly linked to metabolic dysfunction. However, the correlation between mitochondrial metabolism-related genes (MMRGs) and BC remains unclear. The training and validation datasets for BC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. MMRG-related data were obtained from the Molecular Signatures Database. A risk score prognostic model incorporating MMRGs was established based on univariate, LASSO, and multivariate Cox regression analyses. Independent factors affecting BC prognosis were identified through regression analysis and presented in a nomogram. Single-sample gene set enrichment analysis was employed to assess the immune levels of high-risk (HR) and low-risk (LR) groups. The sensitivity of BC patients in the two groups to common anti-tumor drugs was evaluated by utilizing the Genomics of Drug Sensitivity in Cancer database. 12 MMRGs significantly associated with survival were selected from 1234 MMRGs. A 12-gene risk score prognostic model was built. In the multivariate regression analysis incorporating classical clinical factors, the MMRG-related risk score remained an independent prognostic factor. As revealed by tumor immune microenvironment analysis, the LR group with higher survival rates had elevated immune levels. The drug sensitivity results unmasked that the LR group demonstrated higher sensitivity to Irinotecan, Nilotinib, and Oxaliplatin, while the HR group demonstrated higher sensitivity to Lapatinib. The development of MMRG characteristics provides a comprehensive understanding of mitochondrial metabolism in BC, aiding in the prediction of prognosis and tumor microenvironment, and offering promising therapeutic choices for BC patients with different MMRG risk scores.

DNA hypomethylation patterns and their impact on the tumor microenvironment in colorectal cancer

BACKGROUND

Recent research underscores the pivotal role of immune checkpoints as biomarkers in colorectal cancer (CRC) therapy, highlighting the dynamics of resistance and response to immune checkpoint inhibitors. The impact of epigenetic alterations in CRC, particularly in relation to immune therapy resistance, is not fully understood.

METHODS

We integrated a comprehensive dataset encompassing TCGA-COAD, TCGA-READ, and multiple GEO series (GSE14333, GSE37892, GSE41258), along with key epigenetic datasets (TCGA-COAD, TCGA-READ, GSE77718). Hierarchical clustering, based on Euclidean distance and Ward's method, was applied to 330 primary tumor samples to identify distinct clusters. The immune microenvironment was assessed using MCPcounter. Machine learning algorithms were employed to predict DNA methylation patterns and their functional enrichment, in addition to transcriptome expression analysis. Genomic mutation profiles and treatment response assessments were also conducted.

RESULTS

Our analysis delineated a specific tumor cluster with CpG Island (CGI) methylation, termed the Demethylated Phenotype (DMP). DMP was associated with metabolic pathways such as oxidative phosphorylation, implicating increased ATP production efficiency in mitochondria, which contributes to tumor aggressiveness. Furthermore, DMP showed activation of the Myc target pathway, known for tumor immune suppression, and exhibited downregulation in key immune-related pathways, suggesting a tumor microenvironment characterized by diminished immunity and increased fibroblast infiltration. Six potential therapeutic agents-lapatinib, RDEA119, WH.4.023, MG.132, PD.0325901, and AZ628-were identified as effective for the DMP subtype.

CONCLUSION

This study unveils a novel epigenetic phenotype in CRC linked to resistance against immune checkpoint inhibitors, presenting a significant step toward personalized medicine by suggesting epigenetic classifications as a means to identify ideal candidates for immunotherapy in CRC. Our findings also highlight potential therapeutic agents for the DMP subtype, offering new avenues for tailored CRC treatment strategies.

Identification of a CpG-based signature coupled with gene expression as prognostic indicators for melanoma: a preliminary study

DNA methylation is an important part of the genomic biology, which recently allowed the identification of key biomarkers for a variety of cancers, including cutaneous melanoma. Despite the current knowledge in cutaneous melanoma, there is a clear need for new efficient biomarkers in clinical application of detection. We use The Cancer Genome Atlas data as a training set and a multi-stage screening strategy to identify prognostic characteristics of melanoma based on DNA methylation. Three DNA methylation CpG sites were identified to be related to the overall survival in the skin cutaneous melanoma cohort. This signature was validated in two independent datasets from Gene Expression Omnibus. The stratified analysis by clinical stage, age, gender, and grade retained the statistical significance. The methylation signature was significantly correlated with immune cells and anti-tumor immune response. Moreover, gene expression corresponding to the candidate CpG locus was also significantly correlated with the survival rate of the patient. About 49% of the prognostic effects of methylation are mediated by affecting the expression of the corresponding genes. The prognostic characteristics of DNA methylation combined with clinical information provide a better prediction value tool for melanoma patients than the clinical information alone. However, more experiments are required to validate these findings. Overall, this signature presents a prospect of novel and wide-ranging applications for appropriate clinical adjuvant trails.

Toward Explainable Artificial Intelligence for Precision Pathology

The rapid development of precision medicine in recent years has started to challenge diagnostic pathology with respect to its ability to analyze histological images and increasingly large molecular profiling data in a quantitative, integrative, and standardized way. Artificial intelligence (AI) and, more precisely, deep learning technologies have recently demonstrated the potential to facilitate complex data analysis tasks, including clinical, histological, and molecular data for disease classification; tissue biomarker quantification; and clinical outcome prediction. This review provides a general introduction to AI and describes recent developments with a focus on applications in diagnostic pathology and beyond. We explain limitations including the black-box character of conventional AI and describe solutions to make machine learning decisions more transparent with so-called explainable AI. The purpose of the review is to foster a mutual understanding of both the biomedical and the AI side. To that end, in addition to providing an overview of the relevant foundations in pathology and machine learning, we present worked-through examples for a better practical understanding of what AI can achieve and how it should be done.

Immune checkpoints signature-based risk stratification for prognosis of patients with gastric cancer

Until now, few researches have comprehensive explored the role of immune checkpoints (ICIs) and tumor microenvironment (TME) in gastric cancer (GC) patients based on the genomic data. RNA-sequence data and clinical information were obtained from The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) database, GSE84437 and GSE84433. Univariate Cox analysis identified 60 ICIs with prognostic values, and these genes were then subjected to NMF cluster analysis and the GC samples (n = 804) were classified into two distinct subtypes (Cluster 1: n = 583; Cluster 2: n = 221). The Kaplan-Meier curves for OS analysis indicated that C1 predicted a poorer prognosis. The C2 subtype illustrated a relatively better prognosis and characteristics of "hot tumors," including high immune score, overexpression of immune checkpoint molecules, and enriched tumor-infiltrated immune cells, indicating that the NMF clustering in GC was robust and stable. Regarding the patient's heterogeneity, an ICI-score was constructed to quantify the ICI patterns in individual patients. Moreover, the study found that the low ICI-score group contained mostly MSI-low events, and the high ICI-score group contained predominantly MSI-high events. In addition, the ICI-score groups had good responsiveness to CTLA4 and PD-1 based on The Cancer Immunome Atlas (TCIA) database. Our research firstly constructed ICIs signature, as well as identified some hub genes in GC patients.

Analysis of the impact of fatty acid metabolism on immunotherapy for hepatocellular carcinoma

INTRODUCTION AND OBJECTIVES

Hepatocellular carcinoma (HCC), a malignancy with a very dismal prognosis, has drawn a lot of attention, particularly in East Asia, where morbidity and mortality are higher. Although new information about the role of fatty acids (FAs) in HCC is constantly being discovered, it is still vital to investigate how FA metabolism affects the prognosis, immune microenvironment, and responsiveness of HCC to immunotherapy as a whole.

MATERIALS AND METHODS

To determine the significance of FA metabolism in HCC immunotherapy, we first evaluated HCC samples from the single-cell dataset GSE151530. The TCGA-LIHC cohort and GSE140901 were further studied to identify the impact of FA metabolism on prognosis, immune microenvironment, drug sensitivity, and immunotherapy response by developing a fatty acid prediction index (FPI). The heterogeneity and similarity of the involvement of FA metabolism in pan-cancer is also investigated.

RESULTS

Combining single-cell and bulk analyses, we confirmed that FA metabolism regulates tumor malignancy, prognosis, immune microenvironment, drug sensitivity, and immunotherapy response in patients with HCC. Moreover, it can have a considerable impact on the physiological activities of hepatocellular cancer. In addition, we demonstrate that FA metabolism has a comparable or same role in many malignancies.

CONCLUSIONS

Our investigation shows the crucial regulatory role of FA metabolism in HCC and suggests a potential therapeutic method for HCC patients, which may improve their survival.

Machine learning-based establishment and validation of age-related patterns for predicting prognosis in non-small cell lung cancer within the context of the tumor microenvironment

Lung cancer (LC) is a leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for over 80% of cases. The impact of aging on clinical outcomes in NSCLC remains poorly understood, particularly with respect to the immune response. In this study, we explored the effects of aging on NSCLC using 307 genes associated with human aging from the Human Ageing Genomic Resources. We identified 53 aging-associated genes that significantly correlate with overall survival of NSCLC patients, including the clinically validated gene BUB1B. Furthermore, we developed an aging-associated enrichment score to categorize patients based on their aging subtypes and evaluated their prognostic and therapeutic response values in LC. Our analyses yielded two aging-associated subtypes with unique profiles in the tumor microenvironment, demonstrating varying responses to immunotherapy. Consensus clustering based on transcriptome profiles provided insights into the effects of aging on NSCLC and highlighted the potential of personalized therapeutic approaches tailored to aging subtypes. Our findings provide a new target and theoretical support for personalized therapeutic approaches in patients with NSCLC, offering insights into the potential impact of aging on cancer outcomes.

Pan-cancer analysis revealing that PTPN2 is an indicator of risk stratification for acute myeloid leukemia

The non-receptor protein tyrosine phosphatases gene family (PTPNs) is involved in the tumorigenesis and development of many cancers, but the role of PTPNs in acute myeloid leukemia (AML) remains unclear. After a comprehensive evaluation on the expression patterns and immunological effects of PTPNs using a pan-cancer analysis based on RNA sequencing data obtained from The Cancer Genome Atlas, the most valuable gene PTPN2 was discovered. Further investigation of the expression patterns of PTPN2 in different tissues and cells showed a robust correlation with AML. PTPN2 was then systematically correlated with immunological signatures in the AML tumor microenvironment and its differential expression was verified using clinical samples. In addition, a prediction model, being validated and compared with other models, was developed in our research. The systematic analysis of PTPN family reveals that the effect of PTPNs on cancer may be correlated to mediating cell cycle-related pathways. It was then found that PTPN2 was highly expressed in hematologic diseases and bone marrow tissues, and its differential expression in AML patients and normal humans was verified by clinical samples. Based on its correlation with immune infiltrates, immunomodulators, and immune checkpoint, PTPN2 was found to be a reliable biomarker in the immunotherapy cohort and a prognostic predictor of AML. And PTPN2'riskscore can accurately predict the prognosis and response of cancer immunotherapy. These findings revealed the correlation between PTPNs and immunophenotype, which may be related to cell cycle. PTPN2 was differentially expressed between clinical AML patients and normal people. It is a diagnostic biomarker and potentially therapeutic target, providing targeted guidance for clinical treatment.

Chromatin factors: Ready to roll as biomarkers in metastatic colorectal cancer?

Colorectal cancer (CRC) ranks as the third most prevalent cancer globally and stands as the fourth leading cause of cancer-related fatalities in 2020. Survival rates for metastatic disease have slightly improved in recent decades, with clinical trials showing median overall survival of approximately 24-30 months. This progress can be attributed to the integration of chemotherapeutic treatments alongside targeted therapies and immunotherapy. Despite these modest improvements, the primary obstacle to successful treatment for advanced CRC lies in the development of chemoresistance, whether inherent or acquired, which remains the major cause of treatment failure. Epigenetics has emerged as a hallmark of cancer, contributing to master transcription regulation and genome stability maintenance. As a result, epigenetic factors are starting to appear as potential clinical biomarkers for diagnosis, prognosis, and prediction of treatment response in CRC.In recent years, numerous studies have investigated the influence of DNA methylation, histone modifications, and chromatin remodelers on responses to chemotherapeutic treatments. While there is accumulating evidence indicating their significant involvement in various types of cancers, the exact relationship between chromatin landscapes and treatment modulation in CRC remains elusive. This review aims to provide a comprehensive summary of the most pertinent and extensively researched epigenetic-associated mechanisms described between 2015 and 2022 and their potential usefulness as predictive biomarkers in the metastatic disease.

Cross talk between tumor stemness and microenvironment for prognosis and immunotherapy of uveal melanoma

PURPOSE

Tumor stem cells have emerged as a crucial focus of investigation and a therapeutic target in the context of cancer metastasis and drug resistance. They represent a promising novel approach to address the treatment of uveal melanoma (UVM).

METHODS

According to the one-class logistic regression (OCLR) approach, we first estimated two stemness indices (mDNAsi and mRNAsi) in a cohort of UVM (n = 80). The prognostic value of stemness indices among four subtypes of UVM (subtype A-D) was investigated. Moreover, univariate Cox regression and Lasso-penalized algorithms were conducted to identify a stemness-associated signature and verify in several independent cohorts. Besides, UVM patients classified into subgroups based on the stemness-associated signature. The differences in clinical outcomes, tumor microenvironment, and probability of immunotherapeutic response were investigated further.

RESULTS

We observed that mDNAsi was significantly linked with overall survival (OS) time of UVM, but no association was discovered between mRNAsi and OS. Stratification analysis indicated that the prognostic value of mDNAsi was only limited in subtype D of UVM. Besides, we established and verified a prognostic stemness-associated gene signature which can classify UVM patients into subgroups with distinct clinical outcomes, tumor mutation, immune microenvironment, and molecular pathways. The high risk of UVM is more sensitive to immunotherapy. Finally, a well-performed nomogram was constructed to predict the mortality of UVM patients.

CONCLUSIONS

This study offers a comprehensive examination of UVM stemness characteristics. We discovered mDNAsi-associated signatures improved the prediction capacity of individualized UVM prognosis and indicated prospective targets for stemness-regulated immunotherapy. Analysis of the interaction between stemness and tumor microenvironment may shed light on combinational treatment that targets both stem cell and the tumor microenvironment.

Precise subtyping reveals immune heterogeneity for hormone receptor-positive breast cancer

A significant proportion of breast cancer cases are characterized by hormone receptor positivity (HR+). Clinically, the heterogeneity of HR+ breast cancer leads to different therapeutic effects on endocrine. Therefore, definition of subgroups in HR+ breast cancer is important for effective treatment. Here, we have developed a CMBR method utilizing computational functional networks based on DNA methylation to identify conserved subgroups in HR+ breast cancer. Calculated by CMBR, HR+ breast cancer was divided into five subgroups, of which HR+/negative epidermal growth factor receptor-2 (Her2-) was divided into two subgroups, and HR+/positive epidermal growth factor receptor-2 (Her2+) was divided into three subgroups. These subgroups had heterogeneity in the immune microenvironment, tumor infiltrating lymphocyte patterns, somatic mutation patterns and drug sensitivity. Specifically, CMBR identified two subgroups with the "Hot" tumor phenotype. In addition, these conserved subgroups were broadly validated on external validation datasets. CMBR identified the molecular signature of HR+ breast cancer subgroups, providing valuable insights into personalized treatment strategies and management options.

The Role of the IGF2 Methylation Score in Diagnosing Adrenocortical Tumors with Unclear Malignant Potential-Feasibility of Formalin-Fixed Paraffin-Embedded Tissue

The differentiation between benign and malignant adrenocortical tumors based on pathological assessment can be difficult. We present a series of 17 patients with unclear malignant tumors, of whom six had recurrent or metastatic disease. The assessment of the methylation pattern of insulin-like growth factor 2 (IGF2) regulatory regions in fresh frozen material has shown to be valuable in determining the malignancy of adrenocortical tumors, although this has not been elaborately tested in unclear malignant tumors. Since fresh frozen tissue was only available in six of the patients, we determined the feasibility of using formalin-fixed paraffin-embedded (FFPE) tissue for this method. We isolated DNA from FFPE tissue and matched the fresh frozen tissue of three patients with adrenocortical carcinoma. Methylation patterns of IGF2 regulatory regions were determined by pyrosequencing using different amounts of bisulfite-converted DNA (5 ng, 20 ng, 40 ng). Compared to fresh frozen tissue, FFPE tissue had a higher failure rate (fresh frozen 0%; FFPE 18.5%) and poor-to-moderate replicability (fresh frozen rho = 0.89-0.99, median variation 1.6%; FFPE rho = -0.09-0.85, median variation 7.7%). There was only a poor-to-moderate correlation between results from fresh frozen and FFPE tissue (rho = -0.28-0.70, median variation 13.2%). In conclusion, FFPE tissue is not suitable for determining the IGF2 methylation score in patients with an unclear malignant adrenocortical tumor using the currently used method. We, therefore, recommend fresh frozen storage of resection material for diagnostic and biobank purposes.

Lupus nephritis or not? A simple and clinically friendly machine learning pipeline to help diagnosis of lupus nephritis

OBJECTIVE

Diagnosis of lupus nephritis (LN) is a complex process, which usually requires renal biopsy. We aim to establish a machine learning pipeline to help diagnosis of LN.

METHODS

A cohort of 681 systemic lupus erythematosus (SLE) patients without LN and 786 SLE patients with LN was established, and a total of 95 clinical, laboratory data and 17 meteorological indicators were collected. After tenfold cross-validation, the patients were divided into training set and test set. The features selected by collective feature selection method of mutual information (MI) and multisurf were used to construct the models of logistic regression, decision tree, random forest, naive Bayes, support vector machine (SVM), light gradient boosting (LGB), extreme gradient boosting (XGB), and artificial neural network (ANN), the models were compared and verified in post-analysis.

RESULTS

Collective feature selection method screens out antistreptolysin (ASO), retinol binding protein (RBP), lupus anticoagulant 1 (LA1), LA2, proteinuria and other features, and the hyperparameter optimized XGB (ROC: AUC = 0.995; PRC: AUC = 1.000, APS = 1.000; balance accuracy: 0.990) has the best performance, followed by LGB (ROC: AUC = 0.992; PRC: AUC = 0.997, APS = 0.977; balance accuracy: 0.957). The worst performance is naive Bayes model (ROC: AUC = 0.799; PRC: AUC = 0.822, APS = 0.823; balance accuracy: 0.693). In the composite feature importance bar plots, ASO, RF, Up/Ucr, and other features play important roles in LN.

CONCLUSION

We developed and validated a new and simple machine learning pathway for diagnosis of LN, especially the XGB model based on ASO, LA1, LA2, proteinuria, and other features screened out by collective feature selection.

Zinc finger and SCAN domain-containing protein 18 is a potential DNA methylation-modified tumor suppressor and biomarker in breast cancer

Introduction

Zinc finger and SCAN domain-containing protein 18 (ZSCAN18) has been investigated as a putative biomarker of multiple human cancers. However, the expression profile, epigenetic modification, prognostic value, transcription regulation, and molecular mechanism of ZSCAN18 in breast cancer (BC) remain unknown.

Methods

In the study, we present an integrated analysis of ZSCAN18 in BC based on public omics datasets with the use of multiple bioinformatics tools. Genes potentially regulated through restoration of ZSCAN18 expression in MDA-MB-231 cells were investigated to identify pathways associated with BC.

Results

We observed that ZSCAN18 was downregulated in BC and mRNA expression was significantly correlated with clinicopathological parameters. Low expression of ZSCAN18 was found in the HER2-positive and TNBC subtypes. High expression of ZSCAN18 was associated with good prognosis. As compared to normal tissues, the extent of ZSCAN18 DNA methylation was greater with fewer genetic alterations in BC tissues. ZSCAN18 was identified as a transcription factor that might be involved in intracellular molecular and metabolic processes. Low ZSCAN18 expression was associated with the cell cycle and glycolysis signaling pathway. Overexpression of ZSCAN18 inhibited mRNA expression of genes associated with the Wnt/β-catenin and glycolysis signaling pathways, including CTNNB1, BCL9, TSC1, and PFKP. ZSCAN18 expression was negatively correlated with infiltrating B cells and dendritic cells (DCs), as determined by the TIMER web server and reference to the TISIDB. ZSCAN18 DNA methylation was positively correlated with activated B cells, activated CD8+ and CD4+ T cells, macrophages, neutrophils, and activated DCs. Moreover, five ZSCAN18-related hub genes (KDM6B, KAT6A, KMT2D, KDM1A, and HSPBP1) were identified. ZSCAN18, ZNF396, and PGBD1 were identified as components of a physical complex.

Conclusion

ZSCAN18 is a potential tumor suppressor in BC, as expression is modified by DNA methylation and associated with patient survival. In addition, ZSCAN18 plays important roles in transcription regulation, the glycolysis signaling pathway, and the tumor immune microenvironment.

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.

Viewing RCC with a DNA Methylation Lens ENHANCES Understanding of ICI Resistance

Clear-cell renal cell carcinoma tumors with an enhancer demethylator phenotype (TED) harbor a worse prognosis and derive less clinical benefit from immunotherapy. The TED phenotype may help predict immunotherapy resistance. See related article by Lu et al., p. 1279.

Plasma cell-free DNA methylome profiling in pre- and post-surgery oral cavity squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC), a highly heterogeneous disease that involves multiple anatomic sites, is a leading cause of cancer-related mortality worldwide. Although the utility of noninvasive biomarkers based on circulating cell-free DNA (cfDNA) methylation profiling has been widely recognized, limited studies have been reported so far regarding the dynamics of cfDNA methylome in oral cavity squamous cell carcinoma (OCSCC). It is hypothesized in this study that comparison of methylation profiles in pre- and postsurgery plasma samples will reveal OCSCC-specific prognostic and diagnostic biomarkers. As a strategy to further prioritize tumor-specific targets, top differential methylated regions (DMRs) were called by reanalyzing methylation data from paired tumor and normal tissue collected in the the cancer genome atlas head-neck squamous cell carcinoma (TCGA) head and neck cancer cohort. Matched plasma samples from eight patients with OCSCC were collected at Moffitt Cancer Center before and after surgical resection. Plasma-derived cfDNA was analyzed by cfMBD-seq, which is a high-sensitive methylation profiling assay. Differential methylation analysis was then performed based on the matched samples profiled. In the top 200 HNSCC-specific DMRs detected based on the TCGA data set, a total of 23 regions reached significance in the plasma-based DMR test. The top five validated DMR regions (ranked by the significance in the plasma study) are located in the promoter regions of genes PENK, NXPH1, ZIK1, TBXT, and CDO1, respectively. The genome-wide cfDNA DMR analysis further highlighted candidate biomarkers located in genes SFRP4, SOX1, IRF4, and PCDH17. The prognostic relevance of candidate genes was confirmed by survival analysis using the TCGA data. This study supports the utility of cfDNA-based methylome profiling as a promising noninvasive biomarker source for OCSCC and HNSCC.

DNMT3a-dermatopontin axis suppresses breast cancer malignancy via inactivating YAP

Breast cancer (BC) is the most common malignant tumor in women worldwide, and its recurrence and metastasis negatively affect patient prognosis. However, the mechanisms underlying its tumorigenesis and progression remain unclear. Recently, the influence of dermatopontin (DPT), which is an extracellular matrix protein, has been proposed in the development of cancer. Here we found that DNMT3a-mediated DPT, promoter hypermethylation results in the downregulation of DPT expression in breast cancer and its low expression correlated with poor prognosis. Notably, DPT directly interacted with YAP to promote YAP Ser127 phosphorylation, and restricted the translocation of endogenous YAP from the cytoplasm to the nucleus, thereby suppressing malignant phenotypes in BC cells. In addition, Ectopic YAP overexpression reversed the inhibitory effects of DPT on BC growth and metastasis. Our study showed the critical role of DPT in regulating BC progression, making it easier to explore the clinical potential of modulating DPT/YAP activity in BC targeted therapies.

DNA methylation patterns associated with breast cancer prognosis that are specific to tumor subtype and menopausal status

Tumor subtype and menopausal status are strong predictors of breast cancer (BC) prognosis. We aimed to find and validate subtype- or menopausal-status-specific changes in tumor DNA methylation (DNAm) associated with all-cause mortality or BC progression. Associations between site-specific tumor DNAm and BC prognosis were estimated among The Cancer Genome Atlas participants (n = 692) with Illumina Infinium HumanMethylation450 BeadChip array data. All-cause mortality and BC progression were modeled using Cox proportional hazards models stratified by tumor subtypes, adjusting for age, race, stage, menopausal status, tumor purity, and cell type proportion. Effect measure modification by subtype and menopausal status were evaluated by incorporating a product term with DNAm. Site-specific inference was used to identify subtype- or menopausal-status-specific differentially methylated regions (DMRs) and functional pathways. The validation of the results was carried out on an independent dataset (GSE72308; n = 180). We identified a total of fifteen unique CpG probes that were significantly associated ( P ≤ 1 × 10 - 7 with survival outcomes in subtype- or menopausal-status-specific manner. Seven probes were associated with overall survival (OS) or progression-free interval (PFI) for women with luminal A subtype, and four probes were associated with PFI for women with luminal B subtype. Five probes were associated with PFI for post-menopausal women. A majority of significant probes showed a lower risk of OS or BC progression with higher DNAm. We identified subtype- or menopausal-status-specific DMRs and functional pathways of which top associated pathways differed across subtypes or menopausal status. None of significant probes from site-specific analyses met genome-wide significant level in validation analyses while directions and magnitudes of coefficients showed consistent pattern. We have identified subtype- or menopausal-status-specific DNAm biomarkers, DMRs and functional pathways associated with all-cause mortality or BC progression, albeit with limited validation. Future studies with larger independent cohort of non-post-menopausal women with non-luminal A subtypes are warranted for identifying subtype- and menopausal-status-specific DNAm biomarkers for BC prognosis.

Genome-wide DNA methylation profile analysis identifies an individualized predictive signature for melanoma immune response

PURPOSE

The current evaluation methods for tumor infiltrating lymphocytes (TILs), particularly CD8 + TILs, mainly rely on semiquantitative immunohistochemistry with high variability. We aimed to construct an individualized DNA methylation-based signature for CD8 + TILs (CD8 + MeTIL) that may characterize melanoma immune microenvironment and guide therapeutic selection.

METHODS

The transcriptome profiles and DNA methylation data of 457 melanoma patients from The Cancer Genome Atlas (TCGA) database were analyzed. Differential methylation analysis between groups with high and low CD8 + TILs was performed to select differentially methylated positions (DMPs) and define CD8 + MeTIL. The prognostic value of CD8 + MeTIL and its predictive value for immunotherapy response were investigated using multiple melanoma cohorts.

RESULTS

We successfully constructed the CD8 + MeTIL signature based on four DMPs. The survival analyses showed that higher CD8 + MeTIL score was associated with worse survival outcomes in TCGA-SKCM and GSE144487 cohorts. The ROC curve for the predictive analysis revealed that the survival prediction of CD8 + MeTIL score was superior compared with CD8 + TILs (CIBERSORT) and CD8B mRNA expression. Furthermore, we founded that tumors with higher CD8 + MeTIL score were marked with immunosuppressive characteristics, including low immune score and downregulated immune-related pathways. More importantly, the CD8 + MeTIL score showed a potential predictive value for the benefit from immunotherapy in two published cohorts. When combined CD8 + MeTIL with PD-L1 expression, the patient classification showed significantly different immunotherapy response rates and long-term survival outcomes.

CONCLUSIONS

The CD8 + MeTIL signature might be as a novel method to evaluate CD8 + TILs and guide immunotherapy approaches.

Immune response and drug therapy based on ac4C-modified gene in pancreatic cancer typing

N-4 cytidine acetylation (ac4C) is an epitranscriptome modification catalyzed by N-acetyltransferase 10 (NAT10) and is essential for cellular mRNA stability, rRNA biosynthesis, cell proliferation, and epithelial-mesenchymal transition (EMT). Numerous studies have confirmed the inextricable link between NAT10 and the clinical characteristics of malignancies. It is unclear, however, how NAT10 might affect pancreatic ductal adenocarcinoma. We downloaded pancreatic ductal adenocarcinoma patients from the TCGA database. We obtained the corresponding clinical data for data analysis, model construction, differential gene expression analysis, and the GEO database for external validation. We screened the published papers for NAT10-mediated ac4C modifications in 2156 genes. We confirmed that the expression levels and genomic mutation rates of NAT10 differed significantly between cancer and normal tissues. Additionally, we constructed a NAT10 prognostic model and examined immune infiltration and altered biological pathways across the models. The NAT10 isoforms identified in this study can effectively predict clinical outcomes in pancreatic ductal adenocarcinoma. Furthermore, our study showed that elevated levels of NAT10 expression correlated with gemcitabine resistance, that aberrant NAT10 expression may promote the angiogenic capacity of pancreatic ductal adenocarcinoma through activation of the TGF-β pathway, which in turn promotes distal metastasis of pancreatic ductal adenocarcinoma, and that NAT10 knockdown significantly inhibited the migration and clonogenic capacity of pancreatic ductal adenocarcinoma cells. In conclusion, we proposed a predictive model based on NAT10 expression levels, a non-invasive predictive approach for genomic profiling, which showed satisfactory and effective performance in predicting patients' survival outcomes and treatment response. Medicine and electronics will be combined in more interdisciplinary areas in the future.

LRRC3B and its promoter hypomethylation status predicts response to anti-PD-1 based immunotherapy

Background

The leucine rich repeat containing 3B (LRRC3B) gene is a tumor suppressor gene involved in the anti-tumor immune microenvironment. Expression of LRRC3B and DNA methylation at the LRRC3B promoter region may serve as a useful marker to predict response to anti-PD-1 therapy. However, no studies have yet systematically explored the protective role of LRRC3B methylation in tumor progression and immunity.

Methods

Expression of LRRC3B of 33 cancer types in The Cancer Genome Atlas (TCGA) was downloaded from UCSC Xena (http://xena.ucsc.edu/). And, we evaluated the differential expression of LRRC3B according to tumor stage, overall survival, and characteristics of the tumor microenvironment. The immunotherapeutic cohorts included IMvigor21, GSE119144, and GSE72308 which were obtained from the Gene Expression Omnibus database. We conducted pearson correlation analysis of LRRC3B and tumor microenvironment (TME) in pan-cancer. Also, six immune cell types (B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells) and tumor purity were analyzed using the Tumor IMmune Estimation Resource (TIMER1.0) (Tumor IMmune Estimation Resource (TIMER2.0). And, a "silencing score" model base on LRRC3B promoter methylation to predict overall survival (OS) by multivariate Cox regression analysis was constructed. Finally, the model was applied to predict anti-PD-1 therapy in non-small cell lung cancer (NSCLC) and breast cancer (BRCA).

Results

LRRC3B expression associated with less tumor invasion, less severe tumor stage, and decreased metastasis. The inactivation of LRRC3B promoted the enrichment of immuneosuppressive cells, including myeloid-derived suppressor cells (MDSCs), cancer-associated fibroblasts (CAFs), M2 subtype of tumor-associated macrophages (M2-TAMs), M1 subtype of tumor-associated macrophages (M1-TAMs), and regulatory T (Treg) cells. A high silencing score was significantly associated with immune inhibition, low expression of LRRC3B, poor patient survival, and activation of cancer-related pathways.

Conclusion

Our comprehensive analysis demonstrated the potential role of LRRC3B in the anti-tumor microenvironment, clinicopathological features of cancer, and disease prognosis. It suggested that LRRC3B methylation could be used as a powerful biomarker to predict immunotherapy responses in NSCLC and BRCA.

Identification and verification of prognostic cancer subtype based on multi-omics analysis for kidney renal papillary cell carcinoma

Background

Identifying Kidney Renal Papillary Cell Carcinoma (KIRP) patients with high-risk, guiding individualized diagnosis and treatment of patients, and identifying effective prognostic targets are urgent problems to be solved in current research on KIRP.

Methods

In this study, data of multi omics for patients with KIRP were collected from TCGA database, including mRNAs, lncRNAs, miRNAs, data of methylation, and data of gene mutations. Data of multi-omics related to prognosis of patients with KIRP were selected for each omics level. Further, multi omics data related to prognosis were integrated into cluster analysis based on ten clustering algorithms using MOVICS package. The multi omics-based cancer subtype (MOCS) were compared on biological characteristics, immune microenvironmental cell abundance, immune checkpoint, genomic mutation, drug sensitivity using R packages, including GSVA, clusterProfiler, TIMER, CIBERSORT, CIBERSORT-ABS, quanTIseq, MCPcounter, xCell, EPIC, GISTIC, and pRRophetic algorithms.

Results

The top ten OS-related factors for KIRP patients were annotated. Patients with KIRP were divided into MOCS1, MOCS2, and MOCS3. Patients in the MOCS3 subtype were observed with shorter overall survival time than patients in the MOCS1 and MOCS2 subtypes. MOCS1 was negatively correlated with immune-related pathways, and we found global dysfunction of cancer-related pathways among the three MOCS subtypes. We evaluated the activity profiles of regulons among the three MOCSs. Most of the metabolism-related pathways were activated in MOCS2. Several immune microenvironmental cells were highly infiltrated in specific MOCS subtype. MOCS3 showed a significantly lower tumor mutation burden. The CNV occurrence frequency was higher in MOCS1. As for treatment, we found that these MOCSs were sensitive to different drugs and treatments. We also analyzed single-cell data for KIRP.

Conclusion

Based on a variety of algorithms, this study determined the risk classifier based on multi-omics data, which could guide the risk stratification and medication selection of patients with KIRP.

Identification of prognostic biomarkers among ICAMs in the breast cancer microenvironment

BACKGROUND

Intercellular adhesion molecules (ICAMs) in the tumor microenvironment are closely related to immunity and affect the prognosis of cancer patients.

OBJECTIVE

The aim of our study is to explore the correlation between ICAM expression, mutation, methylation and immunity and their prognostic value in breast cancer (BC) is not clear.

METHODS

Online databases and tools such as UALCAN, COSMIC, cBioPortal, MethSurv, PrognoScan, Kaplan-Meier Plotter, GSCA and TIMER were utilized in this study.

RESULTS

We found that the mRNA and protein expression levels of ICAM1 were upregulated in triple-negative breast cancer (TNBC) compared with normal tissues, and TNBC patients with high expression of ICAM1 had better overall survival (OS) and recurrence-free survival (RFS). The main types of ICAM1 gene variants were missense mutation and amplification, and ICAM1 showed a lower level of methylation in TNBC cancer tissues than in normal tissues, which was contrary to the high expression levels of ICAM1 mRNA and protein. Next, the function of ICAM1 was mainly related to the activation of apoptosis, epithelial-mesenchymal transition (EMT) and inhibition of the androgen receptor (AR) and estrogen receptor (ER) pathways. Meanwhile, functional pathway enrichment results showed that ICAM1 was also involved in the immune regulation process of BC. Furthermore, the expression of ICAM1 was positively associated with 6 types of tumor-infiltrating immune cells (CD8+ T cells, CD4+ T cells, B cells, neutrophils, macrophages and dendritic cells) and was also positively related to the expression of programmed cell death-1 (PD-1), programmed cell death-ligand 1 (PD-L1) and cytotoxic T lymphocyte-associated antigen-4 (CTLA4).

CONCLUSIONS

Our research indicated that ICAM1 was likely to be a potential therapeutic target in TNBC.

Epigenetic Mechanisms in Breast Adenocarcinoma: Novel DNA Methylation Patterns

Breast adenocarcinoma is a leading cause of death in females worldwide. A broad spectrum of genetic and epigenetic alterations has been already identified and reported in millions of examined cancerous substrates, evidence of a high-level genomic heterogeneity that characterizes these malignancies. Concerning epigenetic changes and imbalances that critically affect progression and prognosis in the corresponding patients, DNA methylation, histone modifications (acetylation), micro-RNAs (miRs) alterations and chromatin re-organization represent the main mechanisms. Referring to DNA methylation, promoter hyper-hypo methylation in critical tumour suppressor and oncogenes is implicated in normal epithelia transformation to their neoplastic and finally malignant cyto-phenotypes. The current review is focused on the different methylation patterns and mechanisms detected in breast adenocarcinoma and their impact on the corresponding groups of patient response to specific chemotherapeutic regimens and life span prognosis.

Multi-omics consensus portfolio to refine the classification of lung adenocarcinoma with prognostic stratification, tumor microenvironment, and unique sensitivity to first-line therapies

BACKGROUND

Molecular classification of lung adenocarcinoma (LUAD) based on transcriptomic features has been widely studied. The complementarity of data obtained from multilayer molecular biology could help the LUAD classification via combining multi-omics information.

METHODS

We successfully divided samples from the The Cancer Genome Atlas (TCGA) (n=437) into four subtypes (CS1, CS2, CS3 and CS4) by 10 comprehensive multi-omics clustering methods in the "movics" R package. Meanwhile, external validation sets from different sequencing technologies proved the robustness of the grouping model. The relationship between subtypes, prognosis, molecular features, tumor microenvironment and response to first-line therapy was further analyzed. Next we used univariate Cox regression analysis and Lasso regression analysis to explore the application of biomarkers in clinical prognosis and constructed a prognostic model.

RESULTS

CS1 showed the worst overall survival (OS) among all four clusters, possibly related to its poor immune infiltration, higher tumor mutation and worse chromosomal stability. Patients in different subtypes differed significantly in cancer stem cell characteristics, activation of cancer-related pathways, sensitivity to chemotherapy and immunotherapy. The prognostic model showed good predictive performance. The 1-, 2- and 3-year areas under the curve of risk score were 0.779, 0.742 and 0.678, respectively. Seven genes (DKK1, TSPAN7, ID1, DLGAP5, HHIPL2, CD40 and SEMA3C) used to build the model may be potential therapeutic targets for LUAD.

CONCLUSIONS

Four LUAD subtypes with different molecular characteristics and clinical implications were identified successfully through bioinformatic analysis. Our results may contribute to precision medicine and inform the development of rational clinical strategies for targeted and immune therapies.

Identification and validation of immunotherapy for four novel clusters of colorectal cancer based on the tumor microenvironment

The incidence and mortality of colorectal cancer (CRC) are increasing year by year. The accurate classification of CRC can realize the purpose of personalized and precise treatment for patients. The tumor microenvironment (TME) plays an important role in the malignant progression and immunotherapy of CRC. An in-depth understanding of the clusters based on the TME is of great significance for the discovery of new therapeutic targets for CRC. We extracted data on CRC, including gene expression profile, DNA methylation array, somatic mutations, clinicopathological information, and copy number variation (CNV), from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) (four datasets—GSE14333, GSE17538, GSE38832, and GSE39582), cBioPortal, and FireBrowse. The MCPcounter was utilized to quantify the abundance of 10 TME cells for CRC samples. Cluster repetitive analysis was based on the Hcluster function of the Pheatmap package in R. The ESTIMATE package was applied to compute immune and stromal scores for CRC patients. PCA analysis was used to remove batch effects among different datasets and transform genome-wide DNA methylation profiling into methylation of tumor-infiltrating lymphocyte (MeTIL). We evaluated the mutation differences of the clusters using MOVICS, DeconstructSigs, and GISTIC packages. As for therapy, TIDE and SubMap analyses were carried out to forecast the immunotherapy response of the clusters, and chemotherapeutic sensibility was estimated based on the pRRophetic package. All results were verified in the TCGA and GEO data. Four immune clusters (ImmClust-CS1, ImmClust-CS2, ImmClust-CS3, and ImmClust-CS4) were identified for CRC. The four ImmClusts exhibited distinct TME compositions, cancer-associated fibroblasts (CAFs), functional orientation, and immune checkpoints. The highest immune, stromal, and MeTIL scores were observed in CS2, in contrast to the lowest scores in CS4. CS1 may respond to immunotherapy, while CS2 may respond to immunotherapy after anti-CAFs. Among the four ImmClusts, the top 15 markers with the highest mutation frequency were acquired, and CS1 had significantly lower CNA on the focal level than other subtypes. In addition, CS1 and CS2 patients had more stable chromosomes than CS3 and CS4. The most sensitive chemotherapeutic agents in these four ImmClusts were also found. IHC results revealed that CD29 stained significantly darker in the cancer samples, indicating that their CD29 was highly expressed in colon cancer. This work revealed the novel clusters based on TME for CRC, which would guide in predicting the prognosis, biological features, and appropriate treatment for patients with CRC.

Classification of Subgroups with Immune Characteristics Based on DNA Methylation in Luminal Breast Cancer

Luminal breast cancer (BC) accounts for a large proportion of patients in BC, with high heterogeneity. Determining the precise subtype and optimal selection of treatment options for luminal BC is a challenge. In this study, we proposed an MSBR framework that integrate DNA methylation profiles and transcriptomes to identify immune subgroups of luminal BC. MSBR was implemented both on a key module scoring algorithm and “Boruta” feature selection method by DNA methylation. Luminal A was divided into two subgroups and luminal B was divided into three subgroups using the MSBR. Furthermore, these subgroups were defined as different immune subgroups in luminal A and B respectively. The subgroups showed significant differences in DNA methylation levels, immune microenvironment (immune cell infiltration, immune checkpoint PD1/PD-L1 expression, immune cell cracking activity (CYT)) and pathology features (texture, eccentricity, intensity and tumor-infiltrating lymphocytes (TILs)). The results also showed that there is a subgroup in both luminal A and B that has the benefit from immunotherapy. This study proposed a classification of luminal BC from the perspective of epigenetics and immune characteristics, which provided individualized treatment decisions.

Machine Learning-Based Epigenetic Classifiers for Axillary Staging of Patients with ER-Positive Early-Stage Breast Cancer

BACKGROUND

In the era of molecular stratification and effective multimodality therapies, surgical staging of the axilla is becoming less relevant for patients with estrogen receptor (ER)-positive early-stage breast cancer (EBC). Therefore, a nonsurgical method for accurately predicting lymph node disease is the next step in the de-escalation of axillary surgery. This study sought to identify epigenetic signatures in the primary tumor that accurately predict lymph node status.

PATIENTS AND METHODS

We selected a cohort of patients in The Cancer Genome Atlas (TCGA) with ER-positive, HER2-negative invasive ductal carcinomas, and clinically-negative axillae (n = 127). Clinicopathological nomograms from the Memorial Sloan Kettering Cancer Center (MSKCC) and the MD Anderson Cancer Center (MDACC) were calculated. DNA methylation (DNAm) patterns from primary tumor specimens were compared between patients with pN0 and those with > pN0. The cohort was divided into training (n = 85) and validation (n = 42) sets. Random forest was employed to obtain the combinations of DNAm features with the highest accuracy for stratifying patients with > pN0. The most efficient combinations were selected according to the area under the curve (AUC).

RESULTS

Clinicopathological models displayed a modest predictive potential for identifying > pN0 disease (MSKCC AUC 0.76, MDACC AUC 0.69, p = 0.15). Differentially methylated sites (DMS) between patients with pN0 and those with > pN0 were identified (n = 1656). DMS showed a similar performance to the MSKCC model (AUC = 0.76, p = 0.83). Machine learning approaches generated five epigenetic classifiers, which showed higher discriminative potential than the clinicopathological variables tested (AUC > 0.88, p < 0.05).

CONCLUSIONS

Epigenetic classifiers based on primary tumor characteristics can efficiently stratify patients with no lymph node involvement from those with axillary lymph node disease, thereby providing an accurate method of staging the axilla.

Immune depletion of the methylated phenotype of colon cancer is closely related to resistance to immune checkpoint inhibitors

Background

Molecular typing based on single omics data has its limitations and requires effective integration of multiple omics data for tumor typing of colorectal cancer (CRC).

Methods

Transcriptome expression, DNA methylation, somatic mutation, clinicopathological information, and copy number variation were retrieved from TCGA, UCSC Xena, cBioPortal, FireBrowse, or GEO. After pre-processing and calculating the clustering prediction index (CPI) with gap statistics, integrative clustering analysis was conducted via MOVICS. The tumor microenvironment (TME) was deconvolved using several algorithms such as GSVA, MCPcounter, ESTIMATE, and PCA. The metabolism-relevant pathways were extracted through ssGSEA. Differential analysis was based on limma and enrichment analysis was carried out by Enrichr. DNA methylation and transcriptome expression were integrated via ELMER. Finally, nearest template or hemotherapeutic sensitivity prediction was conducted using NTP or pRRophetic.

Results

Three molecular subtypes (CS1, CS2, and CS3) were recognized by integrating transcriptome, DNA methylation, and driver mutations. CRC patients in CS3 had the most favorable prognosis. A total of 90 differentially mutated genes among the three CSs were obtained, and CS3 displayed the highest tumor mutation burden (TMB), while significant instability across the entire chromosome was observed in the CS2 group. A total of 30 upregulated mRNAs served as classifiers were identified and the similar diversity in clinical outcomes of CS3 was validated in four external datasets. The heterogeneity in the TME and metabolism-related pathways were also observed in the three CSs. Furthermore, we found CS2 tended to loss methylations while CS3 tended to gain methylations. Univariate and multivariate Cox regression revealed that the subtypes were independent prognostic factors. For the drug sensitivity analysis, we found patients in CS2 were more sensitive to ABT.263, NSC.87877, BIRB.0796, and PAC.1. By Integrating with the DNA mutation and RNA expression in CS3, we identified that SOX9, a specific marker of CS3, was higher in the tumor than tumor adjacent by IHC in the in-house cohort and public cohort.

Conclusion

The molecular subtypes based on integrated multi-omics uncovered new insights into the prognosis, mechanisms, and clinical therapeutic targets for CRC.

DNA methylation changes in response to neoadjuvant chemotherapy are associated with breast cancer survival

BACKGROUND

Locally advanced breast cancer is a heterogeneous disease with respect to response to neoadjuvant chemotherapy (NACT) and survival. It is currently not possible to accurately predict who will benefit from the specific types of NACT. DNA methylation is an epigenetic mechanism known to play an important role in regulating gene expression and may serve as a biomarker for treatment response and survival. We investigated the potential role of DNA methylation as a prognostic marker for long-term survival (> 5 years) after NACT in breast cancer.

METHODS

DNA methylation profiles of pre-treatment (n = 55) and post-treatment (n = 75) biopsies from 83 women with locally advanced breast cancer were investigated using the Illumina HumanMethylation450 BeadChip. The patients received neoadjuvant treatment with epirubicin and/or paclitaxel. Linear mixed models were used to associate DNA methylation to treatment response and survival based on clinical response to NACT (partial response or stable disease) and 5-year survival, respectively. LASSO regression was performed to identify a risk score based on the statistically significant methylation sites and Kaplan-Meier curve analysis was used to estimate survival probabilities using ten years of survival follow-up data. The risk score developed in our discovery cohort was validated in an independent validation cohort consisting of paired pre-treatment and post-treatment biopsies from 85 women with locally advanced breast cancer. Patients included in the validation cohort were treated with either doxorubicin or 5-FU and mitomycin NACT.

RESULTS

DNA methylation patterns changed from before to after NACT in 5-year survivors, while no significant changes were observed in non-survivors or related to treatment response. DNA methylation changes included an overall loss of methylation at CpG islands and gain of methylation in non-CpG islands, and these changes affected genes linked to transcription factor activity, cell adhesion and immune functions. A risk score was developed based on four methylation sites which successfully predicted long-term survival in our cohort (p = 0.0034) and in an independent validation cohort (p = 0.049).

CONCLUSION

Our results demonstrate that DNA methylation patterns in breast tumors change in response to NACT. These changes in DNA methylation show potential as prognostic biomarkers for breast cancer survival.

Integrative analysis of immune-related multi-omics profiles identifies distinct prognosis and tumor microenvironment patterns in osteosarcoma

Osteosarcoma (OS) is the most common primary malignancy of bone. Epigenetic regulation plays a pivotal role in cancer development in various aspects, including immune response. In this study, we studied the potential association of alterations in the DNA methylation and transcription of immune-related genes with changes in the tumor microenvironment (TME) and tumor prognosis of OS. We obtained multi-omics data for OS patients from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. By referring to curated immune signatures and using a consensus clustering method, we categorized patients based on immune-related DNA methylation patterns (IMPs), and evaluated prognosis and TME characteristics of the resulting patient subgroups. Subsequently, we used a machine-learning approach to construct an IMP-associated prognostic risk model incorporating the expression of a six-gene signature (MYC, COL13A1, UHRF2, MT1A, ACTB, and GBP1), which was then validated in an independent patient cohort. Furthermore, we evaluated TME patterns, transcriptional variation in biological pathways, somatic copy number alteration, anticancer drug sensitivity, and potential responsiveness to immune checkpoint inhibitor therapy with regard to our IMP-associated signature scoring model. By integrative IMP and transcriptomic analysis, we uncovered distinct prognosis and TME patterns in OS. Finally, we constructed a classifying model, which may aid in prognosis prediction and provide a potential rationale for targeted- and immune checkpoint inhibitor therapy in OS.

Tumor Expression Quantitative Trait Methylation Screening Reveals Distinct CpG Panels for Deconvolving Cancer Immune Signatures

DNA methylation signatures in tumors could serve as reliable biomarkers that are accessible in archival tissues for tracking the epigenetic dynamics shaped by both cancer cells and the tumor microenvironment. However, given the ultrahigh dimensionality and noncollapsible nature of the data, it remains challenging to screen all CpG sites to identify the most promising marker panels. In this article, we introduce the concept of tumor-based expression quantitative trait methylation (eQTM) for the prioritization and systematic mining of predictive biomarkers. In melanoma as a disease model, eQTM CpGs and genes represent new and efficient candidate targets to be investigated for both prognostic and immune status monitoring purposes. Three cis-eQTM CpGs (cg07786657, cg12446199, and cg00027570) were strongly associated with and can serve as surrogate biomarkers for the tumor immune cytolytic activity score (CYT). In addition, multiple eQTM genes could be further exploited for predicting immunoregulatory phenotypes. A targeted gene panel analysis identified one eQTM in TCF7 (cg25947408) as a novel candidate biomarker for uncoupling overall T-cell differentiation and exhaustion status in a tumor. The prognostic significance of this eQTM as an independent signature to CYT was validated by both The Cancer Genome Atlas and Moffitt melanoma cohort data. Overall, eQTMs represent a mechanistically distinct class of potential biomarkers that can be used to predict patient prognosis and immune status.

SIGNIFICANCE

This study provides a novel and promising approach to identify targeted epigenetic biomarkers in cancer and will spur further analysis in tumor immune phenotyping.

ENPP2 Promoter Methylation Correlates with Decreased Gene Expression in Breast Cancer: Implementation as a Liquid Biopsy Biomarker

Autotaxin (ATX), encoded by the ctonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2) gene, is a key enzyme in lysophosphatidic acid (LPA) synthesis. We have recently described ENPP2 methylation profiles in health and multiple malignancies and demonstrated correlation to its aberrant expression. Here we focus on breast cancer (BrCa), analyzing in silico publicly available BrCa methylome datasets, to identify differentially methylated CpGs (DMCs) and correlate them with expression. Numerous DMCs were identified between BrCa and healthy breast tissues in the gene body and promoter-associated regions (PA). PA DMCs were upregulated in BrCa tissues in relation to normal, in metastatic BrCa in relation to primary, and in stage I BrCa in relation to normal, and this was correlated to decreased mRNA expression. The first exon DMC was also investigated in circulating cell free DNA (ccfDNA) isolated by BrCa patients; methylation was increased in BrCa in relation to ccfDNA from healthy individuals, confirming in silico results. It also differed between patient groups and was correlated to the presence of multiple metastatic sites. Our data indicate that promoter methylation of ENPP2 arrests its transcription in BrCa and introduce first exon methylation as a putative biomarker for diagnosis and monitoring which can be assessed in liquid biopsy.

Efficient gradient boosting for prognostic biomarker discovery

MOTIVATION

A gradient boosting decision tree (GBDT) is a powerful ensemble machine-learning method that has the potential to accelerate biomarker discovery from high-dimensional molecular data. Recent algorithmic advances, such as extreme gradient boosting (XGB) and light gradient boosting (LGB), have rendered the GBDT training more efficient, scalable and accurate. However, these modern techniques have not yet been widely adopted in discovering biomarkers for censored survival outcomes, which are key clinical outcomes or endpoints in cancer studies.

RESULTS

In this paper, we present a new R package 'Xsurv' as an integrated solution that applies two modern GBDT training frameworks namely, XGB and LGB, for the modeling of right-censored survival outcomes. Based on our simulations, we benchmark the new approaches against traditional methods including the stepwise Cox regression model and the original gradient boosting function implemented in the package 'gbm'. We also demonstrate the application of Xsurv in analyzing a melanoma methylation dataset. Together, these results suggest that Xsurv is a useful and computationally viable tool for screening a large number of prognostic candidate biomarkers, which may facilitate future translational and clinical research.

AVAILABILITY AND IMPLEMENTATION

'Xsurv' is freely available as an R package at: https://github.com/topycyao/Xsurv.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Active demethylation upregulates CD147 expression promoting non-small cell lung cancer invasion and metastasis

Non-small cell lung cancer (NSCLC) is a fatal disease, and its metastatic process is poorly understood. Although aberrant methylation is involved in tumor progression, the mechanisms underlying dynamic DNA methylation remain to be elucidated. It is significant to study the molecular mechanism of NSCLC metastasis and identify new biomarkers for NSCLC early diagnosis. Here, we performed MeDIP-seq and hMeDIP-seq analyses to detect the genes regulated by dynamic DNA methylation. Comparison of the 5mC and 5hmC sites revealed that the CD147 gene underwent active demethylation in NSCLC tissues compared with normal tissues, and this demethylation upregulated CD147 expression. Significantly high levels of CD147 expression and low levels of promoter methylation were observed in NSCLC tissues. Then, we identified the CD147 promoter as a target of KLF6, MeCP2, and DNMT3A. Treatment of cells with TGF-β triggered active demethylation involving loss of KLF6/MeCP2/DNMT3A and recruitment of Sp1, Tet1, TDG, and SMAD2/3 transcription complexes. A dCas9-SunTag-DNMAT3A-sgCD147-targeted methylation system was constructed to reverse CD147 expression. The targeted methylation system downregulated CD147 expression and inhibited NSCLC proliferation and metastasis in vitro and in vivo. Accordingly, we used cfDNA to detect the levels of CD147 methylation in NSCLC tissues and found that the CD147 methylation levels exhibited an inverse relationship with tumor size, lymphatic metastasis, and TNM stage. In conclusion, this study clarified the mechanism of active demethylation of CD147 and suggested that the targeted methylation of CD147 could inhibit NSCLC invasion and metastasis, providing a highly promising therapeutic target for NSCLC.

Methylation statuses of NCOR2, PARK2, and ZSCAN12 signify densities of tumor-infiltrating lymphocytes in gastric carcinoma

Individual cell types of human tissues have their own CpG site methylation profiles, which might be utilized for the development of methylation markers to denote tumor-infiltrating lymphocytes (TILs). We aimed to develop DNA methylation markers that recapitulate the densities of TILs in gastric carcinoma (GC). Through genome-wide methylation profiling, NCOR2, PARK2, and ZSCAN12 were found to be highly methylated in CD3-positive and CD8-positive cells and rarely methylated in tumor cells. Scores of the three methylation markers were analyzed for their relationship with the overall survival and recurrence-free survival of patients with advanced GC (n = 471). The scores of three methylation markers were closely associated with densities of CD3-positive or CD8-positive cells at the tumor center or invasive front of GCs and found to be a significant prognostic factor in univariate analysis of overall survival and recurrence-free survival. In multivariate analysis, the highest score showed hazard ratios of 0.513 (CI 0.306–0.857) and 0.434 (CI 0.261–0.720) for overall survival and recurrence-free survival, respectively. The findings suggest that methylation markers signifying TILs might be utilized for the recapitulation of TIL density in GCs and serve as biomarkers for predicting prognosis in patients with GC.

Characterization of the CpG island methylator phenotype subclass in papillary thyroid carcinoma

CpG island methylator phenotype (CIMP), characterized by the concurrent and widespread hypermethylation of a cluster of CpGs, has been reported to play an important role in carcinogenesis. Limited studies have explored the role of CIMP in papillary thyroid carcinomas (PTCs). Here, in genome-wide DNA methylation analysis of 350 primary PTCs from the Cancer Genome Atlas database that were assessed using the Illumina HumanMethylation450K platform, our study helps to identify two subtypes displayed markedly distinct DNA methylation levels, termed CIMP (high levels of DNA methylation) and nCIMP subgroup (low levels of DNA methylation). Interestingly, PTCs with CIMP tend to have a higher degree of malignancy, since this subtype was tightly associated with older age, advanced pathological stage, and lymph node metastasis (all P < 0.05). Differential methylation analysis showed a broad methylation gain in CIMP and subsequent generalized gene set testing analysis based on the significantly methylated probes in CIMP showed remarkable enrichment in epithelial mesenchymal transition and angiogenesis hallmark pathways, confirming that the CIMP phenotype may promote the tumor progression from another perspective. Analysis of tumor microenvironment showed that CIMP PTCs are in an immune-depletion status, which may affect the effectiveness of immunotherapy. Genetically, the significantly higher tumor mutation burden and copy number alteration both at the genome and focal level confirmed the genomic heterogeneity and chromosomal instability of CIMP. tumor Corresponding to the above findings, PTC patients with CIMP showed remarkable poor clinical outcome as compared to nCIMP regarding overall survival and progression-free survival. More importantly, CIMP was associated with worse survival independent of known prognostic factors.

Discovery of tumor immune infiltration-related snoRNAs for predicting tumor immune microenvironment status and prognosis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) has a high mortality rate and is difficult to diagnose and treat in its early stage. Previous studies have demonstrated that small nucleolar RNAs (snoRNAs) play a critical role in tumor immune infiltration and the development of a variety of solid tumors. However, there have been no studies on the correlation between tumor-infiltrating immune-related snoRNAs (TIISRs) and LUAD. In this study, we filtered six immune-related snoRNAs based on the tissue specificity index (TSI) and expression profile of all snoRNAs between all LUAD cell lines from the Cancer Cell Line Encyclopedia and 21 types of immune cells from the Gene Expression Omnibus database. Further, we performed real-time quantitative polymerase chain reaction (RT-qPCR) to validate the expression status of these snoRNAs on peripheral blood mononuclear cells (PBMCs) and lung cancer cell lines. Next, we developed a TIISR signature based on the expression profiles of snoRNAs from 479 LUAD patients filtered by the random survival forest algorithm. We then analyzed the value of this TIISR signature (TIISR risk score) for assessing tumor immune infiltration, immune checkpoint inhibitor (ICI) treatment response, and the prognosis of LUAD between groups with high and low TIISR risk score. Further, we found that the TIISR risk score groups showed significant differences in biological characteristics and that the risk score could be used to assess the level of tumor immune cell infiltration, thereby predicting prognosis and responsiveness to immunotherapy in LUAD patients.

Epigenome signature as an immunophenotype indicator prompts durable clinical immunotherapy benefits in lung adenocarcinoma

Intertumoral immune heterogeneity is a critical reason for distinct clinical benefits of immunotherapy in lung adenocarcinoma (LUAD). Tumor immunophenotype (immune 'Hot' or 'Cold') suggests immunological individual differences and potential clinical treatment guidelines. However, employing epigenome signatures to determine tumor immunophenotypes and responsive treatment is not well understood. To delineate the tumor immunophenotype and immune heterogeneity, we first distinguished the immune 'Hot' and 'Cold' tumors of LUAD based on five immune expression signatures. In terms of clinical presentation, the immune 'Hot' tumors usually had higher immunoactivity, lower disease stages and better survival outcomes than 'Cold' tumors. At the epigenome levels, we observed that distinct DNA methylation patterns between immunophenotypes were closely associated with LUAD development. Hence, we identified a set of five CpG sites as the immunophenotype-related methylation signature (iPMS) for tumor immunophenotyping and further confirmed its efficiency based on a machine learning framework. Furthermore, we found iPMS and immunophenotype-related immune checkpoints (IPCPs) could contribute to the risk of tumor progression, implying IPCP has the potential to be a novel immunotherapy blockade target. After further parsing of the role of iPMS-predicted immunophenotypes, we found immune 'Hot' was a protective factor leading to better survival outcomes when patients received the anti-PD-1/PD-L1 immunotherapy. And iPMS was also a well-performed signature (AUC = 0.752) for predicting the durable/nondurable clinical benefits. In summary, our study explored the role of epigenome signature in clinical tumor immunophenotyping. Utilizing iPMS to characterize tumor immunophenotypes will facilitate developing personalized epigenetic anticancer approaches.

The Growing Relevance of Immunoregulation in Pediatric Brain Tumors

Pediatric brain tumors are genetically heterogeneous solid neoplasms. With a prevailing poor prognosis and widespread resistance to conventional multimodal therapy, these aggressive tumors are the leading cause of childhood cancer-related deaths worldwide. Advancement in molecular research revealed their unique genetic and epigenetic characteristics and paved the way for more defined prognostication and targeted therapeutic approaches. Furthermore, uncovering the intratumoral metrics on a single-cell level placed non-malignant cell populations such as innate immune cells into the context of tumor manifestation and progression. Targeting immune cells in pediatric brain tumors entails unique challenges but promising opportunities to improve outcome. Herein, we outline the current understanding of the role of the immune regulation in pediatric brain tumors.

A DNA Methylation-Based Gene Signature Can Predict Triple-Negative Breast Cancer Diagnosis

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer (BC) subtype and lacks targeted treatment. It is diagnosed by the absence of immunohistochemical expression of several biomarkers, but this method still displays some interlaboratory variability. DNA methylome aberrations are common in BC, thereby methylation profiling could provide the identification of accurate TNBC diagnosis biomarkers. Here, we generated a signature of differentially methylated probes with class prediction ability between 5 non-neoplastic breast and 7 TNBC tissues (error rate = 0.083). The robustness of this signature was corroborated in larger cohorts of additional 58 non-neoplastic breast, 93 TNBC, and 150 BC samples from the Gene Expression Omnibus repository, where it yielded an error rate of 0.006. Furthermore, we validated by pyrosequencing the hypomethylation of three out of 34 selected probes (FLJ43663, PBX Homeobox 1 (PBX1), and RAS P21 protein activator 3 (RASA3) in 51 TNBC, even at early stages of the disease. Finally, we found significantly lower methylation levels of FLJ43663 in cell free-DNA from the plasma of six TNBC patients than in 15 healthy donors. In conclusion, we report a novel DNA methylation signature with potential predictive value for TNBC diagnosis.

DNA methylation-based signature of CD8+ tumor-infiltrating lymphocytes enables evaluation of immune response and prognosis in colorectal cancer

Background

Tumor-infiltrating lymphocytes (TILs), especially CD8+ TILs, can be used for predicting immunotherapy responsiveness and survival outcome. However, the evaluation of CD8+ TILs currently relies on histopathological methodology with high variability. We therefore aimed to develop a DNA methylation signature for CD8+ TILs (CD8+ MeTIL) that could evaluate immune response and prognosis in colorectal cancer (CRC).

Methods

A CD8+ MeTIL signature score was constructed by using CD8+ T cell-specific differentially methylated positions (DMPs) that were identified from Illumina EPIC methylation arrays. Immune cells, colon epithelial cells, and two CRC cohorts (n=282 and 335) were used to develop a PCR-based assay for quantitative analysis of DNA methylation at single-base resolution (QASM) to determine CD8 + MeTIL signature score.

Results

Three CD8+ T cell-specific DMPs were identified to construct the CD8+ MeTIL signature score, which showed a dramatic discriminability between CD8+ T cells and other cells. The QASM assay we developed for CD8+ MeTIL markers could measure CD8+ TILs distributions in a fully quantitative, accurate, and simple manner. The CD8+ MeTIL score determined by QASM assay showed a strong association with histopathology-based CD8+ TIL counts and a gene expression-based immune marker. Furthermore, the low CD8+ MeTIL score (enriched CD8+ TILs) was associated with MSI-H tumors and predicted better survival in CRC cohorts.

Conclusions

This study developed a quantitative DNA methylation-based signature that was reliable to evaluate CD8+ TILs and prognosis in CRC. This approach has the potential to be a tool for investigations on CD8+ TILs and a biomarker for therapeutic approaches, including immunotherapy.

Identification of tumor immune infiltration-associated lncRNAs for improving prognosis and immunotherapy response of patients with non-small cell lung cancer

BACKGROUND

Increasing evidence has demonstrated the functional relevance of long non-coding RNAs (lncRNAs) to immunity regulation and the tumor microenvironment in non-small cell lung cancer (NSCLC). However, tumor immune infiltration-associated lncRNAs and their value in improving clinical outcomes and immunotherapy remain largely unexplored.

METHODS

We developed a computational approach to identify an lncRNA signature (TILSig) as an indicator of immune cell infiltration in patients with NSCLC through integrative analysis for lncRNA, immune and clinical profiles of 115 immune cell lines, 187 NSCLC cell lines and 1533 patients with NSCLC. Then the influence of the TILSig on the prognosis and immunotherapy in NSCLC was comprehensively investigated.

RESULTS

Computational immune and lncRNA profiling analysis identified an lncRNA signature (TILSig) consisting of seven lncRNAs associated with tumor immune infiltration. The TILSig significantly stratified patients into the immune-cold group and immune-hot group in both training and validation cohorts. These immune-hot patients exhibit significantly improved survival outcome and greater immune cell infiltration compared with immune-cold patients. Multivariate analysis revealed that the TILSig is an independent predictive factor after adjusting for other clinical factors. Further analysis accounting for TILSig and immune checkpoint gene revealed that the TILSig has a discriminatory power in patients with similar expression levels of immune checkpoint genes and significantly prolonged survival was observed for patients with low TILSig and low immune checkpoint gene expression implying a better response to immune checkpoint inhibitor (ICI) immunotherapy.

CONCLUSIONS

Our finding demonstrated the importance and value of lncRNAs in evaluating the immune infiltrate of the tumor and highlighted the potential of lncRNA coupled with specific immune checkpoint factors as predictive biomarkers of ICI response to enable a more precise selection of patients.

A High Epigenetic Risk Score Shapes the Non-Inflamed Tumor Microenvironment in Breast Cancer

Background: Epigenetic dysregulation via aberrant DNA methylation has gradually become recognized as an efficacious signature for predicting tumor prognosis and response to therapeutic targets. However, reliable DNA methylation biomarkers describing tumorigenesis remain to be comprehensively explored regarding their prognostic and therapeutic potential in breast cancer (BC).

Methods: Whole-genome methylation datasets integrated from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were profiled (n = 1,268). A three-stage selection procedure (discovery, training, and external validation) was utilized to screen out the prominent biomarkers and establish a robust risk score from more than 300,000 CpG sites after quality control, rigorous filtering, and reducing dimension. Moreover, gene set enrichment analyses guided us to systematically correlate this epigenetic risk score with immunological characteristics, including immunomodulators, anti-cancer immunity cycle, immune checkpoints, tumor-infiltrating immune cells and a series of signatures upon modulating components within BC tumor microenvironment (TME). Multi-omics data analyses were performed to decipher specific genomic alterations in low- and high-risk patients. Additionally, we also analyzed the role of risk score in predicting response to several treatment options.

Results: A 10-CpG-based prognostic signature which could significantly and independently categorize BC patients into distinct prognoses was established and sufficiently validated. And we hypothesize that this signature designs a non-inflamed TME in BC based on the evidence that the derived risk score is negatively correlated with tumor-associated infiltrating immune cells, anti-cancer immunity cycle, immune checkpoints, immune cytolytic activity, T cell inflamed score, immunophenoscore, and the vast majority of immunomodulators. The identified high-risk patients were characterized by upregulation of immune inhibited oncogenic pathways, higher TP53 mutation and copy number burden, but lower response to cancer immunotherapy and chemotherapy.

Conclusion: Our work highlights the complementary roles of 10-CpG-based signature in estimating overall survival in BC patients, shedding new light on investigating failed events concerning immunotherapy at present.

Methyl Diet Enhanced Sepsis-Induced Mortality Through Altering Gut Microbiota

Introduction

Mortality of sepsis is caused by an inappropriately amplified systemic inflammatory response and bacteremia. Methyl diet has been shown to associate with greater inflammation response in different diseases. This study aimed to determine whether dietary supplementation with methyl donors affects the inflammation response and mortality in sepsis and to investigate the underlying mechanisms.

Methods

Four-week-old male C57BL/6 mice were fed with a high-methyl diet (HMD) or a regulator diet (RD) till the experiment time. Mice septic model was induced by Cecal ligation and puncture (CLP), lipopolysaccharide (LPS), or E.coli. Inflammatory cytokine was analyzed by ELISA and qRT-PCR. Immune cell infiltration was evaluated by H&E and IHC. The composition of gut microbiota was determined by 16S rRNA sequencing. The effect of gut microbiota on sepsis was further verified by fecal microbiome transplantation.

Results

Our results showed that the diet riches in methyl donors exacerbated mortality, organ injury, and circulating levels of inflammatory mediators in CLP-induced septic mice model, compared to the control diet group. However, no significant differences have been observed in the inflammatory responses in the LPS-induced septic model and macrophages activation between the two groups of mice. There was a higher bacterial burden in CLP-induced HMD mice suggested that methyl diet might modulate gut microbiota. Bacterial 16S rRNA sequencing results showed that the composition of gut microbiota was altered. The high methyl donor diet reduced the abundance of Akkermansia and Lachnospiraceae, which were associated with protective effects in sepsis, in the gut. Moreover, fecal microbiome transplantation experiment showed that the transfer of feces, which obtained from high methyl diet mice, aggravated the mortality and inflammation responses in recipient mice.

Discussion

Methyl diet enhanced CLP-induced septic mortality and inflammatory responses through altering the composition of gut microbiota. This result indicated that diet-based gut microbiota may be a new therapeutic strategy for sepsis patients.

Identification and Validation of Immune-Related Methylation Clusters for Predicting Immune Activity and Prognosis in Breast Cancer

The role of DNA methylation of breast cancer-infiltrating immune cells has not been fully explored. We conducted a cohort-based retrospective study analyzing the genome-wide immune-related DNA methylation of 1057 breast cancer patients from the TCGA cohort and GSE72308 cohort. Based on patients' overall survival (OS), a prognostic risk score system using 18 immune-related methylation genes (IRMGs) was established and further validated in an independent cohort. Kaplan-Meier analysis showed a clear separation of OS between the low- and high-risk groups. Patients in the low-risk group had a higher immune score and stromal score compared with the high-risk group. Moreover, the characteristics based on 18-IRMGs signature were related to the tumor immune microenvironment and affected the abundance of tumor-infiltrating immune cells. Consistently, the 18-IRMGs signatures showed similar influences on immune modulation and survival in another external validation cohort (GSE72308). In conclusion, the proposed 18-IRMGs signature could be a potential marker for breast cancer prognostication.

DIMEimmune: Robust estimation of infiltrating lymphocytes in CNS tumors from DNA methylation profiles

The interaction of CNS tumors with infiltrating lymphocytes plays an important role in their initiation and progression and might be related to therapeutic responses. Gene expression-based methods have been successfully used to characterize the tumor microenvironment. However, methylation data are now increasingly used for molecular diagnostics and there are currently only few methods to infer information about the microenvironment from this data type. Using an approach based on differential methylation and principal component analysis, we developed DIMEimmune (Differential Methylation Analysis for Immune Cell Estimation) to estimate CD4⁺ and CD8⁺ T cell abundance as well as tumor-infiltrating lymphocytes (TILs) scores from bulk methylation data. Well-established approaches based on gene expression data and immunohistochemistry-based lymphocyte counts were used as benchmarks. The comparison of DIMEimmune to the previously published MethylCIBERSORT and MeTIL algorithms showed an improved correlation with both gene expression-based and immunohistological results across different brain tumor types. Further, we applied our method to large datasets of glioma, medulloblastoma, atypical teratoid/rhabdoid tumors (ATRTs) and ependymoma. High-grade gliomas showed higher scores of tumor-infiltrating lymphocytes than lower-grade gliomas. There were overall only few tumor-infiltrating lymphocytes in medulloblastoma subgroups. ATRTs were highly infiltrated by lymphocytes, most prominently in the MYC subgroup. DIMEimmune-based estimates of TILs were a significant prognostic factor in the overall cohort of gliomas and medulloblastomas, but not within methylation-based diagnostic subgroups. To conclude, DIMEimmune allows for robust estimates of TIL abundance and might contribute to establishing them as a prognostic or predictive factor in future studies of CNS tumors.