InfiniumPurify: An R package for estimating and accounting for tumor purity in cancer methylation research

Molecular landscape and multi-omic measurements of heterogeneity in fetal adenocarcinoma of the lung

Fetal adenocarcinoma of the lung (FLAC) is a rare form of lung adenocarcinoma and was divided into high-grade (H-FLAC) and low-grade (L-FLAC) subtypes. Despite the existence of some small case series studies, a comprehensive multi-omics study of FLAC has yet to be undertaken. In this study, we depicted the multi-omics landscapes of this rare lung cancer type by performing multi-regional sampling on 20 FLAC cases. A comparison of multi-omics profiles revealed significant differences between H-FLAC and L-FLAC in a multi-omic landscape. Two subtypes also showed distinct relationships between multi-layer intratumor heterogeneity (ITH). We discovered that a lower genetic ITH was significantly associated with worse recurrence-free survival and overall survival in FLAC patients, whereas higher methylation ITH in H-FLAC patients suggested a short survival. Our findings highlight the complex interplay between genetic and transcriptional heterogeneity in FLAC and suggest that different types of ITH may have distinct implications for patient prognosis.

Chromosome evolution screens recapitulate tissue-specific tumor aneuploidy patterns

Whole chromosome and arm-level copy number alterations occur at high frequencies in tumors, but their selective advantages, if any, are poorly understood. Here, utilizing unbiased whole chromosome genetic screens combined with in vitro evolution to generate arm- and subarm-level events, we iteratively selected the fittest karyotypes from aneuploidized human renal and mammary epithelial cells. Proliferation-based karyotype selection in these epithelial lines modeled tissue-specific tumor aneuploidy patterns in patient cohorts in the absence of driver mutations. Hi-C-based translocation mapping revealed that arm-level events usually emerged in multiples of two via centromeric translocations and occurred more frequently in tetraploids than diploids, contributing to the increased diversity in evolving tetraploid populations. Isogenic clonal lineages enabled elucidation of pro-tumorigenic mechanisms associated with common copy number alterations, revealing Notch signaling potentiation as a driver of 1q gain in breast cancer. We propose that intrinsic, tissue-specific proliferative effects underlie tumor copy number patterns in cancer.

Associations in cell type-specific hydroxymethylation and transcriptional alterations of pediatric central nervous system tumors

Although intratumoral heterogeneity has been established in pediatric central nervous system tumors, epigenomic alterations at the cell type level have largely remained unresolved. To identify cell type-specific alterations to cytosine modifications in pediatric central nervous system tumors, we utilize a multi-omic approach that integrated bulk DNA cytosine modification data (methylation and hydroxymethylation) with both bulk and single-cell RNA-sequencing data. We demonstrate a large reduction in the scope of significantly differentially modified cytosines in tumors when accounting for tumor cell type composition. In the progenitor-like cell types of tumors, we identify a preponderance differential Cytosine-phosphate-Guanine site hydroxymethylation rather than methylation. Genes with differential hydroxymethylation, like histone deacetylase 4 and insulin-like growth factor 1 receptor, are associated with cell type-specific changes in gene expression in tumors. Our results highlight the importance of epigenomic alterations in the progenitor-like cell types and its role in cell type-specific transcriptional regulation in pediatric central nervous system tumors.

The Epigenetic Evolution of Glioma Is Determined by the IDH1 Mutation Status and Treatment Regimen

Tumor adaptation or selection is thought to underlie therapy resistance in glioma. To investigate longitudinal epigenetic evolution of gliomas in response to therapeutic pressure, we performed an epigenomic analysis of 132 matched initial and recurrent tumors from patients with IDH-wildtype (IDHwt) and IDH-mutant (IDHmut) glioma. IDHwt gliomas showed a stable epigenome over time with relatively low levels of global methylation. The epigenome of IDHmut gliomas showed initial high levels of genome-wide DNA methylation that was progressively reduced to levels similar to those of IDHwt tumors. Integration of epigenomics, gene expression, and functional genomics identified HOXD13 as a master regulator of IDHmut astrocytoma evolution. Furthermore, relapse of IDHmut tumors was accompanied by histologic progression that was associated with survival, as validated in an independent cohort. Finally, the initial cell composition of the tumor microenvironment varied between IDHwt and IDHmut tumors and changed differentially following treatment, suggesting increased neoangiogenesis and T-cell infiltration upon treatment of IDHmut gliomas. This study provides one of the largest cohorts of paired longitudinal glioma samples with epigenomic, transcriptomic, and genomic profiling and suggests that treatment of IDHmut glioma is associated with epigenomic evolution toward an IDHwt-like phenotype.

SIGNIFICANCE

Standard treatments are related to loss of DNA methylation in IDHmut glioma, resulting in epigenetic activation of genes associated with tumor progression and alterations in the microenvironment that resemble treatment-naïve IDHwt glioma.

Global analysis of iron metabolism-related genes identifies potential mechanisms of gliomagenesis and reveals novel targets

AIMS

This study aimed to investigate key regulators of aberrant iron metabolism in gliomas, and evaluate their effect on biological functions and clinical translational relevance.

METHODS

We used transcriptomic data from multiple cross-platform glioma cohorts to identify key iron metabolism-related genes (IMRGs) based on a series of bioinformatic and machine learning methods. The associations between IMRGs and prognosis, mesenchymal phenotype, and genomic alterations were analyzed in silico. The performance of the IMRGs-based signature in predicting temozolomide (TMZ) treatment sensitivity was evaluated. In vitro and in vivo experiments were used to explore the biological functions of these key IMRGs.

RESULTS

HMOX1, LTF, and STEAP3 were identified as the most essential IMRGs in gliomas. The expression levels of these genes were strongly related to clinicopathological and molecular features. The robust IMRG-based gene signature could be used for prognosis prediction. These genes facilitate mesenchymal transformation, driver gene mutations, and oncogenic alterations in gliomas. The gene signature was also associated with TMZ resistance. HMOX1, LTF, and STEAP3 knockdown in glioma cells significantly reduced cell proliferation, colony formation, migration, and malignant invasion.

CONCLUSION

The study presented a comprehensive view of key regulators underpinning iron metabolism in gliomas and provided new insights into novel therapeutic approaches.

CPVL suppresses metastasis of nasopharyngeal carcinoma through inhibiting epithelial-mesenchymal transition

PURPOSE

Distant metastasis is the main obstacle to treating nasopharyngeal carcinoma (NPC). Tumor distance metastasis is a complex process involving the jointly participation of multiple oncogenes, tumor suppressor genes, and metastasis-associated genes. Enough accurate prognostic genes for evaluating metastasis risk are lacking. We aimed to identify more precise biomarkers for NPC metastasis.

METHODS

We performed weighted gene co-expression network analysis, differentially expressed gene analysis, univariate and multivariate stepwise Cox regression, and Kaplan-Meier (K-M) survival analyses, on data obtained from RNA sequencing of 10 NPC samples and the public database, to identify key genes correlated with NPC metastasis. Wound healing assays, transwell assays, and immunohistochemistry were conducted to validate our bioinformatic conclusions. Western blotting was performed to evaluate and quantify the effect of identified EMT genes on epithelial-mesenchymal transition (EMT) of NPC.

RESULTS

Combined our own RNA sequencing data and public data, we determined carboxypeptidase vitellogenic-like protein (CPVL) as a tumor suppressor for NPC. Pathway enrichment analyses indicated that genes associated with CPVL are involved in EMT. NPC with low CPVL expression had high tumor purity and low levels of immune cells. Experimental results showed that CPVL protein predominantly expressed in cytoplasmic and membranous and it exhibited higher expression levels in NPC tissues without distant metastasis than those with distant metastasis. CPVL inhibits the migration and invasive capability of NPC cells. Overexpression of CPVL upregulates E-cadherin and ZO-1, whereas it downregulates vimentin, suggesting that CPVL suppresses tumor metastasis by inhibiting EMT.

CONCLUSION

CPVL inhibits migration and invasion of NPC cells and is associated with tumor metastasis suppression through upregulating epithelial marker and inhibiting mesenchymal marker expression and could be a prognostic biomarker for metastasis risk evaluation in NPC.

Deconvolution of cancer cell states by the XDec-SM method

Proper characterization of cancer cell states within the tumor microenvironment is a key to accurately identifying matching experimental models and the development of precision therapies. To reconstruct this information from bulk RNA-seq profiles, we developed the XDec Simplex Mapping (XDec-SM) reference-optional deconvolution method that maps tumors and the states of constituent cells onto a biologically interpretable low-dimensional space. The method identifies gene sets informative for deconvolution from relevant single-cell profiling data when such profiles are available. When applied to breast tumors in The Cancer Genome Atlas (TCGA), XDec-SM infers the identity of constituent cell types and their proportions. XDec-SM also infers cancer cells states within individual tumors that associate with DNA methylation patterns, driver somatic mutations, pathway activation and metabolic coupling between stromal and breast cancer cells. By projecting tumors, cancer cell lines, and PDX models onto the same map, we identify in vitro and in vivo models with matching cancer cell states. Map position is also predictive of therapy response, thus opening the prospects for precision therapy informed by experiments in model systems matched to tumors in vivo by cancer cell state.

DNA methylation-based classifier differentiates intrahepatic pancreato-biliary tumours

BACKGROUND

Differentiating intrahepatic cholangiocarcinomas (iCCA) from hepatic metastases of pancreatic ductal adenocarcinoma (PAAD) is challenging. Both tumours have similar morphological and immunohistochemical pattern and share multiple driver mutations. We hypothesised that DNA methylation-based machine-learning algorithms may help perform this task.

METHODS

We assembled genome-wide DNA methylation data for iCCA (n = 259), PAAD (n = 431), and normal bile duct (n = 70) from publicly available sources. We split this cohort into a reference (n = 399) and a validation set (n = 361). Using the reference cohort, we trained three machine learning models to differentiate between these entities. Furthermore, we validated the classifiers on the technical validation set and used an internal cohort (n = 72) to test our classifier.

FINDINGS

On the validation cohort, the neural network, support vector machine, and the random forest classifiers reached accuracies of 97.68%, 95.62%, and 96.5%, respectively. Filtering by anomaly detection and thresholds improved the accuracy to 99.07% (37 samples excluded by filtering), 96.22% (17 samples excluded), and 100% (44 samples excluded) for the neural network, support vector machine and random forest, respectively. Because of best balance between accuracy and number of predictable cases we tested the neural network with applied filters on the in-house cohort, obtaining an accuracy of 95.45%.

INTERPRETATION

We developed a classifier that can differentiate between iCCAs, intrahepatic metastases of a PAAD, and normal bile duct tissue with high accuracy. This tool can be used for improving the diagnosis of pancreato-biliary cancers of the liver.

FUNDING

This work was supported by Berlin Institute of Health (JCS Program), DKTK Berlin (Young Investigator Grant 2022), German Research Foundation (493697503 and 314905040 - SFB/TRR 209 Liver Cancer B01), and German Cancer Aid (70113922).

PUREE: accurate pan-cancer tumor purity estimation from gene expression data

Tumors are complex masses composed of malignant and non-malignant cells. Variation in tumor purity (proportion of cancer cells in a sample) can both confound integrative analysis and enable studies of tumor heterogeneity. Here we developed PUREE, which uses a weakly supervised learning approach to infer tumor purity from a tumor gene expression profile. PUREE was trained on gene expression data and genomic consensus purity estimates from 7864 solid tumor samples. PUREE predicted purity with high accuracy across distinct solid tumor types and generalized to tumor samples from unseen tumor types and cohorts. Gene features of PUREE were further validated using single-cell RNA-seq data from distinct tumor types. In a comprehensive benchmark, PUREE outperformed existing transcriptome-based purity estimation approaches. Overall, PUREE is a highly accurate and versatile method for estimating tumor purity and interrogating tumor heterogeneity from bulk tumor gene expression data, which can complement genomics-based approaches or be used in settings where genomic data is unavailable.

Hydroxymethylation alterations in progenitor-like cell types of pediatric central nervous system tumors are associated with cell type-specific transcriptional changes

Although intratumoral heterogeneity has been established in pediatric central nervous system tumors, epigenomic alterations at the cell type level have largely remained unresolved. To identify cell type-specific alterations to cytosine modifications in pediatric central nervous system tumors we utilized a multi-omic approach that integrated bulk DNA cytosine modification data (methylation and hydroxymethylation) with both bulk and single-cell RNA-sequencing data. We demonstrate a large reduction in the scope of significantly differentially modified cytosines in tumors when accounting for tumor cell type composition. In the progenitor-like cell types of tumors, we identified a preponderance differential CpG hydroxymethylation rather than methylation. Genes with differential hydroxymethylation, like HDAC4 and IGF1R, were associated with cell type-specific changes in gene expression in tumors. Our results highlight the importance of epigenomic alterations in the progenitor-like cell types and its role in cell type-specific transcriptional regulation in pediatric CNS tumors.

DNA methylation in peripheral blood leukocytes for the association with glucose metabolism and invasive breast cancer

BACKGROUND

Insulin resistance (IR) is a well-established factor for breast cancer (BC) risk in postmenopausal women, but the interrelated molecular pathways on the methylome are not explicitly described. We conducted a population-level epigenome-wide association (EWA) study for DNA methylation (DNAm) probes that are associated with IR and prospectively correlated with BC development, both overall and in BC subtypes among postmenopausal women.

METHODS

We used data from Women's Health Initiative (WHI) ancillary studies for our EWA analyses and evaluated the associations of site-specific DNAm across the genome with IR phenotypes by multiple regressions adjusting for age and leukocyte heterogeneities. For our analysis of the top 20 IR-CpGs with BC risk, we used the WHI and the Cancer Genomic Atlas (TCGA), using multiple Cox proportional hazards and logit regressions, respectively, accounting for age, diabetes, obesity, leukocyte heterogeneities, and tumor purity (for TCGA). We further conducted a Gene Set Enrichment Analysis.

RESULTS

We detected several EWA-CpGs in TXNIP, CPT1A, PHGDH, and ABCG1. In particular, cg19693031 in TXNIP was replicated in all IR phenotypes, measured by fasting levels of glucose, insulin, and homeostatic model assessment-IR. Of those replicated IR-genes, 3 genes (CPT1A, PHGDH, and ABCG1) were further correlated with BC risk; and 1 individual CpG (cg01676795 in POR) was commonly detected across the 2 cohorts.

CONCLUSIONS

Our study contributes to better understanding of the interconnected molecular pathways on the methylome between IR and BC carcinogenesis and suggests potential use of DNAm markers in the peripheral blood cells as preventive targets to detect an at-risk group for IR and BC in postmenopausal women.

Longitudinal DNA methylation analysis of adult-type IDH-mutant gliomas

Diffuse gliomas are the most prevalent malignant primary brain tumors in adults and remain incurable despite standard therapy. Tumor recurrence is currently inevitable, which contributes to a persistent high morbidity and mortality in these patients. In this study, we examined the genome-wide DNA methylation profiles of primary and recurrent adult-type IDH-mutant gliomas to elucidate DNA methylation changes associated with tumor progression (with or without malignant transformation). We analyzed DNA methylation profiles of 37 primary IDH-mutant gliomas and 42 paired recurrences using the DNA methylation EPIC beadChip array. DNA methylation-based classification reflected the tumor progression over time. We observed a methylation subtype switch in a proportion of IDH-mutant astrocytomas; the primary tumors were subclassified as low-grade astrocytomas, which progressed to high-grade astrocytomas in the recurrent tumors. The CNS WHO grade 4 IDH-mutant astrocytomas did not always resemble methylation subclasses of higher grades. The number of differentially methylated CpG sites increased over time, and astrocytomas accumulated more differentially methylated CpG sites than oligodendrogliomas during tumor progression. Few differentially methylated CpG sites were shared between patients. We demonstrated that DNA methylation profiles are mostly maintained during IDH-mutant glioma progression, but CpG site-specific methylation alterations can occur.

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.

DNA methylome in pancreatic cancer identified novel promoter hyper-methylation in NPY and FAIM2 genes associated with poor prognosis in Indian patient cohort

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading cancers worldwide and has a poor survival, with a 5-year survival rate of only 8.5%. In this study we investigated altered DNA methylation associated with PDAC severity and prognosis.

Methods

Methylome data, generated using 450 K bead array, was compared between paired PDAC and normal samples in the TCGA cohort (n = 9) and our Indian cohort (n = 7). The total Indian Cohort (n = 75) was split into cohort 1 (n = 7), cohort 2 (n = 22), cohort 3 (n = 26) and cohort 4 (n = 20).Validation of differential methylation (6 selected CpG loci) and associated gene expression for differentially methylated genes (10 selected gDMs) were carried out in separate validation cohorts, using MSP, RT-PCR and IHC correlations between methylation and gene expression were observed in TCGA, GTEx cohorts and in validation cohorts. Kaplan–Meier survival analysis was done to study differential prognosis, during 2–5 years of follow-up.

Results

We identified 156 DMPs, mapped to 91 genes (gDMs), in PDAC; 68 (43.5%) DMPs were found to be differentially methylated both in TCGA cohort and our cohort, with significant concordance at hypo- and hyper-methylated loci. Enrichments of “regulation of ion transport”, “Interferon alpha/beta signalling”, “morphogenesis and development” and “transcriptional dysregulation” pathways were observed among 91 gDMs. Hyper-methylation of NPY and FAIM2 genes with down-regulated expression in PDAC, were significantly associated with poor prognosis in the Indian patient cohort.

Conclusions

Ethnic variations among populations may determine the altered epigenetic landscape in the PDAC patients of the Indian cohort. Our study identified novel differentially methylated genes (mainly NPY and FAIM2) and also validated the previously identified differentially methylated CpG sites associated with PDAC cancer patient’s survival. Comparative analysis of our data with TCGA and CPTAC cohorts showed that both NPY and FAIM2 hyper-methylation and down-regulations can be novel epigenetically regulated genes in the Indian patient population, statistically significantly associated with poor survival and advanced tumour stages.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02737-1.

Methylation subgroup and molecular heterogeneity is a hallmark of glioblastoma: implications for biopsy targeting, classification and therapy

Background

Patients and methods

Results

Conclusions

•

Glioblastoma exhibits significant heterogeneity, from epigenome-wide methylation phenotypes to single molecular targets.

•

Phylogeny showed CDKN2A/B loss and gain of EGFR, PDGFRA, and CDK4 early in tumor development.

•

Intratumoral heterogeneity is of utmost importance for molecular classification as well as for defining therapeutic targets.

•

Assessing single biopsies underestimates the true molecular diversity in a tumor.

Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker-driven learning framework

Current gold standard diagnostic strategies are unable to accurately differentiate malignant from benign small renal masses preoperatively; consequently, 20% of patients undergo unnecessary surgery. Devising a more confident presurgical diagnosis is key to improving treatment decision-making. We therefore developed MethylBoostER, a machine learning model leveraging DNA methylation data from 1228 tissue samples, to classify pathological subtypes of renal tumors (benign oncocytoma, clear cell, papillary, and chromophobe RCC) and normal kidney. The prediction accuracy in the testing set was 0.960, with class-wise ROC AUCs >0.988 for all classes. External validation was performed on >500 samples from four independent datasets, achieving AUCs >0.89 for all classes and average accuracies of 0.824, 0.703, 0.875, and 0.894 for the four datasets. Furthermore, consistent classification of multiregion samples (N = 185) from the same patient demonstrates that methylation heterogeneity does not limit model applicability. Following further clinical studies, MethylBoostER could facilitate a more confident presurgical diagnosis to guide treatment decision-making in the future.

Correlation analysis of tumor purity with clinicopathological, molecular, and imaging features in high-grade gliomas

High-grade gliomas (HGG) have high malignancy, high heterogeneity, and a poor prognosis. Tumor purity is an intrinsic feature of the HGG microenvironment and an independent prognostic factor. The purpose of this study was to analyze the correlation of tumor purity with clinicopathological, molecular, and imaging features. We performed a retrospective analysis of 112 patients diagnosed with HGG (grades III and IV) in our center. Eleven regions of interest (ROI) were randomly selected on whole-slide images (WSI, 40 × magnification) based on HGG tissue paraffin sections and hematoxylin-eosin (H&E) staining. Of these 11 ROIs, five ROIs were visually estimated by pathologists and six ROIs were automatically analyzed using ImageJ software. Last, the average tumor purity (%) of the 11 ROIs was calculated. Correlation analysis of tumor purity with clinicopathological, molecular, and imaging features was conducted. Of the 112 patients included in the study, the mean tumor purity of HGG was 70.96%. There were differences in tumor purity between WHO grades III and IV; the tumor purity of grade IV patients (67.59%) was lower than that of grade III patients (76.00%) (p < 0.001). There were also differences in tumor purity between IDH1 mutant and wild type, and the tumor purity of IDH1 mutant patients was higher than that of IDH1 wild-type patients (p = 0.006). The average range of peritumoral edema was about 19.18 mm, and the diameter of edema, ADC_mean, and ADC_min were negatively correlated with tumor purity(r =  - 0.236, r =  - 0.306, and r =  - 0.242; p < 0.05). The grade of HGG, IDH1 mutant/wild type, peritumoral edema, and ADC value were correlated with tumor purity. HGG grade, IDH1 mutant/wild type, peritumoral edema, and ADC value can predict tumor purity and indirectly reflect patient prognosis.

Tumor purity adjusted beta values improve biological interpretability of high-dimensional DNA methylation data

A common issue affecting DNA methylation analysis in tumor tissue is the presence of a substantial amount of non-tumor methylation signal derived from the surrounding microenvironment. Although approaches for quantifying and correcting for the infiltration component have been proposed previously, we believe these have not fully addressed the issue in a comprehensive and universally applicable way. We present a multi-population framework for adjusting DNA methylation beta values on the Illumina 450/850K platform using generic purity estimates to account for non-tumor signal. Our approach also provides an indirect estimate of the aggregate methylation state of the surrounding normal tissue. Using whole exome sequencing derived purity estimates and Illumina 450K methylation array data generated by The Cancer Genome Atlas project (TCGA), we provide a demonstration of this framework in breast cancer illustrating the effect of beta correction on the aggregate methylation beta value distribution, clustering accuracy, and global methylation profiles.

DNA methylation-based epigenetic signatures predict somatic genomic alterations in gliomas

Molecular classification has improved diagnosis and treatment for patients with malignant gliomas. However, classification has relied on individual assays that are both costly and slow, leading to frequent delays in treatment. Here, we propose the use of DNA methylation, as an emerging clinical diagnostic platform, to classify gliomas based on major genomic alterations and provide insight into subtype characteristics. We show that using machine learning models, DNA methylation signatures can accurately predict somatic alterations and show improvement over existing classifiers. The established Unified Diagnostic Pipeline (UniD) we develop is rapid and cost-effective for genomic alterations and gene expression subtypes diagnostic at early clinical phase and improves over individual assays currently in clinical use. The significant relationship between genetic alteration and epigenetic signature indicates broad applicability of our approach to other malignancies.

No clinical assay currently exists to classify glioma tumours based on gene expression. Here, the authors develop a DNA methylation-based classifier, Unified Diagnostic Pipeline (UniD) that identifies genomic alterations and gene expression subtypes of gliomas.

DNA methylation alterations across time and space in paediatric brain tumours

DNA methylation is increasingly used for tumour classification and has expanded upon the > 100 currently known brain tumour entities. A correct diagnosis is the basis for suitable treatment for patients with brain tumours, which is the leading cause of cancer-related death in children. DNA methylation profiling is required for diagnosis of certain tumours, and used clinically for paediatric brain tumours in several countries. We therefore evaluated if the methylation-based classification is robust in different locations of the same tumour, and determined how the methylation pattern changed over time to relapse. We sampled 3-7 spatially separated biopsies per patient, and collected samples from paired primary and relapse brain tumours from children. Altogether, 121 samples from 46 paediatric patients with brain tumours were profiled with EPIC methylation arrays. The methylation-based classification was mainly homogeneous for all included tumour types that were successfully classified, which is promising for clinical diagnostics. There were indications of multiple subclasses within tumours and switches in the relapse setting, but not confirmed as the classification scores were below the threshold. Site-specific methylation alterations did occur within the tumours and varied significantly between tumour types for the temporal samples, and as a trend in spatial samples. More alterations were present in high-grade tumours compared to low-grade, and significantly more alterations with longer relapse times. The alterations in the spatial and temporal samples were significantly depleted in CpG islands, exons and transcription start sites, while enriched in OpenSea and regions not affiliated with a gene, suggesting a random location of the alterations in less conserved regions. In conclusion, more DNA methylation changes accumulated over time and more alterations occurred in high-grade tumours. The alterations mainly occurred in regions without gene affiliation, and did not affect the methylation-based classification, which largely remained homogeneous in paediatric brain tumours.

A GATA4-regulated secretory program suppresses tumors through recruitment of cytotoxic CD8 T cells

The GATA4 transcription factor acts as a master regulator of development of multiple tissues. GATA4 also acts in a distinct capacity to control a stress-inducible pro-inflammatory secretory program that is associated with senescence, a potent tumor suppression mechanism, but also operates in non-senescent contexts such as tumorigenesis. This secretory pathway is composed of chemokines, cytokines, growth factors, and proteases. Since GATA4 is deleted or epigenetically silenced in cancer, here we examine the role of GATA4 in tumorigenesis in mouse models through both loss-of-function and overexpression experiments. We find that GATA4 promotes non-cell autonomous tumor suppression in multiple model systems. Mechanistically, we show that Gata4-dependent tumor suppression requires cytotoxic CD8 T cells and partially requires the secreted chemokine CCL2. Analysis of transcriptome data in human tumors reveals reduced lymphocyte infiltration in GATA4-deficient tumors, consistent with our murine data. Notably, activation of the GATA4-dependent secretory program combined with an anti-PD-1 antibody robustly abrogates tumor growth in vivo.

BCurve: Bayesian Curve Credible Bands Approach for the Detection of Differentially Methylated Regions

High-throughput assays have been developed to measure DNA methylation, among which bisulfite-based sequencing (BS-seq) and microarray technologies are the most popular for genome-wide profiling. A major goal in DNA methylation analysis is the detection of differentially methylated genomic regions under two different conditions. To accomplish this, many state-of-the-art methods have been proposed in the past few years; only a handful of these methods are capable of analyzing both types of data (BS-seq and microarray), though. On the other hand, covariates, such as sex and age, are known to be potentially influential on DNA methylation; and thus, it would be important to adjust for their effects on differential methylation analysis. In this chapter, we describe a Bayesian curve credible bands approach and the accompanying software, BCurve, for detecting differentially methylated regions for data generated from either microarray or BS-Seq. The unified theme underlying the analysis of these two different types of data is the model that accounts for correlation between DNA methylation in nearby sites, covariates, and between-sample variability. The BCurve R software package also provides tools for simulating both microarray and BS-seq data, which can be useful for facilitating comparisons of methods given the known "gold standard" in the simulated data. We provide detailed description of the main functions in BCurve and demonstrate the utility of the package for analyzing data from both platforms using simulated data from the functions provided in the package. Analyses of two real datasets, one from BS-seq and one from microarray, are also furnished to further illustrate the capability of BCurve.

Spatial heterogeneity in DNA methylation and chromosomal alterations in diffuse gliomas and meningiomas

Adult-type diffuse gliomas and meningiomas are the most common primary intracranial tumors of the central nervous system. DNA methylation profiling is a novel diagnostic technique increasingly used also in the clinic. Although molecular heterogeneity is well described in these tumors, DNA methylation heterogeneity is less studied. We therefore investigated the intratumor genetic and epigenetic heterogeneity in diffuse gliomas and meningiomas, with focus on potential clinical implications. We further investigated tumor purity as a source for heterogeneity in the tumors. We analyzed genome-wide DNA methylation profiles generated from 126 spatially separated tumor biopsies from 39 diffuse gliomas and meningiomas. Moreover, we evaluated five methods for measurement of tumor purity and investigated intratumor heterogeneity by assessing DNA methylation-based classification, chromosomal copy number alterations and molecular markers. Our results demonstrated homogeneous methylation-based classification of IDH-mutant gliomas and further corroborates subtype heterogeneity in glioblastoma IDH-wildtype and high-grade meningioma patients after excluding samples with low tumor purity. We detected a large number of differentially methylated CpG sites within diffuse gliomas and meningiomas, particularly in tumors of higher grades. The presence of CDKN2A/B homozygous deletion differed in one out of two patients with IDH-mutant astrocytomas, CNS WHO grade 4. We conclude that diffuse gliomas and high-grade meningiomas are characterized by intratumor heterogeneity, which should be considered in clinical diagnostics and in the assessment of methylation-based and molecular markers.

DNA methylation variation along the cancer epigenome and the identification of novel epigenetic driver events

While large-scale studies applying various statistical approaches have identified hundreds of mutated driver genes across various cancer types, the contribution of epigenetic changes to cancer remains more enigmatic. This is partly due to the fact that certain regions of the cancer genome, due to their genomic and epigenomic properties, are more prone to dysregulated DNA methylation than others. Thus, it has been difficult to distinguish which promoter methylation changes are really driving carcinogenesis from those that are mostly just a reflection of their genomic location. By developing a novel method that corrects for epigenetic covariates, we reveal a small, concise set of potential epigenetic driver events. Interestingly, those changes suggest different modes of epigenetic carcinogenesis: first, we observe recurrent inactivation of known cancer genes across tumour types suggesting a higher convergence on common tumour suppressor pathways than previously anticipated. Second, in prostate cancer, a cancer type with few recurrently mutated genes, we demonstrate how the epigenome primes tumours towards higher tolerance of other aberrations.

Machine Learning Models Predict the Primary Sites of Head and Neck Squamous Cell Carcinoma Metastases Based on DNA Methylation

In head and neck squamous cell cancers (HNSCs) that present as metastases with an unknown primary (HNSC-CUPs), the identification of a primary tumor improves therapy options and increases patient survival. However, the currently available diagnostic methods are laborious and do not offer a sufficient detection rate. Predictive machine learning models based on DNA methylation profiles have recently emerged as a promising technique for tumor classification. We applied this technique to HNSC to develop a tool that can improve the diagnostic workup for HNSC-CUPs. On a reference cohort of 405 primary HNSC samples, we developed four classifiers based on different machine learning models (random forest (RF), neural network (NN), elastic net penalized logistic regression (LOGREG), support vector machine (SVM)) that predict the primary site of HNSC tumors from their DNA methylation profile. The classifiers achieved high classification accuracies (RF=83%, NN=88%, LOGREG=SVM=89%) on an independent cohort of 64 HNSC metastases. Further, the NN, LOGREG, and SVM models significantly outperformed p16 status as a marker for an origin in the oropharynx. In conclusion, the DNA methylation profiles of HNSC metastases are characteristic for their primary sites and the classifiers developed in this study, which are made available to the scientific community, can provide valuable information to guide the diagnostic workup of HNSC-CUP. This article is protected by copyright. All rights reserved.

Genetic and epigenetic basis of hepatoblastoma diversity

Hepatoblastoma (HB) is the most common pediatric liver malignancy; however, hereditary predisposition and acquired molecular aberrations related to HB clinicopathological diversity are not well understood. Here, we perform an integrative genomic profiling of 163 pediatric liver tumors (154 HBs and nine hepatocellular carcinomas) based on the data acquired from a cohort study (JPLT-2). The total number of somatic mutations is precious low (0.52/Mb on exonic regions) but correlated with age at diagnosis. Telomerase reverse transcriptase (TERT) promoter mutations are prevalent in the tween HBs, selective in the transitional liver cell tumor (TLCT, > 8 years old). DNA methylation profiling reveals that classical HBs are characterized by the specific hypomethylated enhancers, which are enriched with binding sites for ASCL2, a regulatory transcription factor for definitive endoderm in Wnt-pathway. Prolonged upregulation of ASCL2, as well as fetal-liver-like methylation patterns of IGF2 promoters, suggests their "cell of origin" derived from the premature hepatoblast, similar to intestinal epithelial cells, which are highly proliferative. Systematic molecular profiling of HB is a promising approach for understanding the epigenetic drivers of hepatoblast carcinogenesis and deriving clues for risk stratification.

DNA 5-hydroxymethylcytosine in pediatric central nervous system tumors may impact tumor classification and is a positive prognostic marker

Background

Nucleotide-specific 5-hydroxymethylcytosine (5hmC) remains understudied in pediatric central nervous system (CNS) tumors. 5hmC is abundant in the brain, and alterations to 5hmC in adult CNS tumors have been reported. However, traditional approaches to measure DNA methylation do not distinguish between 5-methylcytosine (5mC) and its oxidized counterpart 5hmC, including those used to build CNS tumor DNA methylation classification systems. We measured 5hmC and 5mC epigenome-wide at nucleotide resolution in glioma, ependymoma, and embryonal tumors from children, as well as control pediatric brain tissues using tandem bisulfite and oxidative bisulfite treatments followed by hybridization to the Illumina Methylation EPIC Array that interrogates over 860,000 CpG loci.

Results

Linear mixed effects models adjusted for age and sex tested the CpG-specific differences in 5hmC between tumor and non-tumor samples, as well as between tumor subtypes. Results from model-based clustering of tumors was used to test the relation of cluster membership with patient survival through multivariable Cox proportional hazards regression. We also assessed the robustness of multiple epigenetic CNS tumor classification methods to 5mC-specific data in both pediatric and adult CNS tumors. Compared to non-tumor samples, tumors were hypohydroxymethylated across the epigenome and tumor 5hmC localized to regulatory elements crucial to cell identity, including transcription factor binding sites and super-enhancers. Differentially hydroxymethylated loci among tumor subtypes tended to be hypermethylated and disproportionally found in CTCF binding sites and genes related to posttranscriptional RNA regulation, such as DICER1. Model-based clustering results indicated that patients with low 5hmC patterns have poorer overall survival and increased risk of recurrence. Our results suggest 5mC-specific data from OxBS-treated samples impacts methylation-based tumor classification systems giving new opportunities for further refinement of classifiers for both pediatric and adult tumors.

Conclusions

We identified that 5hmC localizes to super-enhancers, and genes commonly implicated in pediatric CNS tumors were differentially hypohydroxymethylated. We demonstrated that distinguishing methylation and hydroxymethylation is critical in identifying tumor-related epigenetic changes. These results have implications for patient prognostication, considerations of epigenetic therapy in CNS tumors, and for emerging molecular neuropathology classification approaches.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01156-9.

Immune cell deconvolution of bulk DNA methylation data reveals an association with methylation class, key somatic alterations, and cell state in glial/glioneuronal tumors

It is recognized that the tumor microenvironment (TME) plays a critical role in the biology of cancer. To better understand the role of immune cell components in CNS tumors, we applied a deconvolution approach to bulk DNA methylation array data in a large set of newly profiled samples (n = 741) as well as samples from external data sources (n = 3311) of methylation-defined glial and glioneuronal tumors. Using the cell-type proportion data as input, we used dimensionality reduction to visualize sample-wise patterns that emerge from the cell type proportion estimations. In IDH-wildtype glioblastomas (n = 2,072), we identified distinct tumor clusters based on immune cell proportion and demonstrated an association with oncogenic alterations such as EGFR amplification and CDKN2A/B homozygous deletion. We also investigated the immune cluster-specific distribution of four malignant cellular states (AC-like, OPC-like, MES-like and NPC-like) in the IDH-wildtype cohort. We identified two major immune-based subgroups of IDH-mutant gliomas, which largely aligned with 1p/19q co-deletion status. Non-codeleted gliomas showed distinct proportions of a key genomic aberration (CDKN2A/B loss) among immune cell-based groups. We also observed significant positive correlations between monocyte proportion and expression of PD-L1 and PD-L2 (R = 0.54 and 0.68, respectively). Overall, the findings highlight specific roles of the TME in biology and classification of CNS tumors, where specific immune cell admixtures correlate with tumor types and genomic alterations.

Prediction of tumor purity from gene expression data using machine learning

MOTIVATION

Bulk tumor samples used for high-throughput molecular profiling are often an admixture of cancer cells and non-cancerous cells, which include immune and stromal cells. The mixed composition can confound the analysis and affect the biological interpretation of the results, and thus, accurate prediction of tumor purity is critical. Although several methods have been proposed to predict tumor purity using high-throughput molecular data, there has been no comprehensive study on machine learning-based methods for the estimation of tumor purity.

RESULTS

We applied various machine learning models to estimate tumor purity. Overall, the models predicted the tumor purity accurately and showed a high correlation with well-established gold standard methods. In addition, we identified a small group of genes and demonstrated that they could predict tumor purity well. Finally, we confirmed that these genes were mainly involved in the immune system.

AVAILABILITY

The machine learning models constructed for this study are available at https://github.com/BonilKoo/ML_purity.

DNA methylation profiling for molecular classification of adult diffuse lower-grade gliomas

BACKGROUND

DNA methylation profiling has facilitated and improved the classification of a wide variety of tumors of the central nervous system. In this study, we investigated the potential utility of DNA methylation profiling to achieve molecular diagnosis in adult primary diffuse lower-grade glioma (dLGG) according to WHO 2016 classification system. We also evaluated whether methylation profiling could provide improved molecular characterization and identify prognostic differences beyond the classical histological WHO grade together with IDH mutation status and 1p/19q codeletion status. All patients diagnosed with dLGG in the period 2007-2016 from the Västra Götaland region in Sweden were assessed for inclusion in the study.

RESULTS

A total of 166 dLGG cases were subjected for genome-wide DNA methylation analysis. Of these, 126 (76%) were assigned a defined diagnostic methylation class with a class prediction score ≥ 0.84 and subclass score ≥ 0.50. The assigned methylation classes were highly associated with their IDH mutation status and 1p/19q codeletion status. IDH-wildtype gliomas were further divided into subgroups with distinct molecular features.

CONCLUSION

The stratification of the patients by methylation profiling was as effective as the integrated WHO 2016 molecular reclassification at predicting the clinical outcome of the patients. Our study shows that DNA methylation profiling is a reliable and robust approach for the classification of dLGG into molecular defined subgroups, providing accurate detection of molecular markers according to WHO 2016 classification.

Expression of cyclin-dependent kinases and their clinical significance with immune infiltrates could predict prognosis in colorectal cancer

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The expression and prognostic values of AURKA and RB1 may also be significant to CRC diagnosis than previously studies.

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The association of CDKs with immune infiltrates may serve as target molecules for immunotherapy in CRC.

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The expression of CDK is significant among CRC subtypes and therefore, it can be inferred as a potential biomarker in the cancer subtype.

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An increase in tumor purity was positively correlated with the expression of CDK-1 in COAD due to CD4+ cells and CDK-4 in COAD and READ resulting from a fraction of immune cells.

Introduction

Colorectal cancer (CRC) is one of the most cancer-related mortalities worldwide and remains a major public health issue. Despite several attempts to develop promising therapies for CRC, its survival rate decreases with metastasis. Cyclin-dependent kinases (CDKs) are a family of protein kinases with various regulatory activities including cell cycle, mRNA expression, transcription, and differentiation. Aside from their role in cell proliferation when mutated, abnormal expression of these genes has been reported in some human cancer subtypes. This study explored the roles and therapeutic potentials of CDK 1 and 4 as prognostic biomarkers in CRC.

Methods

Bioinformatics analyses were carried out to demonstrate the expression and prognostic values of CDK-1 and CDK-4 with immune infiltrate in CRC.

Discussion

CDK levels in CRC were remarkably higher than those in normal tissues (p < 0.05), and overexpression in CRC tissues was significantly related to nodal metastatic status (p < 0.05) and histological subtypes. Kaplan-Meier analyses showed that patients with CRC who exhibited CDK-1 overexpression had worse overall survival (OS) as against patients with CDK-4 overexpression. The alteration observed was a mutation while the mutation hotspots include E163* and R24A/C/H/L respectively for CDK-1 and CDK-4 on the Pkinase domain. Of the associated genes, AURKA and RB1 were predominantly altered. Furthermore, CDK-4 is positively correlated with tumor purity in both COAD and READ while CDK-1is only positively correlated in COAD. CDK-1 overexpression was significantly associated with poor prognosis as opposed to CDK-4.

Conclusion

The expression and prognostic values of AURKA and RB1 may also be significant to CRC diagnosis. CDKs together with the co-expressed genes and their association with immune infiltrates may serve as target molecules for immunotherapy in CRC.

Association of variably methylated tumour DNA regions with overall survival for invasive lobular breast cancer

Background

Tumour DNA methylation profiling has shown potential to refine disease subtyping and improve the diagnosis and prognosis prediction of breast cancer. However, limited data exist regarding invasive lobular breast cancer (ILBC). Here, we investigated the genome-wide variability of DNA methylation levels across ILBC tumours and assessed the association between methylation levels at the variably methylated regions and overall survival in women with ILBC.

Methods

Tumour-enriched DNA was prepared by macrodissecting formalin-fixed paraffin embedded (FFPE) tumour tissue from 130 ILBCs diagnosed in the participants of the Melbourne Collaborative Cohort Study (MCCS). Genome-wide tumour DNA methylation was measured using the HumanMethylation 450K (HM450K) BeadChip array. Variably methylated regions (VMRs) were identified using the DMRcate package in R. Cox proportional hazards regression models were used to assess the association between methylation levels at the ten most significant VMRs and overall survival. Gene set enrichment analyses were undertaken using the web-based tool Metaspace. Replication of the VMR and survival analysis findings was examined using data retrieved from The Cancer Genome Atlas (TCGA) for 168 ILBC cases. We also examined the correlation between methylation and gene expression for the ten VMRs of interest using TCGA data.

Results

We identified 2771 VMRs (P < 10⁻⁸) in ILBC tumours. The ten most variably methylated clusters were predominantly located in the promoter region of the genes: ISM1, APC, TMEM101, ASCL2, NKX6, HIST3H2A/HIST3H2BB, HCG4P3, HES5, CELF2 and EFCAB4B. Higher methylation level at several of these VMRs showed an association with reduced overall survival in the MCCS. In TCGA, all associations were in the same direction, however stronger than in the MCCS. The pooled analysis of the MCCS and TCGA data showed that methylation at four of the ten genes was associated with reduced overall survival, independently of age and tumour stage; APC: Hazard Ratio (95% Confidence interval) per one-unit M-value increase: 1.18 (1.02–1.36), TMEM101: 1.23 (1.02–1.48), HCG4P3: 1.37 (1.05–1.79) and CELF2: 1.21 (1.02–1.43). A negative correlation was observed between methylation and gene expression for CELF2 (R = − 0.25, P = 0.001), but not for TMEM101 and APC.

Conclusions

Our study identified regions showing greatest variability across the ILBC tumour genome and found methylation at several genes to potentially serve as a biomarker of survival for women with ILBC.

Development of a DNA Methylation-Based Diagnostic Signature to Distinguish Benign Oncocytoma From Renal Cell Carcinoma

PURPOSE

A challenge in the diagnosis of renal cell carcinoma (RCC) is to distinguish chromophobe RCC (chRCC) from benign renal oncocytoma, because these tumor types are histologically and morphologically similar, yet they require different clinical management. Molecular biomarkers could provide a way of distinguishing oncocytoma from chRCC, which could prevent unnecessary treatment of oncocytoma. Such biomarkers could also be applied to preoperative biopsy specimens such as needle core biopsy specimens, to avoid unnecessary surgery of oncocytoma.

METHODS

We profiled DNA methylation in fresh-frozen oncocytoma and chRCC tumors and adjacent normal tissue and used machine learning to identify a signature of differentially methylated cytosine-phosphate-guanine sites (CpGs) that robustly distinguish oncocytoma from chRCC.

RESULTS

Unsupervised clustering of Stanford and preexisting RCC data from The Cancer Genome Atlas (TCGA) revealed that of all RCC subtypes, oncocytoma is most similar to chRCC. Unexpectedly, however, oncocytoma features more extensive, overall abnormal methylation than does chRCC. We identified 79 CpGs with large methylation differences between oncocytoma and chRCC. A diagnostic model trained on 30 CpGs could distinguish oncocytoma from chRCC in 10-fold cross-validation (area under the receiver operating curve [AUC], 0.96 (95% CI, 0.88 to 1.00)) and could distinguish TCGA chRCCs from an independent set of oncocytomas from a previous study (AUC, 0.87). This signature also separated oncocytoma from other RCC subtypes and normal tissue, revealing it as a standalone diagnostic biomarker for oncocytoma.

CONCLUSION

This CpG signature could be developed as a clinical biomarker to support differential diagnosis of oncocytoma and chRCC in surgical samples. With improved biopsy techniques, this signature could be applied to preoperative biopsy specimens.

Intratumor DNA methylation heterogeneity in glioblastoma: implications for DNA methylation-based classification

BACKGROUND

A feature of glioblastoma (GBM) is cellular and molecular heterogeneity, both within and between tumors. This variability causes a risk for sampling bias and potential tumor escape from future targeted therapy. Heterogeneous intratumor gene expression in GBM is well documented, but little is known regarding the epigenetic heterogeneity. Variability in DNA methylation within tumors would have implications for diagnostics, as methylation can be used for tumor classification, subtyping, and determination of the clinically used biomarker O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation. We therefore aimed to profile the intratumor DNA methylation heterogeneity in GBM and its effect on diagnostic properties.

METHODS

Three to 4 spatially separated biopsies per tumor were collected from 12 GBM patients. We performed genome-wide DNA methylation analysis and investigated intratumor variation.

RESULTS

All samples were classified as GBM isocitrate dehydrogenase (IDH) wild type (wt)/mutated by methylation profiling, but the subclass differed within 5 tumors. Some GBM samples exhibited higher DNA methylation differences within tumors than between, and many cytosine-phosphate-guanine (CpG) sites (mean: 17 000) had different methylation levels within the tumors. MGMT methylation status differed in IDH mutated patients (1/1).

CONCLUSIONS

We demonstrated that intratumor DNA methylation heterogeneity is a feature of GBM. Although all biopsies were classified as GBM IDH wt/mutated by methylation analysis, the assigned subclass differed in samples from the same patient. The observed heterogeneity within tumors is important to consider for methylation-based biomarkers and future improvements in stratification of GBM patients.

PESM: A novel approach of tumor purity estimation based on sample specific methylation sites

Background: Tumor purity is of great significance for the study of tumor genotyping and the prediction of recurrence, which is significantly affected by tumor heterogeneity. Tumor heterogeneity is the basis of drug resistance in various cancer treatments, and DNA methylation plays a core role in the generation of tumor heterogeneity. Almost all types of cancer cells are associated with abnormal DNA methylation in certain regions of the genome. The selection of tumor-related differential methylation sites, which can be used as an indicator of tumor purity, has important implications for purity assessment. At present, the selection of information sites mostly focuses on inter-tumor heterogeneity and ignores the heterogeneity of tumor growth space that is sample specificity. Results: Considering the specificity of tumor samples and the information gain of individual tumor sample relative to the normal samples, we present an approach, PESM, to evaluate the tumor purity through the specificity difference methylation sites of tumor samples. Applied to more than 200 tumor samples of Prostate adenocarcinoma (PRAD) and Kidney renal clear cell carcinoma (KIRC), it shows that the tumor purity estimated by PESM is highly consistent with other existing methods. In addition, PESM performs better than the method that uses the integrated signal of methylation sites to estimate purity. Therefore, different information sites selection methods have an important impact on the estimation of tumor purity, and the selection of sample specific information sites has a certain significance for accurate identification of tumor purity of samples.

Histoepigenetic analysis of the mesothelin network within pancreatic ductal adenocarcinoma cells reveals regulation of retinoic acid receptor gamma and AKT by mesothelin

To enable computational analysis of regulatory networks within the cancer cell in its natural tumor microenvironment, we develop a two-stage histoepigenetic analysis method. The first stage involves iterative computational deconvolution to estimate sample-specific cancer-cell intrinsic expression of a gene of interest. The second stage places the gene within a network module. We validate the method in simulation experiments, show improved performance relative to differential expression analysis from bulk samples, and apply it to illuminate the role of the mesothelin (MSLN) network in pancreatic ductal adenocarcinoma (PDAC). The network analysis and subsequent experimental validation in a panel of PDAC cell lines suggests AKT activation by MSLN through two known activators, retinoic acid receptor gamma (RARG) and tyrosine kinase non receptor 2 (TNK2). Taken together, these results demonstrate the potential of histoepigenetic analysis to reveal cancer-cell specific molecular interactions directly from patient tumor profiles.

PEIS: a novel approach of tumor purity estimation by identifying information sites through integrating signal based on DNA methylation data

BACKGROUND

Tumor purity plays an important role in understanding the pathogenic mechanism of tumors. The purity of tumor samples is highly sensitive to tumor heterogeneity. Due to Intratumoral heterogeneity of genetic and epigenetic data, it is suitable to study the purity of tumors. Among them, there are many purity estimation methods based on copy number variation, gene expression and other data, while few use DNA methylation data and often based on selected information sites. Consequently, how to choose methylation sites as information sites has an important influence on the purity estimation results. At present, the selection of information sites was often based on the differentially methylated sites that only consider the mean signal, without considering other possible signals and the strong correlation among adjacent sites.

RESULTS

Considering integrating multi-signals and strong correlation among adjacent sites, we propose an approach, PEIS, to estimate the purity of tumor samples by selecting informative differential methylation sites. Application to 12 publicly available tumor datasets, it is shown that PEIS provides accurate results in the estimation of tumor purity which has a high consistency with other existing methods. Also, through comparing the results of different information sites selection methods in the evaluation of tumor purity, it shows the PEIS is superior to other methods.

CONCLUSIONS

A new method to estimate the purity of tumor samples is proposed. This approach integrates multi-signals of the CpG sites and the correlation between the sites. Experimental analysis shows that this method is in good agreement with other existing methods for estimating tumor purity.

RF_Purify: a novel tool for comprehensive analysis of tumor-purity in methylation array data based on random forest regression

Background

With the advent of array-based techniques to measure methylation levels in primary tumor samples, systematic investigations of methylomes have widely been performed on a large number of tumor entities. Most of these approaches are not based on measuring individual cell methylation but rather the bulk tumor sample DNA, which contains a mixture of tumor cells, infiltrating immune cells and other stromal components. This raises questions about the purity of a certain tumor sample, given the varying degrees of stromal infiltration in different entities. Previous methods to infer tumor purity require or are based on the use of matching control samples which are rarely available. Here we present a novel, reference free method to quantify tumor purity, based on two Random Forest classifiers, which were trained on ABSOLUTE as well as ESTIMATE purity values from TCGA tumor samples. We subsequently apply this method to a previously published, large dataset of brain tumors, proving that these models perform well in datasets that have not been characterized with respect to tumor purity .

Results

Using two gold standard methods to infer purity – the ABSOLUTE score based on whole genome sequencing data and the ESTIMATE score based on gene expression data- we have optimized Random Forest classifiers to predict tumor purity in entities that were contained in the TCGA project. We validated these classifiers using an independent test data set and cross-compared it to other methods which have been applied to the TCGA datasets (such as ESTIMATE and LUMP).

Using Illumina methylation array data of brain tumor entities (as published in Capper et al. (Nature 555:469-474,2018)) we applied this model to estimate tumor purity and find that subgroups of brain tumors display substantial differences in tumor purity.

Conclusions

Random forest- based tumor purity prediction is a well suited tool to extrapolate gold standard measures of purity to novel methylation array datasets. In contrast to other available methylation based tumor purity estimation methods, our classifiers do not need a priori knowledge about the tumor entity or matching control tissue to predict tumor purity.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3014-z) contains supplementary material, which is available to authorized users.

Absence of an embryonic stem cell DNA methylation signature in human cancer

BACKGROUND

Differentiated cells that arise from stem cells in early development contain DNA methylation features that provide a memory trace of their fetal cell origin (FCO). The FCO signature was developed to estimate the proportion of cells in a mixture of cell types that are of fetal origin and are reminiscent of embryonic stem cell lineage. Here we implemented the FCO signature estimation method to compare the fraction of cells with the FCO signature in tumor tissues and their corresponding nontumor normal tissues.

METHODS

We applied our FCO algorithm to discovery data sets obtained from The Cancer Genome Atlas (TCGA) and replication data sets obtained from the Gene Expression Omnibus (GEO) data repository. Wilcoxon rank sum tests, linear regression models with adjustments for potential confounders and non-parametric randomization-based tests were used to test the association of FCO proportion between tumor tissues and nontumor normal tissues. P-values of < 0.05 were considered statistically significant.

RESULTS

Across 20 different tumor types we observed a consistently lower FCO signature in tumor tissues compared with nontumor normal tissues, with 18 observed to have significantly lower FCO fractions in tumor tissue (total n = 6,795 tumor, n = 922 nontumor, P < 0.05). We replicated our findings in 15 tumor types using data from independent subjects in 15 publicly available data sets (total n = 740 tumor, n = 424 nontumor, P < 0.05).

CONCLUSIONS

The results suggest that cancer development itself is substantially devoid of recapitulation of normal embryologic processes. Our results emphasize the distinction between DNA methylation in normal tightly regulated stem cell driven differentiation and cancer stem cell reprogramming that involves altered methylation in the service of great cell heterogeneity and plasticity.

Genome-Wide Analysis of Glioblastoma Patients with Unexpectedly Long Survival

Glioblastoma (GBM), representing WHO grade IV astrocytoma, is a relatively common primary brain tumor in adults with an exceptionally dismal prognosis. With an incidence rate of over 10 000 cases in the United States annually, the median survival rate ranges from 10-15 months in IDH1/2-wildtype tumors and 24-31 months in IDH1/2-mutant tumors, with further variation depending on factors such as age, MGMT methylation status, and treatment regimen. We present a cohort of 4 patients, aged 37-60 at initial diagnosis, with IDH1-mutant GBMs that were associated with unusually long survival intervals after the initial diagnosis, currently ranging from 90 to 154 months (all still alive). We applied genome-wide profiling with a methylation array (Illumina EPIC Array 850k) and a next-generation sequencing panel to screen for genetic and epigenetic alterations in these tumors. All 4 tumors demonstrated methylation patterns and genomic alterations consistent with GBM. Three out of four cases showed focal amplification of the CCND2 gene or gain of the region on 12p that included CCND2, suggesting that this may be a favorable prognostic factor in GBM. As this study has a limited sample size, further evaluation of patients with similar favorable outcome is warranted to validate these findings.

Dissecting heterogeneity in malignant pleural mesothelioma through histo-molecular gradients for clinical applications

Malignant pleural mesothelioma (MPM) is recognized as heterogeneous based both on histology and molecular profiling. Histology addresses inter-tumor and intra-tumor heterogeneity in MPM and describes three major types: epithelioid, sarcomatoid and biphasic, a combination of the former two types. Molecular profiling studies have not addressed intra-tumor heterogeneity in MPM to date. Here, we use a deconvolution approach and show that molecular gradients shed new light on the intra-tumor heterogeneity of MPM, leading to a reconsideration of MPM molecular classifications. We show that each tumor can be decomposed as a combination of epithelioid-like and sarcomatoid-like components whose proportions are highly associated with the prognosis. Moreover, we show that this more subtle way of characterizing MPM heterogeneity provides a better understanding of the underlying oncogenic pathways and the related epigenetic regulation and immune and stromal contexts. We discuss the implications of these findings for guiding therapeutic strategies, particularly immunotherapies and targeted therapies.