Genome-wide DNA methylation analysis of lung carcinoma reveals one neuroendocrine and four adenocarcinoma epitypes associated with patient outcome

Metabolic reprogramming by histone deacetylase inhibition preferentially targets NRF2-activated tumors

The interplay between metabolism and chromatin signaling is implicated in cancer progression. However, whether and how metabolic reprogramming in tumors generates chromatin vulnerabilities remain unclear. Lung adenocarcinoma (LUAD) tumors frequently harbor aberrant activation of the NRF2 antioxidant pathway, which drives aggressive and chemo-resistant disease. Using a chromatin-focused CRISPR screen, we report that NRF2 activation sensitizes LUAD cells to genetic and chemical inhibition of class I histone deacetylases (HDACs). This association is observed across cultured cells, mouse models, and patient-derived xenografts. Integrative epigenomic, transcriptomic, and metabolomic analysis demonstrates that HDAC inhibition causes widespread redistribution of H4ac and its reader protein, which transcriptionally downregulates metabolic enzymes. This results in reduced flux into amino acid metabolism and de novo nucleotide synthesis pathways that are preferentially required for the survival of NRF2-active cancer cells. Together, our findings suggest NRF2 activation as a potential biomarker for effective repurposing of HDAC inhibitors to treat solid tumors.

Identifying New Contributors to Brain Metastasis in Lung Adenocarcinoma: A Transcriptomic Meta-Analysis

Lung tumors frequently metastasize to the brain. Brain metastasis (BM) is common in advanced cases, and a major cause of patient morbidity and mortality. The precise molecular mechanisms governing BM are still unclear, in part attributed to the rarity of BM specimens. In this work, we compile a unique transcriptomic dataset encompassing RNA-seq, microarray, and single-cell analyses from BM samples obtained from patients with lung adenocarcinoma (LUAD). By integrating this comprehensive dataset, we aimed to enhance understanding of the molecular landscape of BM, thereby facilitating the identification of novel and efficient treatment strategies. We identified 102 genes with significantly deregulated expression levels in BM tissues, and discovered transcriptional alterations affecting the key driver 'hub' genes CD69 (a type II C-lectin receptor) and GZMA (Granzyme A), indicating an important role of the immune system in the development of BM from primary LUAD. Our study demonstrated a BM-specific gene expression pattern and revealed the presence of dendritic cells and neutrophils in BM, suggesting an immunosuppressive tumor microenvironment. These findings highlight key drivers of LUAD-BM that may yield therapeutic targets to improve patient outcomes.

Depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma

BACKGROUND

Tumours with no evidence of neuroendocrine transformation histologically but harbouring neuroendocrine features are collectively referred to as non-small cell lung cancer (NSCLC) with neuroendocrine differentiation (NED). Investigating the mechanisms underlying NED is conducive to designing appropriate treatment options for NSCLC patients.

METHODS

In the present study, we integrated multiple lung cancer datasets to identify neuroendocrine features using a one-class logistic regression (OCLR) machine learning algorithm trained on small cell lung cancer (SCLC) cells, a pulmonary neuroendocrine cell type, based on the transcriptome of NSCLC and named the NED index (NEDI). Single-sample gene set enrichment analysis, pathway enrichment analysis, ESTIMATE algorithm analysis, and unsupervised subclass mapping (SubMap) were performed to assess the altered pathways and immune characteristics of lung cancer samples with different NEDI values.

RESULTS

We developed and validated a novel one-class predictor based on the expression values of 13,279 mRNAs to quantitatively evaluate neuroendocrine features in NSCLC. We observed that a higher NEDI correlated with better prognosis in patients with LUAD. In addition, we observed that a higher NEDI was significantly associated with reduced immune cell infiltration and immune effector molecule expression. Furthermore, we found that etoposide-based chemotherapy might be more effective in the treatment of LUAD with high NEDI values. Moreover, we noted that tumours with low NEDI values had better responses to immunotherapy than those with high NEDI values.

CONCLUSIONS

Our findings improve the understanding of NED and provide a useful strategy for applying NEDI-based risk stratification to guide decision-making in the treatment of LUAD.

Metabolic Reprogramming by Histone Deacetylase Inhibition Selectively Targets NRF2-activated tumors

Interplay between metabolism and chromatin signaling have been implicated in cancer initiation and progression. However, whether and how metabolic reprogramming in tumors generates specific epigenetic vulnerabilities remain unclear. Lung adenocarcinoma (LUAD) tumors frequently harbor mutations that cause aberrant activation of the NRF2 antioxidant pathway and drive aggressive and chemo-resistant disease. We performed a chromatin-focused CRISPR screen and report that NRF2 activation sensitized LUAD cells to genetic and chemical inhibition of class I histone deacetylases (HDAC). This association was consistently observed across cultured cells, syngeneic mouse models and patient-derived xenografts. HDAC inhibition causes widespread increases in histone H4 acetylation (H4ac) at intergenic regions, but also drives re-targeting of H4ac reader protein BRD4 away from promoters with high H4ac levels and transcriptional downregulation of corresponding genes. Integrative epigenomic, transcriptomic and metabolomic analysis demonstrates that these chromatin changes are associated with reduced flux into amino acid metabolism and de novo nucleotide synthesis pathways that are preferentially required for the survival of NRF2-active cancer cells. Together, our findings suggest that metabolic alterations such as NRF2 activation could serve as biomarkers for effective repurposing of HDAC inhibitors to treat solid tumors.

A trans-omics assessment of gene-gene interaction in early-stage NSCLC

Epigenome-wide gene-gene (G × G) interactions associated with non-small-cell lung cancer (NSCLC) survival may provide insights into molecular mechanisms and therapeutic targets. Hence, we proposed a three-step analytic strategy to identify significant and robust G × G interactions that are relevant to NSCLC survival. In the first step, among 49 billion pairs of DNA methylation probes, we identified 175 775 G × G interactions with P_Bonferroni  ≤ 0.05 in the discovery phase of epigenomic analysis; among them, 15 534 were confirmed with P ≤ 0.05 in the validation phase. In the second step, we further performed a functional validation for these G × G interactions at the gene expression level by way of a two-phase (discovery and validation) transcriptomic analysis, and confirmed 25 significant G × G interactions enriched in the 6p21.33 and 6p22.1 regions. In the third step, we identified two G × G interactions using the trans-omics analysis, which had significant (P ≤ 0.05) epigenetic cis-regulation of transcription and robust G × G interactions at both the epigenetic and transcriptional levels. These interactions were cg14391855 × cg23937960 (β_interaction  = 0.018, P = 1.87 × 10^-12 ), which mapped to RELA × HLA-G (β_interaction  = 0.218, P = 8.82 × 10^-11 ) and cg08872738 × cg27077312 (β_interaction  = -0.010, P = 1.16 × 10^-11 ), which mapped to TUBA1B × TOMM40 (β_interaction =-0.250, P = 3.83 × 10^-10 ). A trans-omics mediation analysis revealed that 20.3% of epigenetic effects on NSCLC survival were significantly (P = 0.034) mediated through transcriptional expression. These statistically significant trans-omics G × G interactions can also discriminate patients with high risk of mortality. In summary, we identified two G × G interactions at both the epigenetic and transcriptional levels, and our findings may provide potential clues for precision treatment of NSCLC.

Microbiome dysbiosis and epigenetic modulations in lung cancer: From pathogenesis to therapy

The lung microbiome plays an essential role in maintaining healthy lung function, including host immune homeostasis. Lung microbial dysbiosis or disruption of the gut-lung axis can contribute to lung carcinogenesis by causing DNA damage, inducing genomic instability, or altering the host's susceptibility to carcinogenic insults. Thus far, most studies have reported the association of microbial composition in lung cancer. Mechanistic studies describing host-microbe interactions in promoting lung carcinogenesis are limited. Considering cancer as a multifaceted disease where epigenetic dysregulation plays a critical role, epigenetic modifying potentials of microbial metabolites and toxins and their roles in lung tumorigenesis are not well studied. The current review explains microbial dysbiosis and epigenetic aberrations in lung cancer and potential therapeutic opportunities.

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 Demethylation Switches Oncogenic ΔNp63 to Tumor Suppressive TAp63 in Squamous Cell Carcinoma

The TP63 gene encodes two major protein variants; TAp63 contains a p53-like transcription domain and consequently has tumor suppressor activities whereas ΔNp63 lacks this domain and acts as an oncogene. The two variants show distinct expression patterns in normal tissues and tumors, with lymphocytes and lymphomas/leukemias expressing TAp63, and basal epithelial cells and some carcinomas expressing high levels of ΔNp63, most notably squamous cell carcinomas (SCC). Whilst the transcriptional functions of TAp63 and ΔNp63 isoforms are known, the mechanisms involved in their regulation are poorly understood. Using squamous epithelial cells that contain high levels of ΔNp63 and low/undetectable TAp63, the DNA demethylating agent decitabine (5-aza-2’-deoxycytidine, 5-dAza) caused a dose-dependent increase in TAp63, with a simultaneous reduction in ΔNp63, indicating DNA methylation-dependent regulation at the isoform-specific promoters. The basal cytokeratin KRT5, a direct ΔNp63 transcriptional target, was also reduced, confirming functional alteration of p63 activity after DNA demethylation. We also showed high level methylation of three CpG sites in the TAP63 promoter in these cells, which was reduced by decitabine. DNMT1 depletion using inducible shRNAs partially replicated these effects, including an increase in the ratio of TAP63:ΔNP63 mRNAs, a reduction in ΔNp63 protein and reduced KRT5 mRNA levels. Finally, high DNA methylation levels were found at the TAP63 promoter in clinical SCC samples and matched normal tissues. We conclude that DNA methylation at the TAP63 promoter normally silences transcription in squamous epithelial cells, indicating DNA methylation as a therapeutic approach to induce this tumor suppressor in cancer. That decitabine simultaneously reduced the oncogenic activity of ΔNp63 provides a “double whammy” for SCC and other p63-positive carcinomas. Whilst a variety of mechanisms may be involved in producing the opposite effects of DNA demethylation on TAp63 and ΔNp63, we propose an “either or” mechanism in which TAP63 transcription physically interferes with the ability to initiate transcription from the downstream ΔNP63 promoter on the same DNA strand. This mechanism can explain the observed inverse expression of p63 isoforms in normal cells and cancer.

DNA methylation profiling identifies subgroups of lung adenocarcinoma with distinct immune cell composition, DNA methylation age, and clinical outcome

PURPOSE

Lung adenocarcinoma (LUAD) is a clinically heterogenous disease, which is highlighted by the unpredictable recurrence in low-stage tumors and highly variable responses observed in patients treated with immunotherapies, which cannot be explained by mutational profiles. DNA methylation-based classification and understanding of microenviromental heterogeneity may allow stratification into clinically relevant molecular subtypes of LUADs.

EXPERIMENTAL DESIGN

We characterize the genome-wide DNA methylation landscape of 88 resected LUAD tumors. Exome sequencing focusing on a panel of cancer-related genes was used to genotype these adenocarcinoma samples. Bioinformatic and statistical tools, the immune cell composition, DNA methylation age (DNAm age), and DNA methylation clustering were used to identify clinically relevant subgroups.

RESULTS

Deconvolution of DNA methylation data identified immunologically hot and cold subsets of lung adenocarcinomas. Additionally, concurrent factors were analyzed that could affect the immune microenvironment, such as smoking history, ethnicity, or presence of KRAS or TP53 mutations. When the DNAm age was calculated, a lower DNAm age was correlated with the presence of a set of oncogenic drivers, poor overall survival, and specific immune cell populations. Unsupervised DNA methylation clustering identified 6 molecular subgroups of LUAD tumors with distinct clinical and microenvironmental characteristics.

CONCLUSIONS

Our results demonstrate that DNA methylation signatures can stratify lung adenocarcinoma into clinically relevant subtypes, and thus such classification of LUAD at the time of resection may lead to better methods in predicting tumor recurrence and therapy responses.

Precision Oncology: Artificial Intelligence and DNA Methylation Analysis of Circulating Cell-Free DNA for Lung Cancer Detection

Background

Lung cancer (LC) is a leading cause of cancer-deaths globally. Its lethality is due in large part to the paucity of accurate screening markers. Precision Medicine includes the use of omics technology and novel analytic approaches for biomarker development. We combined Artificial Intelligence (AI) and DNA methylation analysis of circulating cell-free tumor DNA (ctDNA), to identify putative biomarkers for and to elucidate the pathogenesis of LC.

Methods

Illumina Infinium MethylationEPIC BeadChip array analysis was used to measure cytosine (CpG) methylation changes across the genome in LC. Six different AI platforms including support vector machine (SVM) and Deep Learning (DL) were used to identify CpG biomarkers and for LC detection. Training set and validation sets were generated, and 10-fold cross validation performed. Gene enrichment analysis using g:profiler and GREAT enrichment was used to elucidate the LC pathogenesis.

Results

Using a stringent GWAS significance threshold, p-value <5x10^-8, we identified 4389 CpGs (cytosine methylation loci) in coding genes and 1812 CpGs in non-protein coding DNA regions that were differentially methylated in LC. SVM and three other AI platforms achieved an AUC=1.00; 95% CI (0.90-1.00) for LC detection. DL achieved an AUC=1.00; 95% CI (0.95-1.00) and 100% sensitivity and specificity. High diagnostic accuracies were achieved with only intragenic or only intergenic CpG loci. Gene enrichment analysis found dysregulation of molecular pathways involved in the development of small cell and non-small cell LC.

Conclusion

Using AI and DNA methylation analysis of ctDNA, high LC detection rates were achieved. Further, many of the genes that were epigenetically altered are known to be involved in the biology of neoplasms in general and lung cancer in particular.

Hyper-Methylated Hub Genes of T-Cell Receptor Signaling Predict a Poor Clinical Outcome in Lung Adenocarcinoma

Background

Immune checkpoint inhibitors (ICIs) emerge as the first-line treatment of lung adenocarcinoma (LUAD); selection of subpopulations acquiring clinical benefit is required. Associations between epigenetic modulation of tumor microenvironment (TME) and clinical outcome are far from clear. We focused on immune-related genes closely regulated by DNA methylation to identify the potential clinical outcome indicators.

Methods

We systematically calculated immunophenotype score (IMpS) and classified immunophenotypes based on seven TME features in three independent cohorts. The overlapping of differential expressed genes and methylated probes targeted genes was regarded as genes closely regulated by DNA methylation. Then, probe/gene pairs which highly correlated with each other and IMpS were identified and named as immune-related probe/gene pairs (mIMg). Prognostic mIMg were selected and verified in seven independent validation cohorts.

Results

Three immune phenotypes were clustered, and similar results were obtained in the three independent training cohorts. C2 displayed as an immunologically hot phenotype, whereas C3 corresponded with immunologically cold phenotype. Average methylation level was decreased from C2 to C3 (C2 > C1 > C3). Similarly, ICIs nonresponders showed global hypo-methylation compared with responders. Genes in mIMg were mainly enriched, especially in T-cell receptor activation, and repressed in noninflamed TME by hyper-methylation. Among mIMg, low expression and hyper-methylation of CD247, LCK, and PSTPIP1 were risk factors of overall survival (OS). ICIs nonresponders were more likely to be hyper-methylated in the three genes. By integrating with the oncogenes status, we demonstrated that EGFR wt and SRGN overexpressed patients were associated with chronic inflammation and immune evasion, showing an immunologically hot phenotype, which might lead to the short OS but derive clinical benefit from ICIs.

Conclusions

This study identifies hyper-methylation and concurrent repression of CD247, LCK, PSTPIP1 as immune negative indicators and risk factors for prognosis in LUAD. Moreover, EGFR/SRGN axis may participate in immune modification to influence ICIs response and clinical outcome in LUAD.

A novel HIF1α-STIL-FOXM1 axis regulates tumor metastasis

BACKGROUND

Metastasis is the major cause of morbidity and mortality in cancer that involves in multiple steps including epithelial-mesenchymal transition (EMT) process. Centrosome is an organelle that functions as the major microtubule organizing center (MTOC), and centrosome abnormalities are commonly correlated with tumor aggressiveness. However, the conclusive mechanisms indicating specific centrosomal proteins participated in tumor progression and metastasis remain largely unknown.

METHODS

The expression levels of centriolar/centrosomal genes in various types of cancers were first examined by in silico analysis of the data derived from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and European Bioinformatics Institute (EBI) datasets. The expression of STIL (SCL/TAL1-interrupting locus) protein in clinical specimens was further assessed by Immunohistochemistry (IHC) analysis and the oncogenic roles of STIL in tumorigenesis were analyzed using in vitro and in vivo assays, including cell migration, invasion, xenograft tumor formation, and metastasis assays. The transcriptome differences between low- and high-STIL expression cells were analyzed by RNA-seq to uncover candidate genes involved in oncogenic pathways. The quantitative polymerase chain reaction (qPCR) and reporter assays were performed to confirm the results. The chromatin immunoprecipitation (ChIP)-qPCR assay was applied to demonstrate the binding of transcriptional factors to the promoter.

RESULTS

The expression of STIL shows the most significant increase in lung and various other types of cancers, and is highly associated with patients' survival rate. Depletion of STIL inhibits tumor growth and metastasis. Interestingly, excess STIL activates the EMT pathway, and subsequently enhances cancer cell migration and invasion. Importantly, we reveal an unexpected role of STIL in tumor metastasis. A subset of STIL translocate into nucleus and associate with FOXM1 (Forkhead box protein M1) to promote tumor metastasis and stemness via FOXM1-mediated downstream target genes. Furthermore, we demonstrate that hypoxia-inducible factor 1α (HIF1α) directly binds to the STIL promoter and upregulates STIL expression under hypoxic condition.

CONCLUSIONS

Our findings indicate that STIL promotes tumor metastasis through the HIF1α-STIL-FOXM1 axis, and highlight the importance of STIL as a promising therapeutic target for lung cancer treatment.

Viral Status Predicts the Patterns of Genome Methylation and Decitabine Response in Merkel Cell Carcinoma

Merkel cell carcinoma (MCC) is an aggressive cutaneous neuroendocrine carcinoma that is classified as Merkel cell polyomavirus-positive (virus positive [VP]) or Merkel cell polyomavirus-negative (virus negative [VN]). Epigenetic changes, such as DNA methylation, can alter gene expression and influence cancer progression. However, patterns of DNA methylation and the therapeutic efficacy of hypomethylating agents have not been fully explored in MCC. We characterized genome-wide DNA methylation in 16 MCC cell lines from both molecular subclasses in comparison with other cancer types and found that the overall profile of MCC is similar to that of small-cell lung carcinoma. Comparison of VP MCC with VN MCC revealed 2,260 differentially methylated positions. The hypomethylating agent decitabine upregulated the expression of antigen-presenting machinery in MCC cell lines and stimulated membrane expression of HLA-A in VP and VN MCC xenograft tumors. Decitabine also induced prominent caspase- and large T antigen‒independent cell death in VP MCC, whereas VN MCC cell lines displayed decreased proliferation without increased cell death. In mouse xenografts, decitabine significantly decreased the size of VP tumors but not that of VN tumors. Our findings indicate that viral status predicts genomic methylation patterns in MCC and that decitabine may be therapeutically effective against MCC through antiproliferative effects, cell death, and increased immune recognition.

DNA Methylation in Lung Cancer: Mechanisms and Associations with Histological Subtypes, Molecular Alterations, and Major Epidemiological Factors

Lung cancer is the major leading cause of cancer-related mortality worldwide. Multiple epigenetic factors-in particular, DNA methylation-have been associated with the development of lung cancer. In this review, we summarize the current knowledge on DNA methylation alterations in lung tumorigenesis, as well as their associations with different histological subtypes, common cancer driver gene mutations (e.g., KRAS, EGFR, and TP53), and major epidemiological risk factors (e.g., sex, smoking status, race/ethnicity). Understanding the mechanisms of DNA methylation regulation and their associations with various risk factors can provide further insights into carcinogenesis, and create future avenues for prevention and personalized treatments. In addition, we also highlight outstanding questions regarding DNA methylation in lung cancer to be elucidated in future studies.

Deciphering the etiology and role in oncogenic transformation of the CpG island methylator phenotype: a pan-cancer analysis

Numerous cancer types have shown to present hypermethylation of CpG islands, also known as a CpG island methylator phenotype (CIMP), often associated with survival variation. Despite extensive research on CIMP, the etiology of this variability remains elusive, possibly due to lack of consistency in defining CIMP. In this work, we utilize a pan-cancer approach to further explore CIMP, focusing on 26 cancer types profiled in the Cancer Genome Atlas (TCGA). We defined CIMP systematically and agnostically, discarding any effects associated with age, gender or tumor purity. We then clustered samples based on their most variable DNA methylation values and analyzed resulting patient groups. Our results confirmed the existence of CIMP in 19 cancers, including gliomas and colorectal cancer. We further showed that CIMP was associated with survival differences in eight cancer types and, in five, represented a prognostic biomarker independent of clinical factors. By analyzing genetic and transcriptomic data, we further uncovered potential drivers of CIMP and classified them in four categories: mutations in genes directly involved in DNA demethylation; mutations in histone methyltransferases; mutations in genes not involved in methylation turnover, such as KRAS and BRAF; and microsatellite instability. Among the 19 CIMP-positive cancers, very few shared potential driver events, and those drivers were only IDH1 and SETD2 mutations. Finally, we found that CIMP was strongly correlated with tumor microenvironment characteristics, such as lymphocyte infiltration. Overall, our results indicate that CIMP does not exhibit a pan-cancer manifestation; rather, general dysregulation of CpG DNA methylation is caused by heterogeneous mechanisms.

Epigenome-wide three-way interaction study identifies a complex pattern between TRIM27, KIAA0226, and smoking associated with overall survival of early-stage NSCLC

The interaction between DNA methylation of tripartite motif containing 27 (cg05293407_TRIM27 ) and smoking has previously been identified to reveal histologically heterogeneous effects of TRIM27 DNA methylation on early-stage non-small-cell lung cancer (NSCLC) survival. However, to understand the complex mechanisms underlying NSCLC progression, we searched three-way interactions. A two-phase study was adopted to identify three-way interactions in the form of pack-year of smoking (number of cigarettes smoked per day × number of years smoked) × cg05293407_TRIM27  × epigenome-wide DNA methylation CpG probe. Two CpG probes were identified with FDR-q ≤ 0.05 in the discovery phase and P ≤ 0.05 in the validation phase: cg00060500_KIAA0226 and cg17479956_EXT2 . Compared to a prediction model with only clinical information, the model added 42 significant three-way interactions using a looser criterion (discovery: FDR-q ≤ 0.10, validation: P ≤ 0.05) had substantially improved the area under the receiver operating characteristic curve (AUC) of the prognostic prediction model for both 3-year and 5-year survival. Our research identified the complex interaction effects among multiple environment and epigenetic factors, and provided therapeutic target for NSCLC patients.

A Sight of the Diagnostic Value of Aberrant Cell-Free DNA Methylation in Lung Cancer

Background

Lung cancer is one of the most commonly diagnosed cancer worldwide. As one of the liquid biopsy analytes, alternations in cell-free DNA (cfDNA) methylation could function as promising biomarkers for lung cancer detection.

Methods

In this study, differential methylation analysis was performed to identify candidate markers, and lasso regression with 10-fold cross-validation (CV) was used to establish the diagnostic marker panel. The performance of the binary classifier was evaluated using the receiver operating characteristic (ROC) curve and the precision-recall (PR) curve.

Results

We identified 4072 differentially methylated regions (DMRs) based on cfDNA methylation data, and then a 10-DMR marker panel was established. The panel achieved an area under the ROC curve (AUROC) of 0.922 and an area under the PR curve (AUPR) of 0.899 in a cfDNA cohort containing 29 lung cancer and 74 normal samples, showing outstanding performance. Besides, the cfDNA-derived markers also performed well in primary tissue datasets, which were more robust than the tissue-derived markers.

Conclusion

Our study suggested that the 10-DMR marker panel attained high accuracy and robustness and may function as a novel and promising target for lung cancer detection.

SRIQ clustering: A fusion of Random Forest, QT clustering, and KNN concepts

Gene expression profiling together with unsupervised analysis methods, typically clustering methods, has been used extensively in cancer research to unravel, e.g., new molecular subtypes that hold promise of disease refinement that may ultimately benefit patients. However, many of the commonly used methods require a prespecified number of clusters to extract and frequently require some type of feature pre-selection, e.g. variance filtering. This introduces subjectivity to the process of cluster discovery and the definition of putative novel tumor subtypes. Here, we introduce SRIQ, a novel unsupervised clustering method that could circumvent some of the issues in commonly used unsupervised analysis methods. SRIQ incorporates concepts from random forest machine learning as well as quality threshold- and k-nearest neighbor clustering. It is implemented as a Java and Python pipeline including data pre-processing, differential expression analysis, and pathway analysis. Using 434 lung adenocarcinomas profiled by RNA sequencing, we demonstrate the technical reproducibility of SRIQ and benchmark its performance compared to the commonly used consensus clustering method. Based on differential gene expression analysis and auxiliary molecular data we show that SRIQ can define new tumor subsets that appear biologically relevant and consistent compared and that these new subgroups seem to refine existing transcriptional subtypes that were defined using consensus clustering. Together, this provides support that SRIQ may be a useful new tool for unsupervised analysis of gene expression data from human malignancies.

Immune Score-based Molecular Subtypes and Signature Associated with Clinical Outcome in Hepatoblastoma

Background: This study aimed to identify genes related to the immune score of hepatoblastoma, examine the characteristics of the immune microenvironment of hepatoblastoma, and construct a risk scoring system for predicting the prognosis of hepatoblastoma. Methods: Through using the gene chip data of patients with hepatoblastoma with survival data in the ArrayExpress and GEO databases, the immune score of hepatoblastoma was calculated by the ESITIMATE algorithm, and the prognostic value of immune score in patients with hepatoblastoma was studied by the survival analysis. Genes related to the immune score were identified by the WGCNA algorithm. According to these genes, patients with hepatoblastoma were clustered unsupervised. Finally, the risk scoring system was constructed according to the immune score-related genes. Results: The immune score calculated by the ESTIMATE algorithm had a good prognostic value in patients with hepatoblastoma. Patients with high immune scores had better OS than those with low immune scores (P < 0.001). A total of 146 immune score-related genes were identified by WGCNA analysis, and univariate COX regression analysis indicated that 59 of the genes had prognostic value. According to the unsupervised clustering results of the 146 immune score-related genes, patients with hepatoblastoma could be divided into two subtypes with different prognoses, namely molecular subtype 1 and subtype 2, with molecular subtype 1 having a better prognosis. The immunocyte infiltration analysis results showed that the difference between the two subtypes was mainly in activated CD4 T cells, activated dendritic cells, CD56 bright natural killer cells, the macrophage, and regulatory T cells. According to the immune score-related genes, a risk scoring system was constructed based on a five-gene signature. After the cut-off value was determined, patients with hepatoblastoma were divided into a high-risk group and a low-risk group. The prognosis of the two groups was different. Conclusions: The immune score has a good prognostic value in patients with hepatoblastoma. Based on the different expression patterns of immune score-related genes, hepatoblastoma can be divided into two different prognostic molecular subtypes, showing different immunocyte infiltration patterns. The established risk scoring system based on a five-gene signature has a good predictive value in patients with hepatoblastoma.

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.

Genetic and epigenetic regulation of the NRF2-KEAP1 pathway in human lung cancer

Electrophilic and oxidative stress is caused when homeostatic mechanisms are disrupted. A major defense mechanism involves the activation of the nuclear factor erythroid 2-related factor 2 (NRF2) transcription factor encoded by the NFE2L2 gene, which can accelerate the detoxification of electrophilic carcinogens and prevent cancer and on the other hand in certain exposure contexts may exacerbate the carcinogenic process. NRF2-target genes activated under these conditions can be used as biomarkers of stress signalling, while activation of NRF2 can also reveal the epigenetic mechanisms that modulate NFE2L2 expression. Epigenetic mechanisms that regulate NFE2L2 and the gene for its adaptor protein KEAP1 include DNA methylation, histone modifications and microRNA. Understanding the activation of the NRF2-KEAP1 signalling pathway in human lung cancer, its epigenetic regulation and its role in oncogenesis is the subject of this review.

Proteogenomics of non-small cell lung cancer reveals molecular subtypes associated with specific therapeutic targets and immune evasion mechanisms

Despite major advancements in lung cancer treatment, long-term survival is still rare, and a deeper understanding of molecular phenotypes would allow the identification of specific cancer dependencies and immune evasion mechanisms. Here we performed in-depth mass spectrometry (MS)-based proteogenomic analysis of 141 tumors representing all major histologies of non-small cell lung cancer (NSCLC). We identified six distinct proteome subtypes with striking differences in immune cell composition and subtype-specific expression of immune checkpoints. Unexpectedly, high neoantigen burden was linked to global hypomethylation and complex neoantigens mapped to genomic regions, such as endogenous retroviral elements and introns, in immune-cold subtypes. Further, we linked immune evasion with LAG3 via STK11 mutation-dependent HNF1A activation and FGL1 expression. Finally, we develop a data-independent acquisition MS-based NSCLC subtype classification method, validate it in an independent cohort of 208 NSCLC cases and demonstrate its clinical utility by analyzing an additional cohort of 84 late-stage NSCLC biopsy samples.

Genome-wide analysis of methylation CpG sites in gene promoters identified four pairs of CpGs-mRNAs associated with lung adenocarcinoma prognosis

BACKGROUND

Activation of oncogenes through promoter hypomethylation and silencing of tumor suppressor genes induced by promoter hypermethylation played essential roles in the progression of lung adenocarcinoma (LUAD). This study aimed to identify the LUAD prognostic CpG sites and the regulated genes which contributed to LUAD progression.

METHODS

Methylation profiles from TCGA and GSE60645 were used to screen the differentially methylated CpGs. Then, the Log-rank test was adopted to identify LUAD prognosis-associated CpGs. Differential gene expression and survival analyses were further performed to suggest the roles of methylation-driven genes in LUAD prognosis. Finally, models and nomograms were constructed to predict the prognosis of LUAD.

RESULTS

A total of 1891 CpGs at gene promoters were differentially methylated. Among them, 54 CpGs were significantly associated with LUAD prognosis. Nine of them showed significant correlations with the expression of four genes (CCDC181, CFTR, PPP1R16B, MYEOV). CCDC181, CFTR and PPP1R16B were aberrantly down-regulated in LUAD, while MYEOV was up-regulated. All of them were significantly associated with LUAD prognosis. The LASSO regression analysis indicated that tumor stages, cg09181792, cg16998150, cg22779330 and PPP1R16B were promising prognostic factors. The AUC (area under the curve) of the model containing the clinical predictors was 0.643. The combination of CpGs and PPP1R16B with clinical variables significantly improved the predictive efficiency with an AUC of 0.714 (P = 0.036).

CONCLUSION

This study identified four pairs of promoter CpGs and genes that were significantly associated with LUAD prognosis. The integration of CpGs methylation and gene expression showed better predictive ability for LUAD prognosis.

Performance of gene expression-based single sample predictors for assessment of clinicopathological subgroups and molecular subtypes in cancers: a case comparison study in non-small cell lung cancer

The development of multigene classifiers for cancer prognosis, treatment prediction, molecular subtypes or clinicopathological groups has been a cornerstone in transcriptomic analyses of human malignancies for nearly two decades. However, many reported classifiers are critically limited by different preprocessing needs like normalization and data centering. In response, a new breed of classifiers, single sample predictors (SSPs), has emerged. SSPs classify samples in an N-of-1 fashion, relying on, e.g. gene rules comparing expression values within a sample. To date, several methods have been reported, but there is a lack of head-to-head performance comparison for typical cancer classification problems, representing an unmet methodological need in cancer bioinformatics. To resolve this need, we performed an evaluation of two SSPs [k-top-scoring pair classifier (kTSP) and absolute intrinsic molecular subtyping (AIMS)] for two case examples of different magnitude of difficulty in non-small cell lung cancer: gene expression–based classification of (i) tumor histology and (ii) molecular subtype. Through the analysis of ~2000 lung cancer samples for each case example (n = 1918 and n = 2106, respectively), we compared the performance of the methods for different sample compositions, training data set sizes, gene expression platforms and gene rule selections. Three main conclusions are drawn from the comparisons: both methods are platform independent, they select largely overlapping gene rules associated with actual underlying tumor biology and, for large training data sets, they behave interchangeably performance-wise. While SSPs like AIMS and kTSP offer new possibilities to move gene expression signatures/predictors closer to a clinical context, they are still importantly limited by the difficultness of the classification problem at hand.

A methylation-based nomogram for predicting survival in patients with lung adenocarcinoma

BACKGROUND

DNA methylation alteration is frequently observed in Lung adenocarcinoma (LUAD) and may play important roles in carcinogenesis, diagnosis, and prognosis. Thus, this study aimed to construct a reliable methylation-based nomogram, guiding prognostic classification screening and personalized medicine for LUAD patients.

METHOD

The DNA methylation data, gene expression data and corresponding clinical information of lung adenocarcinoma samples were extracted from The Cancer Genome Atlas (TCGA) database. Differentially methylated sites (DMSs) and differentially expressed genes (DEGs) were obtained and then calculated correlation by pearson correlation coefficient. Functional enrichment analysis and Protein-protein interaction network were used to explore the biological roles of aberrant methylation genes. A prognostic risk score model was constructed using univariate Cox and LASSO analysis and was assessed in an independent cohort. A methylation-based nomogram that included the risk score and the clinical risk factors was developed, which was evaluated by concordance index and calibration curves.

RESULT

We identified a total of 1362 DMSs corresponding to 471 DEGs with significant negative correlation, including 752 hypermethylation sites and 610 hypomethylation sites. Univariate cox regression analysis showed that 59 DMSs were significantly associated with overall survival. Using LASSO method, we constructed a three-DMSs signature that was independent predictive of prognosis in the training cohort. Patients in high-risk group had a significant shorter overall survival than patients in low-risk group classified by three-DMSs signature (log-rank p = 1.9E-04). Multivariate cox regression analysis proved that the three-DMSs signature was an independent prognostic factor for LUAD in TCGA-LUAD cohort (HR = 2.29, 95%CI: 1.47-3.57, P = 2.36E-04) and GSE56044 cohort (HR = 2.16, 95%CI: 1.19-3.91, P = 0.011). Furthermore, a nomogram, combining the risk score with clinical risk factors, was developed with C-indexes of 0.71 and 0.70 in TCGA-LUAD and GSE56044 respectively.

CONCLUSIONS

The present study established a robust three-DMSs signature for the prediction of overall survival and further developed a nomogram that could be a clinically available guide for personalized treatment of LUAD patients.

Homeobox gene amplification and methylation in oral squamous cell carcinoma

OBJECTIVES

Investigate the DNA copy number and the methylation profile of the homeobox genes HOXA5, HOXA7, HOXA9, HOXB5, HOXB13, HOXC12, HOXC13, HOXD10, HOXD11, IRX4 and ZHX1, and correlate them with clinicopathological parameters and overall survival.

MATERIAL AND METHODS

DNA from OSCC samples and surgical margins were submitted to DNA amplification by qPCR and to DNA methylation analysis using a DNA Methylation PCR Array System.

RESULTS

HOXA5, HOXB5 and HOXD10 were amplified in surgical margins while HOXA9, HOXB13 and IRX4 were amplified in OSCC. HOXD10 demonstrated hypermethylation in half of the tumor while ZHX1 did not show hypermethylation. No correlation of DNA copy number or methylation with clinicopathological parameters or survival was observed.

CONCLUSION

HOXA9, HOXB13 and IRX4 genes appears to be regulated by amplification and HOXD10 by methylation in OSCC. Further studies are needed to determine the role of these events in OSCC development.

Pan-cancer methylation analysis reveals an inverse correlation of tumor immunogenicity with methylation aberrancy

Tumor immunogenicity is driven by various genomic and transcriptomic factors but the association with the overall status of methylation aberrancy is not well established. We analyzed The Cancer Genome Atlas pan-cancer database to investigate whether the overall methylation aberrancy links to the immune evasion of tumor. We created the definitions of hypermethylation burden, hypomethylation burden and methylation burden to establish the values that represent the degree of methylation aberrancy from human methylation 450 K array data. Both hypermethylation burden and hypomethylation burden significantly correlated with global methylation level as well as methylation subtypes defined in previous literatures. Then we evaluated whether methylation burden correlates with tumor immunogenicity and found that methylation burden showed a significant negative correlation with cytolytic activity score, which represent cytotoxic T cell activity, in pan-cancer (Spearman rho = - 0.37, p < 0.001) and 30 of 33 individual cancer types. Furthermore, this correlation was independent of mutation burden and chromosomal instability in multivariate regression analysis. We validated the findings in the external cohorts and outcomes of patients who were treated with immune checkpoint inhibitors, which showed that high methylation burden group had significantly poor progression-free survival (Hazard ratio 1.74, p = 0.038). Overall, the degree of methylation aberrancy negatively correlated with tumor immunogenicity. These findings emphasize the importance of methylation aberrancy for tumors to evade immune surveillance and warrant further development of methylation biomarker.

Pan-cancer application of a lung-adenocarcinoma-derived gene-expression-based prognostic predictor

Gene-expression profiling can be used to classify human tumors into molecular subtypes or risk groups, representing potential future clinical tools for treatment prediction and prognostication. However, it is less well-known how prognostic gene signatures derived in one malignancy perform in a pan-cancer context. In this study, a gene-rule-based single sample predictor (SSP) called classifier for lung adenocarcinoma molecular subtypes (CLAMS) associated with proliferation was tested in almost 15 000 samples from 32 cancer types to classify samples into better or worse prognosis. Of the 14 malignancies that presented both CLAMS classes in sufficient numbers, survival outcomes were significantly different for breast, brain, kidney and liver cancer. Patients with samples classified as better prognosis by CLAMS were generally of lower tumor grade and disease stage, and had improved prognosis according to other type-specific classifications (e.g. PAM50 for breast cancer). In all, 99.1% of non-lung cancer cases classified as better outcome by CLAMS were comprised within the range of proliferation scores of lung adenocarcinoma cases with a predicted better prognosis by CLAMS. This finding demonstrates the potential of tuning SSPs to identify specific levels of for instance tumor proliferation or other transcriptional programs through predictor training. Together, pan-cancer studies such as this may take us one step closer to understanding how gene-expression-based SSPs act, which gene-expression programs might be important in different malignancies, and how to derive tools useful for prognostication that are efficient across organs.

Diagnostic Value of Insulinoma-Associated Protein 1 (INSM1) and Comparison With Established Neuroendocrine Markers in Pulmonary Cancers

CONTEXT.—

The diagnostic distinction of pulmonary neuroendocrine (NE) tumors from non-small cell lung carcinomas (NSCLCs) is clinically relevant for prognostication and treatment. Diagnosis is based on morphology and immunohistochemical staining.

OBJECTIVE.—

To determine the diagnostic value of insulinoma-associated protein 1 (INSM1), in comparison with established NE markers, in pulmonary tumors.

DESIGN.—

Fifty-four pulmonary NE tumors and 632 NSCLCs were stained for INSM1, CD56, chromogranin A, and synaptophysin. In a subset, gene expression data were available for analysis. Also, 419 metastases to the lungs were stained for INSM1. A literature search identified 39 additional studies with data on NE markers in lung cancers from the last 15 years. Seven of these included data on INSM1.

RESULTS.—

A positive INSM1 staining was seen in 39 of 54 NE tumors (72%) and 6 of 623 NSCLCs (1%). The corresponding numbers were 47 of 54 (87%) and 14 of 626 (2%) for CD56, 30 of 54 (56%) and 6 of 629 (1%) for chromogranin A, and 46 of 54 (85%) and 49 of 630 (8%) for synaptophysin, respectively. Analysis of literature data revealed that CD56 and INSM1 were the best markers for identification of high-grade NE pulmonary tumors when considering both sensitivity and specificity, while synaptophysin also showed good sensitivity. INSM1 gene expression was clearly associated with NE histology.

CONCLUSIONS.—

The solid data of both our and previous studies confirm the diagnostic value of INSM1 as a NE marker in pulmonary pathology. The combination of CD56 with INSM1 and/or synaptophysin should be the first-hand choice to confirm pulmonary high-grade NE tumors. INSM1 gene expression could be used to predict NE tumor histology.

New molecular classification of large cell neuroendocrine carcinoma and small cell lung carcinoma with potential therapeutic impacts

Large cell neuroendocrine carcinoma (LCNECs) and small cell lung carcinomas (SCLCs) are high-grade neuroendocrine carcinomas of the lung with very aggressive behavior and poor prognosis. Their histological classification as well as their therapeutic management has not changed much in recent years, but genomic and transcriptomic analyses have revealed different molecular subtypes raising hopes for more personalized treatment. Indeed, four subtypes of SCLCs have been recently described, SCLC-A driven by the master gene ASCL1, SCLC-N driven by NEUROD1, SCLC-Y by YAP1 and SCLC-P by POU2F3. Whereas SCLC standard of care is based on concurrent chemoradiation for limited stages and on chemotherapy alone or chemotherapy combined with anti-PD-L1 checkpoint inhibitors for extensive stage SCLC, SCLC-A variants could benefit from DLL3 or BCL2 inhibitors, and SCLC-N variants from Aurora kinase inhibitors combined with chemotherapy, or PI3K/mTOR or HSP90 inhibitors. In addition, a new SCLC variant (SCLC-IM) with high-expression of immune checkpoints has been also reported, which could benefit from immunotherapies. PARP inhibitors also gave promising results in combination with chemotherapy in a subset of SCLCs. Regarding LCNECs, they represent a heterogeneous group of tumors, some of them exhibiting mutations also found in SCLC but with a pattern of expression of NSCLC, while others harbor mutations also found in NSCLC but with a pattern of expression of SCLC, questioning their clinical management as NSCLCs or SCLCs. Overall, we are probably entering a new area, which, if personalized treatments are effective, will also lead to the implementation in practice of molecular testing or biomarkers detection for the selection of patients who can benefit from them.

Epigenetic-smoking interaction reveals histologically heterogeneous effects of TRIM27 DNA methylation on overall survival among early-stage NSCLC patients

Tripartite motif containing 27 (TRIM27) is highly expressed in lung cancer, including non-small-cell lung cancer (NSCLC). Here, we profiled DNA methylation of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tumours from 613 early-stage NSCLC patients and evaluated associations between CpG methylation of TRIM27 and overall survival. Significant CpG probes were confirmed in 617 samples from The Cancer Genome Atlas. The methylation of the CpG probe cg05293407_TRIM27 was significantly associated with overall survival in patients with LUSC (HR = 1.65, 95% CI: 1.30-2.09, P = 4.52 × 10^-5), but not in patients with LUAD (HR = 1.08, 95% CI: 0.87-1.33, P = 0.493). As incidence of LUSC is associated with higher smoking intensity compared to LUAD, we investigated whether smoking intensity impacted on the prognostic effect of cg05293407_TRIM27 methylation in NSCLC. LUSC patients had a higher average pack-year of smoking (37.49_LUAD vs 54.79_LUSC, P = 1.03 × 10^-19) and included a higher proportion of current smokers than LUAD patients (28.24%_LUAD vs 34.09%_LUSC, P = 0.037). cg05293407_TRIM27 was significantly associated with overall survival only in NSCLC patients with medium-high pack-year of smoking (HR = 1.58, 95% CI: 1.26-1.96, P = 5.25 × 10^-5). We conclude that cg05293407_TRIM27 methylation is a potential predictor of LUSC prognosis, and smoking intensity may impact on its prognostic value across the various types of NSCLC.

Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases

Head and neck squamous cell carcinoma (HNSC) patients are at risk of suffering from both pulmonary metastases or a second squamous cell carcinoma of the lung (LUSC). Differentiating pulmonary metastases from primary lung cancers is of high clinical importance, but not possible in most cases with current diagnostics. To address this, we performed DNA methylation profiling of primary tumors and trained three different machine learning methods to distinguish metastatic HNSC from primary LUSC. We developed an artificial neural network that correctly classified 96.4% of the cases in a validation cohort of 279 patients with HNSC and LUSC as well as normal lung controls, outperforming support vector machines (95.7%) and random forests (87.8%). Prediction accuracies of more than 99% were achieved for 92.1% (neural network), 90% (support vector machine), and 43% (random forest) of these cases by applying thresholds to the resulting probability scores and excluding samples with low confidence. As independent clinical validation of the approach, we analyzed a series of 51 patients with a history of HNSC and a second lung tumor, demonstrating the correct classifications based on clinicopathological properties. In summary, our approach may facilitate the reliable diagnostic differentiation of pulmonary metastases of HNSC from primary LUSC to guide therapeutic decisions.

A gene expression-based single sample predictor of lung adenocarcinoma molecular subtype and prognosis

Disease recurrence in surgically treated lung adenocarcinoma (AC) remains high. New approaches for risk stratification beyond tumor stage are needed. Gene expression-based AC subtypes such as the Cancer Genome Atlas Network (TCGA) terminal-respiratory unit (TRU), proximal-inflammatory (PI) and proximal-proliferative (PP) subtypes have been associated with prognosis, but show methodological limitations for robust clinical use. We aimed to derive a platform independent single sample predictor (SSP) for molecular subtype assignment and risk stratification that could function in a clinical setting. Two-class (TRU/nonTRU=SSP2) and three-class (TRU/PP/PI=SSP3) SSPs using the AIMS algorithm were trained in 1655 ACs (n = 9659 genes) from public repositories vs TCGA centroid subtypes. Validation and survival analysis were performed in 977 patients using overall survival (OS) and distant metastasis-free survival (DMFS) as endpoints. In the validation cohort, SSP2 and SSP3 showed accuracies of 0.85 and 0.81, respectively. SSPs captured relevant biology previously associated with the TCGA subtypes and were associated with prognosis. In survival analysis, OS and DMFS for cases discordantly classified between TCGA and SSP2 favored the SSP2 classification. In resected Stage I patients, SSP2 identified TRU-cases with better OS (hazard ratio [HR] = 0.30; 95% confidence interval [CI] = 0.18-0.49) and DMFS (TRU HR = 0.52; 95% CI = 0.33-0.83) independent of age, Stage IA/IB and gender. SSP2 was transformed into a NanoString nCounter assay and tested in 44 Stage I patients using RNA from formalin-fixed tissue, providing prognostic stratification (relapse-free interval, HR = 3.2; 95% CI = 1.2-8.8). In conclusion, gene expression-based SSPs can provide molecular subtype and independent prognostic information in early-stage lung ACs. SSPs may overcome critical limitations in the applicability of gene signatures in lung cancer.

A new clinical prognostic nomogram for liver cancer based on immune score

Background

Increased attention is being paid to the relationship between the immune status of the tumor microenvironment and tumor prognosis. The application of immune scoring in evaluating the clinical prognosis of liver cancer patients has not yet been explored. This study sought to clarify the association between immune score and prognosis and construct a clinical nomogram to predict the survival of patients with liver cancer.

Methods

A total of 346 patients were included in our analysis datasets downloaded from The Cancer Genome Atlas (TCGA) dataset. A Cox proportional-hazards regression model was used to estimate the adjusted hazard ratios (HRs). A nomogram was built based on the results of multivariate analysis and was subjected to bootstrap internal validation. The predictive accuracy and discriminative ability were measured by the concordance index (C-index) and the calibration curve. Through the functional analysis of differential expression of genes with different immune scores, the target genes were screened out.

Results

In comparison with patients with low immune scores, those with intermediate and high immune scores had significantly improved survival time [HR and 95% confidence interval (CI): 0.54 (0.30–0.97) and 0.51 (0.27–0.97), respectively]. The C-index for survival time prediction was 0.66 (95% CI: 0.60–0.71). The calibration plot for the probability of survival at three or five years showed good agreement between prediction by the nomogram and actual observations. The top 10 hub genes were CXCL8(chemokine (C-X-C motif) ligand 8), SYK(spleen tyrosine kinase), CXCL12(chemokine (C-X-C motif) ligand 12), CXCL10 (chemokine (C-X-C motif) ligand10), CXCL1(chemokine (C-X-C motif) ligand1), CCL5(chemokine (C-C motif) ligand 5), CCL20(chemokine (C-C motif) ligand 20), LCK, CXCL11(chemokine (C-X-C motif) ligand 11), CCR5(chemokine (C-C motif) receptor 5). More importantly, we found that the high expression of CXCL8 and CXCL1 were related to the prognosis.

Conclusions

High and/or intermediate immune scores are significantly correlated with better survival time in patients with liver cancer. Moreover, nomograms for predicting prognosis may help to estimate the survival of patients. We also propose that CXCL8 and CXCL1 may be a potential therapeutic target for liver cancer treatment.

Methylation Patterns and Chromatin Accessibility in Neuroendocrine Lung Cancer

Lung cancer is the worldwide leading cause of death from cancer. Epigenetic modifications such as methylation and changes in chromatin accessibility are major gene regulatory mechanisms involved in tumorigenesis and cellular lineage commitment. We aimed to characterize these processes in the context of neuroendocrine (NE) lung cancer. Illumina 450K DNA methylation data were collected for 1407 lung cancers including 27 NE tumors. NE differentially methylated regions (NE-DMRs) were identified and correlated with gene expression data for 151 lung cancers and 31 human tissue entities from the Genotype-Tissue Expression (GTEx) consortium. Assay for transposase-accessible chromatin sequencing (ATAC-seq) and RNA sequencing (RNA-seq) were performed on eight lung cancer cell lines, including three NE cell lines, to identify neuroendocrine specific gene regulatory elements. We identified DMRs with methylation patterns associated with differential gene expression and an NE tumor phenotype. DMR-associated genes could further be split into six functional modules, including one highly specific gene module for NE lung cancer showing high expression in both normal and malignant brain tissue. The regulatory potential of NE-DMRs was further validated in vitro using paired ATAC- and RNA-seq and revealed both proximal and distal regulatory elements of canonical NE-marker genes such as CHGA, NCAM1, INSM1, as well as a number of novel candidate markers of NE lung cancer. Using multilevel genomic analyses of both tumor bulk tissue and lung cancer cell lines, we identified a large catalogue of gene regulatory elements related to the NE phenotype of lung cancer.

Identification and validation of novel DNA methylation markers for early diagnosis of lung adenocarcinoma

Lung cancer has the highest mortality of all cancers worldwide. Epigenetic alterations have emerged as potential biomarkers for early diagnosis of various cancer tissue types. To identify methylation markers for early diagnosis of lung adenocarcinoma, we aimed to integrate genome‐wide DNA methylation and gene expression data from The Cancer Genome Atlas. To this end, we first examined the global DNA methylation pattern of lung adenocarcinoma and investigated the relationship between DNA methylation subtypes and clinical features. We then extracted differentially methylated and expressed genes, and adopted feature selection techniques to determine the final methylation markers. The performance of the markers in predicting lung adenocarcinoma was evaluated on three independent datasets from Gene Expression Omnibus. Protein levels of marker genes were validated by immunohistochemistry, and their biological function was further verified in vivo. We identified three novel methylation markers in lung adenocarcinoma including cg08032924, cg14823851, and cg19161124, mapping to CMTM2, TBX4, and DPP6, respectively. Validating these results on three independent datasets indicated that the three markers can achieve extremely high sensitivity and specificity in distinguishing lung adenocarcinoma from normal samples. Immunohistochemistry quantification results confirmed that markers are weakly expressed in human lung adenocarcinoma, and CMTM2 decreased tumor growth of mouse Lewis lung carcinoma in vivo. Overall, our study identified three novel methylation markers in lung adenocarcinoma which may contribute toward an improved diagnosis potentially leading to a better outcome for patients with lung adenocarcinoma.

Methylation in HOX Clusters and Its Applications in Cancer Therapy

HOX genes are commonly known for their role in embryonic development, defining the positional identity of most structures along the anterior–posterior axis. In postembryonic life, HOX gene aberrant expression can affect several processes involved in tumorigenesis such as proliferation, apoptosis, migration and invasion. Epigenetic modifications are implicated in gene expression deregulation, and it is accepted that methylation events affecting HOX gene expression play crucial roles in tumorigenesis. In fact, specific methylation profiles in the HOX gene sequence or in HOX-associated histones are recognized as potential biomarkers in several cancers, helping in the prediction of disease outcomes and adding information for decisions regarding the patient’s treatment. The methylation of some HOX genes can be associated with chemotherapy resistance, and its identification may suggest the use of other treatment options. The use of epigenetic drugs affecting generalized or specific DNA methylation profiles, an approach that now deserves much attention, seems likely to be a promising weapon in cancer therapy in the near future. In this review, we summarize these topics, focusing particularly on how the regulation of epigenetic processes may be used in cancer therapy.

Blood-derived DNA methylation predictors of mortality discriminate tumor and healthy tissue in multiple organs

Evidence has shown that certain methylation markers derived from blood can mirror corresponding methylation signatures in internal tissues. In the current study, we aimed to investigate two strong epigenetic predictors for life span, derived from blood DNA methylation data, in tissue samples of solid cancer patients. Using data from the Cancer Genome Atlas (TCGA) and the German DACHS study, we compared a mortality risk score (MRscore) and DNAmPhenoAge in paired tumor and adjacent normal tissue samples of patients with lung (N = 69), colorectal (n = 299), breast (n = 90), head/neck (n = 50), prostate (n = 50), and liver (n = 50) cancer. To explore the concordance across tissue and blood, we additionally assessed the two markers in blood samples of colorectal cancer (CRC) cases and matched controls (n = 93) in the DACHS+ study. The MRscore was significantly elevated in tumor tissues compared to normal tissues of all cancers except prostate cancer, for which an opposite pattern was observed. DNAmPhenoAge was consistently higher in all tumor tissues. The MRscore discriminated lung, colorectal, and prostate tumor tissues from normal tissues with very high accuracy [AUCs of 0.87, 0.99 (TCGA) /0.94 (DACHS), and 0.92, respectively]. DNAmPhenoAge accurately discriminated five types of tumor tissues from normal tissues (except prostate cancer), with AUCs of 0.82–0.93. The MRscore was also significantly higher in blood samples of CRC cases than in controls, with areas under the curve (AUC) of 0.74, whereas DNAmPhenoAge did not distinguish cases from controls, with AUC of 0.54. This study provides compelling evidence that blood‐derived DNAm markers could reflect methylation changes in less accessible tissues. Further research should explore the potential use of these findings for cancer diagnosis and early detection.

The Impact of the Ubiquitin System in the Pathogenesis of Squamous Cell Carcinomas

The ubiquitin system is a dynamic regulatory pathway controlling the activity, subcellular localization and stability of a myriad of cellular proteins, which in turn affects cellular homeostasis through the regulation of a variety of signaling cascades. Aberrant activity of key components of the ubiquitin system has been functionally linked with numerous human diseases including the initiation and progression of human tumors. In this review, we will contextualize the importance of the two main components of the ubiquitin system, the E3 ubiquitin ligases (E3s) and deubiquitinating enzymes (DUBs), in the etiology of squamous cell carcinomas (SCCs). We will discuss the signaling pathways regulated by these enzymes, emphasizing the genetic and molecular determinants underlying their deregulation in SCCs.

Epigenome-wide gene-age interaction analysis reveals reversed effects of PRODH DNA methylation on survival between young and elderly early-stage NSCLC patients

DNA methylation changes during aging, but it remains unclear whether the effect of DNA methylation on lung cancer survival varies with age. Such an effect could decrease prediction accuracy and treatment efficacy. We performed a methylation–age interaction analysis using 1,230 early-stage lung adenocarcinoma patients from five cohorts. A Cox proportional hazards model was used to investigate lung adenocarcinoma and squamous cell carcinoma patients for methylation–age interactions, which were further confirmed in a validation phase. We identified one adenocarcinoma-specific CpG probe, cg14326354_PRODH, with effects significantly modified by age (HR_interaction = 0.989; 95% CI: 0.986–0.994; P = 9.18×10–7). The effect of low methylation was reversed for young and elderly patients categorized by the boundary of 95% CI standard (HR_young = 2.44; 95% CI: 1.26–4.72; P = 8.34×10-3; HR_elderly = 0.58; 95% CI: 0.42–0.82; P = 1.67×10-3). Moreover, there was an antagonistic interaction between low cg14326354_PRODH methylation and elderly age (HR_interaction = 0.21; 95% CI: 0.11–0.40; P = 2.20×10−6). In summary, low methylation of cg14326354PRODH might benefit survival of elderly lung adenocarcinoma patients, providing new insight to age-specific prediction and potential drug targeting.

Comprehensive analysis of gene expression and DNA methylation data identifies potential biomarkers and functional epigenetic modules for lung adenocarcinoma

Lung cancer has one of the highest mortality rates of malignant neoplasms. Lung adenocarcinoma (LUAD) is one of the most common types of lung cancer. DNA methylation is more stable than gene expression and could be used as a biomarker for early tumor diagnosis. This study is aimed to screen potential DNA methylation signatures to facilitate the diagnosis and prognosis of LUAD and integrate gene expression and DNA methylation data of LUAD to identify functional epigenetic modules. We systematically integrated gene expression and DNA methylation data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), bioinformatic models and algorithms were implemented to identify signatures and functional modules for LUAD. Three promising diagnostic and five potential prognostic signatures for LUAD were screened by rigorous filtration, and our tumor-normal classifier and prognostic model were validated in two separate data sets. Additionally, we identified functional epigenetic modules in the TCGA LUAD dataset and GEO independent validation data set. Interestingly, the MUC1 module was identified in both datasets. The potential biomarkers for the diagnosis and prognosis of LUAD are expected to be further verified in clinical practice to aid in the diagnosis and treatment of LUAD.

EWASdb: epigenome-wide association study database

DNA methylation, the most intensively studied epigenetic modification, plays an important role in understanding the molecular basis of diseases. Furthermore, epigenome-wide association study (EWAS) provides a systematic approach to identify epigenetic variants underlying common diseases/phenotypes. However, there is no comprehensive database to archive the results of EWASs. To fill this gap, we developed the EWASdb, which is a part of 'The EWAS Project', to store the epigenetic association results of DNA methylation from EWASs. In its current version (v 1.0, up to July 2018), the EWASdb has curated 1319 EWASs associated with 302 diseases/phenotypes. There are three types of EWAS results curated in this database: (i) EWAS for single marker; (ii) EWAS for KEGG pathway and (iii) EWAS for GO (Gene Ontology) category. As the first comprehensive EWAS database, EWASdb has been searched or downloaded by researchers from 43 countries to date. We believe that EWASdb will become a valuable resource and significantly contribute to the epigenetic research of diseases/phenotypes and have potential clinical applications. EWASdb is freely available at http://www.ewas.org.cn/ewasdb or http://www.bioapp.org/ewasdb.

Integrated data analysis reveals significant associations of KEAP1 mutations with DNA methylation alterations in lung adenocarcinomas

KEAP1 regulates the cytoprotection induced by NRF2 and has been reported to be a candidate tumor suppressor. Recent evidence has shown that mutations in several driver genes cause aberrant DNA methylation patterns, a hallmark of cancer. However, the correlation between KEAP1 mutations and DNA methylation in lung cancer has still not been investigated. In this study, we systematically carried out an integrated multi-omics analysis to explore the correlation between KEAP1 mutations and DNA methylation and its effect on gene expression in lung adenocarcinoma (LUAD). We found that most of the DNA aberrations associated with KEAP1 mutations in LAUD were hypomethylation. Surprisingly, we found several NRF2-regulated genes among the genes that showed differential DNA methylation. Moreover, we identified an 8-gene signature with altered DNA methylation pattern and elevated gene expression levels in LUAD patients with mutated KEAP1, and evaluated the prognostic value of this signature in various clinical datasets. These results establish that KEAP1 mutations are associated with DNA methylation changes capable of shaping regulatory network functions. Combining both epigenomic and transcriptomic changes along with KEAP1 mutations may provide a better understanding of the molecular mechanisms associated with the progression of lung cancer and may help to provide better therapeutic approaches.

Independent Validation of Early-Stage Non-Small Cell Lung Cancer Prognostic Scores Incorporating Epigenetic and Transcriptional Biomarkers With Gene-Gene Interactions and Main Effects

Background

DNA methylation and gene expression are promising biomarkers of various cancers, including non-small cell lung cancer (NSCLC). Besides the main effects of biomarkers, the progression of complex diseases is also influenced by gene-gene (G×G) interactions.

Research Question

Would screening the functional capacity of biomarkers on the basis of main effects or interactions, using multiomics data, improve the accuracy of cancer prognosis?

Study Design and Methods

Biomarker screening and model validation were used to construct and validate a prognostic prediction model. NSCLC prognosis-associated biomarkers were identified on the basis of either their main effects or interactions with two types of omics data. A prognostic score incorporating epigenetic and transcriptional biomarkers, as well as clinical information, was independently validated.

Results

Twenty-six pairs of biomarkers with G×G interactions and two biomarkers with main effects were significantly associated with NSCLC survival. Compared with a model using clinical information only, the accuracy of the epigenetic and transcriptional biomarker-based prognostic model, measured by area under the receiver operating characteristic curve (AUC), increased by 35.38% (95% CI, 27.09%-42.17%; P = 5.10 × 10^–17) and 34.85% (95% CI, 26.33%-41.87%; P = 2.52 × 10^–18) for 3- and 5-year survival, respectively, which exhibited a superior predictive ability for NSCLC survival (AUC_{3 year}, 0.88 [95% CI, 0.83-0.93]; and AUC_{5 year}, 0.89 [95% CI, 0.83-0.93]) in an independent Cancer Genome Atlas population. G×G interactions contributed a 65.2% and 91.3% increase in prediction accuracy for 3- and 5-year survival, respectively.

Interpretation

The integration of epigenetic and transcriptional biomarkers with main effects and G×G interactions significantly improves the accuracy of prognostic prediction of early-stage NSCLC survival.

Integrative Analysis of Methylation and Gene Expression in Lung Adenocarcinoma and Squamous Cell Lung Carcinoma

Lung cancer is a highly prevalent type of cancer with a poor 5-year survival rate of about 4-17%. Eighty percent lung cancer belongs to non-small-cell lung cancer (NSCLC). For a long time, the treatment of NSCLC has been mostly guided by tumor stage, and there has been no significant difference between the therapy strategy of lung adenocarcinoma (LUAD) and squamous cell lung carcinoma (SCLC), the two major subtypes of NSCLC. In recent years, important molecular differences between LUAD and SCLC are increasingly identified, indicating that targeted therapy will be more and more histologically specific in the future. To investigate the LUAD and SCLC difference on multi-omics scale, we analyzed the methylation and gene expression data together. With the Boruta method to remove irrelevant features and the MCFS (Monte Carlo Feature Selection) method to identify the significantly important features, we identified 113 key methylation features and 23 key gene expression features. HNF1B and TP63 were found to be dysfunctional on both methylation and gene expression levels. The experimentally determined interaction network suggested that TP63 may play an important role in connecting methylation genes and expression genes. Many of the discovered signature genes have been supported by literature. Our results may provide directions of precision diagnosis and therapy of LUAD and SCLC.

Common and distinct features of potentially predictive biomarkers in small cell lung carcinoma and large cell neuroendocrine carcinoma of the lung by systematic and integrated analysis

BACKGROUND

Large-cell neuroendocrine carcinoma of the lung (LCNEC) and small-cell lung carcinoma (SCLC) are neuroendocrine neoplasms. However, the underlying mechanisms of common and distinct genetic characteristics between LCNEC and SCLC are currently unclear. Herein, protein expression profiles and possible interactions with miRNAs were provided by integrated bioinformatics analysis, in order to explore core genes associated with tumorigenesis and prognosis in SCLC and LCNEC.

METHODS

GSE1037 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in LCNEC and SCLC, as compared with normal lung tissues, were selected using the GEO2R online analyzer and Venn diagram software. Gene ontology (GO) analysis was performed using Database for Annotation, Visualization and Integrated Discovery. The biological pathway analysis was performed using the FunRich database. Subsequently, a protein-protein interaction (PPI) network of DEGs was generated using Search Tool for the Retrieval of Interacting Genes and displayed via Cytoscape software. The PPI network was analyzed by the Molecular Complex Detection app from Cytoscape, and 16 upregulated hub genes were selected. The Oncomine database was used to detect expression patterns of hub genes for validation. Furthermore, the biological pathways of these 16 hub genes were re-analyzed, and potential interactions between these genes and miRNAs were explored via FunRich.

RESULTS

A total of 384 DEGs were identified. A Venn diagram determined 88 common DEGs. The PPI network was constructed with 48 nodes and 221 protein pairs. Among them, 16 hub genes were extracted, 14 of which were upregulated in SCLC samples, as compared with normal lung specimens, and 10 were correlated with the cell cycle pathway. Furthermore, 57 target miRNAs for 8 hub genes were identified, among which 31 miRNAs were correlated with the progression of carcinoma, drug-resistance, radio-sensitivity, or autophagy in lung cancer.

CONCLUSION

This study provided effective biomarkers and novel therapeutic targets for diagnosis and prognosis of SCLC and LCNEC.

Genetic landscape of prognostic value in pancreatic ductal adenocarcinoma microenvironment

Background

The prognosis of pancreatic ductal adenocarcinoma (PDAC) remains dismally poor and is widely considered as an intricate genetic disorder. The mutational landscape of PDAC may directly reflect cancer immunogenicity and dictate the extent and phenotype of immune cell infiltration. In adverse, the microenvironment may also effect the gene expression of cancer cells, which is associated with clinical prognosis. Thus, it is crucial to identify genomic alterations in PDAC microenvironment and its impacts on clinical prognosis.

Methods

The gene expression profiles, mutation data and clinical information of 179 pancreatic cancer patients with an initial pathologic diagnosis ranging from 2001 to 2013 were retrieved from The Cancer Genome Atlas (TCGA) database. The MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm for calculating immune scores and stromal scores and Tumor IMmune Estimation Resource (TIMER) resource for cell infiltrations were applied in this study.

Results

The average immune score or stromal score of PDAC subtype was significantly higher than that of other specific subtypes. KRAS mutant cases had significantly lower immune scores (P=0.001) and stromal scores (P=0.007), in concert with lower immune scores in TP53 mutant cases (P=0.030). However, no significant difference was found in SMAD4 and CDKN2A mutations. In the cohort OS/RFS, the infiltration levels of CD8+ T cells, B cells, Macrophages, Neutrophils and DCs in high stromal score group were higher than those in the low score group (all P<0.001), as well as in immune score groups except for Macrophages in the cohort RFS. In the cohort OS/RFS, 317/379 upregulated genes and 9/6 downregulated genes were observed in the high immune score group, while 227/205 upregulated genes and 17/6 downregulated genes in the high stromal score group. With the analysis for prognostic value of DEGs, 82 and 58 DEGs respectively in the high immune and stromal score group were verified to be significantly associated with better OS (P<0.05), while 53 and 17 DEGs respectively with longer RFS (P<0.05). Functional enrichment analysis showed genes of prognostic values were significantly related to immune response.

Conclusions

A list of genes with prognostic value in PDAC microenvironment were obtained from functional enrichment analysis based on immune and stromal scores, which indicates a series of potential auxiliary prognostic biomarkers for PDAC are available. Further research on these genes may be valuable and helpful to understand the crosstalk between tumor and microenvironment, new immune evasion mechanisms and underlying novel therapeutic targets in an integrated manner.

Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma

Limited studies have focused on developing prognostic models with trans-omics biomarkers for early-stage lung adenocarcinoma (LUAD). We performed integrative analysis of clinical information, DNA methylation, and gene expression data using 825 early-stage LUAD patients from 5 cohorts. Ranger algorithm was used to screen prognosis-associated biomarkers, which were confirmed with a validation phase. Clinical and biomarker information was fused using an iCluster plus algorithm, which significantly distinguished patients into high- and low-mortality risk groups (Pdiscovery = 0.01 and Pvalidation = 2.71×10-3). Further, potential functional DNA methylation-gene expression-overall survival pathways were evaluated by causal mediation analysis. The effect of DNA methylation level on LUAD survival was significantly mediated through gene expression level. By adding DNA methylation and gene expression biomarkers to a model of only clinical data, the AUCs of the trans-omics model improved by 18.3% (to 87.2%) and 16.4% (to 85.3%) in discovery and validation phases, respectively. Further, concordance index of the nomogram was 0.81 and 0.77 in discovery and validation phases, respectively. Based on systematic review of published literatures, our model was superior to all existing models for early-stage LUAD. In summary, our trans-omics model may help physicians accurately identify patients with high mortality risk.

A novel CpG island methylation panel predicts survival in lung adenocarcinomas

The lack of clinically useful biomarkers compromise the personalized management of lung adenocarcinomas (ADCs); epigenetic events and DNA methylation in particular have exhibited potential value as biomarkers. By comparing genome-wide DNA methylation data of paired lung ADCs and normal tissues from 6 public datasets, cancer-specific CpG island (CGI) methylation changes were identified with a pre-specified criterion. Correlations between DNA methylation and expression data for each gene were assessed by Pearson correlation analysis. A prognostically relevant CGI methylation signature was constructed by risk-score analysis, and was validated using a training-validation approach. Survival data were analyzed by log-rank test and Cox regression model. In total, 134 lung ADC-specific CGI CpGs were identified, among which, a panel of 9 CGI loci were selected as prognostic candidates, and were used to construct a risk-score signature. The novel CGI methylation signature was identified to classify distinct prognostic subgroups across different datasets, and was demonstrated to be a potent independent prognostic factor for overall survival time of patients with lung ADCs. In addition, it was identified that cancer-specific CGI hypomethylation of RPL39L, along with the corresponding gene expression, provided optimized prognostication of lung ADCs. In summary, cancer-specific CGI methylation aberrations are optimal candidates for novel biomarkers of lung ADCs; the 9-CpG methylation panel and hypomethylation of RPL39L exhibited particularly promising significance.

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.