Enhancer methylation dynamics drive core transcriptional regulatory circuitry in pan-cancer

Enhancer demethylation-regulated gene score identified molecular subtypes, inspiring immunotherapy or CDK4/6 inhibitor therapy in oesophageal squamous cell carcinoma

BACKGROUND

The 5-year survival rate of oesophageal squamous cell carcinoma (ESCC) is approximately 20%. The prognosis and drug response exhibit substantial heterogeneity in ESCC, impeding progress in survival outcomes. Our goal is to identify a signature for tumour subtype classification, enabling precise clinical treatments.

METHODS

Utilising pre-treatment multi-omics data from an ESCC dataset (n = 310), an enhancer methylation-eRNA-target gene regulation network was constructed and validated by in vitro experiments. Four machine learning methods collectively identified core target genes, establishing an Enhancer Demethylation-Regulated Gene Score (EDRGS) model for classification. The molecular function of EDRGS subtyping was explored in scRNA-seq (n = 60) and bulk-seq (n = 310), and the EDRGS's potential to predict treatment response was assessed in datasets of various cancer types.

FINDINGS

EDRGS stratified ESCCs into EDRGS-high/low subtypes, with EDRGS-high signifying a less favourable prognosis in ESCC and nine additional cancer types. EDRGS-high exhibited an immune-hot but immune-suppressive phenotype with elevated immune checkpoint expression, increased T cell infiltration, and IFNγ signalling in ESCC, suggesting a better response to immunotherapy. Notably, EDRGS outperformed PD-L1 in predicting anti-PD-1/L1 therapy effectiveness in ESCC (n = 42), kidney renal clear cell carcinoma (KIRC, n = 181), and bladder urothelial carcinoma (BLCA, n = 348) cohorts. EDRGS-low showed a cell cycle-activated phenotype with higher CDK4 and/or CDK6 expression, demonstrating a superior response to the CDK4/6 inhibitor palbociclib, validated in ESCC (n = 26), melanoma (n = 18), prostate cancer (n = 15) cells, and PDX models derived from patients with pancreatic cancer (n = 30).

INTERPRETATION

Identification of EDRGS subtypes enlightens ESCC categorisation, offering clinical insights for patient management in immunotherapy (anti-PD-1/L1) and CDK4/6 inhibitor therapy across cancer types.

FUNDING

This study was supported by funding from the National Key R&D Program of China (2021YFC2501000, 2020YFA0803300), the National Natural Science Foundation of China (82030089, 82188102), the CAMS Innovation Fund for Medical Sciences (2021-I2M-1-018, 2022-I2M-2-001, 2021-I2M-1-067), the Fundamental Research Funds for the Central Universities (3332021091).

Super enhancer-driven core transcriptional regulatory circuitry crosstalk with cancer plasticity and patient mortality in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a clinically aggressive subtype of breast cancer. Core transcriptional regulatory circuitry (CRC) consists of autoregulated transcription factors (TFs) and their enhancers, which dominate gene expression programs and control cell fate. However, there is limited knowledge of CRC in TNBC. Herein, we systemically characterized the activated super-enhancers (SEs) and interrogated 14 CRCs in breast cancer. We found that CRCs could be broadly involved in DNA conformation change, metabolism process, and signaling response affecting the gene expression reprogramming. Furthermore, these CRC TFs are capable of coordinating with partner TFs bridging the enhancer-promoter loops. Notably, the CRC TF and partner pairs show remarkable specificity for molecular subtypes of breast cancer, especially in TNBC. USF1, SOX4, and MYBL2 were identified as the TNBC-specific CRC TFs. We further demonstrated that USF1 was a TNBC immunophenotype-related TF. Our findings that the rewiring of enhancer-driven CRCs was related to cancer immune and mortality, will facilitate the development of epigenetic anti-cancer treatment strategies.

Identifying immune checkpoint-related lncRNA biomarkers for immunotherapy response and prognosis in cancers

Long non-coding RNAs (lncRNAs) could modulate expression of immune checkpoints (ICPs) in tumor-immune. However, precise functions in immunity and potential for predicting ICP inhibitors (ICI) response have been described for only a few lncRNAs. Here, a multiple-step pipeline was developed to identify cancer- and immune-context ICP and lncRNA cooperative regulation pairs (ICPaLncCRPs) across cancers. Immune-related ICPs and lncRNAs were extracted follow immune cell lines and immunologic constant of rejection groups. ICPaLncCRP networks were constructed, which likely to modulate tumor-immune by specific patterns. Common and specific hub ICPaLncs such as MIR155HG, TRG-AS1 and PCED1B-AS1 maybe play central roles in prognosis and circulating. Moreover, these hub ICPaLncs were significantly correlated with immune cell infiltration based on bulk and single-cell RNA sequencing data. Some ICPaLncCRPs such as IDO1-MIR155HG could predict three- and five-year prognosis of melanoma in two independent datasets. We also validated that some ICPaLncCRPs could effectively predict ICI-response follow six independent datasets. Collectively, this study will enhance our understanding of lncRNA functions and accelerate discovery of lncRNA-based biomarkers in ICI treatment.

Enhancer-associated regulatory network and gene signature based on transcriptome and methylation data to predict the survival of patients with lung adenocarcinoma

Accumulating evidence has proved that aberrant methylation of enhancers plays regulatory roles in gene expression for various cancers including lung adenocarcinoma (LUAD). In this study, the transcriptome and methylation data of The Cancer Genome Atlas (TCGA)-LUAD cohort were comprehensively analyzed with a five-step Enhancer Linking by Methylation/Expression Relationships (ELMER) process. Step 1: 131,371 distal (2 kb upstream from the transcription start site) probes were obtained. Step 2: 10,665 distal hypomethylated probes were identified in an unsupervised mode with the get.diff.meth function. Step 3: 699 probe-gene pairs with negative correlations were screened using the get.pair function in an unsupervised mode. Step 4: After mapping with probes, 768 motifs were obtained and 24 of them were enriched. Step 5: 127 transcription factors (TFs) with differential expressions and negative correlations with methylation levels were screened, which were corresponding to 21 motifs. After the ELMER process, a prognostic “TFs-motifs-genes” regulatory network was constructed. The Least absolute shrinkage and selection operator (LASSO) and Stepwise regression analyses were further applied to identify variables in the TCGA-LUAD cohort and an eight-gene signature was constructed for calculating the risk score. The risk score was verified in two independent validation cohorts. The area under curve values of receiver operating characteristic curves predicting 1-, 3-, and 5-years survival ranged from 0.633 to 0.764. With the increase of the risk scores, both the survival statuses and clinical traits showed a worse tendency. There were significant differences in the degrees of immune cell infiltration, TMB values, and TIDE scores between the high-risk and low-risk groups. Finally, a better-performing prognostic nomogram was integrated with the risk score and other clinical traits. In short, this multi-omics analysis demonstrated the application of ELMER in analyzing enhancer-associated regulatory network in LUAD, which provided promising strategies for epigenetic therapy and prognostic biomarkers.