ABSTRACT
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).
Subject(s)
Biomarkers, Tumor , Cyclin-Dependent Kinase 4 , Cyclin-Dependent Kinase 6 , Esophageal Neoplasms , Esophageal Squamous Cell Carcinoma , Gene Expression Regulation, Neoplastic , Immunotherapy , Humans , Cyclin-Dependent Kinase 6/antagonists & inhibitors , Cyclin-Dependent Kinase 6/genetics , Cyclin-Dependent Kinase 4/antagonists & inhibitors , Cyclin-Dependent Kinase 4/genetics , Esophageal Squamous Cell Carcinoma/genetics , Esophageal Squamous Cell Carcinoma/drug therapy , Esophageal Squamous Cell Carcinoma/mortality , Esophageal Squamous Cell Carcinoma/therapy , Esophageal Squamous Cell Carcinoma/immunology , Esophageal Squamous Cell Carcinoma/metabolism , Esophageal Squamous Cell Carcinoma/pathology , Immunotherapy/methods , Esophageal Neoplasms/genetics , Esophageal Neoplasms/drug therapy , Esophageal Neoplasms/therapy , Esophageal Neoplasms/mortality , Esophageal Neoplasms/metabolism , Prognosis , DNA Methylation , Enhancer Elements, Genetic , Gene Expression Profiling , Computational Biology/methods , Cell Line, Tumor , Protein Kinase Inhibitors/therapeutic use , Protein Kinase Inhibitors/pharmacology , Gene Regulatory Networks , AnimalsABSTRACT
OBJECTIVE: Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy. Differentiation from chronic pancreatitis (CP) is currently inaccurate in about one-third of cases. Misdiagnoses in both directions, however, have severe consequences for patients. We set out to identify molecular markers for a clear distinction between PDAC and CP. DESIGN: Genome-wide variations of DNA-methylation, messenger RNA and microRNA level as well as combinations thereof were analysed in 345 tissue samples for marker identification. To improve diagnostic performance, we established a random-forest machine-learning approach. Results were validated on another 48 samples and further corroborated in 16 liquid biopsy samples. RESULTS: Machine-learning succeeded in defining markers to differentiate between patients with PDAC and CP, while low-dimensional embedding and cluster analysis failed to do so. DNA-methylation yielded the best diagnostic accuracy by far, dwarfing the importance of transcript levels. Identified changes were confirmed with data taken from public repositories and validated in independent sample sets. A signature of six DNA-methylation sites in a CpG-island of the protein kinase C beta type gene achieved a validated diagnostic accuracy of 100% in tissue and in circulating free DNA isolated from patient plasma. CONCLUSION: The success of machine-learning to identify an effective marker signature documents the power of this approach. The high diagnostic accuracy of discriminating PDAC from CP could have tremendous consequences for treatment success, once the result from still a limited number of liquid biopsy samples would be confirmed in a larger cohort of patients with suspected pancreatic cancer.
Subject(s)
Carcinoma, Pancreatic Ductal , Pancreatic Neoplasms , Pancreatitis, Chronic , Humans , Pancreatic Neoplasms/diagnosis , Pancreatic Neoplasms/genetics , Pancreatic Neoplasms/pathology , Pancreatitis, Chronic/diagnosis , Pancreatitis, Chronic/genetics , Carcinoma, Pancreatic Ductal/diagnosis , Carcinoma, Pancreatic Ductal/genetics , DNA Methylation , DNA , Biomarkers, Tumor/genetics , Pancreatic NeoplasmsABSTRACT
Cancer stem cells (CSCs) are crucial for tumorigenesis, metastasis, and therapy resistance in esophageal squamous cell carcinoma (ESCC). To further elucidate the mechanism underlying characteristics of CSCs and develop CSCs-targeted therapy, an efficient culture system that could expand and maintain CSCs is needed. CSCs reside in a complex tumor microenvironment, and three-dimensional (3D) culture systems of biomimetic scaffolds are expected to better support the growth of CSCs by recapitulating the biophysical properties of the extracellular matrix (ECM). Here, we established gelatin-based 3D biomimetic scaffolds mimicking the stiffness and collagen content of ESCC, which could enrich ESCC CSCs efficiently. Biological changes of ESCC cells laden in scaffolds with three different viscoelasticity emulating physiological stiffness of esophageal tissues were thoroughly investigated in varied aspects such as cell morphology, viability, cell phenotype markers, and transcriptomic profiling. The results demonstrated the priming effects of viscoelasticity on the stemness of ESCC. The highly viscous scaffolds (G': 6-403 Pa; G'': 2-75 Pa) better supported the enrichment of ESCC CSCs, and the TGF-beta signaling pathway might be involved in regulating the stemness of ESCC cells. Compared to two-dimensional (2D) cultures, highly viscous scaffolds significantly promoted the clonal expansion of ESCC cells in vitro and tumor formation ability in vivo. Our findings highlight the crucial role of biomaterials' viscoelasticity for the 3D culture of ESCC CSCs in vitro, and this newly-established culture system represents a valuable platform to support their growth, which could facilitate the CSCs-targeted therapy in the future.
Subject(s)
Esophageal Neoplasms , Esophageal Squamous Cell Carcinoma , Humans , Esophageal Squamous Cell Carcinoma/metabolism , Esophageal Squamous Cell Carcinoma/pathology , Esophageal Squamous Cell Carcinoma/therapy , Gelatin/pharmacology , Esophageal Neoplasms/metabolism , Esophageal Neoplasms/pathology , Biomimetics , Cell Line, Tumor , Neoplastic Stem Cells/pathology , Tumor MicroenvironmentABSTRACT
Recognition and quantitative analytics of histopathological cells are the golden standard for diagnosing multiple cancers. Despite recent advances in deep learning techniques that have been widely investigated for the automated segmentation of various types of histopathological cells, the heavy dependency on specific histopathological image types with sufficient supervised annotations, as well as the limited access to clinical data in hospitals, still pose significant challenges in the application of computer-aided diagnosis in pathology. In this paper, we focus on the model generalization of cell segmentation towards cross-tissue histopathological images. Remarkably, a novel target-specific finetuning-based self-supervised domain adaptation framework is proposed to transfer the cell segmentation model to unlabeled target datasets, without access to source datasets and annotations. When performed on the target unlabeled histopathological image set, the proposed method only needs to tune very few parameters of the pre-trained model in a self-supervised manner. Considering the morphological properties of pathological cells, we introduce two constraint terms at both local and global levels into this framework to access more reliable predictions. The proposed cross-domain framework is validated on three different types of histopathological tissues, showing promising performance in self-supervised cell segmentation. Additionally, the whole framework can be further applied to clinical tools in pathology without accessing the original training image data. The code and dataset are released at: https://github.com/NeuronXJTU/SFDA-CellSeg.
Subject(s)
Diagnosis, Computer-Assisted , Image Processing, Computer-Assisted , Supervised Machine LearningABSTRACT
Integrated molecular analysis of human cancer has yielded molecular classification for precise management of cancer patients. Here, we analyzed the whole genomic, epigenomic, transcriptomic, and proteomic data of 155 esophageal squamous cell carcinomas (ESCCs). Multi-omics analysis led to the classification of ESCCs into four subtypes: cell cycle pathway activation, NRF2 oncogenic activation, immune suppression (IS), and immune modulation (IM). IS and IM cases were highly immune infiltrated but differed in the type and distribution of immune cells. IM cases showed better response to immune checkpoint blockade therapy than other subtypes in a clinical trial. We further developed a classifier with 28 features to identify the IM subtype, which predicted anti-PD-1 therapy response with 85.7% sensitivity and 90% specificity. These results emphasize the clinical value of unbiased molecular classification based on multi-omics data and have the potential to further improve the understanding and treatment of ESCC.
Subject(s)
Carcinoma, Squamous Cell , Esophageal Neoplasms , Esophageal Squamous Cell Carcinoma , Humans , Esophageal Squamous Cell Carcinoma/genetics , Esophageal Squamous Cell Carcinoma/pathology , Esophageal Neoplasms/genetics , Esophageal Neoplasms/pathology , Carcinoma, Squamous Cell/genetics , Multiomics , ProteomicsABSTRACT
Short instrumental streamflow records in the South and East Tibetan Plateau (SETP) limit understanding of the full range and long-term variability in streamflow, which could greatly impact freshwater resources for about one billion people downstream. Here we reconstruct eight centuries (1200-2012 C.E.) of annual streamflow from the Monsoon Asia Drought Atlas in five headwater regions across the SETP. We find two regional patterns, including northern (Yellow, Yangtze, and Lancang-Mekong) and southern (Nu-Salween and Yarlung Zangbo-Brahmaputra) SETP regions showing ten contrasting wet and dry periods, with a dividing line of regional moisture regimes at ~32°-33°N identified. We demonstrate strong temporal nonstationarity in streamflow variability, and reveal much greater high/low mean flow periods in terms of duration and magnitude: mostly pre-instrumental wetter conditions in the Yarlung Zangbo-Brahmaputra and drier conditions in other rivers. By contrast, the frequency of extreme flows during the instrumental periods for the Yangtze, Nu-Salween, and Yarlung Zangbo-Brahmaputra has increased by ~18% relative to the pre-instrumental periods.
Subject(s)
Droughts , Rivers , Humans , Tibet , AsiaABSTRACT
Studies have indicated that some genes involved in carcinogenesis are highly methylated in their promoter regions but nevertheless strongly transcribed. It has been proposed that transcription factors could bind specifically to methylated promoters and trigger transcription. We looked at this rather comprehensively for pancreatic ductal adenocarcinoma (PDAC) and studied some cases in more detail. Some 2% of regulated genes in PDAC exhibited higher transcription coupled to promoter hypermethylation in comparison to healthy tissue. Screening 661 transcription factors, several were found to bind specifically to methylated promoters, in particular molecules of the NFAT family. One of them-NFATc1-was substantially more strongly expressed in PDAC than control tissue and exhibited a strong oncogenic role. Functional studies combined with computational analyses allowed determining affected genes. A prominent one was gene ALDH1A3, which accelerates PDAC metastasis and correlates with a bad prognosis. Further studies confirmed the direct up-regulation of ALDH1A3 transcription by NFATc1 promoter binding in a methylation-dependent process, providing insights into the oncogenic role of transcription activation in PDAC that is promoted by DNA methylation.
ABSTRACT
Given the challenge to estimate representative long-term natural variability of streamflow from limited observed data, a hierarchical, multilevel Bayesian regression (HBR) was developed to reconstruct the 1489-2006 annual streamflow data at six Athabasca River Basin (ARB) gauging stations based on 14 tree ring chronologies. Seven nested models were developed to maximize the applications of available tree ring predictors. Based on results of goodness-of-fit tests, the HBR developed was skillful and reliable in reconstructing the streamflow of ARB. From five centuries of reconstructed streamflow for ARB, five or six abrupt change points are detected. The streamflow time series obtained from a backward moving, 46-year window for six gauging sites in ARB vary significantly over five centuries (1489-2006) and at times could exceed the 90% and/or 95% confidence intervals, denoting significant non-stationarities. Apparently changes in the mean state and the lag-1 autocorrelation of reconstructed streamflow across the gauging sites can be similar or radically different from each other. These nonstationary features imply that the default stationary assumption is not applicable in ARB. Further, the reconstructed streamflow shows statistically significant oscillations at interannual, interdecadal and multidecadal time scales and are teleconnected to climate patterns such as El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO). A composite analysis shows that La Niña (El Niño), cold (warm) PDO, and cold (warm) AMO events are typically associated with increased (decreased) streamflow anomalies of ARB. The reconstructed streamflow data provides us the full range of streamflow variability and recurrence characteristics of extremes spanned over five centuries from which it is useful for us to evaluate and manage the current water systems of ARB more effectively and a better risk analysis of future droughts of ARB.
ABSTRACT
Aberrant DNA methylation is often involved in carcinogenesis. Our initial goal was to identify DNA methylation biomarkers associated with pancreatic cancer. A genomewide methylation study was performed on DNA from pancreatic ductal adenocarcinoma (PDAC) and endocrine pancreas tumors. Validation of DNA methylation patterns and concomitant alterations in expression of gene candidates was performed on patient samples and pancreatic cancer cell lines. Furthermore, validation was done on independent data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Finally, droplet digital PCR was employed to detect DNA methylation marks in cell-free (cf) DNA isolated from plasma samples of PDAC patients and cancer-free blood donors. Hypermethylation of the SST gene (encoding somatostatin) and concomitant downregulation of its expression were discovered in PDAC and endocrine tumor tissues while not being present in chronic pancreatitis (inflamed) tissues and normal pancreas. Fittingly, treatment with a somatostatin agonist (octreotide) reduced cell proliferation and migration of pancreatic cancer cells. Diagnostic performance of SST methylation in a receiver operating characteristic curve analysis was 100% and 89% for tissue and plasma samples, respectively. A large body of TCGA and GEO data confirmed SST hypermethylation and downregulation in PDAC and showed a similar effect in a broad spectrum of other tumor entities. SST promoter methylation represents a sensitive and promising molecular, pan-cancer biomarker detectable in tumor tissue, and liquid biopsy samples.
Subject(s)
Adenocarcinoma/genetics , Biomarkers, Tumor/genetics , Carcinoma, Pancreatic Ductal/blood , Carcinoma, Pancreatic Ductal/genetics , DNA Methylation/genetics , Pancreatic Neoplasms/blood , Pancreatic Neoplasms/genetics , Somatostatin/genetics , Adenocarcinoma/blood , Adenocarcinoma/diagnosis , Adenocarcinoma/pathology , Biomarkers, Tumor/metabolism , Carcinoma, Pancreatic Ductal/diagnosis , Carcinoma, Pancreatic Ductal/pathology , Cell Movement/genetics , Cell Proliferation/genetics , Down-Regulation/genetics , Gene Expression Regulation, Neoplastic , Genetic Loci , Genome, Human , Humans , Kaplan-Meier Estimate , Pancreatic Neoplasms/diagnosis , Pancreatic Neoplasms/pathology , Principal Component Analysis , Prognosis , Reproducibility of Results , Somatostatin/agonists , Pancreatic NeoplasmsABSTRACT
Binding of transcription factors to mutated DNA sequences is a likely regulator of cancer progression. Noncoding regulatory mutations such as those on the core promoter of the gene encoding human telomerase reverse transcriptase have been shown to affect gene expression in cancer. Using a protein microarray of 667 transcription factor DNA-binding domains and subsequent functional assays, we looked for transcription factors that preferentially bind the mutant hTERT promoter and characterized their downstream effects. One of them, friend leukemia integration 1 (FLI1), which belongs to the E26 transforming-specific family of transcription factors, exhibited particularly strong effects with respect to regulating hTERT expression, while the even better binding ELK3 did not. Depletion of FLI1 decreased expression of the genes for cyclin D1 (CCND1) and E2F transcription factor 2 (E2F2) resulting in a G1/S cell cycle arrest and in consequence a reduction of cell proliferation. FLI1 also affected CMTM7, another gene involved in G1/S transition, although by another process that suggests a balanced regulation of the tumor suppressor gene's activity via opposing regulation processes. FLI1 expression was found upregulated and correlated with an increase in CCND1 expression in pancreatic cancer and brain tumors. In non-neoplastic lung cells, however, FLI1 depletion led to rapid progression through the cell cycle. This coincides with the fact that FLI1 is downregulated in lung tumors. Taken together, our data indicate a cell cycle regulatory hub involving FLI1, hTERT, CCND1 and E2F2 in a tissue- and context-dependent manner.
Subject(s)
Neoplasms/genetics , Neoplasms/metabolism , Proto-Oncogene Protein c-fli-1/genetics , Proto-Oncogene Protein c-fli-1/metabolism , Cell Cycle/physiology , Cell Cycle Proteins/genetics , Cell Cycle Proteins/metabolism , Cell Line, Tumor , Cyclin D1/biosynthesis , Cyclin D1/genetics , Cyclin D1/metabolism , Disease Progression , E2F2 Transcription Factor/genetics , E2F2 Transcription Factor/metabolism , Gene Expression Regulation, Neoplastic , Humans , Mutation , Neoplasms/pathology , Promoter Regions, Genetic , Protein Array Analysis , Proto-Oncogene Protein c-fli-1/biosynthesis , Telomerase/genetics , Telomerase/metabolismABSTRACT
Chicken embryonic stem cells (cESCs) obtained from stage X embryos provide a novel model for the study of avian embryonic development. A new way to maintain cESCs for a long period in vitro still remains unexplored. We found that the cESCs showed stem cell-like properties in vitro for a long term with the support of DF-1 feeder and basic culture medium supplemented with human basic fibroblast growth factor (hbFGF), mouse stem cell factor (mSCF), and human leukemia inhibitory factor (hLIF). During the long culture period, the cESCs showed typical ES cell morphology and expressed primitive stem cell markers with a relatively stable proliferation rate and high telomerase activity. These cells also exhibited the capability to differentiate into cardiac myocytes, smooth muscle cells, neural cells, osteoblast, and adipocyte in vitro. Chimera chickens were produced by cESCs cultured for 25 passages with this new culture system. The experiments showed that DF-1 was the optimal feeder and hbFGF was an important factor for maintaining the pluripotency of cESCs in vitro.