Integrative modeling of tumor genomes and epigenomes for enhanced cancer diagnosis by cell-free DNA.
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PROVIDER: EGAS00001006553 | EGA |
REPOSITORIES: EGA
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Project description:Multi-cancer early detection remains a key challenge in cell-free DNA (cfDNA)-based liquid biopsy. Here, we perform cfDNA whole-genome sequencing to generate two test datasets covering 2125 patient samples of 9 cancer types and 1241 normal control samples, and also a reference dataset for background variant filtering based on 20,529 low-depth healthy samples. An external cfDNA dataset consisting of 208 cancer and 214 normal control samples is used for additional evaluation. Accuracy for cancer detection and tissue-of-origin localization is achieved using our algorithm, which incorporates cancer type-specific profiles of mutation distribution and chromatin organization in tumor tissues as model references. Our integrative model detects early-stage cancers, including those of pancreatic origin, with high sensitivity that is comparable to that of late-stage detection. Model interpretation reveals the contribution of cancer type-specific genomic and epigenomic features. Our methodologies may lay the groundwork for accurate cfDNA-based cancer diagnosis, especially at early stages.
| S-EPMC10085982 | biostudies-literature
Project description:BackgroundLung cancer is a leading cause of cancer mortality, highlighting the need for innovative non-invasive early detection methods. Although cell-free DNA (cfDNA) analysis shows promise, its sensitivity in early-stage lung cancer patients remains a challenge. This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung cancer detection model.MethodsTo address this issue, a multi-centre prospective cohort study was conducted, with participants harbouring suspicious malignant lung nodules and healthy volunteers recruited from two clinical centres. Plasma cfDNA was analysed for its epigenetic and fragmentomic profiles using chromatin immunoprecipitation sequencing, reduced representation bisulphite sequencing and low-pass whole-genome sequencing. Machine learning algorithms were then employed to integrate the multi-omics data, aiding in the development of a precise lung cancer detection model.ResultsCancer-related changes in cfDNA fragmentomics were significantly enriched in specific genes marked by cell-free epigenomes. A total of 609 genes were identified, and the corresponding cfDNA fragmentomic features were utilised to construct the ensemble model. This model achieved a sensitivity of 90.4% and a specificity of 83.1%, with an AUC of 0.94 in the independent validation set. Notably, the model demonstrated exceptional sensitivity for stage I lung cancer cases, achieving 95.1%. It also showed remarkable performance in detecting minimally invasive adenocarcinoma, with a sensitivity of 96.2%, highlighting its potential for early detection in clinical settings.ConclusionsWith feature selection guided by multiple epigenetic sequencing approaches, the cfDNA fragmentomics-based machine learning model demonstrated outstanding performance in the independent validation cohort. These findings highlight its potential as an effective non-invasive strategy for the early detection of lung cancer.KeypointsOur study elucidated the regulatory relationships between epigenetic modifications and their effects on fragmentomic features. Identifying epigenetically regulated genes provided a critical foundation for developing the cfDNA fragmentomics-based machine learning model. The model demonstrated exceptional clinical performance, highlighting its substantial potential for translational application in clinical practice.
| S-EPMC11798665 | biostudies-literature
Project description:The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.
| S-EPMC4530010 | biostudies-literature
Project description:Allelic differences between the two homologous chromosomes can affect the propensity of inheritance in humans; however, the extent of such differences in the human genome has yet to be fully explored. Here we delineate allelic chromatin modifications and transcriptomes among a broad set of human tissues, enabled by a chromosome-spanning haplotype reconstruction strategy. The resulting large collection of haplotype-resolved epigenomic maps reveals extensive allelic biases in both chromatin state and transcription, which show considerable variation across tissues and between individuals, and allow us to investigate cis-regulatory relationships between genes and their control sequences. Analyses of histone modification maps also uncover intriguing characteristics of cis-regulatory elements and tissue-restricted activities of repetitive elements. The rich data sets described here will enhance our understanding of the mechanisms by which cis-regulatory elements control gene expression programs.
| S-EPMC4449149 | biostudies-literature
Project description:Epigenomic modifications are instrumental for transcriptional regulation, but comprehensive reference epigenomes remain unexplored in rice. Here, we develop an enhanced chromatin immunoprecipitation (eChIP) approach for plants, and generate genome-wide profiling of five histone modifications and RNA polymerase II occupancy with it. By integrating chromatin accessibility, DNA methylation, and transcriptome datasets, we construct comprehensive epigenome landscapes across various tissues in 20 representative rice varieties. Approximately 81.8% of rice genomes are annotated with different epigenomic properties. Refinement of promoter regions using open chromatin and H3K4me3-marked regions provides insight into transcriptional regulation. We identify extensive enhancer-like promoters with potential enhancer function on transcriptional regulation through chromatin interactions. Active and repressive histone modifications and the predicted enhancers vary largely across tissues, whereas inactive chromatin states are relatively stable. Together, these datasets constitute a valuable resource for functional element annotation in rice and indicate the central role of epigenomic information in understanding transcriptional regulation.
| S-EPMC7253419 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2018-06-01 | GSE55383 | GEO
Project description:Purpose: Tamoxifen is the most commonly used antiestrogen drug for breast cancer treatment, however, acquired resistance is a considerable challenge in clinic. Complexity and redundancy in biological system implies tamoxifen resistance is associated with complex biological processes. The aim of this study is to attempt to reveal the mechanism of tamoxifen resistance using high-throughput sequencing technique. Methods: We applied digital gene expression (DGE), RNA-seq, Small RNA-seq, bisulifte-seq (reduced representation bisuflite sequencing (RRBS)) with Illumina sequencing platforms, and Array CGH to analyze two well-established tamoxifen resistant cell line models: one is the LCC cell line model that was derived from MCF-7 breast cancer cell line and developed tamoxifen resistance following a step-wise strategy. This cell line model includes LCC0 (estrogen-dependent and tamoxifen sensitive), LCC1 (estrogen-independent and tamoxifen sensitive), LCC2 (derived from LCC1 with increased small-dosage tamoxifen treatment, estrogen-independent, and tamoxifen-resistant), and LCC9 (derived from LCC1 with increased small-dosage fluvestrant treatment, estrogen-independent, and cross-resistance to both fluvestrant and tamoxifen). The other is the TAMR cell line model that was derived from MCF-7 breast cancer cell line and developed four tamoxifen-resistant sublines following the strategy of high-dosage of tamoxifen treatment. Each subline was developed from the survived single cell after the tamoxifen treatment. Results: The DGE and RNA-seq results showed characteristic expression features for the both cell line models, for instance, high expression of SOX2 and alterations of other SOX gene family members, decreased expression of PGR, PTEN, TP53, EZH2, SMAD3, TGF-β1, DNMT1, APOBEC3B, FOXO3 and FOXO4, and increased expression of CDH1, APOBEC3H, FOXA1, TET2, TET3, TDG, MBD4, DNMT3a and DNMT3b between the tamoxifen-resistant cell lines with their matched parental cell lines. Small RNA seq showed the decreased expressions of miR-1, miR-196-3p, miR-18a-5p, miR-125b-5p, and the increased expression of miR-23a-3p, miR-26b-5p, miR-320d, miR-141-3p, miR-23a-3p, miR-30a-5p, miR-9-5p, miR-200c, miR-129-5p, miR-125b-5p, miR-1285-3p and miR-30b-5p between the tamoxifen-resistant cell lines and their matched parental cell lines. Additionally, let-7a and let-7c have been found to present high expression levels in the tamoxifen resistant cells in the both cell line models, even it showed significantly decreased in some TAMR cells compared with their parental cell line. The RRBS showed similar DNA methylation levels between the LCC cell lines. Array CGH showed a small number of copy number variants between the LCC cell lines. Conclusion: Our present study suggests the development of tamoxifen resistance is associated with a dynamic process of cell fate changing. Therefore, new therapeutic method that targets whole cell system and cell fate dynamics, will be a promising strategy.
2018-06-01 | GSE55380 | GEO
Project description:Purpose: Tamoxifen is the most commonly used antiestrogen drug for breast cancer treatment, however, acquired resistance is a considerable challenge in clinic. Complexity and redundancy in biological system implies tamoxifen resistance is associated with complex biological processes. The aim of this study is to attempt to reveal the mechanism of tamoxifen resistance using high-throughput sequencing technique. Methods: We applied digital gene expression (DGE), RNA-seq, Small RNA-seq, bisulifte-seq (reduced representation bisuflite sequencing (RRBS)) with Illumina sequencing platforms, and Array CGH to analyze two well-established tamoxifen resistant cell line models: one is the LCC cell line model that was derived from MCF-7 breast cancer cell line and developed tamoxifen resistance following a step-wise strategy. This cell line model includes LCC0 (estrogen-dependent and tamoxifen sensitive), LCC1 (estrogen-independent and tamoxifen sensitive), LCC2 (derived from LCC1 with increased small-dosage tamoxifen treatment, estrogen-independent, and tamoxifen-resistant), and LCC9 (derived from LCC1 with increased small-dosage fluvestrant treatment, estrogen-independent, and cross-resistance to both fluvestrant and tamoxifen). The other is the TAMR cell line model that was derived from MCF-7 breast cancer cell line and developed four tamoxifen-resistant sublines following the strategy of high-dosage of tamoxifen treatment. Each subline was developed from the survived single cell after the tamoxifen treatment. Results: The DGE and RNA-seq results showed characteristic expression features for the both cell line models, for instance, high expression of SOX2 and alterations of other SOX gene family members, decreased expression of PGR, PTEN, TP53, EZH2, SMAD3, TGF-β1, DNMT1, APOBEC3B, FOXO3 and FOXO4, and increased expression of CDH1, APOBEC3H, FOXA1, TET2, TET3, TDG, MBD4, DNMT3a and DNMT3b between the tamoxifen-resistant cell lines with their matched parental cell lines. Small RNA seq showed the decreased expressions of miR-1, miR-196-3p, miR-18a-5p, miR-125b-5p, and the increased expression of miR-23a-3p, miR-26b-5p, miR-320d, miR-141-3p, miR-23a-3p, miR-30a-5p, miR-9-5p, miR-200c, miR-129-5p, miR-125b-5p, miR-1285-3p and miR-30b-5p between the tamoxifen-resistant cell lines and their matched parental cell lines. Additionally, let-7a and let-7c have been found to present high expression levels in the tamoxifen resistant cells in the both cell line models, even it showed significantly decreased in some TAMR cells compared with their parental cell line. The RRBS showed similar DNA methylation levels between the LCC cell lines. Array CGH showed a small number of copy number variants between the LCC cell lines. Conclusion: Our present study suggests the development of tamoxifen resistance is associated with a dynamic process of cell fate changing. Therefore, new therapeutic method that targets whole cell system and cell fate dynamics, will be a promising strategy.
2018-06-01 | GSE55343 | GEO
Project description:Building models of a biological system that are consistent with the myriad data available is one of the key challenges in biology. Modeling the structure and dynamics of macromolecular assemblies, for example, can give insights into how biological systems work, evolved, might be controlled, and even designed. Integrative structure modeling casts the building of structural models as a computational optimization problem, for which information about the assembly is encoded into a scoring function that evaluates candidate models. Here, we describe our open source software suite for integrative structure modeling, Integrative Modeling Platform (https://integrativemodeling.org), and demonstrate its use.
| S-EPMC5734277 | biostudies-literature
Project description:Despite large experimental and computational efforts aiming to dissect the mechanisms underlying disease risk, mapping cis-regulatory elements to target genes remains a challenge. Here, we introduce a matrix factorization framework to integrate physical and functional interaction data of genomic segments. The framework was used to predict a regulatory network of chromatin interaction edges linking more than 20 000 promoters and 1.8 million enhancers across 127 human reference epigenomes, including edges that are present in any of the input datasets. Our network integrates functional evidence of correlated activity patterns from epigenomic data and physical evidence of chromatin interactions. An important contribution of this work is the representation of heterogeneous data with different qualities as networks. We show that the unbiased integration of independent data sources suggestive of regulatory interactions produces meaningful associations supported by existing functional and physical evidence, correlating with expected independent biological features.
| S-EPMC6698807 | biostudies-literature