Similar Datasets
Project description:Adrenocortical carcinoma (ACC) is a rare but aggressive malignancy originating in the adrenal cortex, characterized by significant intra- and intertumoral heterogeneity. In this study, we employed a combination of whole-genome sequencing, single-cell RNA sequencing, T cell receptor sequencing, spatial transcriptome sequencing, and multiple fluorescent staining to construct a comprehensive multi-omics landscape of ACC. Our findings demonstrated that, although all tumor cells exhibited a "confused cell identity" feature, distinct subpopulations were present in each ACC patient. Among these, the LDLR+ and MKI67+ subpopulations expressed elevated levels of C1A signature genes and established robust communication with endothelial cells through NECTIN-PVR and NOTCH2-JAG pairs, both significantly associated with poor prognosis. Interestingly, the PCDH15+ subpopulations displayed moderate levels of both C1A signature genes and extracellular matrix related genes. On the other hand, the HLA-B+ and subpopulation exhibited the highest levels of antigen-processing related genes, a characteristic not previously reported. The amalgamation of these distinct subpopulations facilitated the stratification of ACC patients into subtypes with varying prognoses. Patients (Group II) dominated by LDLR+ and MKI67+ subpopulations exhibited the worst prognosis, while those (Group III) with a higher proportion of PCDH15+ subpopulation demonstrated the most favorable outcomes. Significantly, patients (Group I) with a higher prevalence of the HLA-B+ subpopulation also showed notably increased LAG3+ memory CD8 T cells, indicating their potential suitability for immunotherapy. Despite responding averagely to conventional treatment, this subgroup was predicted to be responsive to immunotherapy. Notably, one patient from Group I exhibited a positive response to camrelizumab treatment after relapse with mitotane, leading to a successful outcome, while two patients from Group II did not respond favorably. Our study offers insights into the single-cell level heterogeneity of ACC and provides valuable implications for precision treatments for this disease.
2025-12-26 | GSE252108 | GEO
Project description:Adrenocortical carcinoma (ACC) is a rare but aggressive malignancy originating in the adrenal cortex, characterized by significant intra- and intertumoral heterogeneity. In this study, we employed a combination of whole-genome sequencing, single-cell RNA sequencing, T cell receptor sequencing, spatial transcriptome sequencing, and multiple fluorescent staining to construct a comprehensive multi-omics landscape of ACC. Our findings demonstrated that, although all tumor cells exhibited a "confused cell identity" feature, distinct subpopulations were present in each ACC patient. Among these, the LDLR+ and MKI67+ subpopulations expressed elevated levels of C1A signature genes and established robust communication with endothelial cells through NECTIN-PVR and NOTCH2-JAG pairs, both significantly associated with poor prognosis. Interestingly, the PCDH15+ subpopulations displayed moderate levels of both C1A signature genes and extracellular matrix related genes. On the other hand, the HLA-B+ and subpopulation exhibited the highest levels of antigen-processing related genes, a characteristic not previously reported. The amalgamation of these distinct subpopulations facilitated the stratification of ACC patients into subtypes with varying prognoses. Patients (Group II) dominated by LDLR+ and MKI67+ subpopulations exhibited the worst prognosis, while those (Group III) with a higher proportion of PCDH15+ subpopulation demonstrated the most favorable outcomes. Significantly, patients (Group I) with a higher prevalence of the HLA-B+ subpopulation also showed notably increased LAG3+ memory CD8 T cells, indicating their potential suitability for immunotherapy. Despite responding averagely to conventional treatment, this subgroup was predicted to be responsive to immunotherapy. Notably, one patient from Group I exhibited a positive response to camrelizumab treatment after relapse with mitotane, leading to a successful outcome, while two patients from Group II did not respond favorably. Our study offers insights into the single-cell level heterogeneity of ACC and provides valuable implications for precision treatments for this disease.
2025-12-26 | GSE252112 | GEO
Project description:Human cancer cell lines have long served as tools for cancer research and drug discovery, but the presence and the source of intra-cell-line heterogeneity remain elusive. Here, we perform single-cell RNA-sequencing and ATAC-sequencing on 42 and 39 human cell lines, respectively, to illustrate both transcriptomic and epigenetic heterogeneity within individual cell lines. Our data reveal that transcriptomic heterogeneity is frequently observed in cancer cell lines of different tissue origins, often driven by multiple common transcriptional programs. Copy number variation, as well as epigenetic variation and extrachromosomal DNA distribution all contribute to the detected intra-cell-line heterogeneity. Using hypoxia treatment as an example, we demonstrate that transcriptomic heterogeneity could be reshaped by environmental stress. Overall, our study performs single-cell multi-omics of commonly used human cancer cell lines and offers mechanistic insights into the intra-cell-line heterogeneity and its dynamics, which would serve as an important resource for future cancer cell line-based studies.
| S-EPMC10710513 | biostudies-literature
Project description:BackgroundBiopsies obtained from primary oesophageal squamous cell carcinoma (ESCC) guide diagnosis and treatment. However, spatial intra-tumoral heterogeneity (ITH) influences biopsy-derived information and patient responsiveness to therapy. Here, we aimed to elucidate the spatial ITH of ESCC and matched lymph node metastasis (LNmet ).MethodsPrimary tumour superficial (PTsup ), deep (PTdeep ) and LNmet subregions of patients with locally advanced resectable ESCC were evaluated using whole-exome sequencing (WES), whole-transcriptome sequencing and spatially resolved digital spatial profiling (DSP). To validate the findings, immunohistochemistry was conducted and a single-cell transcriptomic dataset was analysed.ResultsWES revealed 15.72%, 5.02% and 32.00% unique mutations in PTsup , PTdeep and LNmet , respectively. Copy number alterations and phylogenetic trees showed spatial ITH among subregions both within and among patients. Driver mutations had a mixed intra-tumoral clonal status among subregions. Transcriptome data showed distinct differentially expressed genes among subregions. LNmet exhibited elevated expression of immunomodulatory genes and enriched immune cells, particularly when compared with PTsup (all P < .05). DSP revealed orthogonal support of bulk transcriptome results, with differences in protein and immune cell abundance between subregions in a spatial context. The integrative analysis of multi-omics data revealed complex heterogeneity in mRNA/protein levels and immune cell abundance within each subregion.ConclusionsThis study comprehensively characterised spatial ITH in ESCC, and the findings highlight the clinical significance of unbiased molecular classification based on multi-omics data and their potential to improve the understanding and management of ESCC. The current practices for tissue sampling are insufficient for guiding precision medicine for ESCC, and routine profiling of PTdeep and/or LNmet should be systematically performed to obtain a more comprehensive understanding of ESCC and better inform treatment decisions.
| S-EPMC10679972 | biostudies-literature
Project description:Malignant pleural mesothelioma (MPM) is a deadly pleural malignancy with over 90% mortality within five years and a median survival of approximately 7 months without treatment. The current first-line chemotherapy, a combination of pemetrexed and cisplatin, modestly extends survival, with the addition of bevacizumab offering a further 2-month benefit. Radiation therapy, while not effective as a primary treatment due to the extensive field required, plays a role in multimodal approaches or as localized therapy. The diversity in patient outcomes and responses to treatments indicates significant tumor heterogeneity in MPM, underscoring the need for therapies that consider this variability and target the molecular specifics of individual MPM cases. Tumor heterogeneity is observed at both intertumor and intratumor levels, encompassing tumor cells and their microenvironments. Molecular profiling studies have further highlighted this heterogeneity, complicating the identification of a unique biomarker for MPM.
2024-01-04 | GSE247203 | GEO
Project description:ObjectiveTo investigate the mechanism by which heterogeneity in breast cancer developed and acted in single-cell transcriptomes.MethodsThe composition of breast cancer based on the single-cell transcriptomes of 54,055 high-quality cells from clinical specimens of 4 malignant and 4 non-malignant patients were investigated.ResultsWe identified six common expression programs and six subtype-specific expression programs form malignant epithelial cells. The expression program of malignant epithelial cells exhibited activated EMT (Epithelial Mesenchymal Transition) in TME, which might indicate EMT intervention have a general therapeutic effect on various subtypes. Gene set enrichment analysis (GSEA) based on the top 50 highly NMF (non-negative matrix factorization) score genes in each program depicted the distinct function of each program in breast cancer progression. Moreover, we revealed the profound cellular heterogeneity of myeloid cell lineages in breast cancer. In macrophages, two mainly tumor associated macrophages (TAMs), TAM1 and TAM2, were also detected and the highly variable genes in TAM2 were strongly enriched in IFN-α and IFN-γ. The changes of lipid metabolism pathways in macrophages are closely related to the microenvironment of breast cancer.ConclusionWe constructed a comprehensive single-cell transcriptome atlas of 54,055 cells from 4 malignant and 4 nonmalignant patients, providing insights into the mechanisms underlying breast cancer progression and the development of potential therapeutic strategies in breast cancer.
| S-EPMC11671554 | biostudies-literature
Project description:Tumor heterogeneity is a major obstacle to the success of cancer treatment. An accurate understanding and recognition of tumor heterogeneity is critical in the clinical management of cancer patients. Here, we utilized single-cell RNA sequencing (scRNA-seq) to uncover the intra- and intertumoral heterogeneity of liver metastases from a patient with metastatic uveal melanoma. The two metastases analyzed were largely infiltrated by noncancerous cells with significant variability in the proportion of different cell types. Analysis of copy-number variations (CNVs) showed gain of 8q and loss of 6q in both tumors, but loss of Chromosome 3 was only detected in one of the tumors. Single-nucleotide polymorphism (SNP) array revealed a uniparental isodisomy 3 in the tumor with two copies of Chromosome 3, indicating a regain of Chromosome 3 during the development of the metastatic disease. In addition, both tumors harbored subclones with additional CNVs. Pathway enrichment analysis of differentially expressed genes revealed that cancer cells in the metastasis with isodisomy 3 showed up-regulation in epithelial-mesenchymal transition and myogenesis related genes. In contrast, up-regulation in interferon signaling was observed in the metastasis with monosomy 3 and increased T-cell infiltrate. This study highlights the complexity and heterogeneity of different metastases within an individual case of uveal melanoma.
| S-EPMC8559622 | biostudies-literature
Project description:This study profiled global single cell-spatial-bulk transcriptome landscapes of hepatocellular carcinoma (HCC) ecosystem from six HCC cases and a non-carcinoma liver control donor. We discovered that intratumoral heterogeneity mainly derived from HCC cells diversity and pervaded the genome-transcriptome-proteome-metabolome network. HCC cells are the core driving force of taming tumor-associated macrophages (TAMs) with pro-tumorigenic phenotypes for favor its dominant growth. Remarkably, M1-types TAMs had been characterized by disturbance of metabolism, poor antigen-presentation and immune-killing abilities. Besides, we found simultaneous cirrhotic and HCC lesions in an individual patient shared common origin and displayed parallel clone evolution via driving disparate immune reprograms for better environmental adaptation. Moreover, endothelial cells exhibited phenotypically conserved but executed differential functions in a space-dependent manner. Further, the spatiotemporal traits of rapid recurrence niche genes were identified and validated by immunohistochemistry. Our data unravels the great significance of HCC cells in shaping vibrant tumor ecosystems corresponding to clinical scenarios.
| S-EPMC11568154 | biostudies-literature
Project description:The heterogeneity of Major Depressive Disorder (MDD) has been increasingly recognized, challenging traditional symptom-based diagnostics and the development of mechanism-targeted therapies. This study aims to identify neuroimaging-based MDD subtypes and dissect their predominant biological characteristics using multi-omics data. A total of 807 participants were included in this study, comprising 327 individuals with MDD and 480 healthy controls (HC). The amplitude of low-frequency fluctuations (ALFF), a functional neuroimaging feature, was extracted for each participant and used to identify MDD subtypes through machine learning clustering. Multi-omics data, including profiles of genetic, epigenetics, metabolomics, and pro-inflammatory cytokines, were obtained. Comparative analyses of multi-omics data were conducted between each MDD subtype and HC to explore the molecular underpinnings involved in each subtype. We identified three neuroimaging-based MDD subtypes, each characterized by unique ALFF pattern alterations compared to HC. Multi-omics analysis showed a strong genetic predisposition for Subtype 1, primarily enriched in neuronal development and synaptic regulation pathways. This subtype also exhibited the most severe depressive symptoms and cognitive decline compared to the other subtypes. Subtype 2 is characterized by immuno-inflammation dysregulation, supported by elevated IL-1 beta levels, altered epigenetic inflammatory measures, and differential metabolites correlated with IL-1 beta levels. No significant biological markers were identified for Subtype 3. Our results identify neuroimaging-based MDD subtypes and delineate the distinct biological features of each subtype. This provides a proof of concept for mechanism-targeted therapy in MDD, highlighting the importance of personalized treatment approaches based on neurobiological and molecular profiles.
| S-EPMC11876359 | biostudies-literature
Project description:Background: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC. Methods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser (GDC TCGA Adrenocortical Carcinoma cohort). A highly correlated multi-omics signature discriminating groups of samples was identified with the data integration analysis for biomarker discovery using latent components (DIABLO) method. Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). The discriminative power of the multi-omics signature and their regulators was delineated by training a random forest classifier using 55 samples, by employing a 10-fold cross validation with five iterations. The prognostic value of the identified biomarkers was further assessed on an external ACC dataset obtained from GEO (GSE49280) using the Kaplan-Meier estimator method. An optimal prognostic signature was finally derived using the stepwise Akaike Information Criterion (AIC) that allowed categorization of samples into high and low-risk groups. Results: A multi-omics signature including genes, micro RNA's and methylation sites was generated. Systems biology tools identified additional genes regulating the features included in the multi-omics signature. RNA-seq, miRNA-seq and DNA methylation sets of features revealed a high power to classify patients from stages I-II and stages III-IV, outperforming previously identified prognostic biomarkers. Using an independent dataset, associations of the genes included in the signature with Overall Survival (OS) data demonstrated that patients with differential expression levels of 8 genes and 4 micro RNA's showed a statistically significant decrease in OS. We also found an independent prognostic signature for ACC with potential use in clinical practice, combining 9-gene/micro RNA features, that successfully predicted high-risk ACC cancer patients. Conclusion: Machine learning and integrative analysis of multi-omics data, in combination with Clarivate CBDD systems biology tools, identified a set of biomarkers with high prognostic value for ACC disease. Multi-omics data is a promising resource for the identification of drivers and new prognostic biomarkers in rare diseases that could be used in clinical practice.
| S-EPMC10658191 | biostudies-literature