scholarly journals Single-cell epigenomics in cancer: charting a course to clinical impact

Epigenomics ◽  
2020 ◽  
Vol 12 (13) ◽  
pp. 1139-1151 ◽  
Author(s):  
Danielle R Bond ◽  
Kumar Uddipto ◽  
Anoop K Enjeti ◽  
Heather J Lee
Keyword(s):  
Single Cell ◽  
Patient Outcomes ◽  
Cancer Biology ◽  
Cell Types ◽  
Disease Relapse ◽  
Multiple Cancer ◽  
Heterogeneous Nature ◽  
Numerous Cell ◽  
Cancer Types ◽  
Exciting Possibility

Cancer is a disease of global epigenetic dysregulation. Mutations in epigenetic regulators are common events in multiple cancer types and epigenetic therapies are emerging as a treatment option in several malignancies. A major challenge for the clinical management of cancer is the heterogeneous nature of this disease. Cancers are composed of numerous cell types and evolve over time. This heterogeneity confounds decisions regarding treatment and promotes disease relapse. The emergence of single-cell epigenomic technologies has introduced the exciting possibility of linking genetic and transcriptional heterogeneity in the context of cancer biology. The next challenge is to leverage these tools for improved patient outcomes. Here we consider how single-cell epigenomic technologies may address the current challenges faced by cancer clinicians.

484 Bioturing browser: interactively explore public single cell sequencing data

2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A520-A520
Author(s):  
Son Pham ◽  
Tri Le ◽  
Tan Phan ◽  
Minh Pham ◽  
Huy Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cell ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Cell Types ◽  
Sequencing Data ◽  
Single Cell Sequencing ◽  
Data Formats ◽  
Cancer Types ◽  
Cell Data

BackgroundSingle-cell sequencing technology has opened an unprecedented ability to interrogate cancer. It reveals significant insights into the intratumoral heterogeneity, metastasis, therapeutic resistance, which facilitates target discovery and validation in cancer treatment. With rapid advancements in throughput and strategies, a particular immuno-oncology study can produce multi-omics profiles for several thousands of individual cells. This overflow of single-cell data poses formidable challenges, including standardizing data formats across studies, performing reanalysis for individual datasets and meta-analysis.MethodsN/AResultsWe present BioTuring Browser, an interactive platform for accessing and reanalyzing published single-cell omics data. The platform is currently hosting a curated database of more than 10 million cells from 247 projects, covering more than 120 immune cell types and subtypes, and 15 different cancer types. All data are processed and annotated with standardized labels of cell types, diseases, therapeutic responses, etc. to be instantly accessed and explored in a uniform visualization and analytics interface. Based on this massive curated database, BioTuring Browser supports searching similar expression profiles, querying a target across datasets and automatic cell type annotation. The platform supports single-cell RNA-seq, CITE-seq and TCR-seq data. BioTuring Browser is now available for download at www.bioturing.com.ConclusionsN/A

MicroRNAs: New non-invasive diagnostic biomarkers and therapeutic method for cancer treatment

2021 ◽  
Vol 3 (1) ◽  
pp. 1-12
Author(s):  
Tamadir Aledani ◽  
Kassim Abdulkareem
Keyword(s):  
Early Diagnosis ◽  
Low Cost ◽  
Diagnosis And Treatment ◽  
Messenger Rnas ◽  
Multiple Cancer ◽  
Diagnostic Biomarkers ◽  
Non Invasive ◽  
Cancer Types ◽  
Tumor Suppressive Mirnas ◽  
Exciting Possibility

Background: Cancer is a global health problem and the main cause of mortality. Most cancerassociated cases of mortality are the consequences of lack of effective treatment and biomarkers for early diagnosis. New hopes for the improvement of the early diagnosis and treatment of cancer synchronize with the emergence of microRNAs (miRNAs). MicroRNAs are small, noncoding, single-stranded RNAs, the length of which is approximately 18–25 nucleotides and which bind to 3’ untranslated region (3’UTR) of the target messenger RNAs (mRNAs), leading to mRNA degradation or translational inhibition; thereby regulating gene expression posttranscriptionally. Aim: Using microRNAs as promising and potential biomarkers for diagnosis and therapeutic targets. Methods: The microRNA expression changes in peripheral blood and can be assayed using non-invasive, low-cost, precise, and rapid tools. Results: It is noteworthy that miRNAs participate in multiple cancer-related biological processes, including proliferation, apoptosis, angiogenesis, drug resistance, invasion, and metastasis. Interestingly, the identified cancer-associated miRNAs, including over-expressed oncogenic miRNAs (oncomiRs) or underexpressed tumor-suppressive miRNAs, are diverse and specific for different tissues and cancer types. Conclusion: The genetic testing of microRNAs opens up the exciting possibility of early diagnosis and treatment before the onset of metastasis. Keywords: microRNAs, gene silencing, circulating biomarkers, cancer diagnosis, anticancer therapy, miRNAs detection.

scholarly journals TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment

2020 ◽  
Vol 49 (D1) ◽  
pp. D1420-D1430
Author(s):  
Dongqing Sun ◽  
Jin Wang ◽  
Ya Han ◽  
Xin Dong ◽  
Jun Ge ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
Large Scale ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Multiple Cancer ◽  
Web Resource ◽  
Cancer Types

Abstract Cancer immunotherapy targeting co-inhibitory pathways by checkpoint blockade shows remarkable efficacy in a variety of cancer types. However, only a minority of patients respond to treatment due to the stochastic heterogeneity of tumor microenvironment (TME). Recent advances in single-cell RNA-seq technologies enabled comprehensive characterization of the immune system heterogeneity in tumors but posed computational challenges on integrating and utilizing the massive published datasets to inform immunotherapy. Here, we present Tumor Immune Single Cell Hub (TISCH, http://tisch.comp-genomics.org), a large-scale curated database that integrates single-cell transcriptomic profiles of nearly 2 million cells from 76 high-quality tumor datasets across 27 cancer types. All the data were uniformly processed with a standardized workflow, including quality control, batch effect removal, clustering, cell-type annotation, malignant cell classification, differential expression analysis and functional enrichment analysis. TISCH provides interactive gene expression visualization across multiple datasets at the single-cell level or cluster level, allowing systematic comparison between different cell-types, patients, tissue origins, treatment and response groups, and even different cancer-types. In summary, TISCH provides a user-friendly interface for systematically visualizing, searching and downloading gene expression atlas in the TME from multiple cancer types, enabling fast, flexible and comprehensive exploration of the TME.

scholarly journals A Pan-cancer Blueprint of the Heterogeneous Tumour Microenvironment Revealed by Single-Cell Profiling

2020 ◽  
Author(s):  
Junbin Qian ◽  
Siel Olbrecht ◽  
Bram Boeckx ◽  
Hanne Vos ◽  
Damya Laoui ◽  
...  
Keyword(s):  
Single Cell ◽  
Stromal Cell ◽  
Single Cells ◽  
Cell Types ◽  
Tumour Microenvironment ◽  
Cell Phenotype ◽  
Marker Genes ◽  
Stromal Compartment ◽  
Cancer Types ◽  
Pan Cancer

AbstractThe stromal compartment of the tumour microenvironment consists of a heterogeneous set of tissue-resident and tumour-infiltrating cells, which are profoundly moulded by cancer cells. An outstanding question is to what extent this heterogeneity is similar between cancers affecting different organs. Here, we profile 233,591 single cells from patients with lung, colorectal, ovary and breast cancer (n=36) and construct a pan-cancer blueprint of stromal cell heterogeneity using different single-cell RNA and protein-based technologies. We identify 68 stromal cell populations, of which 46 are shared between cancer types and 22 are unique. We also characterise each population phenotypically by highlighting its marker genes, transcription factors, metabolic activities and tissue-specific expression differences. Resident cell types are characterised by substantial tissue specificity, while tumour-infiltrating cell types are largely shared across cancer types. Finally, by applying the blueprint to melanoma tumours treated with checkpoint immunotherapy and identifying a naïve CD4+ T-cell phenotype predictive of response to checkpoint immunotherapy, we illustrate how it can serve as a guide to interpret scRNA-seq data. In conclusion, by providing a comprehensive blueprint through an interactive web server, we generate a first panoramic view on the shared complexity of stromal cells in different cancers.

Functional cell profiling (FCP) of ~100,000 CTCs from multiple cancer types identifies morphologically distinguishable CTC subtypes within and between cancer types.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e14553-e14553
Author(s):  
Gordon Vansant ◽  
Adam Jendrisak ◽  
Ramsay Sutton ◽  
Sarah Orr ◽  
David Lu ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Single Cell ◽  
Digital Pathology ◽  
Disease Patient ◽  
Therapy Response ◽  
K Nearest Neighbor ◽  
Training Set ◽  
Multiple Cancer ◽  
Cluster Assignment ◽  
Cancer Types

e14553 Background: Different cancers subtypes can often be effectively treated with similar Rx classes (i.e. platinum or taxane Rx). Yet, within a disease patient therapy benefit can be variable. The origins of precision medicine derive from pathologic sub-stratification to guide therapy (e.g. SCLC vs. NSCLC). Using the Epic Sciences platform, we performed FPC analysis of ~100,000 single CTCs from multiple indications and sought to utilize high resolution digital pathology and machine learning to index metastatic cancers for the purpose of improving our understanding of therapy response and precision medicine. Methods: 92,300 CTCs underwent FCP analysis (single cell digital pathology features of cellular and sub-cellular morphometrics) were collected from prostate (1641 pts, 70,747 CTCs), breast (268 pts, 8,718 CTCs), NSCLC ( 110 pts, 1884 CTCs), SCLC ( 141 pts, 8,872 CTCs) and bladder (65 pts, 2079 CTCs) cancer pts. After pre-processing the raw data, a training set was balanced by sampling the same number of CTCs from each indication. K-means clustering was applied on the training set and optimized number of clusters were determined by using the elbow approach. After generating the clusters on the training set, the cluster centers were extracted from k-means, and used to train a k-Nearest Neighbor (k-NN) classifier to predict the cluster assignment for the remaining CTCs (test set). Results: The optimized # of clusters was 9. The % and characteristics of CTCs in each indication are listed below. BCa CTCs were more enriched in cluster c1, which had higher CK expression, while SCLC and some of mCRPC shared the small cell features (c5). Conclusions: Heterogeneous CTC phenotypic subtypes were observed across multiple indications. Each indication harbored subtype heterogeneity and shared clusters with other disease subtypes. Patient cluster subtype analysis to prognosis and therapy benefit are on-going. Analysis of linking of CTC subtypes genotypes (by single cell sequencing) and to patient survival on multiple indications is ongoing.[Table: see text]

scholarly journals Perturbation of Interaction Networks for Application to Cancer Therapy

2007 ◽  
Vol 5 ◽  
pp. 117693510700500 ◽  
Author(s):  
Adrian P. Quayle ◽  
Asim S. Siddiqui ◽  
Steven J. M. Jones
Keyword(s):  
Protein Interaction ◽  
Experimental Testing ◽  
Cell Types ◽  
Drug Combinations ◽  
Interaction Networks ◽  
Data Sources ◽  
Model Organisms ◽  
Multiple Cancer ◽  
Protein Protein Interaction ◽  
Cancer Types

We present a computational approach for studying the effect of potential drug combinations on the protein networks associated with tumor cells. The majority of therapeutics are designed to target single proteins, yet most diseased states are characterized by a combination of many interacting genes and proteins. Using the topology of protein-protein interaction networks, our methods can explicitly model the possible synergistic effect of targeting multiple proteins using drug combinations in different cancer types. The methodology can be conceptually split into two distinct stages. Firstly, we integrate protein interaction and gene expression data to develop network representations of different tissue types and cancer types. Secondly, we model network perturbations to search for target combinations which cause significant damage to a relevant cancer network but only minimal damage to an equivalent normal network. We have developed sets of predicted target and drug combinations for multiple cancer types, which are validated using known cancer and drug associations, and are currently in experimental testing for prostate cancer. Our methods also revealed significant bias in curated interaction data sources towards targets with associations compared with high-throughput data sources from model organisms. The approach developed can potentially be applied to many other diseased cell types.

scholarly journals Aging-associated alterations in the mammary gland revealed by single-cell RNA sequencing

2019 ◽  
Author(s):  
Carman Man-Chung Li ◽  
Hana Shapiro ◽  
Christina Tsiobikas ◽  
Laura Selfors ◽  
Huidong Chen ◽  
...  
Keyword(s):  
Mammary Gland ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Patterns ◽  
Cell Types ◽  
Single Cell Rna Sequencing ◽  
Numerous Cell ◽  
Luminal Cells ◽  
Increased Susceptibility ◽  
Mammary Tissues

AbstractAging of the mammary gland is closely associated with increased susceptibility to diseases such as cancer, but there have been limited systematic studies of aging-induced alterations within this organ. We performed high-throughput single-cell RNA-sequencing (scRNA-seq) profiling of mammary tissues from young and old nulliparous mice, including both epithelial and stromal cell types. Our analysis identified altered proportions and distinct gene expression patterns in numerous cell populations as a consequence of the aging process, independent of parity and lactation. In addition, we detected a subset of luminal cells that express both hormone-sensing and alveolar markers and decrease in relative abundance with age. These data provide a high-resolution landscape of aging mammary tissues, with potential implications for normal tissue functions and cancer predisposition.

scholarly journals Single-Cell RNA Sequencing Reveals Multiple Pathways and the Tumor Microenvironment Could Lead to Chemotherapy Resistance in Cervical Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Meijia Gu ◽  
Ti He ◽  
Yuncong Yuan ◽  
Suling Duan ◽  
Xin Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cancer Biology ◽  
Chemotherapy Resistance ◽  
Cell Types ◽  
Functional Enrichment ◽  
Single Cell Rna Sequencing

BackgroundCervical cancer is one of the most common gynecological cancers worldwide. The tumor microenvironment significantly influences the therapeutic response and clinical outcome. However, the complex tumor microenvironment of cervical cancer and the molecular mechanisms underlying chemotherapy resistance are not well studied. This study aimed to comprehensively analyze cells from pretreated and chemoresistant cervical cancer tissues to generate a molecular census of cell populations.MethodsBiopsy tissues collected from patients with cervical squamous cell carcinoma, cervical adenocarcinoma, and chronic cervicitis were subjected to single-cell RNA sequencing using the 10× Genomics platform. Unsupervised clustering analysis of cells was performed to identify the main cell types, and important cell clusters were reclustered into subpopulations. Gene expression profiles and functional enrichment analysis were used to explore gene expression and functional differences between cell subpopulations in cervicitis and cervical cancer samples and between chemoresistant and chemosensitive samples.ResultsA total of 24,371 cells were clustered into nine separate cell types, including immune and non-immune cells. Differentially expressed genes between chemoresistant and chemosensitive patients enriched in the phosphoinositide 3-kinase (PI3K)/AKT pathway were involved in tumor development, progression, and apoptosis, which might lead to chemotherapy resistance.ConclusionsOur study provides a comprehensive overview of the cancer microenvironment landscape and characterizes its gene expression and functional difference in chemotherapy resistance. Consequently, our study deepens the insights into cervical cancer biology through the identification of gene markers for diagnosis, prognosis, and therapy.

Tumor-microbiome links subtype, cellular programs, and immunity in pancreatic cancer

2021 ◽  
Author(s):  
Martin Blaser ◽  
Bassel Ghaddar ◽  
Antara Biswas ◽  
Chris Harris ◽  
M. Bishr Omary ◽  
...  
Keyword(s):  
Tumor Infiltrating Lymphocytes ◽  
Cell Types ◽  
Sequencing Data ◽  
Multiple Cancer ◽  
Clinical Prognosis ◽  
Tumor Subtypes ◽  
Cancer Types ◽  
Specific Tissue ◽  
Bacteria And Fungi ◽  
Infiltrating Lymphocytes

Abstract Microorganisms are detected in multiple cancer types, including in putatively sterile organs, but it is unclear whether this relates to specific tissue contexts and influences oncogenesis or anti-tumor responses in humans. We developed SAHMI, a framework to analyze host-microbiome interactions using single-cell sequencing data. Interrogating pancreatic ductal adenocarcinomas (PDA), we identified an altered and diverse tumor microbiome that includes known and novel tumor-associated bacteria and fungi. Specific somatic cell-types were enriched with particular microbes whose abundances correlated with select host gene expression and cancer hallmark activities. Nearly all tumor-infiltrating lymphocytes had infection-reactive transcriptional profiles. Pseudotime analysis provided evidence for tumor-microbial co-evolution and identified three tumor subtypes with distinct microbial, molecular, and clinical characteristics. Finally, using multiple independent datasets, a signature of increased intra-tumoral microbial diversity predicted clinical prognosis. Collectively, tumor-microbiome cross-talk appears to modulate tumorigenesis with implications for clinical management.

scholarly journals Virtual methylome dissection facilitated by single-cell analyses

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Liduo Yin ◽  
Yanting Luo ◽  
Xiguang Xu ◽  
Shiyu Wen ◽  
Xiaowei Wu ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Nonnegative Matrix ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Cell Type ◽  
Mixed Cell ◽  
Numerous Cell ◽  
Cell Type Specific ◽  
Methylation Patterns

Abstract Background Numerous cell types can be identified within plant tissues and animal organs, and the epigenetic modifications underlying such enormous cellular heterogeneity are just beginning to be understood. It remains a challenge to infer cellular composition using DNA methylomes generated for mixed cell populations. Here, we propose a semi-reference-free procedure to perform virtual methylome dissection using the nonnegative matrix factorization (NMF) algorithm. Results In the pipeline that we implemented to predict cell-subtype percentages, putative cell-type-specific methylated (pCSM) loci were first determined according to their DNA methylation patterns in bulk methylomes and clustered into groups based on their correlations in methylation profiles. A representative set of pCSM loci was then chosen to decompose target methylomes into multiple latent DNA methylation components (LMCs). To test the performance of this pipeline, we made use of single-cell brain methylomes to create synthetic methylomes of known cell composition. Compared with highly variable CpG sites, pCSM loci achieved a higher prediction accuracy in the virtual methylome dissection of synthetic methylomes. In addition, pCSM loci were shown to be good predictors of the cell type of the sorted brain cells. The software package developed in this study is available in the GitHub repository (https://github.com/Gavin-Yinld). Conclusions We anticipate that the pipeline implemented in this study will be an innovative and valuable tool for the decoding of cellular heterogeneity.

Sign in / Sign up

Export Citation Format

Share Document