Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Evidence of drug-response heterogeneity rapidly generated from a single cancer cell.

ABSTRACT: One cancer cell line is believed to be composed of numerous clones with different drug sensitivity. We sought to investigate the difference of drug-response pattern in clones from a cell line or from a single cell. We showed that 22 clones derived from 4T1 cells were drastically different from each other with respect to drug-response pattern against 11 anticancer drugs and expression profile of 19 genes associated with drug resistance or sensitivity. Similar results were obtained using daughter clones derived from a single 4T1 cell. Each daughter clone showed distinct drug-response pattern and gene expression profile. Similar results were also obtained using Bcap37 cells. We conclude that a single cancer cell can rapidly produce a population of cells with high heterogeneity of drug response and the acquisition of drug-response heterogeneity is random.

SUBMITTER: Wang R

PROVIDER: S-EPMC5522224 | biostudies-other | 2017 Jun

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Publications

Evidence of drug-response heterogeneity rapidly generated from a single cancer cell.

Wang Rong R Jin Chengmeng C Hu Xun X

Oncotarget 20170601 25

One cancer cell line is believed to be composed of numerous clones with different drug sensitivity. We sought to investigate the difference of drug-response pattern in clones from a cell line or from a single cell. We showed that 22 clones derived from 4T1 cells were drastically different from each other with respect to drug-response pattern against 11 anticancer drugs and expression profile of 19 genes associated with drug resistance or sensitivity. Similar results were obtained using daughter ...[more]

PMID: 28467802

Similar Datasets

Single-Cell Transcriptomics Reveals Heterogeneity and Drug Response of Human Colorectal Cancer Organoids.

Project description:Organoids are three-dimensional cell cultures that mimic organ functions and structures. The organoid model has been developed as a versatile in vitro platform for stem cell biology and diseases modeling. Tumor organoids are shown to share ~ 90% of genetic mutations with biopsies from same patients. However, it's not clear whether tumor organoids recapitulate the cellular heterogeneity observed in patient tumors. Here, we used single-cell RNA-Seq to investigate the transcriptomics of tumor organoids derived from human colorectal tumors, and applied machine learning methods to unbiasedly cluster subtypes in tumor organoids. Computational analysis reveals cancer heterogeneity sustained in tumor organoids, and the subtypes in organoids displayed high diversity. Furthermore, we treated the tumor organoids with a first-line cancer drug, Oxaliplatin, and investigated drug response in single-cell scale. Diversity of tumor cell populations in organoids were significantly perturbed by drug treatment. Single-cell analysis detected the depletion of chemosensitive subgroups and emergence of new drug tolerant subgroups after drug treatment. Our study suggests that the organoid model is capable of recapitulating clinical heterogeneity and its evolution in response to chemotherapy.

| S-EPMC6317967 | biostudies-literature

A single-cell analysis of breast cancer cell lines to study tumour heterogeneity and drug response.

Project description:Cancer cells within a tumour have heterogeneous phenotypes and exhibit dynamic plasticity. How to evaluate such heterogeneity and its impact on outcome and drug response is still unclear. Here, we transcriptionally profile 35,276 individual cells from 32 breast cancer cell lines to yield a single cell atlas. We find high degree of heterogeneity in the expression of biomarkers. We then train a deconvolution algorithm on the atlas to determine cell line composition from bulk gene expression profiles of tumour biopsies, thus enabling cell line-based patient stratification. Finally, we link results from large-scale in vitro drug screening in cell lines to the single cell data to computationally predict drug responses starting from single-cell profiles. We find that transcriptional heterogeneity enables cells with differential drug sensitivity to co-exist in the same population. Our work provides a framework to determine tumour heterogeneity in terms of cell line composition and drug response.

| S-EPMC8971486 | biostudies-literature

Drug-induced death signaling strategy rapidly predicts cancer response to chemotherapy.

Project description:There is a lack of effective predictive biomarkers to precisely assign optimal therapy to cancer patients. While most efforts are directed at inferring drug response phenotype based on genotype, there is very focused and useful phenotypic information to be gained from directly perturbing the patient's living cancer cell with the drug(s) in question. To satisfy this unmet need, we developed the Dynamic BH3 Profiling technique to measure early changes in net pro-apoptotic signaling at the mitochondrion ("priming") induced by chemotherapeutic agents in cancer cells, not requiring prolonged ex vivo culture. We find in cell line and clinical experiments that early drug-induced death signaling measured by Dynamic BH3 Profiling predicts chemotherapy response across many cancer types and many agents, including combinations of chemotherapies. We propose that Dynamic BH3 Profiling can be used as a broadly applicable predictive biomarker to predict cytotoxic response of cancers to chemotherapeutics in vivo.

| S-EPMC4391197 | biostudies-literature

Anti-Cancer Drug Sensitivity Assay with Quantitative Heterogeneity Testing Using Single-Cell Raman Spectroscopy.

Project description:A novel anti-cancer drug sensitivity testing (DST) approach was developed based on in vitro single-cell Raman spectrum intensity (RSI). Generally, the intensity of Raman spectra (RS) for a single living cell treated with drugs positively relates to the sensitivity of the cells to the drugs. In this study, five cancer cell lines (BGC 823, SGC 7901, MGC 803, AGS, and NCI-N87) were exposed to three cytotoxic compounds or to combinations of these compounds, and then they were evaluated for their responses with RSI. The results of RSI were consistent with conventional DST methods. The parametric correlation coefficient for the RSI and Methylthiazolyl tetrazolium assay (MTT) was 0.8558 ± 0.0850, and the coefficient of determination was calculated as R² = 0.9529 ± 0.0355 for fitting the dose⁻response curve. Moreover, RSI data for NCI-N87 cells treated by trastuzumab, everolimus (cytostatic), and these drugs in combination demonstrated that the RSI method was suitable for testing the sensitivity of cytostatic drugs. Furthermore, a heterogeneity coefficient H was introduced for quantitative characterization of the heterogeneity of cancer cells treated by drugs. The largest possible variance between RSs of cancer cells were quantitatively obtained using eigenvalues of principal component analysis (PCA). The ratio of H between resistant cells and sensitive cells was greater than 1.5, which suggested the H-value was effective to describe the heterogeneity of cancer cells. Briefly, the RSI method might be a powerful tool for simple and rapid detection of the sensitivity of tumor cells to anti-cancer drugs and the heterogeneity of their responses to these drugs.

| S-EPMC6278387 | biostudies-literature

Intratumoural heterogeneity generated by Notch signalling promotes small-cell lung cancer.

Project description:The Notch signalling pathway mediates cell fate decisions and is tumour suppressive or oncogenic depending on the context. During lung development, Notch pathway activation inhibits the differentiation of precursor cells to a neuroendocrine fate. In small-cell lung cancer, an aggressive neuroendocrine lung cancer, loss-of-function mutations in NOTCH genes and the inhibitory effects of ectopic Notch activation indicate that Notch signalling is tumour suppressive. Here we show that Notch signalling can be both tumour suppressive and pro-tumorigenic in small-cell lung cancer. Endogenous activation of the Notch pathway results in a neuroendocrine to non-neuroendocrine fate switch in 10-50% of tumour cells in a mouse model of small-cell lung cancer and in human tumours. This switch is mediated in part by Rest (also known as Nrsf), a transcriptional repressor that inhibits neuroendocrine gene expression. Non-neuroendocrine Notch-active small-cell lung cancer cells are slow growing, consistent with a tumour-suppressive role for Notch, but these cells are also relatively chemoresistant and provide trophic support to neuroendocrine tumour cells, consistent with a pro-tumorigenic role. Importantly, Notch blockade in combination with chemotherapy suppresses tumour growth and delays relapse in pre-clinical models. Thus, small-cell lung cancer tumours generate their own microenvironment via activation of Notch signalling in a subset of tumour cells, and the presence of these cells may serve as a biomarker for the use of Notch pathway inhibitors in combination with chemotherapy in select patients with small-cell lung cancer.

| S-EPMC5776014 | biostudies-literature

Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells.

Project description:BackgroundIntra-tumoral genetic and functional heterogeneity correlates with cancer clinical prognoses. However, the mechanisms by which intra-tumoral heterogeneity impacts therapeutic outcome remain poorly understood. RNA sequencing (RNA-seq) of single tumor cells can provide comprehensive information about gene expression and single-nucleotide variations in individual tumor cells, which may allow for the translation of heterogeneous tumor cell functional responses into customized anti-cancer treatments.ResultsWe isolated 34 patient-derived xenograft (PDX) tumor cells from a lung adenocarcinoma patient tumor xenograft. Individual tumor cells were subjected to single cell RNA-seq for gene expression profiling and expressed mutation profiling. Fifty tumor-specific single-nucleotide variations, including KRAS(G12D), were observed to be heterogeneous in individual PDX cells. Semi-supervised clustering, based on KRAS(G12D) mutant expression and a risk score representing expression of 69 lung adenocarcinoma-prognostic genes, classified PDX cells into four groups. PDX cells that survived in vitro anti-cancer drug treatment displayed transcriptome signatures consistent with the group characterized by KRAS(G12D) and low risk score.ConclusionsSingle-cell RNA-seq on viable PDX cells identified a candidate tumor cell subgroup associated with anti-cancer drug resistance. Thus, single-cell RNA-seq is a powerful approach for identifying unique tumor cell-specific gene expression profiles which could facilitate the development of optimized clinical anti-cancer strategies.

| S-EPMC4506401 | biostudies-literature

Single-cell analyses of transcriptional heterogeneity during drug tolerance transition in cancer cells by RNA sequencing.

Project description:The acute cellular response to stress generates a subpopulation of reversibly stress-tolerant cells under conditions that are lethal to the majority of the population. Stress tolerance is attributed to heterogeneity of gene expression within the population to ensure survival of a minority. We performed whole transcriptome sequencing analyses of metastatic human breast cancer cells subjected to the chemotherapeutic agent paclitaxel at the single-cell and population levels. Here we show that specific transcriptional programs are enacted within untreated, stressed, and drug-tolerant cell groups while generating high heterogeneity between single cells within and between groups. We further demonstrate that drug-tolerant cells contain specific RNA variants residing in genes involved in microtubule organization and stabilization, as well as cell adhesion and cell surface signaling. In addition, the gene expression profile of drug-tolerant cells is similar to that of untreated cells within a few doublings. Thus, single-cell analyses reveal the dynamics of the stress response in terms of cell-specific RNA variants driving heterogeneity, the survival of a minority population through generation of specific RNA variants, and the efficient reconversion of stress-tolerant cells back to normalcy.

| S-EPMC4226127 | biostudies-literature

Massively parallel quantification of phenotypic heterogeneity in single-cell drug responses.

Project description:Single-cell analysis tools have made substantial advances in characterizing genomic heterogeneity; however, tools for measuring phenotypic heterogeneity have lagged due to the increased difficulty of handling live biology. Here, we report a single-cell phenotyping tool capable of measuring image-based clonal properties at scales approaching 100,000 clones per experiment. These advances are achieved by exploiting a previously unidentified flow regime in ladder microfluidic networks that, under appropriate conditions, yield a mathematically perfect cell trap. Machine learning and computer vision tools are used to control the imaging hardware and analyze the cellular phenotypic parameters within these images. Using this platform, we quantified the responses of tens of thousands of single cell–derived acute myeloid leukemia (AML) clones to targeted therapy, identifying rare resistance and morphological phenotypes at frequencies down to 0.05%. This approach can be extended to higher-level cellular architectures such as cell pairs and organoids and on-chip live-cell fluorescence assays.

| S-EPMC8448449 | biostudies-literature

Single-cell epigenomic variability reveals functional cancer heterogeneity.

Project description:BackgroundCell-to-cell heterogeneity is a major driver of cancer evolution, progression, and emergence of drug resistance. Epigenomic variation at the single-cell level can rapidly create cancer heterogeneity but is difficult to detect and assess functionally.ResultsWe develop a strategy to bridge the gap between measurement and function in single-cell epigenomics. Using single-cell chromatin accessibility and RNA-seq data in K562 leukemic cells, we identify the cell surface marker CD24 as co-varying with chromatin accessibility changes linked to GATA transcription factors in single cells. Fluorescence-activated cell sorting of CD24 high versus low cells prospectively isolated GATA1 and GATA2 high versus low cells. GATA high versus low cells express differential gene regulatory networks, differential sensitivity to the drug imatinib mesylate, and differential self-renewal capacity. Lineage tracing experiments show that GATA/CD24hi cells have the capability to rapidly reconstitute the heterogeneity within the entire starting population, suggesting that GATA expression levels drive a phenotypically relevant source of epigenomic plasticity.ConclusionSingle-cell chromatin accessibility can guide prospective characterization of cancer heterogeneity. Epigenomic subpopulations in cancer impact drug sensitivity and the clonal dynamics of cancer evolution.

| S-EPMC5259890 | biostudies-literature

SpaRx: elucidate single-cell spatial heterogeneity of drug responses for personalized treatment.

Project description:Spatial cellular authors heterogeneity contributes to differential drug responses in a tumor lesion and potential therapeutic resistance. Recent emerging spatial technologies such as CosMx, MERSCOPE and Xenium delineate the spatial gene expression patterns at the single cell resolution. This provides unprecedented opportunities to identify spatially localized cellular resistance and to optimize the treatment for individual patients. In this work, we present a graph-based domain adaptation model, SpaRx, to reveal the heterogeneity of spatial cellular response to drugs. SpaRx transfers the knowledge from pharmacogenomics profiles to single-cell spatial transcriptomics data, through hybrid learning with dynamic adversarial adaption. Comprehensive benchmarking demonstrates the superior and robust performance of SpaRx at different dropout rates, noise levels and transcriptomics coverage. Further application of SpaRx to the state-of-the-art single-cell spatial transcriptomics data reveals that tumor cells in different locations of a tumor lesion present heterogenous sensitivity or resistance to drugs. Moreover, resistant tumor cells interact with themselves or the surrounding constituents to form an ecosystem for drug resistance. Collectively, SpaRx characterizes the spatial therapeutic variability, unveils the molecular mechanisms underpinning drug resistance and identifies personalized drug targets and effective drug combinations.

| S-EPMC10555713 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data