Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR7174600_1.fastq.gz | Fastqsanger.gz | |||
| SRR7174600_2.fastq.gz | Fastqsanger.gz | |||
| SRR7174610_1.fastq.gz | Fastqsanger.gz | |||
| SRR7174610_2.fastq.gz | Fastqsanger.gz | |||
| SRR7174601_1.fastq.gz | Fastqsanger.gz |
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Project description:The identification and characterisation of Circulating Tumour Cells (CTCs) is important to get insights into the biology of metastatic cancers, monitoring disease progression, and potential use in liquid biopsy-based personalised cancer treatment. The major limitation of CTC isolation is due to their heterogeneous nature, altered phenotype from primary tumour and availability in limited numbers. In the past years, several techniques have been developed to detect CTCs from peripheral blood, based on canonical markers. These methods are, however, prone to miss a larger set of CTCs due to variable or no expression of these markers. Furthermore, CTC enrichment processes are not free from White Blood Cell (WBC) contamination. Single cell RNA sequencing (scRNA-Seq) of CTCs provides a wealth of information about their tumors of origin as well as their fate. The first and most important roadblock encountered in CTC scRNA-Seq data analysis is confirmation of CTC capture. We present unCTC, an R software for unsupervised and semi-supervised characterisation of CTC transcriptomes, in contrast with WBCs. unCTC features various standard and novel computational/statistical modules for clustering, Copy Number Variation (CNV) inference, and marker based verification of the malignant phenotypes. Notably, we propose Deep Dictionary Learning using K-means clustering cost (DDLK) that robustly clusters scRNA-Seq profiles of CTCs and WBC contaminates to characterise heterogeneity among the concerned cell population. Interestingly, DDLK performs better as gene expression data is transformed into pathway enrichment profiles. To demonstrate the utility of unCTC, we produce scRNA-Seq profiles of breast CTCs enriched by the integrated ClearCell® FX/PolarisTM workflow that works on the principles of size selection and negative enrichment for CD45, a pan leukocyte marker.
2022-11-09 | GSE210651 | GEO
Project description:The identification and characterisation of Circulating Tumour Cells (CTCs) is important to get insights into the biology of metastatic cancers, monitoring disease progression, and potential use in liquid biopsy-based personalised cancer treatment. The major limitation of CTC isolation is due to their heterogeneous nature, altered phenotype from primary tumour and availability in limited numbers. In the past years, several techniques have been developed to detect CTCs from peripheral blood, based on canonical markers. These methods are, however, prone to miss a larger set of CTCs due to variable or no expression of these markers. Furthermore, CTC enrichment processes are not free from White Blood Cell (WBC) contamination. Single cell RNA sequencing (scRNA-Seq) of CTCs provides a wealth of information about their tumors of origin as well as their fate. The first and most important roadblock encountered in CTC scRNA-Seq data analysis is confirmation of CTC capture. We present unCTC, an R software for unsupervised and semi-supervised characterisation of CTC transcriptomes, in contrast with WBCs. unCTC features various standard and novel computational/statistical modules for clustering, Copy Number Variation (CNV) inference, and marker based verification of the malignant phenotypes. Notably, we propose Deep Dictionary Learning using K-means clustering cost (DDLK) that robustly clusters scRNA-Seq profiles of CTCs and WBC contaminates to characterise heterogeneity among the concerned cell population. Interestingly, DDLK performs better as gene expression data is transformed into pathway enrichment profiles. To demonstrate the utility of unCTC, we produce scRNA-Seq profiles of breast CTCs enriched by the integrated ClearCell® FX/PolarisTM workflow that works on the principles of size selection and negative enrichment for CD45, a pan leukocyte marker.
2024-12-31 | GSE181219 | GEO
Project description:The identification and characterisation of Circulating Tumour Cells (CTCs) is important to get insights into the biology of metastatic cancers, monitoring disease progression, and potential use in liquid biopsy-based personalised cancer treatment. The major limitation of CTC isolation is due to their heterogeneous nature, altered phenotype from primary tumour and availability in limited numbers. In the past years, several techniques have been developed to detect CTCs from peripheral blood, based on canonical markers. These methods are, however, prone to miss a larger set of CTCs due to variable or no expression of these markers. Furthermore, CTC enrichment processes are not free from White Blood Cell (WBC) contamination. Single cell RNA sequencing (scRNA-Seq) of CTCs provides a wealth of information about their tumors of origin as well as their fate. The first and most important roadblock encountered in CTC scRNA-Seq data analysis is confirmation of CTC capture. We present unCTC, an R software for unsupervised and semi-supervised characterisation of CTC transcriptomes, in contrast with WBCs. unCTC features various standard and novel computational/statistical modules for clustering, Copy Number Variation (CNV) inference, and marker based verification of the malignant phenotypes. Notably, we propose Deep Dictionary Learning using K-means clustering cost (DDLK) that robustly clusters scRNA-Seq profiles of CTCs and WBC contaminates to characterise heterogeneity among the concerned cell population. Interestingly, DDLK performs better as gene expression data is transformed into pathway enrichment profiles. To demonstrate the utility of unCTC, we produce scRNA-Seq profiles of breast CTCs enriched by the integrated ClearCell® FX/PolarisTM workflow that works on the principles of size selection and negative enrichment for CD45, a pan leukocyte marker.
2022-11-09 | GSE186288 | GEO
Project description:Circulating tumor cells (CTCs) and disseminated tumour cells with mesenchymal traits are difficult to detect by epithelial marker proteins. Particularly, triple negative breast cancers (TNBC) that are prone to therapy failure release a subpopulation of circulating tumour cells (CTCs) with mesenchymal traits. To provide tools that support their detection and analysis, the cell line BC-M1 established from disseminated tumour cells in the bone marrow of a breast cancer patient and a bone metastasis subline of MDA-MB-231 were analysed. Mass spectrometry analysis revealed high levels of CUB domain-containing protein 1 (CDCP1) in BC-M1. CDCP1 was found in other carcinoma cell lines (MDA-MB-231, MDA-MB-468) and other DTC cell lines (LC-M1, PC-E1) as well. Peripheral blood mononuclear cells were virtually negative for CDCP1 by Western Blot and immunofluorescent staining. Presence of CDCP1 in CTCs was confirmed by CellSearch. Here, CDCP1 positive CTCs were detected in eight of 30 analysed breast cancer patients. For the isolation of CTCs from the blood of breast cancer patients, we established a sandwich magnetic-activated cell sorting (MACS). The extracellular domain of CDCP1 served for cell catching and the cytoplasmic domain of CDCP1 for immunofluorescent detection of CDCP1 in CTCs. We showed that the MACS approach is suitable for the isolation of EpCam/keratin negative breast cancer cells from the blood and isolated CDCP1 positive CTCs from breast cancer patients by MACS. Hence, our approach is particularly suited for the detection and isolation of CTCs from TNBC when low EpCam or keratin levels limit the application of conventional approaches.
2023-03-20 | PXD035203 | Pride
Project description:Metastasis is responsible for the vast majority of breast cancer related deaths. Metastatic breast cancer (MBC) is inherently different than primary breast cancer (BC), evolving under selection pressure at different organ sites or during systemic therapy. The current ASCO guidelines call for biopsy of a metastatic site to guide decision making for systemic therapy. Yet, biopsies of macro metastasis are oftentimes not feasible in the clinical setting. Circulating tumor cells (CTCs) have been shown to be prognostic in MBC, but their use as clinical biomarker beyond CTC enumeration has been limited. A better understanding of CTC-biology compared to metastasis may shed light on treatment opportunities and help advance the application of CTCs as liquid biopsies in clinical practice. The ANGLE Parsortix system is a microfluidics device that separates CTCs based on size and deformability, without the need for cell-surface marker selection. Our lab has previously demonstrated the feasibility of gene expression profiling of rare CTCs. Here, we evaluated whether whole transcriptome sequencing (RNA Seq) gene expression profiling of ANGLE Parsortix isolated CTCs may serve as a surrogate for biopsies of macro metastases. CTCs from 21 MBC patients were enumerated and captured from 10mL peripheral blood (PB) via the ANGLE Parsortix system. RNA Seq was performed on fresh metastatic tumor biopsies (mets), CTCs and peripheral blood from all patients. Biopsy sites included: skin (n=1), lung (n=1), pleural effusion (n=5), pericardial effusion (n=1), breast (n=3), lymph node (n=2), brain (n=4), liver (n=1), ascites (n=3), cerebrospinal fluid (n=2) and bone (n=1). 19/21 patients were included in subsequent data analysis. We present the whole transcriptomic landscape of CTCs with comparison to metastases and peripheral blood all acquired prior to treatment of Stage IV breast cancer. Multiple genes, including oncogenes, breast epithelial, mesenchymal genes and cancer stem cell genes were found with higher expression in CTCs versus metastases. When focusing on 66 known potentially clinically actionable genes in breast cancer, represented by 7 molecular signaling pathways, CTCs did not show significantly different patterns of expression versus mets compared to PB. RNA Seq of CTCs may be utilized to identify alterations in MBC patients that are potentially clinically actionable.
2022-05-01 | GSE113890 | GEO
Project description:We developed a method to isolate pure circulating tumor cells (CTC). RNA from such CTCs isolated from the peripheral blood of metastatic breats cnacer patients and gene expression was performed using cDNAmicroarray. we used cDNA array to compare gene expression of CTCs with normal epithelial and breast tumor samples CTCs vs. breast tumors
2014-12-18 | E-GEOD-45964 | biostudies-arrayexpress
Project description:Circulating tumor cells (CTCs) potential utility as a liquid biopsy is of great interest in breast cancer. The goal of this study is to use RNA-seq to show that we can capture CTCs using EpCAM based gating for most of the breast cancer subtypes and detection of breast cancer specific genese are independent of sequencing plateformt. Using an optimized data analysis workflow, we mapped about 25 million sequence reads per sample to the hg 18. Data analysis revealed a significant detction of breast cancer specific genes in CTCs and such signature genes overlap between two sets of data yet provided complementary insights in transcriptome profiling.
2018-07-22 | GSE71192 | GEO
Project description:We developed a method to isolate pure circulating tumor cells (CTC). RNA from such CTCs isolated from the peripheral blood of metastatic breast cancer patients and gene expression was performed using cDNAmicroarray. we used cDNA array to compare gene expression of CTCs with normal epithelial and breast tumor samples normal blood vs. breast tumor
2014-12-18 | E-GEOD-45962 | biostudies-arrayexpress