Transcriptional signatures derived from murine tumor-associated macrophages predict outcome in breast cancer patients
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ABSTRACT:
PROVIDER: PRJNA521461 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR8550010.fastq.gz | Fastqsanger.gz | |||
| SRR8550020.fastq.gz | Fastqsanger.gz | |||
| SRR8550030.fastq.gz | Fastqsanger.gz | |||
| SRR8550040.fastq.gz | Fastqsanger.gz | |||
| SRR8550050.fastq.gz | Fastqsanger.gz |
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Project description:Macrophages are frequently the most abundant immune cells in murine and human cancers. Studies in various transgenic mouse tumor models have revealed pro-tumor functions of tumor-associated macrophages (TAMs), but despite their association with poor clinical outcome in human patients, molecular signatures for the prediction of clinical outcome in humans are still missingbeen demonstrated. Here we generated molecular signatures from F4/80+CD11b+ TAMs from two transgenic breast cancer models: K14cre;Cdh1flox/flox;Trp53flox/flox (KEP), which resembles human invasive lobular carcinoma (ILC) and MMTV-NeuT (NeuT), which resembles HER2-overexpressing breast cancer. Determination of truly specific TAM transcriptome signatures in breast cancer required relationship analysis with healthy mammary gland tissue macrophages (MTMs), since comparison with macrophages from tissues overestimated TAM-specific gene expression. Furthermore, translation of the TAM signatures to outcome prediction in patients required consideration of the breast cancer subtype. TAM signatures derived from the KEP, but not the NeuT model reliably predicted outcome in ILC patients. Collectively, we show that a transgenic mouse tumor model can be utilized to derive a TAM-based signature for human breast cancer outcome prediction and provide a generalizable strategy for determining and applying specific molecular signatures of immune cells to, in principle, any cancer provided the murine model reflects the human disease.
2019-10-30 | GSE126268 | GEO
Project description:This SuperSeries is composed of the following subset Series: GSE6871: Gene expression signatures that predict radiation exposure (human) GSE6873: Gene expression signatures that predict radiation exposure (mouse) Keywords: SuperSeries Refer to individual Series
2010-06-12 | E-GEOD-6874 | biostudies-arrayexpress
Project description:Studies of miRNA profiling to predict outcome in cardiac arrest patients treated with therapeutic hypothermia (TH)
2013-01-11 | GSE34643 | GEO
Project description:Background: Cardiac arrest (CA) represents the third leading cause of death worldwide. Among survivors, severe neurological sequelae are frequent but difficult to predict. Novel prognostic biomarkers would offer clinicians the possibility to deliver personalized healthcare. The potential of small circulating noncoding RNAs (microRNAs) to predict neurological outcome and survival after CA has been reported. Objective: This study aims to identify circulating circular RNAs (circRNAs) associated with clinical outcome after CA. Methods and Results: Methods and Results: Whole blood samples obtained 48h after return of spontaneous circulation from 23 sex-matched survivors and 23 deceased cardiac arrest (CA) patients were enrolled in this study. Whole transcriptome RNA sequencing identified candidate RNAs associated with neurological outcome and survival. Conclusion: We have identified candidate RNAs associated with clinical outcome after CA whose predictive value remains to be confirmed in large populations.
2022-11-02 | GSE197764 | GEO
Project description:Transcriptional Signatures Predict Phenotypic Variations of Candida auris
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Project description:Expression profiling of breast cancer tumours, comparing 10 year survivors to deceased patients Background It is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated. Methods 46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used to evaluate the gene lists on the different data sets, a correlation-based classifier and a VFI (Voting Features Interval) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients which had developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material. Results A list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively). Conclusion The list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients should have had different adjuvant treatment than they did. Keywords: Expression Microarray, Lymph-node-negative Breast Cancer, Clinical Outcome, Classification
2008-08-29 | GSE12071 | GEO
Project description:Recently, expression profiling of breast carcinomas has revealed gene signatures that predict clinical outcome, and discerned prognostically relevant breast cancer subtypes. Measurement of the degree of genomic instability provides a very similar stratification of prognostic groups. We therefore hypothesized that these features are linked. We used gene expression profiling of 48 breast cancer specimens that profoundly differed in their degree of genomic instability and identified a set of 12 genes that defines the two groups. The biological and prognostic significance of this gene set was established through survival prediction in published datasets from patients with breast cancer. Of note, the gene expression signatures that define specific prognostic subtypes in other breast cancer datasets predicted genomic instability in our samples. This remarkable congruence suggests a biological dependency of poor-prognosis gene signatures, breast cancer subtypes, genomic instability, and clinical outcome. Keywords: disease state analysis 44 samples
2009-11-30 | E-GEOD-11901 | biostudies-arrayexpress
Project description:Over the last two decades, tumor-derived RNA expression signatures have been developed for the two most commonly diagnosed tumors worldwide, namely prostate and breast tumors, in order to improve both outcome prediction and treatment decision-making. In this context, molecular signatures gained by main components of the tumor microenvironment, such as cancer-associated fibroblasts (CAFs), have been explored as prognostic and therapeutic tools. Nevertheless, a deeper understanding of the significance of CAFs-related gene signatures in breast and prostate cancers still remains to be disclosed. RNA sequencing technology (RNA-seq) was employed to profile and compare the transcriptome of CAFs isolated from patients affected by breast and prostate tumors. The differentially expressed genes (DEGs) characterizing breast and prostate CAFs were intersected with data from public datasets derived from bulk RNA-seq profiles of breast and prostate tumor patients. Pathway enrichment analyses allowed us to appreciate the biological significance of the DEGs. K-means clustering was applied to construct CAFs-related gene signatures specific for breast and prostate cancer and to stratify independent cohorts of patients into high and low gene expression clusters. Kaplan-Meier survival curves and log-rank tests were employed to predict differences in the outcome parameters of the clusters of patients. Decision-tree analysis was used to validate the clustering results and boosting calculations were then employed to improve the results obtained by the decision-tree algorithm. Data obtained in breast CAFs allowed us to assess a signature that includes 8 genes (ITGA11, THBS1, FN1, EMP1, ITGA2, FYN, SPP1, and EMP2) belonging to pro-metastatic signaling routes, such as the focal adhesion pathway. Survival analyses indicated that the cluster of breast cancer patients showing a high expression of the aforementioned genes displays worse clinical outcomes. Next, we identified a prostate CAFs-related signature that includes 11 genes (IL13RA2, GDF7, IL33, CXCL1, TNFRSF19, CXCL6, LIFR, CXCL5, IL7, TSLP, and TNFSF15) associated with immune responses. A low expression of these genes was predictive of poor survival rates in prostate cancer patients. The results obtained were significantly validated through a two-step approach, based on unsupervised (clustering) and supervised (classification) learning techniques, showing a high prediction accuracy (≥ 90%) in independent RNA-seq cohorts. We identified a huge heterogeneity in the transcriptional profile of CAFs derived from breast and prostate tumors. Of note, the two novel CAFs-related gene signatures might be considered as reliable prognostic indicators and valuable biomarkers for a better management of breast and prostate cancer patients.
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Project description:Identification for microRNA signatures to predict breast cancer recurrence in young women were performed through expression profiling of a total 2565 miRNAs in 45 samples diagnosed as invasive ducatal carcinoma of no special type (NST) cases which had been consecutively operated on within a defined short period of time.
2017-11-22 | GSE97811 | GEO
Project description:Recently, expression profiling of breast carcinomas has revealed gene signatures that predict clinical outcome, and discerned prognostically relevant breast cancer subtypes. Measurement of the degree of genomic instability provides a very similar stratification of prognostic groups. We therefore hypothesized that these features are linked. We used gene expression profiling of 48 breast cancer specimens that profoundly differed in their degree of genomic instability and identified a set of 12 genes that defines the two groups. The biological and prognostic significance of this gene set was established through survival prediction in published datasets from patients with breast cancer. Of note, the gene expression signatures that define specific prognostic subtypes in other breast cancer datasets predicted genomic instability in our samples. This remarkable congruence suggests a biological dependency of poor-prognosis gene signatures, breast cancer subtypes, genomic instability, and clinical outcome. Keywords: disease state analysis
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