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Transcriptional signatures derived from murine tumor-associated macrophages predict outcome in breast cancer patients

ABSTRACT: Transcriptional signatures derived from murine tumor-associated macrophages predict outcome in breast cancer patients

PROVIDER: PRJNA521461 | ENA |

REPOSITORIES: ENA

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			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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Transcriptional signatures derived from murine tumor-associated macrophages predict outcome in breast cancer patients

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

Gene expression signatures that predict radiation exposure

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

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Circulating microRNAs to predict neurological outcome after sudden cardiac arrest

Project description:Studies of miRNA profiling to predict outcome in cardiac arrest patients treated with therapeutic hypothermia (TH)

2013-01-11 | GSE34643 | GEO

Circular RNAs to predict clinical outcome after cardiac arrest

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

Circular RNAs to predict clinical outcome after cardiac arrest

Project description:Circular RNAs to predict clinical outcome after cardiac arrest

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Transcriptional Signatures Predict Phenotypic Variations of Candida auris

Project description:Transcriptional Signatures Predict Phenotypic Variations of Candida auris

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Multi-omic Signatures of Host Response Associated with Presence, Type, and Outcome of Enterococcal Bacteremia proteomics data

Project description:Proteomics data from the manuscript Multi-omic Signatures of Host Response Associated with the Presence, Type, and Outcome of Enterococcal Bacteremia.

2024-12-24 | MSV000096728 | MassIVE

Multi-omic Signatures of Host Response Associated with Presence, Type, and Outcome of Enterococcal Bacteremia metabolomics data

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Murine macrophages: Peritoneal exudate macrophages (PECs) vs tumor-associated macrophages (TAMs)

Project description:Murine macrophages: Peritoneal exudate macrophages (PECs) vs tumor-associated macrophages (TAMs)

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Gene expression signatures predict outcome in non-muscle invasive bladder carcinoma - a multi-center validation study

Project description:Background Clinically useful molecular markers predicting the clinical course of patients diagnosed with non-muscle invasive bladder cancer are needed to improve treatment outcome. Methods We used custom designed oligonucleotide microarrays to validate four previously reported gene expression signatures for molecular diagnosis of disease stage and carcinoma in situ, and for predicting disease recurrence and progression. We analyzed tumors from 404 patients diagnosed with bladder cancer in hospitals in Denmark, Sweden, England, Spain and France. Molecular classifications were compared to pathological diagnosis and clinical outcome. The median follow-up time for the patients was 3.5 years. Results Classification of disease stage using a 52-gene classifier was found to be highly significantly correlated with pathological stage (P<0.001). Furthermore, the classifier added information regarding future disease progression of Ta or T1 tumors (P<0.001). The molecular 77-gene progression classifier was highly significantly correlated with progression free survival (P<0.001) and cancer specific survival (P=0.001). Furthermore, multivariate Cox´s regression analysis showed the progression classifier to be an independently significant variable associated with disease progression after adjustment for age, sex, stage, grade and treatment (hazard ratio 2.4, P=0.005). The diagnosis of carcinoma in situ (CIS) using a 68-gene classifier showed a highly significant correlation with histopathological CIS diagnosis (odds ratio 5.8, P<0.001) in multivariate logistic regression analysis. Conclusions We conclude that this multicenter validation study confirms the clinical utility of molecular classifiers to guide treatment decisions for patients initially diagnosed with non-muscle invasive bladder cancer. Keywords: Multi center validation study of gene expression signatures

2007-06-15 | GSE5479 | GEO

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