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Development of an exosomal gene signature to detect residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning [canine data]

ABSTRACT: Development of an exosomal gene signature to detect residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning [canine data]

PROVIDER: PRJNA759587 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR15687543_1.fastq.gz	Fastqsanger.gz
	SRR15687543_2.fastq.gz	Fastqsanger.gz
	SRR15687544_1.fastq.gz	Fastqsanger.gz
	SRR15687544_2.fastq.gz	Fastqsanger.gz
	SRR15687545_1.fastq.gz	Fastqsanger.gz

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Development of an exosomal gene signature to detect residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning

Project description:Development of an exosomal gene signature to detect residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning

| PRJNA759584 | ENA

Development of an exosomal gene signature to detect residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning [mouse data]

Project description:We developed a method using canine osteosarcoma in mouse xenografts to distinguish tumor-derived from host-response exosomal mRNAs. The model allows for the identification of canine osteosarcoma-specific gene signatures by RNA sequencing and a species-differentiating bioinformatics pipeline. An osteosarcoma-associated signature consisting of five gene transcripts was validated in dogs with osteosarcoma. Serum/plasma exosomes were isolated from 53 dogs in distinct clinical groups (“healthy”, “osteosarcoma”, “other bone tumor”, or “non-neoplastic disease”). Pre-treatment samples from osteosarcoma cases were used as the training set and a validation set from post-treatment samples was used for testing, classifying as “osteosarcoma–detected” or “osteosarcoma–NOT detected”. Dogs in a validation set whose post-treatment samples were classified as “osteosarcoma–NOT detected” had longer remissions, up to 15 months after treatment. In conclusion, we identified a gene signature predictive of molecular remissions with potential applications in the early detection and minimal residual disease settings. These results provide proof-of-concept for our discovery platform and its utilization in future studies to inform cancer risk, diagnosis, prognosis, and therapeutic response.

2021-09-03 | GSE183190 | GEO

Development of an exosomal gene signature to detect residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning [canine data]

2021-09-03 | GSE183189 | GEO

Development of an exosomal gene signature to detect residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning [mouse data]

Project description:Development of an exosomal gene signature to detect residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning [mouse data]

| PRJNA759588 | ENA

Development of an exosomal gene signature to detect residual disease in dogs with osteosarcoma using a novel xenograft platform and machine learning

Project description:This SuperSeries is composed of the SubSeries listed below.

2021-09-03 | GSE183191 | GEO

Detect-seq on transfected HEK293T cells [Detect-seq data]

Project description:Detect-seq on transfected HEK293T cells [Detect-seq data]

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Proof of concept for the use of trained sniffer dogs to detect osteosarcoma.

Project description:Sarcomas are mesenchymal cancers which often show an aggressive behavior and patient survival largely depends on an early detection. In last years, much attention has been given to the fact that cancer patients release specific odorous volatile organic compounds (VOCs) that can be efficiently detected by properly trained sniffer dogs. Here, we have evaluated for the first time the ability of sniffer dogs (n = 2) to detect osteosarcoma cell cultures and patient samples. One of the two dogs was successfully trained to discriminate osteosarcoma patient-derived primary cells from mesenchymal stem/stromal cells (MSCs) obtained from healthy individuals. After the training phase, the dog was able to detect osteosarcoma specific odor cues in a different panel of 6 osteosarcoma cell lines with sensitivity and specificity rates between 95 and 100%. Moreover, the same VOCs were also detected by the sniffer dog in saliva samples from osteosarcoma patients (n = 2) and discriminated from samples from healthy individuals with a similar efficacy. Altogether, these results indicate that there are common odor profiles shared by cultures of osteosarcoma cells and body fluid samples from patients and provide a first proof of concept about the potential of canine odor detection as a non-invasive screening method to detect osteosarcomas.

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Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.

2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress

Detect-seq on transfected HEK293T cells [Detect-seq data]

Project description:We used a new technology, named Detect-seq, to perform genome sequencing on transfected HEK293T cells to see DdCBEs' off-target mutations. Besides, targeted-amplicon sequencing and ATAC-seq data were applied to validate the results of Detect-seq.

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In vivo isolated Wnt1-Prim1 Primary, Residual, and Recurrent Tumor Cells

Project description:Residual disease represents a major obstacle to the successful treatment of breast cancer, however little is known about the biology of residual tumor cells in vivo. To identify genes differentially expressed in vivo in residual tumor cells compared to primary tumor cells or recurrent tumor cells, we used flow cytometry to isolate GFP-labeled tumor cells from primary, residual, or recurrent tumors grown orthotopically in nu/nu mice. WTA was used to amplify RNA from all tumor cells.

2020-07-24 | GSE60610 | GEO

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