Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1085531 | ENA |
REPOSITORIES: ENA
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Project description:Liver cancer is one of the most lethal cancers worldwide. Liquid biopsy provides a noninvasive approach in detecting and monitoring cancer biomarkers to overcome current limitations associated with tissue biopsies, comprising the analysis of circulating tumor-derived material. In this study, we profiled plasma cell-free RNA-seq to identify recurrently dysregulated RNA biomarkers for the liquid biopsy of cancer.
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Project description:Longitudinal monitoring of patients with advanced cancers is crucial to evaluate both disease burden and treatment response. Current liquid biopsy approaches mostly rely on the detection of DNA-based biomarkers. However, plasma RNA analysis can unleash tremendous opportunities for tumor state interrogation and molecular subtyping. Through the application of deep learning algorithms to the deconvolved transcriptomes of RNA within plasma extracellular vesicles (evRNA), we successfully predict consensus molecular subtypes in metastatic colorectal cancer patients. We further demonstrate the ability to monitor changes in transcriptomic subtype under treatment selection pressure and identify molecular pathways in evRNA associated with recurrence. Our approach also identified expressed gene fusions and neoepitopes from evRNA. These results demonstrate the feasibility of transcriptomic-based liquid biopsy platforms for precision oncology approaches, spanning from the longitudinal monitoring of tumor subtype changes to identification of expressed fusions and neoantigens as cancer-specific therapeutic targets, sans the need for tissue-based sampling.
2024-02-29 | GSE255774 | GEO
Project description:Longitudinal monitoring of patients with advanced cancers is crucial to evaluate both disease burden and treatment response. Current liquid biopsy approaches mostly rely on the detection of DNA-based biomarkers. However, plasma RNA analysis can unleash tremendous opportunities for tumor state interrogation and molecular subtyping. Through the application of deep learning algorithms to the deconvolved transcriptomes of RNA within plasma extracellular vesicles (evRNA), we successfully predict consensus molecular subtypes in metastatic colorectal cancer patients. We further demonstrate the ability to monitor changes in transcriptomic subtype under treatment selection pressure and identify molecular pathways in evRNA associated with recurrence. Our approach also identified expressed gene fusions and neoepitopes from evRNA. These results demonstrate the feasibility of transcriptomic-based liquid biopsy platforms for precision oncology approaches, spanning from the longitudinal monitoring of tumor subtype changes to identification of expressed fusions and neoantigens as cancer-specific therapeutic targets, sans the need for tissue-based sampling.
2024-02-29 | GSE255773 | GEO
Project description:Longitudinal monitoring of patients with advanced cancers is crucial to evaluate both disease burden and treatment response. Current liquid biopsy approaches mostly rely on the detection of DNA-based biomarkers. However, plasma RNA analysis can unleash tremendous opportunities for tumor state interrogation and molecular subtyping. Through the application of deep learning algorithms to the deconvolved transcriptomes of RNA within plasma extracellular vesicles (evRNA), we successfully predict consensus molecular subtypes in metastatic colorectal cancer patients. We further demonstrate the ability to monitor changes in transcriptomic subtype under treatment selection pressure and identify molecular pathways in evRNA associated with recurrence. Our approach also identified expressed gene fusions and neoepitopes from evRNA. These results demonstrate the feasibility of transcriptomic-based liquid biopsy platforms for precision oncology approaches, spanning from the longitudinal monitoring of tumor subtype changes to identification of expressed fusions and neoantigens as cancer-specific therapeutic targets, sans the need for tissue-based sampling.
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Project description:Longitudinal monitoring of patients with advanced cancers is crucial to evaluate both disease burden and treatment response. Current liquid biopsy approaches mostly rely on the detection of DNA-based biomarkers. However, plasma RNA analysis can unleash tremendous opportunities for tumor state interrogation and molecular subtyping. Through the application of deep learning algorithms to the deconvolved transcriptomes of RNA within plasma extracellular vesicles (evRNA), we successfully predict consensus molecular subtypes in metastatic colorectal cancer patients. We further demonstrate the ability to monitor changes in transcriptomic subtype under treatment selection pressure and identify molecular pathways in evRNA associated with recurrence. Our approach also identified expressed gene fusions and neoepitopes from evRNA. These results demonstrate the feasibility of transcriptomic-based liquid biopsy platforms for precision oncology approaches, spanning from the longitudinal monitoring of tumor subtype changes to identification of expressed fusions and neoantigens as cancer-specific therapeutic targets, sans the need for tissue-based sampling.
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