Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA118601 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Molecular characterization of Georgia cohort of primary breast cancer FFPE tissues on custom breast cancer DASL panel
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Project description:Analysis of 97 formalin-fixed, paraffin-embedded (FFPE) primary breast tumors using Illumina DASL microarray technology on a Custom Breast Cancer Panel and the Illumina Human Cancer Panel. Molecular markers between the pathology defined subtypes of breast cancer were assessed to hypothesize potential therapeutic targets specific to the subtypes Molecular Characterization of 97 primary breast tumor formalin-fixed, paraffin-embedded (FFPE) specimens including 24 triple negative (TN: ER-, PR-, HER2-), 9 HER2-positive (HER2+: ER-, PR-, HER2+), and 64 hormone receptor-positive (HR+: ER+ and/or PR+). 91 of the 97 specimens were characterized on the Illumina Human Cancer DASL Panel and 86 of 97 specimens were characterized on a custom Breast Cancer DASL Panel, 80 of these specimens were common to both the Human Cancer DASL Panel and the custom Breast Cancer DASL Panel.
2009-10-23 | E-GEOD-17650 | biostudies-arrayexpress
Project description:Analysis of 143 formalin-fixed, paraffin-embedded (FFPE) primary breast tumors using a Custom Breast Cancer Panel and Human Cancer Panel for the DASL platform. Molecular markers between the pathology defined subtypes of breast cancer were assessed to hypothesize potential therapeutic targets specific to the subtypes Molecular Characterization of 143 primary breast carcinomas including 101 triple negative (TN: ER-, PR-, HER2-), 3 HER2-positive (HER2+: ER-, PR-, HER2+), and 39 hormone receptor-positive (HR+: ER+ and/or PR+)
2009-10-23 | E-GEOD-18539 | biostudies-arrayexpress
Project description:Analysis of 143 formalin-fixed, paraffin-embedded (FFPE) primary breast tumors using a Custom Breast Cancer Panel and Human Cancer Panel for the DASL platform. Molecular markers between the pathology defined subtypes of breast cancer were assessed to hypothesize potential therapeutic targets specific to the subtypes
2009-10-23 | GSE18539 | GEO
Project description:Analysis of 97 formalin-fixed, paraffin-embedded (FFPE) primary breast tumors using Illumina DASL microarray technology on a Custom Breast Cancer Panel and the Illumina Human Cancer Panel. Molecular markers between the pathology defined subtypes of breast cancer were assessed to hypothesize potential therapeutic targets specific to the subtypes
2009-10-23 | GSE17650 | GEO
Project description:Accurate identification of the primary site of metastatic cancer is critical to guide the subsequent treatments. There is a significant portion of patients whose primary sites are initially classified as uncertain, and 3-9% of cancer patients are diagnosed with cancer of unknown primary (CUP) even after comprehensive diagnostic workups. Yet, a widely accepted molecular test is still not available. Here, we presented the combination of a novel DNA methylation sequencing-based method and an algorithm to predict the tissues of origin for metastatic cancers. The assay applied degraded DNA from formalin-fixed, paraffin-embedded (FFPE) tissues to generate reduced represent bisulfite sequencing libraries (FFPE-RRBS). Comparable DNA methylation metrics were obtained for the paired fresh frozen (FF) RRBS and FFPE-RRBS libraries and the FFPE-RRBS libraries of matched primary and metastatic cancer tissues. We generated and systemically evaluated 28 molecular classifiers built on four methylation evaluation methods and seven machine-learning approaches from a training data set of 498 primary cancer patients. Of those classifiers, the beta values-based (mean methylation) linear support vector (BELIVE) performed the best, achieving overall accuracies of 81-95% for identifying the primary sites of 215 metastatic cancer patients by utilizing the top-k predictions (k=1, 2, 3). The prediction accuracies ranged from 92% to 98% for 4702 patients with primary tumors in a cross-validation cohort. Lastly, BELIVE successfully identified the tissues of origin for approximately 81-93% of cases in a cohort of 68 patients initially diagnosed with CUP.
2023-06-11 | GSE233088 | GEO
Project description:Accurate identification of the primary site of metastatic cancer is critical to guide the subsequent treatments. There is a significant portion of patients whose primary sites are initially classified as uncertain, and 3-9% of cancer patients are diagnosed with cancer of unknown primary (CUP) even after comprehensive diagnostic workups. Yet, a widely accepted molecular test is still not available. Here, we presented the combination of a novel DNA methylation sequencing-based method and an algorithm to predict the tissues of origin for metastatic cancers. The assay applied degraded DNA from formalin-fixed, paraffin-embedded (FFPE) tissues to generate reduced represent bisulfite sequencing libraries (FFPE-RRBS). Comparable DNA methylation metrics were obtained for the paired fresh frozen (FF) RRBS and FFPE-RRBS libraries and the FFPE-RRBS libraries of matched primary and metastatic cancer tissues. We generated and systemically evaluated 28 molecular classifiers built on four methylation evaluation methods and seven machine-learning approaches from a training data set of 498 primary cancer patients. Of those classifiers, the beta values-based (mean methylation) linear support vector (BELIVE) performed the best, achieving overall accuracies of 81-95% for identifying the primary sites of 215 metastatic cancer patients by utilizing the top-k predictions (k=1, 2, 3). The prediction accuracies ranged from 92% to 98% for 4702 patients with primary tumors in a cross-validation cohort. Lastly, BELIVE successfully identified the tissues of origin for approximately 81-93% of cases in a cohort of 68 patients initially diagnosed with CUP.
2023-06-11 | GSE233087 | GEO
Project description:Accurate identification of the primary site of metastatic cancer is critical to guide the subsequent treatments. There is a significant portion of patients whose primary sites are initially classified as uncertain, and 3-9% of cancer patients are diagnosed with cancer of unknown primary (CUP) even after comprehensive diagnostic workups. Yet, a widely accepted molecular test is still not available. Here, we presented the combination of a novel DNA methylation sequencing-based method and an algorithm to predict the tissues of origin for metastatic cancers. The assay applied degraded DNA from formalin-fixed, paraffin-embedded (FFPE) tissues to generate reduced represent bisulfite sequencing libraries (FFPE-RRBS). Comparable DNA methylation metrics were obtained for the paired fresh frozen (FF) RRBS and FFPE-RRBS libraries and the FFPE-RRBS libraries of matched primary and metastatic cancer tissues. We generated and systemically evaluated 28 molecular classifiers built on four methylation evaluation methods and seven machine-learning approaches from a training data set of 498 primary cancer patients. Of those classifiers, the beta values-based (mean methylation) linear support vector (BELIVE) performed the best, achieving overall accuracies of 81-95% for identifying the primary sites of 215 metastatic cancer patients by utilizing the top-k predictions (k=1, 2, 3). The prediction accuracies ranged from 92% to 98% for 4702 patients with primary tumors in a cross-validation cohort. Lastly, BELIVE successfully identified the tissues of origin for approximately 81-93% of cases in a cohort of 68 patients initially diagnosed with CUP.
2023-06-11 | GSE230193 | GEO
Project description:Accurate identification of the primary site of metastatic cancer is critical to guide the subsequent treatments. There is a significant portion of patients whose primary sites are initially classified as uncertain, and 3-9% of cancer patients are diagnosed with cancer of unknown primary (CUP) even after comprehensive diagnostic workups. Yet, a widely accepted molecular test is still not available. Here, we presented the combination of a novel DNA methylation sequencing-based method and an algorithm to predict the tissues of origin for metastatic cancers. The assay applied degraded DNA from formalin-fixed, paraffin-embedded (FFPE) tissues to generate reduced represent bisulfite sequencing libraries (FFPE-RRBS). Comparable DNA methylation metrics were obtained for the paired fresh frozen (FF) RRBS and FFPE-RRBS libraries and the FFPE-RRBS libraries of matched primary and metastatic cancer tissues. We generated and systemically evaluated 28 molecular classifiers built on four methylation evaluation methods and seven machine-learning approaches from a training data set of 498 primary cancer patients. Of those classifiers, the beta values-based (mean methylation) linear support vector (BELIVE) performed the best, achieving overall accuracies of 81-95% for identifying the primary sites of 215 metastatic cancer patients by utilizing the top-k predictions (k=1, 2, 3). The prediction accuracies ranged from 92% to 98% for 4702 patients with primary tumors in a cross-validation cohort. Lastly, BELIVE successfully identified the tissues of origin for approximately 81-93% of cases in a cohort of 68 patients initially diagnosed with CUP.
2023-06-11 | GSE231969 | GEO
Project description:Accurate identification of the primary site of metastatic cancer is critical to guide the subsequent treatments. There is a significant portion of patients whose primary sites are initially classified as uncertain, and 3-9% of cancer patients are diagnosed with cancer of unknown primary (CUP) even after comprehensive diagnostic workups. Yet, a widely accepted molecular test is still not available. Here, we presented the combination of a novel DNA methylation sequencing-based method and an algorithm to predict the tissues of origin for metastatic cancers. The assay applied degraded DNA from formalin-fixed, paraffin-embedded (FFPE) tissues to generate reduced represent bisulfite sequencing libraries (FFPE-RRBS). Comparable DNA methylation metrics were obtained for the paired fresh frozen (FF) RRBS and FFPE-RRBS libraries and the FFPE-RRBS libraries of matched primary and metastatic cancer tissues. We generated and systemically evaluated 28 molecular classifiers built on four methylation evaluation methods and seven machine-learning approaches from a training data set of 498 primary cancer patients. Of those classifiers, the beta values-based (mean methylation) linear support vector (BELIVE) performed the best, achieving overall accuracies of 81-95% for identifying the primary sites of 215 metastatic cancer patients by utilizing the top-k predictions (k=1, 2, 3). The prediction accuracies ranged from 92% to 98% for 4702 patients with primary tumors in a cross-validation cohort. Lastly, BELIVE successfully identified the tissues of origin for approximately 81-93% of cases in a cohort of 68 patients initially diagnosed with CUP.
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