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DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE231984 | GEO | 2023/06/11

REPOSITORIES: GEO

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DNA methylation profiling to determine the primary sites of metastatic cancers using formalin-fixed paraffin-embedded tissues [wgbs_set]

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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