Dataset Information

NGS-dataset of putative driver mutations associated with benign peritoneal strumosis.

ABSTRACT: A rare case of benign peritoneal strumosis was screened for driver mutations in genes relevant to currently approved cancer therapies. Therefore, three formalin fixed paraffin embedded issue sections were screened with the GeneReader Actionable Insights NGS panel (Qiagen, Hilden, Germany) for the occurrence of driver mutations. Several mutations were identified in drug-targetable genes, such as ALK, EGFR, and BRAF. The majority of identified mutations were single nucleotide variant, but also a insertion/deletion mutation was identified. The presented dataset is the first NGS dataset available from a patient with benign peritoneal strumosis.

SUBMITTER: Brockmann M

PROVIDER: S-EPMC6122335 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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NGS-dataset of putative driver mutations associated with benign peritoneal strumosis.

Brockmann Michael M Schildgen Verena V Schildgen Oliver O Lüsebrink Jessica J Pieper Monika M Gudima Alexandru A

Data in brief 20180816

A rare case of benign peritoneal strumosis was screened for driver mutations in genes relevant to currently approved cancer therapies. Therefore, three formalin fixed paraffin embedded issue sections were screened with the GeneReader Actionable Insights NGS panel (Qiagen, Hilden, Germany) for the occurrence of driver mutations. Several mutations were identified in drug-targetable genes, such as ALK, EGFR, and BRAF. The majority of identified mutations were single nucleotide variant, but also a i ...[more]

PMID: 30186896

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Project description:BackgroundEvolutionary models of breast cancer progression differ on the extent to which metastatic potential is pre-encoded within primary tumors. Although metastatic recurrences often harbor putative driver mutations that are not detected in their antecedent primary tumor using standard sequencing technologies, whether these mutations were acquired before or after dissemination remains unclear.MethodsTo ascertain whether putative metastatic driver mutations initially deemed specific to the metastasis by whole exome sequencing were, in actuality, present within rare ancestral subclones of the primary tumors from which they arose, we employed error-controlled ultra-deep sequencing (UDS-UMI) coupled with FFPE artifact mitigation by uracil-DNA glycosylase (UDG) to assess the presence of 132 "metastasis-specific" mutations within antecedent primary tumors from 21 patients. Maximum mutation detection sensitivity was ~1% of primary tumor cells. A conceptual framework was developed to estimate relative likelihoods of alternative models of mutation acquisition.ResultsThe ancestral primary tumor subclone responsible for seeding the metastasis was identified in 29% of patients, implicating several putative drivers in metastatic seeding including LRP5 A65V and PEAK1 K140Q. Despite this, 93% of metastasis-specific mutations in putative metastatic driver genes remained undetected within primary tumors, as did 96% of metastasis-specific mutations in known breast cancer drivers, including ERRB2 V777L, ESR1 D538G, and AKT1 D323H. Strikingly, even in those cases in which the rare ancestral subclone was identified, 87% of metastasis-specific putative driver mutations remained undetected. Modeling indicated that the sequential acquisition of multiple metastasis-specific driver or passenger mutations within the same rare subclonal lineage of the primary tumor was highly improbable.ConclusionsOur results strongly suggest that metastatic driver mutations are sequentially acquired and selected within the same clonal lineage both before, but more commonly after, dissemination from the primary tumor, and that these mutations are biologically consequential. Despite inherent limitations in sampling archival primary tumors, our findings indicate that tumor cells in most patients continue to undergo clinically relevant genomic evolution after their dissemination from the primary tumor. This provides further evidence that metastatic recurrence is a multi-step, mutation-driven process that extends beyond primary tumor dissemination and underscores the importance of longitudinal tumor assessment to help guide clinical decisions.

Dataset Information

NGS-dataset of putative driver mutations associated with benign peritoneal strumosis.

Publications

NGS-dataset of putative driver mutations associated with benign peritoneal strumosis.

Similar Datasets

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