ID
45649
Description
Principal Investigator: Todd R. Golub, MD, Harvard Medical School; Pediatric Oncology, Dana-Farber Cancer Institute; Broad Institute of MIT and Harvard, Cambridge, MA, USA MeSH: Breast Neoplasms,Sequence Analysis, DNA,Hepatocyte Nuclear Factor 3-alpha https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001250 Genomic analysis of tumor samples has led to the identification of hundreds of cancer genes based on the presence of mutations in protein-coding regions. By contrast, much less is known about cancer-causing mutations in non-coding regions. Here, we performed deep sequencing in 360 primary breast cancers and developed computational methods to identify significantly mutated promoters. Clear signals were found in the promoters of four genes. FOXA1, a known driver of hormone-receptor positive breast cancer, harbors a mutational hotspot in its promoter that leads to overexpression through increased E2F binding. RMRP and NEAT1, two non-coding RNA genes, carry mutations that alter protein binding to the promoter and impact expression levels. Overall, our study shows that recurrent mutations in or near gene promoters in cancers have functional consequences. Power analyses indicate that more such genes remain to be discovered through deep sequencing of adequately sized patient cohorts.
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Versions (1)
- 3/20/23 3/20/23 - Simon Heim
Copyright Holder
Todd R. Golub, MD, Harvard Medical School; Pediatric Oncology, Dana-Farber Cancer Institute; Broad Institute of MIT and Harvard, Cambridge, MA, USA
Uploaded on
March 20, 2023
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License
Creative Commons BY 4.0
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dbGaP phs001250 Sequencing of Coding and Non-coding Regions in Primary Breast Cancers and Patient-matched Controls
The subject consent file includes subject IDs and consent information.
- StudyEvent: SEV1
- The subject consent file includes subject IDs and consent information.
- This data table contains a mapping of study subject IDs to sample IDs. Samples are the final preps submitted for genotyping, sequencing, and/or expression data. For example, if one patient (subject ID) gave one sample, and that sample was processed differently to generate 2 sequencing runs, there would be two rows, both using the same subject ID, but having 2 unique sample IDs. The data table also includes sample use.
- This subject phenotype table contains subject ID, tumor in normal contamination, estrogen receptor status , progesterone receptor status, HER2 (human epidermal growth factor receptor 2) status, age, tumor histology, and ethnicity of participant.
- This sample attributes table contains sample ID, sample type, tumor type, and analyte type.
Similar models
The subject consent file includes subject IDs and consent information.
- StudyEvent: SEV1
- The subject consent file includes subject IDs and consent information.
- This data table contains a mapping of study subject IDs to sample IDs. Samples are the final preps submitted for genotyping, sequencing, and/or expression data. For example, if one patient (subject ID) gave one sample, and that sample was processed differently to generate 2 sequencing runs, there would be two rows, both using the same subject ID, but having 2 unique sample IDs. The data table also includes sample use.
- This subject phenotype table contains subject ID, tumor in normal contamination, estrogen receptor status , progesterone receptor status, HER2 (human epidermal growth factor receptor 2) status, age, tumor histology, and ethnicity of participant.
- This sample attributes table contains sample ID, sample type, tumor type, and analyte type.
C0441833 (UMLS CUI [1,2])
C0242481 (UMLS CUI [1,2])