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Gafni E, Harvey A, Jaroszewicz A, Solari OS, Landolin J, Barbirou M, Miller A, Tonellato PJ, Kundaje A, Jeffrey SS, Curtis C, Sledge GW, Giresi P, Boley N. Abstract 2105: Cell-free DNA fragments inform epigenomic mechanisms for early detection of breast cancer. Cancer Res 2021. [DOI: 10.1158/1538-7445.am2021-2105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Introduction: Chromatin accessibility and cell-free DNA fragmentation patterns can be used to identify epigenomic mechanisms (Sharma et al. 2010) and infer cell-types contributing to cfDNA in pathological states such as cancer (Snyder et al. 2016; Ulz et al. 2017). We describe results from a novel blood-based cell-free DNA (cfDNA) assay using epigenomic signatures that have high sensitivity for detecting early stages of breast cancer, a cancer type that is characterized by low tumor burden (Phallen et al. 2017). We present the results from a prospective, case-control study demonstrating improved sensitivity to the screening mammogram and other published blood-based assays.
Methods: Assay performance was evaluated using a case-control study design enrolling 123 total subjects (58% Healthy, 18% Stage I, 13% Stage II, 11% Stage III). Cases were defined as subjects with a confirmatory diagnosis of invasive breast cancer, at any stage, by tissue biopsy. Controls were composed of subjects with either a negative finding by mammography (BI-RADS 1 or 2) or self-declared cancer-free. Whole blood samples were collected in Streck BCT tubes and shipped to a central laboratory for processing. Total cell-free DNA was extracted from plasma and prepped for next-generation sequencing. Sequencing libraries were enriched using a custom panel targeting genomic regions with distinct epigenomic activity in breast cancer. We trained a neural net to predict regulatory events in each of these regions, and then identified those events that were predictive of the presence of breast cancer. Final classification was performed by logistic regression over the predicted regulatory events.
Results: Performance was tested using a held-out test set and achieved an overall sensitivity of 92.5% (95% CI: 88.1%, 97%) at specificity of 88.9% with an overall AUC of 95.8%. Performance of screening mammography is reported to be 86.9% (95% CI: 86.3%, 87.6%) sensitive at 88.9% specificity on data obtained from six Breast Cancer Surveillance Consortium (BCSC) registries on 792808 women (Lehman et al. 2017).
Conclusion: These results support the utility for detecting epigenomic signals from cell-free DNA to enhance early detection of breast cancer. A prospective breast cancer screening study in a larger cohort is needed to further validate performance.
Citation Format: Erik Gafni, Adam Harvey, Artur Jaroszewicz, Omid Shams Solari, Jane Landolin, Mouadh Barbirou, Amanda Miller, Peter J. Tonellato, Anshul Kundaje, Stefanie S. Jeffrey, Christina Curtis, George W. Sledge, Paul Giresi, Nathan Boley. Cell-free DNA fragments inform epigenomic mechanisms for early detection of breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2105.
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Affiliation(s)
- Erik Gafni
- 1Ravel Biotechnology Inc., San Francisco, CA
| | - Adam Harvey
- 1Ravel Biotechnology Inc., San Francisco, CA
| | | | | | | | - Mouadh Barbirou
- 2Biomedical Informatics, University of Missouri, Columbia, MO
| | - Amanda Miller
- 2Biomedical Informatics, University of Missouri, Columbia, MO
| | | | | | | | | | | | - Paul Giresi
- 1Ravel Biotechnology Inc., San Francisco, CA
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Brooks AN, Duff MO, May G, Yang L, Bolisetty M, Landolin J, Wan K, Sandler J, Booth BW, Celniker SE, Graveley BR, Brenner SE. Regulation of alternative splicing in Drosophila by 56 RNA binding proteins. Genome Res 2015; 25:1771-80. [PMID: 26294686 PMCID: PMC4617972 DOI: 10.1101/gr.192518.115] [Citation(s) in RCA: 67] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2015] [Accepted: 08/19/2015] [Indexed: 12/26/2022]
Abstract
Alternative splicing is regulated by RNA binding proteins (RBPs) that recognize pre-mRNA sequence elements and activate or repress adjacent exons. Here, we used RNA interference and RNA-seq to identify splicing events regulated by 56 Drosophila proteins, some previously unknown to regulate splicing. Nearly all proteins affected alternative first exons, suggesting that RBPs play important roles in first exon choice. Half of the splicing events were regulated by multiple proteins, demonstrating extensive combinatorial regulation. We observed that SR and hnRNP proteins tend to act coordinately with each other, not antagonistically. We also identified a cross-regulatory network where splicing regulators affected the splicing of pre-mRNAs encoding other splicing regulators. This large-scale study substantially enhances our understanding of recent models of splicing regulation and provides a resource of thousands of exons that are regulated by 56 diverse RBPs.
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Affiliation(s)
- Angela N Brooks
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA; Broad Institute, Cambridge, Massachusetts 02142, USA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA
| | - Michael O Duff
- Department of Genetics and Genome Sciences, Institute for Systems Genomics, University of Connecticut Health Center, Farmington, Connecticut 06030, USA
| | - Gemma May
- Department of Genetics and Genome Sciences, Institute for Systems Genomics, University of Connecticut Health Center, Farmington, Connecticut 06030, USA
| | - Li Yang
- Department of Genetics and Genome Sciences, Institute for Systems Genomics, University of Connecticut Health Center, Farmington, Connecticut 06030, USA
| | - Mohan Bolisetty
- Department of Genetics and Genome Sciences, Institute for Systems Genomics, University of Connecticut Health Center, Farmington, Connecticut 06030, USA
| | - Jane Landolin
- Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Ken Wan
- Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Jeremy Sandler
- Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Benjamin W Booth
- Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Susan E Celniker
- Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA
| | - Brenton R Graveley
- Department of Genetics and Genome Sciences, Institute for Systems Genomics, University of Connecticut Health Center, Farmington, Connecticut 06030, USA
| | - Steven E Brenner
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA; Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
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