Leveraging sequences missing from the human genome to diagnose cancer
Abstract Background Cancer diagnosis using cell-free DNA (cfDNA) has the potential to improve treatment and survival but has several technical limitations. Methods In this study, we developed a prediction model based on neomers, DNA sequences 13–17 nucleotides in length that are predominantly absent...
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Nature Portfolio
2025-08-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-025-01067-3 |
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| author | Ilias Georgakopoulos-Soares Ofer Yizhar-Barnea Ioannis Mouratidis Candace S. Y. Chan Michail Patsakis Akshatha Nayak Rachael Bradley Mayank Mahajan Jasmine Sims Dianne Laboy Cintron Ryder Easterlin Julia S. Kim Emmalyn Chen Geovanni Pineda Guillermo E. Parada John S. Witte Christopher A. Maher Felix Feng Ioannis Vathiotis Nikolaos Syrigos Emmanouil Panagiotou Andriani Charpidou Konstantinos Syrigos Jocelyn Chapman Mark Kvale Martin Hemberg Nadav Ahituv |
| author_facet | Ilias Georgakopoulos-Soares Ofer Yizhar-Barnea Ioannis Mouratidis Candace S. Y. Chan Michail Patsakis Akshatha Nayak Rachael Bradley Mayank Mahajan Jasmine Sims Dianne Laboy Cintron Ryder Easterlin Julia S. Kim Emmalyn Chen Geovanni Pineda Guillermo E. Parada John S. Witte Christopher A. Maher Felix Feng Ioannis Vathiotis Nikolaos Syrigos Emmanouil Panagiotou Andriani Charpidou Konstantinos Syrigos Jocelyn Chapman Mark Kvale Martin Hemberg Nadav Ahituv |
| author_sort | Ilias Georgakopoulos-Soares |
| collection | DOAJ |
| description | Abstract Background Cancer diagnosis using cell-free DNA (cfDNA) has the potential to improve treatment and survival but has several technical limitations. Methods In this study, we developed a prediction model based on neomers, DNA sequences 13–17 nucleotides in length that are predominantly absent from the genomes of healthy individuals and are created by tumor-associated mutations. Results We show that neomer-based classifiers can accurately detect cancer, including early stages, and distinguish subtypes and features. Analysis of 2577 cancer genomes from 21 cancer types shows that neomers can distinguish tumor types with higher accuracy than state-of-the-art methods. Generation and analysis of 465 cfDNA whole-genome sequences demonstrates that neomers can precisely detect lung and ovarian cancer, including early stages, with an area under the curve ranging from 0.89 to 0.94. By testing various promoters or over 9000 candidate enhancer sequences with massively parallel reporter assays, we show that neomers can identify cancer-associated mutations that alter regulatory activity. Conclusions Combined, our results identify a sensitive, specific, and simple cancer diagnostic tool that can also identify cancer-associated mutations in gene regulatory elements. |
| format | Article |
| id | doaj-art-8405fa3f697e477cb26541b19e4d2f39 |
| institution | Kabale University |
| issn | 2730-664X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj-art-8405fa3f697e477cb26541b19e4d2f392025-08-24T11:47:37ZengNature PortfolioCommunications Medicine2730-664X2025-08-015111410.1038/s43856-025-01067-3Leveraging sequences missing from the human genome to diagnose cancerIlias Georgakopoulos-Soares0Ofer Yizhar-Barnea1Ioannis Mouratidis2Candace S. Y. Chan3Michail Patsakis4Akshatha Nayak5Rachael Bradley6Mayank Mahajan7Jasmine Sims8Dianne Laboy Cintron9Ryder Easterlin10Julia S. Kim11Emmalyn Chen12Geovanni Pineda13Guillermo E. Parada14John S. Witte15Christopher A. Maher16Felix Feng17Ioannis Vathiotis18Nikolaos Syrigos19Emmanouil Panagiotou20Andriani Charpidou21Konstantinos Syrigos22Jocelyn Chapman23Mark Kvale24Martin Hemberg25Nadav Ahituv26Department of Bioengineering and Therapeutic Sciences, University of California San FranciscoDepartment of Bioengineering and Therapeutic Sciences, University of California San FranciscoInstitute for Personalized Medicine, Department of Molecular and Precision Medicine, The Pennsylvania State University College of MedicineDepartment of Bioengineering and Therapeutic Sciences, University of California San FranciscoInstitute for Personalized Medicine, Department of Molecular and Precision Medicine, The Pennsylvania State University College of MedicineInstitute for Personalized Medicine, Department of Molecular and Precision Medicine, The Pennsylvania State University College of MedicineDepartment of Bioengineering and Therapeutic Sciences, University of California San FranciscoThe Gene Lay Institute of Immunology and Inflammation, Brigham and Women’s Hospital, Massachusetts General Hospital and Harvard Medical SchoolDepartment of Bioengineering and Therapeutic Sciences, University of California San FranciscoDepartment of Bioengineering and Therapeutic Sciences, University of California San FranciscoDepartment of Bioengineering and Therapeutic Sciences, University of California San FranciscoDepartment of Bioengineering and Therapeutic Sciences, University of California San FranciscoDepartment of Epidemiology and Biostatistics, University of California San FranciscoDivision of Gynecologic Oncology, University of California San FranciscoDonnelly Centre for Cellular and Biomolecular Research, University of TorontoDepartment of Epidemiology and Biostatistics, University of California San FranciscoDivision of Oncology, Department of Internal Medicine, Washington University School of MedicineDivision of Hematology/Oncology, Department of Medicine, University of California San FranciscoThird Department of Internal Medicine, Sotiria Hospital, National and Kapodistrian University of Athens, School of MedicineThird Department of Internal Medicine, Sotiria Hospital, National and Kapodistrian University of Athens, School of MedicineThird Department of Internal Medicine, Sotiria Hospital, National and Kapodistrian University of Athens, School of MedicineThird Department of Internal Medicine, Sotiria Hospital, National and Kapodistrian University of Athens, School of MedicineThird Department of Internal Medicine, Sotiria Hospital, National and Kapodistrian University of Athens, School of MedicineHelen Diller Comprehensive Cancer Center, University of California San FranciscoInstitute for Human Genetics, University of California San FranciscoThe Gene Lay Institute of Immunology and Inflammation, Brigham and Women’s Hospital, Massachusetts General Hospital and Harvard Medical SchoolDepartment of Bioengineering and Therapeutic Sciences, University of California San FranciscoAbstract Background Cancer diagnosis using cell-free DNA (cfDNA) has the potential to improve treatment and survival but has several technical limitations. Methods In this study, we developed a prediction model based on neomers, DNA sequences 13–17 nucleotides in length that are predominantly absent from the genomes of healthy individuals and are created by tumor-associated mutations. Results We show that neomer-based classifiers can accurately detect cancer, including early stages, and distinguish subtypes and features. Analysis of 2577 cancer genomes from 21 cancer types shows that neomers can distinguish tumor types with higher accuracy than state-of-the-art methods. Generation and analysis of 465 cfDNA whole-genome sequences demonstrates that neomers can precisely detect lung and ovarian cancer, including early stages, with an area under the curve ranging from 0.89 to 0.94. By testing various promoters or over 9000 candidate enhancer sequences with massively parallel reporter assays, we show that neomers can identify cancer-associated mutations that alter regulatory activity. Conclusions Combined, our results identify a sensitive, specific, and simple cancer diagnostic tool that can also identify cancer-associated mutations in gene regulatory elements.https://doi.org/10.1038/s43856-025-01067-3 |
| spellingShingle | Ilias Georgakopoulos-Soares Ofer Yizhar-Barnea Ioannis Mouratidis Candace S. Y. Chan Michail Patsakis Akshatha Nayak Rachael Bradley Mayank Mahajan Jasmine Sims Dianne Laboy Cintron Ryder Easterlin Julia S. Kim Emmalyn Chen Geovanni Pineda Guillermo E. Parada John S. Witte Christopher A. Maher Felix Feng Ioannis Vathiotis Nikolaos Syrigos Emmanouil Panagiotou Andriani Charpidou Konstantinos Syrigos Jocelyn Chapman Mark Kvale Martin Hemberg Nadav Ahituv Leveraging sequences missing from the human genome to diagnose cancer Communications Medicine |
| title | Leveraging sequences missing from the human genome to diagnose cancer |
| title_full | Leveraging sequences missing from the human genome to diagnose cancer |
| title_fullStr | Leveraging sequences missing from the human genome to diagnose cancer |
| title_full_unstemmed | Leveraging sequences missing from the human genome to diagnose cancer |
| title_short | Leveraging sequences missing from the human genome to diagnose cancer |
| title_sort | leveraging sequences missing from the human genome to diagnose cancer |
| url | https://doi.org/10.1038/s43856-025-01067-3 |
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