Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer
Abstract Liquid biopsies have the potential to revolutionize cancer care through non-invasive early detection of tumors. Developing a robust liquid biopsy test requires collecting high-dimensional data from a large number of blood samples across heterogeneous groups of patients. We propose that the...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-53851-9 |
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| author | Mehran Karimzadeh Amir Momen-Roknabadi Taylor B. Cavazos Yuqi Fang Nae-Chyun Chen Michael Multhaup Jennifer Yen Jeremy Ku Jieyang Wang Xuan Zhao Philip Murzynowski Kathleen Wang Rose Hanna Alice Huang Diana Corti Dang Nguyen Ti Lam Seda Kilinc Patrick Arensdorf Kimberly H. Chau Anna Hartwig Lisa Fish Helen Li Babak Behsaz Olivier Elemento James Zou Fereydoun Hormozdiari Babak Alipanahi Hani Goodarzi |
| author_facet | Mehran Karimzadeh Amir Momen-Roknabadi Taylor B. Cavazos Yuqi Fang Nae-Chyun Chen Michael Multhaup Jennifer Yen Jeremy Ku Jieyang Wang Xuan Zhao Philip Murzynowski Kathleen Wang Rose Hanna Alice Huang Diana Corti Dang Nguyen Ti Lam Seda Kilinc Patrick Arensdorf Kimberly H. Chau Anna Hartwig Lisa Fish Helen Li Babak Behsaz Olivier Elemento James Zou Fereydoun Hormozdiari Babak Alipanahi Hani Goodarzi |
| author_sort | Mehran Karimzadeh |
| collection | DOAJ |
| description | Abstract Liquid biopsies have the potential to revolutionize cancer care through non-invasive early detection of tumors. Developing a robust liquid biopsy test requires collecting high-dimensional data from a large number of blood samples across heterogeneous groups of patients. We propose that the generative capability of variational auto-encoders enables learning a robust and generalizable signature of blood-based biomarkers. In this study, we analyze orphan non-coding RNAs (oncRNAs) from serum samples of 1050 individuals diagnosed with non-small cell lung cancer (NSCLC) at various stages, as well as sex-, age-, and BMI-matched controls. We demonstrate that our multi-task generative AI model, Orion, surpasses commonly used methods in both overall performance and generalizability to held-out datasets. Orion achieves an overall sensitivity of 94% (95% CI: 87%–98%) at 87% (95% CI: 81%–93%) specificity for cancer detection across all stages, outperforming the sensitivity of other methods on held-out validation datasets by more than ~ 30%. |
| format | Article |
| id | doaj-art-bfa41ff610b944e99ea17c73581fe401 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-bfa41ff610b944e99ea17c73581fe4012024-11-24T12:35:12ZengNature PortfolioNature Communications2041-17232024-11-0115111210.1038/s41467-024-53851-9Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancerMehran Karimzadeh0Amir Momen-Roknabadi1Taylor B. Cavazos2Yuqi Fang3Nae-Chyun Chen4Michael Multhaup5Jennifer Yen6Jeremy Ku7Jieyang Wang8Xuan Zhao9Philip Murzynowski10Kathleen Wang11Rose Hanna12Alice Huang13Diana Corti14Dang Nguyen15Ti Lam16Seda Kilinc17Patrick Arensdorf18Kimberly H. Chau19Anna Hartwig20Lisa Fish21Helen Li22Babak Behsaz23Olivier Elemento24James Zou25Fereydoun Hormozdiari26Babak Alipanahi27Hani Goodarzi28Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Exai Bio Inc.Weill Cornell MedicineStanford UniversityExai Bio Inc.Exai Bio Inc.University of CaliforniaAbstract Liquid biopsies have the potential to revolutionize cancer care through non-invasive early detection of tumors. Developing a robust liquid biopsy test requires collecting high-dimensional data from a large number of blood samples across heterogeneous groups of patients. We propose that the generative capability of variational auto-encoders enables learning a robust and generalizable signature of blood-based biomarkers. In this study, we analyze orphan non-coding RNAs (oncRNAs) from serum samples of 1050 individuals diagnosed with non-small cell lung cancer (NSCLC) at various stages, as well as sex-, age-, and BMI-matched controls. We demonstrate that our multi-task generative AI model, Orion, surpasses commonly used methods in both overall performance and generalizability to held-out datasets. Orion achieves an overall sensitivity of 94% (95% CI: 87%–98%) at 87% (95% CI: 81%–93%) specificity for cancer detection across all stages, outperforming the sensitivity of other methods on held-out validation datasets by more than ~ 30%.https://doi.org/10.1038/s41467-024-53851-9 |
| spellingShingle | Mehran Karimzadeh Amir Momen-Roknabadi Taylor B. Cavazos Yuqi Fang Nae-Chyun Chen Michael Multhaup Jennifer Yen Jeremy Ku Jieyang Wang Xuan Zhao Philip Murzynowski Kathleen Wang Rose Hanna Alice Huang Diana Corti Dang Nguyen Ti Lam Seda Kilinc Patrick Arensdorf Kimberly H. Chau Anna Hartwig Lisa Fish Helen Li Babak Behsaz Olivier Elemento James Zou Fereydoun Hormozdiari Babak Alipanahi Hani Goodarzi Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer Nature Communications |
| title | Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer |
| title_full | Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer |
| title_fullStr | Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer |
| title_full_unstemmed | Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer |
| title_short | Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer |
| title_sort | deep generative ai models analyzing circulating orphan non coding rnas enable detection of early stage lung cancer |
| url | https://doi.org/10.1038/s41467-024-53851-9 |
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