| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Nature Communications, Vol 15, Iss 1, Pp 1-12 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-024-53851-9 |
| Popis: |
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%. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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