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Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary
Open accessPublished version of a preprint
Abstract
Cancer of unknown primary (CUP) is a type of cancer that cannot be traced back to its primary site and accounts for 3-5% of all cancers. Established targeted therapies are lacking for CUP, leading to generally poor outcomes. We developed OncoNPC, a machine-learning classifier trained on targeted next-generation sequencing (NGS) data from 36,445 tumors across 22 cancer types from three institutions. Oncology NGS-based primary cancer-type classifier (OncoNPC) achieved a weighted F1 score of 0.942 for high confidence predictions ([Formula: see text]) on held-out tumor samples, which made up 65.2% of all the held-out samples. When applied to 971 CUP tumors collected at the Dana-Farber Cancer Institute, OncoNPC predicted primary cancer types with high confidence in 41.2% of the tumors. OncoNPC also identified CUP subgroups with significantly higher polygenic germline risk for the predicted cancer types and with significantly different survival outcomes. Notably, patients with CUP who received first palliative intent treatments concordant with their OncoNPC-predicted cancers had significantly better outcomes (hazard ratio (HR) = 0.348; 95% confidence interval (CI) = 0.210-0.570; P = [Formula: see text]). Furthermore, OncoNPC enabled a 2.2-fold increase in patients with CUP who could have received genomically guided therapies. OncoNPC thus provides evidence of distinct CUP subgroups and offers the potential for clinical decision support for managing patients with CUP.
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“This includes genomics-based algorithms, where targeted NGS algorithms have been rigorously evaluated and are currently being deployed and assessed for clinical utility (134, 135).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [135]
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Metadata
- Authors
- Moon, I., LoPiccolo, J., Baca, S. C., Sholl, L. M., Kehl, K. L., Hassett, M. J., Liu, D., Schrag, D., & Gusev, A.
- Affiliations
- Harvard University (US)
- Dana-Farber Cancer Institute (US)
- Brigham and Women's Hospital (US)
- Broad Institute (US)
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Gusev, Alexander
- ORCID
- Venue
- Nature Medicine
- Published
- 2023-08
- Volume
- 29
- Issue
- 8
- Pages
- 2057-2067
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC11484892
- OpenAlex
- W4385628901
- Cited by
- 122
- References
- 52
- Citations by year
- 2026: 32 · 2025: 44 · 2024: 42 · 2023: 4
- FWCI
- 14.88
- Type
- journal article
- Language
- en
- Update notices
- correction (2023-11-15)
- Open access
- green
- dx.doi.org
- Licence
- Locations
- Nature Medicine (publishedVersion)
- Research Square (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- PubMed Central (submittedVersion, cc-by)
- Funders
- Dana-Farber Cancer Institute
- Emerson Collective
- National Cancer Institute (R01CA227237, R01CA244569, R01CA259058, P30CA008748, T32CA009172)
- Topics
- Health Sciences › Medicine › Oncology › Cancer Diagnosis and Treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- cancer of unknown primary, targeted next-generation sequencing, treatment response prediction, clinical decision support, cancer subgroups, survival outcomes
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