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Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary
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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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Cites
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
Regression Models and Life-Tables
Cox, D. R. (1972) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1972.tb00899.x · Cited by 39,793
From local explanations to global understanding with explainable AI for trees
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551
Proportional hazards tests and diagnostics based on weighted residuals
GRAMBSCH, P. M., & THERNEAU, T. M. (1994) · Biometrika · doi:10.1093/biomet/81.3.515 · Cited by 5,885
COSMIC: the Catalogue Of Somatic Mutations In Cancer
Tate, J. G., Bamford, S., Jubb, H. C., Sondka, Z., Beare, D. M., Bindal, N., Boutselakis, H., Cole, C. G., Creatore, C., Dawson, E., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1015 · Cited by 4,867
The repertoire of mutational signatures in human cancer
Alexandrov, L. B., Kim, J., Haradhvala, N. J., Huang, M. N., Tian Ng, A. W., Wu, Y., Boot, A., Covington, K. R., Gordenin, D. A., Bergstrom, E. N., et al. (2020) · Nature · doi:10.1038/s41586-020-1943-3 · Cited by 3,496
OncoKB: A Precision Oncology Knowledge Base
Chakravarty, D., Gao, J., Phillips, S. M., Kundra, R., Zhang, H., Wang, J., Rudolph, J. E., Yaeger, R. D., Soumerai, T. E., Nissan, M. H., et al. (2017) · JCO Precision Oncology · doi:10.1200/po.17.00011 · Cited by 2,783
Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT)
Cheng, D. T., Mitchell, T. N., Zehir, A., Shah, R. H., Benayed, R., Syed, A., Chandramohan, R., Liu, Z. Y., Won, H. H., Scott, S. N., et al. (2015) · The Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2014.12.006 · Cited by 2,119
Tobacco Smoke Carcinogens and Lung Cancer
Hecht, S. S. (1999) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/91.14.1194 · Cited by 1,976
AACR Project GENIE: Powering Precision Medicine through an International Consortium
The AACR Project GENIE Consortium, The AACR Project GENIE Consortium, André, F., Arnedos, M., Baras, A. S., Baselga, J., Bedard, P. L., Berger, M. F., Bierkens, M., Calvo, F., et al. (2017) · Cancer Discovery · doi:10.1158/2159-8290.cd-17-0151 · Cited by 1,964
Research works printed but not linked: 3
- Moon, I., Groha, S., & Gusev, A. SurvLatent ODE: a neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated venous thromboembolism (VTE) prediction. In Proceedings of the 7th Machine Learning for Healthcare Conference. 800– 827 (PMLR, 2022).
- Bergstra, J. & Bengio, Y. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012).
- Janzing, D., Minorics, L. & Blöbaum, P. Feature relevance quantification in explainable AI: a causal problem. In Proceedings of International Conference on Artificial Intelligence and Statistics 2907–2916 (PMLR, 2020).
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Wei, Y., Liu, M., Mou, K., Wang, L., & Zheng, Y. (2025) · Clinical Cosmetic and Investigational Dermatology · doi:10.2147/ccid.s557553
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Artificial intelligence in oncology: An introduction
Khan, N. A., Mahajan, A. A., Tendulkar, D., Das, N. R., Mondal, S., Dey, A., Ghosh, S., & Rashid, G. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45004-4.00008-6
AI in cancer clinical trials enhancing study design and patient selection
Sharma, R., Kumar, V., Mirza, A. A., & Pathak, Y. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45004-4.00014-1
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Chen, Z., Honglin, Y., Xie, T., Xiang, L., & Yuan, J. (2026) · iScience · doi:10.1016/j.isci.2026.116351
Löptien, J., Haas, M., Pouyiourou, M., Müller, C., Coith, C., Bochtler, T., Cai, M., Forouzmand, E., He, Y., Neumann, O., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.18.732338
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Zhao, Y., Xie, D., Liu, H., Peng, B., Li, X., Wang, X., Leng, Y., Liu, J., Yang, T., Li, H., et al. (2026) · Bio-Design and Manufacturing · doi:10.1631/bdm.2500394
Zhang, Y., Liu, L., Bao, H., Xu, K., Zhang, H., Wang, S., Chang, S., Zhu, D., Huang, Z., Wang, Z., et al. (2026) · Molecular Biomedicine · doi:10.1186/s43556-026-00497-2
Zeng, X., Lao, I. W., Sun, Y., Wang, Q., Zhang, X., Luo, Z., Zhou, X., & Xu, M. (2026) · Biomarker Research · doi:10.1186/s40364-026-00983-3
Yang, T., Jiang, T., Liu, D., Yu, M., & Li, C. (2026) · Research Square · doi:10.21203/rs.3.rs-9465974/v1
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Schuerch, M., Geisberg, J., Flower, C. T., Bektaş, A. B., McDonald, T. O., Mishra, S., Graser, C. J., Altreuter, J., Ananda, G., Boland, G. M., et al. (2026) · medRxiv · doi:10.64898/2026.08.17.26354679
Emerging biomarkers in cardiovascular disease
Ahmed, Z. (2026) · Advances in clinical chemistry · doi:10.1016/bs.acc.2026.08.004
Llanos, P., Russell‐Guzmán, J., Monsalves‐Álvarez, M., Aravena, M., Maturana, M., Olivares, R., Olivares, P., López-Gil, J. F., Yáñez-Sepúlveda, R., & Buvinic, S. (2026) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2026.109643
CT-based prediction of EGFR mutation subtypes and TP53 co-mutation status in lung adenocarcinoma
Liu, H., Li, Z., Zhang, Y., Yu, G., Miao, Y., Teng, X., Xu, Y., Xu, W., Gao, C., Bai, F., et al. (2026) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2026.1898841
The Cost of Uncertainty: A Microcosting Analysis of the Cancer of Unknown Primary Diagnostic Pathway
Spurgeon, L. C., Mitchell, C. L., Conway, A., Cook, N., & Gavan, S. P. (2026) · BJC Reports · doi:10.1038/s44276-026-00252-8
Kim, J., Ko, Y., Jang, S. Y., Shin, J. E., Kim, J., Shin, J., Park, S., Kwon, M., Lim, S. H., Jung, H. A., et al. (2026) · Research Square · doi:10.21203/rs.3.rs-11257888/v1
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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