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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Genomics-based targeted NGS algorithms for cell-of-origin prediction have been rigorously evaluated and are being deployed and assessed for clinical utility.
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 2,735 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Vemurafenib in Multiple Nonmelanoma Cancers with <i>BRAF</i> V600 Mutations
Hyman, D. M., Puzanov, I., Subbiah, V., Faris, J. E., Chau, I., Blay, J.-Y., Wolf, J., Raje, N. S., Diamond, E. L., Hollebecque, A., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1502309 · Cited by 1,764 · see citing works
Rosenthal, R., McGranahan, N., Herrero, J., Taylor, B. S., & Swanton, C. (2016) · Genome biology · doi:10.1186/s13059-016-0893-4 · Cited by 1,163 · see citing works
EGFR Mutations and Lung Cancer
Santos, G. D. C., Shepherd, F. A., & Tsao, M. (2011) · Annual Review of Pathology Mechanisms of Disease · doi:10.1146/annurev-pathol-011110-130206 · Cited by 948 · see citing works
Zhang, Y., Yuan, J.-Q., Wang, K., Fu, X., Han, X., Threapleton, D. E., Yang, Z., Mao, C., & Tang, J.-L. (2016) · Oncotarget · doi:10.18632/oncotarget.12587 · Cited by 831 · see citing works
XGBoost Model for Chronic Kidney Disease Diagnosis
Ogunleye, A., & Wang, Q. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2911071 · Cited by 768 · see citing works
AI-based pathology predicts origins for cancers of unknown primary
Lu, M. Y., Chen, T. Y., Williamson, D. F. K., Zhao, M., Shady, M., Lipkova, J., & Mahmood, F. (2021) · Nature · doi:10.1038/s41586-021-03512-4 · Cited by 636 · see citing works
Garcia, E. P., Minkovsky, A., Jia, Y., Ducar, M. D., Shivdasani, P., Gong, X., Ligon, A. H., Sholl, L. M., Kuo, F. C., MacConaill, L. E., et al. (2017) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2016-0527-oa · Cited by 521 · see citing works
Xie, J. H., & Liu, C. (2005) · Statistics in Medicine · doi:10.1002/sim.2174 · Cited by 511 · see citing works
Epigenetic profiling to classify cancer of unknown primary: a multicentre, retrospective analysis
Morán, S., Martínez‐Cardús, A., Sayols, S., Musulén, E., Balañá, C., Estival-Gonzalez, A., Moutinho, C., Heyn, H., Díaz‐Lagares, Á., de Moura, M. C., et al. (2016) · The Lancet Oncology · doi:10.1016/s1470-2045(16)30297-2 · Cited by 489 · see citing works
Fizazi, K. S., Greco, F. A., Pavlidis, N. A., Daugaard, G., Oien, K. A., & Pentheroudakis, G. E. (2015) · Annals of Oncology · doi:10.1093/annonc/mdv305 · Cited by 455 · see citing works
Hainsworth, J. D., Rubin, M. S., Spigel, D. R., Boccia, R. V., Raby, S., Quinn, R., & Greco, F. A. (2012) · Journal of Clinical Oncology · doi:10.1200/jco.2012.43.3755 · Cited by 365 · see citing works
KRAS mutation in pancreatic cancer
Luo, J. (2021) · Seminars in Oncology · doi:10.1053/j.seminoncol.2021.02.003 · Cited by 360 · see citing works
Evaluating eligibility criteria of oncology trials using real-world data and AI
Liu, R., Rizzo, S., Whipple, S., Pal, N., Pineda, A. L., Lu, M. W., Arnieri, B., Lü, Y., Capra, W. B., Copping, R., et al. (2021) · Nature · doi:10.1038/s41586-021-03430-5 · Cited by 326 · see citing works
Cancer of Unknown Primary Site
Varadhachary, G. R., & Raber, M. N. (2014) · New England Journal of Medicine · doi:10.1056/nejmra1303917 · Cited by 267 · see citing works
AI-based pathology predicts origins for cancers of unknown primary
Lu, M. Y., Chen, T., Williamson, D. F. K., Zhao, M. M., Shady, M., Lipková, J., & Mahmood, F. (2021) · RePEc: Research Papers in Economics · Cited by 263 · see citing works
glm2: Fitting Generalized Linear Models with Convergence Problems
Ian, C. M. (2011) · The R Journal · doi:10.32614/rj-2011-012 · Cited by 233 · see citing works
Jiao, W., Atwal, G., Polak, P. P., Karlić, R., Cuppen, E. P. J. G., Al‐Shahrour, F., Atwal, G., Bailey, P. J., Biankin, A. V., Boutros, P. C., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-13825-8 · Cited by 216 · see citing works
Hayashi, H., Kurata, T., Takiguchi, Y., Arai, M., Takeda, K., Akiyoshi, K., Matsumoto, K., Onoe, T., Mukai, H., Matsubara, N., et al. (2019) · Journal of Clinical Oncology · doi:10.1200/jco.18.00771 · Cited by 171 · see citing works
Elsheikh, S. E., Green, A. R., Aleskandarany, M. A., Grainge, M. J., Paish, C. E., Lambros, M. B. K., Reis-Filho, J. S., & Ellis, I. O. (2007) · Breast Cancer Research and Treatment · doi:10.1007/s10549-007-9659-8 · Cited by 154 · see citing works
Diagnostic work-up of carcinoma of unknown primary: from immunohistochemistry to molecular profiling
Oien, K. A., & Dennis, J. L. (2012) · Annals of Oncology · doi:10.1093/annonc/mds357 · Cited by 130 · see citing works
A mini review on cancer of unknown primary site: A clinical puzzle for the oncologists
Pavlidis, N. A., Khaled, H. M., & Gaafar, R. M. (2014) · Journal of Advanced Research · doi:10.1016/j.jare.2014.11.007 · Cited by 117 · see citing works
Zhao, Y., Pan, Z., Namburi, S., Pattison, A., Posner, A., Balachander, S., Paisie, C. A., Reddi, H. V., Rueter, J., Gill, A. J., et al. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.103030 · Cited by 117 · see citing works
Hayashi, H., Takiguchi, Y., Minami, H., Akiyoshi, K., Segawa, Y., Ueda, H., Iwamoto, Y., Kondoh, C. N., Matsumoto, K., Takahashi, S. I., et al. (2020) · JAMA Oncology · doi:10.1001/jamaoncol.2020.4643 · Cited by 117 · see citing works
Development of genome-derived tumor type prediction to inform clinical cancer care
Penson, A., Camacho, N., Zheng, Y., Varghese, A. M., Al-Ahmadie, H., Razavi, P., Chandarlapaty, S., Vallejo, C. E., Vakiani, E., Gilewski, T., et al. (2020) · JAMA Oncology · doi:10.1001/jamaoncol.2019.3985 · Cited by 115 · see citing works
Hatton, C. M., Paton, L. W., McMillan, D., Cussens, J., Gilbody, S. M., & Tiffin, P. A. (2018) · Journal of Affective Disorders · doi:10.1016/j.jad.2018.12.095 · Cited by 107 · see citing works
Conway, A., Mitchell, C. L., Kilgour, E., Brady, G., Dive, C., & Cook, N. (2018) · British Journal of Cancer · doi:10.1038/s41416-018-0332-2 · Cited by 96 · see citing works
Unfavourable prognosis associated with K-ras gene mutation in pancreatic cancer surgical margins
Kim, J., Reber, H. A., Dry, S., Elashoff, D. A., Chen, S. L., Umetani, N., Kitago, M., Hines, O. J., Kazanjian, K. K., Hiramatsu, S., et al. (2006) · Gut · doi:10.1136/gut.2005.083063 · Cited by 95 · see citing works
Determining Tissue of Origin for Metastatic Cancers
Anderson, G. G., & Weiss, L. M. (2009) · Applied immunohistochemistry & molecular morphology · doi:10.1097/pai.0b013e3181a75e6d · Cited by 94 · see citing works
Natural Language Processing to Ascertain Cancer Outcomes from Medical Oncologist Notes
Kehl, K. L., Xu, W., Lepisto, E., Elmarakeby, H., Hassett, M. J., Van Allen, E. M., Johnson, B. E., & Schrag, D. (2020) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00020 · Cited by 90 · see citing works
He, B., Zhang, Y., Zhou, Z. X., Wang, B., Liang, Y., Lang, J., Lin, H., Bing, P., Yu, L., Sun, D., et al. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00737 · Cited by 83 · see citing works
Nguyen, L., van Hoeck, A., & Cuppen, E. P. J. G. (2022) · Nature Communications · doi:10.1038/s41467-022-31666-w · Cited by 81 · see citing works
Chen, Y., Wang, X., Jung, Y., Abedi, V., Zand, R., Bikak, M., & Adibuzzaman, M. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aadf0f · Cited by 68 · see citing works
Mutation distributions and clinical correlations of PIK3CA gene mutations in breast cancer
Dirican, E., Akkіprіk, M., & Özer, A. (2016) · Tumor Biology · doi:10.1007/s13277-016-4924-2 · Cited by 64 · see citing works
Yoon, H., Foster, N. R., Meyers, J. P., Steen, P. D., Visscher, D. W., Pillai, R., Prow, D. M., Reynolds, C. M., Marchello, B. T., Mowat, R. B., et al. (2015) · Annals of Oncology · doi:10.1093/annonc/mdv543 · Cited by 54 · see citing works
Does Cancer of Unknown Primary (CUP) Truly Exist as a Distinct Cancer Entity?
Bochtler, T., & Krämer, A. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.00402 · Cited by 53 · see citing works
Liu, R., Rizzo, S., Waliany, S., Garmhausen, M. R., Pal, N., Huang, Z., Chaudhary, N., Wang, L. E., Harbron, C. G., Neal, J. W., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01873-5 · Cited by 53 · see citing works
Cancer of Unknown Primary Site: New Treatment Paradigms in the Era of Precision Medicine
Hainsworth, J. D., & Greco, F. A. (2018) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_100014 · Cited by 50 · see citing works
Constructing germline research cohorts from the discarded reads of clinical tumor sequences
Gusev, A., Groha, S., Taraszka, K., Semenov, Y. R., & Zaitlen, N. (2021) · Genome Medicine · doi:10.1186/s13073-021-00999-4 · Cited by 33 · see citing works
Developmental Deconvolution for Classification of Cancer Origin
Moiso, E., Farahani, A. A., Marble, H. D., Hendricks, A., Mildrum, S., Levine, S. S., Lennerz, J. K., & Garg, S. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-1443 · Cited by 30 · see citing works
Posner, A., Prall, O. W. J., Sivakumaran, T., Etemadamoghadam, D., Thio, N., Pattison, A., Balachander, S., Fisher, K., Webb, S., Wood, C. S., et al. (2022) · The Journal of Pathology · doi:10.1002/path.6022 · Cited by 28 · see citing works
Mileshkin, L., Bochtler, T., Gatta, G., Kurzrock, R., Beringer, A., Mueller-Ohldach, M., Surinach, A., Perret, C., Thomas, M., Gondos, Á., et al. (2022) · Cancers · doi:10.3390/cancers14122905 · Cited by 8 · see citing works
Challenge of the Unknown: How Can We Improve Clinical Outcomes in Cancer of Unknown Primary?
Conway, A., Mitchell, C., & Cook, N. (2019) · Journal of Clinical Oncology · doi:10.1200/jco.19.00449 · Cited by 7 · see citing works
Flagged first, then by citations
Research works printed but not linked: 8
- [40]Moon I, Groha S, and 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 Machine Learning for Healthcare Conference, PMLR, 2022, pp. 800– 827.
- [1]A. P. G. Consortium, “ Aacr project genie: Powering precision medicine through an international consortium,” Cancer discovery, vol. 7, no. 8, pp. 818– 831, 2017. 28572459 PMC5611790
- [4]Chen T and Guestrin C, “ Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785– 794.
- [8]Bergstra J and Bengio Y, “ Random search for hyper-parameter optimization.,” Journal of machine learning research, vol. 13, no. 2, 2012.
- [11]Lundberg SM, “ From local explanations to global understanding with explainable ai for trees,” Nature machine intelligence, vol. 2, no. 1, pp. 56– 67, 2020. PMC7326367 32607472
- [12]Janzing D, Minorics L, and Blöbaum P, “ Feature relevance quantification in explainable ai: A causal problem,” in International Conference on artificial intelligence and statistics, PMLR, 2020, pp. 2907– 2916.
- [16]Marschner I, Donoghoe MW, and Donoghoe MMW, “ Package ‘glm2’,” Journal, Vol, vol. 3, no. 2, pp. 12– 15, 2018.
- [17]Chakravarty D, “ Oncokb: A precision oncology knowledge base,” JCO precision oncology, vol. 1, pp. 1– 16, 2017. PMC5586540 28890946
Cited by
Sorted by citations
The Challenges of Machine Learning: A Critical Review
Barbierato, E., & Gatti, A. (2024) · Electronics · doi:10.3390/electronics13020416 · Cited by 217 · see citing works
Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196 · see citing works
A cfDNA methylation-based tissue-of-origin classifier for cancers of unknown primary
Conway, A., Pearce, S. P., Clipson, A., Hill, S. M., Chemi, F., Slane-Tan, D., Ferdous, S., Hossain, A., Kamieniecka, K., White, D. J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-47195-7 · Cited by 74 · see citing works
Zhao, Y. S., Xiong, S., Ren, Q., Wang, J., Li, M., Yang, L., Wu, D., Tang, K., Pan, X., Chen, F., et al. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(24)00599-0 · Cited by 67 · see citing works
Liu, X., Zhang, X., Jiang, S., Mo, M., Wang, Q., Wang, Y., Zhou, L., Hu, S., Yang, H., Hou, Y., et al. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(24)00313-9 · Cited by 65 · see citing works
Progress and opportunities of foundation models in bioinformatics
Li, Q., Hu, Z., Wang, Y., Li, L., Fan, Y., King, I., Jia, G., Wang, S., Song, L., & Li, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae548 · Cited by 62 · see citing works
Integration of artificial intelligence in clinical laboratory medicine: Advancements and challenges
Xie, H., Jia, Y. H., & Liu, S. (2024) · Interdisciplinary medicine · doi:10.1002/inmd.20230056 · Cited by 52 · see citing works
Progress and challenges of artificial intelligence in lung cancer clinical translation
Zhu, E., Muneer, A., Zhang, J., Yang, X., Li, X., Zhou, C., Heymach, J. V., Wu, J., & Le, X. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00986-7 · Cited by 52 · see citing works
Multi-omics based artificial intelligence for cancer research
Li, L., Sun, M., Wang, J., & Wan, S. (2024) · Advances in cancer research · doi:10.1016/bs.acr.2024.06.005 · Cited by 47 · see citing works
Liu, F., Beck, S., Yang, L., Luo, H., & Zhang, K. C. (2026) · Nature reviews. Cancer · doi:10.1038/s41568-026-00922-2 · Cited by 52 · see citing works
Harnessing the power of artificial intelligence for human living organoid research
Wang, H., Li, X., You, X., & Zhao, G. (2024) · Bioactive Materials · doi:10.1016/j.bioactmat.2024.08.027 · Cited by 43 · see citing works
Towards the Prediction of Responses to Cancer Immunotherapy: A Multi-Omics Review
Tao, W., Sun, Q., Xu, B., & Wang, R. (2025) · Life · doi:10.3390/life15020283 · Cited by 39 · see citing works
Zhang, P., Wu, X., Wang, D., Zhang, M., Zhang, B., & Zhang, Z. (2024) · Environmental Toxicology · doi:10.1002/tox.24230 · Cited by 35 · see citing works
Machine learning in cardiovascular risk assessment: Towards a precision medicine approach
Wang, Y., Aivalioti, E., Stamatelopoulos, K., Zervas, G., Mortensen, M. B., Zeller, M., Liberale, L., Vece, D. D., Schweiger, V., Camici, G. G., et al. (2025) · European Journal of Clinical Investigation · doi:10.1111/eci.70017 · Cited by 32 · see citing works
Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data
Darmofal, M., Suman, S., Atwal, G., Chen, J.-F., Chen, J., Toomey, M., Vakiani, E., Varghese, A. M., Rema, A. B., Syed, A., et al. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-0996 · Cited by 31 · see citing works
Precision Oncology: Current Landscape, Emerging Trends, Challenges, and Future Perspectives
Qiao, D., Wang, R. C., & Wang, Z. (2025) · Cells · doi:10.3390/cells14221804 · Cited by 38 · see citing works
Advances in Cancer Research: Current and Future Diagnostic and Therapeutic Strategies
Liu, X., Jiang, H., & Wang, X. (2024) · Biosensors · doi:10.3390/bios14020100 · Cited by 28 · see citing works
Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges
Mondello, A., Bo, M. D., Toffoli, G., & Polano, M. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1260276 · Cited by 27 · see citing works
The leading global health challenges in the artificial intelligence era
Zaidan, A. M. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1328918 · Cited by 24 · see citing works
Artificial intelligence in bioinformatics: a survey
Jiang, J., Li, Y., Cao, S., Shan, Y., Liu, Y., Fei, T., Yu, Y., Feng, Y., Li, Y., Li, Y., et al. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf576 · Cited by 24 · see citing works
The Rise of Hypothesis-Driven Artificial Intelligence in Oncology
Xianyu, Z., Correia, C., Ung, C. Y., Zhu, S., Billadeau, D. D., & Li, H. (2024) · Cancers · doi:10.3390/cancers16040822 · Cited by 25 · see citing works
Artificial intelligence-aided data mining of medical records for cancer detection and screening
Haue, A. D., Hjaltelin, J. X., Holm, P. C., Placido, D., & Brunak, S. (2024) · The Lancet Oncology · doi:10.1016/s1470-2045(24)00277-8 · Cited by 23 · see citing works
Rebello, R. J., Posner, A., Dong, R., Prall, O. W. J., Sivakumaran, T., Mitchell, C. B., Flynn, A., Caneborg, A., Mitchell, C., Kanwal, S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59661-x · Cited by 18 · see citing works
Liang, A., Wu, X., Zhu, Y., Pan, L., Wang, A., Wu, C., & Xia, J. (2025) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2025.102936 · Cited by 18 · see citing works
Xin, H., Zhang, Y., Lai, Q., Liao, N., Zhang, J., Liu, Y., Chen, Z., He, P., He, J., Liu, J., et al. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102464 · Cited by 17 · see citing works
Discovery of predictive biomarkers for cancer therapy through computational approaches
Wang, X., Nguyen, J., Nader, K. M., Miihkinen, M., Wall, P., Singhal, A., Bedard, P. L., Ideker, T., Aittokallio, T. A., & Haibe‐Kains, B. (2026) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-025-01109-8 · Cited by 18 · see citing works
Spurgeon, L. C., Mitchell, C. L., Cook, N., & Conway, A. (2025) · British Journal of Cancer · doi:10.1038/s41416-025-03073-7 · Cited by 14 · see citing works
Rethinking cancer of unknown primary: from diagnostic challenge to targeted treatment
Pouyiourou, M., Bochtler, T., Pauli, C., Moch, H., Brobeil, A., Pantel, K., Stenzinger, A., & Krämer, A. (2025) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-025-01060-8 · Cited by 14 · see citing works
Jakka, A. L., Chacko, R. M., Vasam, M., Alagarsamy, S., Chandragiri, S. S., Gavini, S. D., & Mathew, M. J. (2025) · Pharmacogenomics · doi:10.1080/14622416.2025.2591596 · Cited by 15 · see citing works
A review on trends in development and translation of omics signatures in cancer
Ma, W., Tang, W., Kwok, J. S.-L., Tong, A. H. Y., Lo, C. W. S., Chu, A. T.-W., Chung, B. H., & Hong Kong Genome Project (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.01.024 · Cited by 15 · see citing works
Interpretable discovery of patterns in tabular data via spatially semantic topographic maps
Yan, R., Islam, M. T., & Xing, L. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01268-6 · Cited by 13 · see citing works
Li, Q., Teodoro, G., Jiang, Y., & Kong, J. (2024) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2024.102467 · Cited by 11 · see citing works
Rostami, S., Stutterheim, H. W., Maxouri, O., Willemse, J. R. J., Sánchez, D. I. R., Schats, W., van Golen, L. W., Vollebergh, M. A., Cheung, Z. J., Vogel, W. V., et al. (2025) · British Journal of Radiology · doi:10.1093/bjr/tqaf039 · Cited by 11 · see citing works
Convergence of machine learning and genomics for precision oncology
Reardon, B. M., Culhane, A. C., & Allen, E. M. V. (2026) · Nature reviews. Cancer · doi:10.1038/s41568-025-00897-6 · Cited by 14 · see citing works
Advances in machine learning for tumour classification in cancer of unknown primary: A mini-review
Oróstica, K., Mardones, F., Bernal, Y. A., Molina, S., Orchard, M., Verdugo, R. A., Carvajal‐Hausdorf, D., Marcelain, K., Contreras, S., & Armisén, R. (2024) · Cancer Letters · doi:10.1016/j.canlet.2024.217348 · Cited by 10 · see citing works
From silicon to solutions: AI's impending impact on research and discovery
Markowitz, D. M., Boyd, R. L., & Blackburn, K. G. (2024) · Frontiers in Social Psychology · doi:10.3389/frsps.2024.1392128 · Cited by 10 · see citing works
Yang, P., Feng, P., Tian, G., Zhao, G., Yuan, G., & Pan, Y. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108510 · Cited by 8 · see citing works
GPSai: A Clinically Validated AI Tool for Tissue of Origin Prediction during Routine Tumor Profiling
Ghani, H., Helmstetter, A., Ribeiro, J. R., Maney, T., Rock, S. L., Feldman, R. A., Swensen, J. J., Abdulla, F. R., Spetzler, D. B., Florento, E., et al. (2025) · Cancer Research Communications · doi:10.1158/2767-9764.crc-25-0171 · Cited by 8 · see citing works
Bulić, L., Brlek, P., Hrvatin, N., Brenner, E., Škaro, V., Projić, P., Rogan, S. A., Bebek, M., Shah, P. S., & Primorac, D. (2026) · AI · doi:10.3390/ai7010011 · Cited by 13 · see citing works
Precise, pragmatic and inclusive: the modern era of oncology clinical trials
Grant, M. J., & Goldberg, S. B. (2023) · Nature Medicine · doi:10.1038/s41591-023-02466-6 · Cited by 7 · see citing works
Brlek, P., Bulić, L., Shah, N., Shah, P., & Primorac, D. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26062568 · Cited by 6 · see citing works
Clinical prediction of pathological complete response in breast cancer: a machine learning study
He, C., Yu, T., Yang, L., He, L., Zhu, J., & Chen, J. (2025) · BMC Cancer · doi:10.1186/s12885-025-14335-1 · Cited by 6 · see citing works
Measuring Progress in Precision Oncology
Horak, P., & Fröhling, S. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1237 · Cited by 6 · see citing works
Metastasis to and from the breast: a guide to differential diagnosis and ancillary testing
Šidlauskas, K., Elliott, P., Makhija, P., & Jones, J. L. (2025) · Diagnostic histopathology · doi:10.1016/j.mpdhp.2025.01.001 · Cited by 5 · see citing works
Advancements in the Application of the Intersection of Medicine and Engineering in Cancer Research
Chen, H., Zhang, G.-M., Qian, Y., Yang, P., Li, X., Wang, J., Wang, J., Hu, Y.-X., Xiao, H., Zhong, Q., et al. (2025) · Cancer Nexus · doi:10.1002/cnx2.70003 · Cited by 5 · see citing works
Zhao, T., Zhang, X., Liu, X., Wang, Q., Hu, X., & Luo, Z. (2025) · MedComm · doi:10.1002/mco2.70161 · Cited by 5 · see citing works
Zou, M., Li, H., Su, D., Xiong, Y., Wei, H., Wang, S., Sun, H., Wang, T., Xi, Q., Zuo, Y., et al. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad430 · Cited by 5 · see citing works
From Code to Cure: The Impact of Artificial Intelligence in Biomedical Applications
Gromiha, M. M., Preethi, P., & Pandey, M. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4010030 · Cited by 5 · see citing works
Jeong, Y. U., Chu, J., Kang, J., Baek, S., Lee, J., Jung, D.-S., Kim, W.-W., Kim, Y. R., Kang, J.-H., & Do, I. (2024) · Current Issues in Molecular Biology · doi:10.3390/cimb46070432 · Cited by 5 · see citing works
Metastatic renal cell carcinoma with occult primary: a multicenter prospective cohort
Jacquin, N., Flippot, R., Masliah‐Planchon, J., Grisay, G., Brillet, R., Dupain, C., Kamal, M. T., Guillou, I., Gruel, N., Servant, N., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00648-0 · Cited by 5 · see citing works
Liu, J., Yang, M., Bi, Y., Zhang, J., Yang, Y., Li, Y., Shen, H., Chen, K., & Li, X. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102332 · Cited by 4 · see citing works
Yang, W., Duan, L., Zhao, X., Niu, L., Wang, C., Fan, D., & Hong, L. (2025) · Pathology - Research and Practice · doi:10.1016/j.prp.2025.156083 · Cited by 4 · see citing works
Advancing cancer nanomedicine with machine learning
Qin, X., Lu, T., & Pang, Z. (2024) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2024.06.018 · Cited by 4 · see citing works
Xie, N., Liao, D., Liu, B., Zhang, J., Liu, L., Huang, G., & Ouyang, Q. (2024) · Journal of Clinical Laboratory Analysis · doi:10.1002/jcla.25124 · Cited by 4 · see citing works
Cai, C., Li, J., Liu, M., Jiao, Y., & Xu, J. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822073 · Cited by 4 · see citing works
Wang, F. (2025) · American Journal of Cancer Research · doi:10.62347/whuq1208 · Cited by 4 · see citing works
Lee, K. J., Park, S. J., Kim, J., Hong, S. H., Kim, I.-H., Lee, J., Lee, M. A., Shin, K., & Mun, H. S. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14092947 · Cited by 3 · see citing works
Association between polygenic risk and survival in breast cancer patients
Kurant, D. E., Groha, S., Ding, Y., German, C., Wang, W., Granka, J. M., Holmes, M. V., Aslibekyan, S. W., Auton, A., Babalola, E., et al. (2025) · BMC Cancer · doi:10.1186/s12885-025-14640-9 · Cited by 3 · see citing works
Chen, K., Wang, L., Wang, X., Yang, L., Zhang, X., Lin, Y., & Cao, L. (2025) · Surgical Endoscopy · doi:10.1007/s00464-025-12169-3 · Cited by 3 · see citing works
Yalikun, Y., Shen, Y., Mao, A., Zhou, Q., Wei, J., Zhu, Y., & Long, M. (2025) · Cancers · doi:10.3390/cancers17213562 · Cited by 3 · see citing works
Dupain, C., Jacquin, N., Latouche, A., Nevière, Z., Gestraud, P., Hamza, A., Nedara, K., Cockenpot, V., Sèlves, J., Allory, Y., et al. (2025) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2025.101524 · Cited by 3 · see citing works
Mauvieux, L., Herbrecht, R., Eischen, A., Galoisy, A.-C., Rolland, D. C. M., Gervais, C., Mayeur‐Rousse, C., Hueber-Bonnot, S., Nicolae, A., Fornecker, L., et al. (2025) · Blood Advances · doi:10.1182/bloodadvances.2025016424 · Cited by 3 · see citing works
Hu, Y., He, Z., Li, S., Han, B., Yang, X., & Chen, X. (2026) · Environmental Pollution · doi:10.1016/j.envpol.2026.128275 · Cited by 4 · see citing works
Genomics in Cancer of Unknown Primary: Utility in Modern Clinical Practice
Dooley, A. J., Bowden, A. R., Whatling, H., Watkins, J. A., & Greef, B. (2025) · Clinical Oncology · doi:10.1016/j.clon.2025.103793 · Cited by 3 · see citing works
Li, L., Li, L., Wang, Y., Wu, B., Guan, Y., Chen, Y., & Zhao, J. (2024) · Genes · doi:10.3390/genes15111458 · Cited by 3 · see citing works
Lu, M., Kim, C., Covert, I., White, N. J., & Lee, S. (2024) · medRxiv · doi:10.1101/2024.09.04.24312866 · Cited by 3 · see citing works
Huang, Y., Li, J., An, H., Luo, T., Ji, Z., Song, Y. F., Liu, H., Kaicheng, U., Zhang, S. M., Zhao, J., et al. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107953 · Cited by 2 · see citing works
Impact of whole genome sequencing on the care pathway for patients with cancer of unknown primary
Droogers, E., Teunissen, Y., van Puffelen, A. J., Groenendijk, F. H., van Zanten, S. E. M. V., Wagner, A., Verheul, H. M. W., & Robbrecht, D. G. J. (2025) · ESMO Open · doi:10.1016/j.esmoop.2025.105069 · Cited by 2 · see citing works
Wu, S., Dong, J., Shi, J., Qu, X., Bao, Y., Mao, X., Lv, M., Xuan, C., & Ying, H. (2025) · Biomedicines · doi:10.3390/biomedicines13092057 · Cited by 2 · see citing works
Vissers, J. H. A., Mitchell, C., Prall, O. W. J., Lo, W., Kanwal, S., Luen, S. J., Watts, S. C., Angel, C. M., Khoo, C., Pang, J., et al. (2025) · Genome Medicine · doi:10.1186/s13073-025-01534-5 · Cited by 2 · see citing works
Cutting-edge AI technologies in skin cancer applications
Diao, Y., Chen, X., Huang, Z., Tan, Q., Yang, M., Yu, H., Xu, Y., & Hu, X. (2026) · Cancer Letters · doi:10.1016/j.canlet.2026.218256 · Cited by 2 · see citing works
Lu, L., Wei, X., Chen, Y., Meng, D., Mo, S., Sun, Z., Song, F., Liao, K., & Huang, W. (2026) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1711029 · Cited by 2 · see citing works
SEOM-GECOD clinical guideline for cancer of unknown primary (update 2025)
Gaspà, F. L., Etxániz, O., Giménez, A., Gomila, P., Iglesias, L., Longo, F., Nogales, E., Sánchez, A. C., Soler, G., & Fernández, I. (2026) · Clinical & Translational Oncology · doi:10.1007/s12094-026-04241-8 · Cited by 2 · see citing works
Fang, S., Wang, Y., Zhou, M., Shao, Y., Jiang, Z., Guo, Y., Jiang, T., Feng, W., Fang, J., & Hong, T. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag549 · Cited by 2 · see citing works
Archetypal Analysis of Kidney Allograft Biopsies Using Next-generation Sequencing Technology
Garcia, E. C., Giarraputo, A., Racapé, M., Goutaudier, V., Ursule‐Dufait, C., de la Grange, P., Letourneur, F., Raynaud, M., Couderau, C., Mezine, F., et al. (2024) · Transplantation · doi:10.1097/tp.0000000000005181 · Cited by 2 · see citing works
Adaptive Multiparticle Swarm Neural Architecture Search for High-Incidence Cancer Prediction
Xu, L., Zheng, J., He, C., Wang, J., Zheng, B., & Lv, J. (2025) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2025.3543822 · Cited by 2 · see citing works
Liu, N., Li, X., Luo, X., Liu, B., Tang, J., Xiao, F., Wang, W., Tang, Y., Shu, P., Zhang, B., et al. (2025) · Translational Lung Cancer Research · doi:10.21037/tlcr-24-875 · Cited by 2 · see citing works
Rebello, R. J., Posner, A., Dong, R., Prall, O. W. J., Sivakumaran, T., Mitchell, C. B., Flynn, A., Caneborg, A., Mitchell, C., Kanwal, S., et al. (2024) · medRxiv · doi:10.1101/2024.08.09.24311642 · Cited by 2 · see citing works
Qin, H. (2024) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v4i1.31813 · Cited by 1 · see citing works
Tu, H., Zierow, L., Lennartsson, M., & Schweitzer, S. (2025) · PLoS ONE · doi:10.1371/journal.pone.0321268 · Cited by 1 · see citing works
Liu, J., Zhao, P., Jiang, D., Li, S. B., Jin, C., Xu, D., Wang, X. M., Chen, Y., Tang, B., & Qu, X. (2026) · Gut Microbes · doi:10.1080/19490976.2026.2672791 · Cited by 3 · see citing works
Chen, Y., Li, H., Chen, S. H., Ji, H., Xin, J., & Du, M. (2026) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2026.07.003 · Cited by 1 · see citing works
Application of machine learning to classify cancers of unknown primary
Sarkar, S., & Baptista‐Hon, D. T. (2023) · MedComm – Oncology · doi:10.1002/mog2.63 · Cited by 1 · see citing works
An Adaptive Combined Learning of Grading System for Early Stage Emerging Diseases
Wen, L., Pan, W., Shi, Y., Pan, W., Hu, C., Kong, W., Wang, R., Zhang, W., & Liao, S. (2024) · International Journal of Intelligent Systems · doi:10.1155/2024/6619263 · Cited by 1 · see citing works
Deep learning in personalized genomics and gene editing
Naorem, L. D., Shrivastava, K., & Chandrani, P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-27523-4.00010-x · Cited by 1 · see citing works
A context-aware analytical framework for DNA methylation biomarker discoveryPreprint
Angeloni, A. R., Samartino, C. G., Arbona, J., Vaquer, C. C., Wetten, P. A., Bocanegra, V., Militello, R. D., Sanguinetti, G., Correa, A., Carlen, M., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.20.706794 · Cited by 1 · see citing works
Diener, M. K., & Bruns, C. J. (2024) · Die Onkologie · doi:10.1007/s00761-024-01477-3
Decoding Precision Cancer Care: AI’s Evolution from Data to Decision and Recent Breakthroughs
Zhao, X., Blum, J., Menta, A. K., Yang, V., & Subbiah, V. (2024) · AI in Precision Oncology · doi:10.1089/aipo.2024.0013
Wu, S., Bao, Y., Shi, J., Qu, X., Dong, J., Mao, X., & Ying, H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4891735
Smith, H., Jean, J., Duckett, D., Vormittag-Nocito, E. R., Nezami, B. G., Jennings, L. J., Ahrendsen, J. T., Yang, X. J., Primdahl, D., Conway, K. S., et al. (2024) · Journal of Neuropathology & Experimental Neurology · doi:10.1093/jnen/nlae086
Machine Learning for Flow Cytometry-Based Classification of B-Cell ProliferationsPreprint
Mauvieux, L., Herbrecht, R., Vogt, M., Eischen, A., Galoisy, A.-C., Rolland, D. C. M., Gervais, C., Mayeur‐Rousse, C., Hueber-Bonnot, S., Nicolae, A., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5004506
Zhang, J.-X., Yan, H., Fan, W.-L., Wei, J., Liu, S., Shi, H., Cheng, Y., Feng, Z.-L., & Zu, Q. (2024) · Research Square · doi:10.21203/rs.3.rs-5304318/v1
Hechtman, J. F., Baskovich, B. W., Fussell, A. M., Geiersbach, K. B., Iorgulescu, J. B., Sirohi, D., Snow, A. N., & Sidiropoulos, N. (2024) · Journal of Molecular Diagnostics · doi:10.1016/j.jmoldx.2024.08.010
Vela, V., Sabani, B., & Spahn, G. (2025) · Springer eBooks · doi:10.1007/978-3-031-84297-9_3
Bravo, M. C., Parrado-Hernández, E., McNamara, P. J., & Pellicer, A. (2025) · Journal of Perinatology · doi:10.1038/s41372-025-02346-6
Cai, C., Chen, J., Liu, M., Li, J., & Xu, J. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-0027-7_11
Vela, V., & Spahn, G. (2025) · Springer eBooks · doi:10.1007/978-3-662-70291-8_3
Integrated histopathologic modeling of detailed tumor subtypes and actionable biomarkersPreprint
Boehm, K. M., Darmofal, M., Pasha, A., Aukerman, A. T., Lim, R. S., Seffar, E., Pollard, T., Rekhtman, N., Chang, J. C., Kohli, A., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.14.670351 · Cited by 1 · see citing works
Prediction of Risk Factors from Gastric Cancer Genetic Data Using Machine Learning
Kumar, B. S., Shanmuganandam, S., Kumar, N. S., Lalhruaizela, S., Hruaitluanga, L., & Hmingliana, L. (2025) · Human-Centric Intelligent Systems · doi:10.1007/s44230-025-00118-1
Learning Patient Similarity from Genomics for Precision OncologyPreprint
Shady, M., Reardon, B. M., Jiang, S., Pimenta, E. M., O’Meara, T. A., Park, J., Kehl, K. L., Elmarakeby, H., Sunyaev, S. R., & Allen, E. M. V. (2025) · medRxiv · doi:10.64898/2025.12.17.25342480
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
