The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.
Precision medicine for cancer with next-generation functional diagnostics
Open accessAbstract
Precision medicine is about matching the right drugs to the right patients. Although this approach is technology agnostic, in cancer there is a tendency to make precision medicine synonymous with genomics. However, genome-based cancer therapeutic matching is limited by incomplete biological understanding of the relationship between phenotype and cancer genotype. This limitation can be addressed by functional testing of live patient tumour cells exposed to potential therapies. Recently, several 'next-generation' functional diagnostic technologies have been reported, including novel methods for tumour manipulation, molecularly precise assays of tumour responses and device-based in situ approaches; these address the limitations of the older generation of chemosensitivity tests. The promise of these new technologies suggests a future diagnostic strategy that integrates functional testing with next-generation sequencing and immunoprofiling to precisely match combination therapies to individual cancer patients.
Overview
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
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Organoids are ideally suited for FPM due to their ease of establishment, similarity with the tissue of origin and tractability158,167 and have been deployed to identify therapeutic leads in a number of tumors168, including rare cancers that tend to be understudied and poorly characterized at the molecular level16,151,169,170.”
From Organoids (2022) · cites this paper as [168]
Experimental — accuracy not yet measuredIn the citing paper
“Microenvironmental interactions are known to influence in vivo drug response, but cellular ‘memory’ of these interactions may degrade during the course of ex vivo treatment9.”
From Drug sensitivity of single cancer cells is predicted by changes in mass accumulation rate (2016) · cites this paper as [9]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cerami, E., Gao, J., Dogrusoz, U., Gross, B. E., Sumer, S. O., Aksoy, B. A., Jacobsen, A., Byrne, C. J., Heuer, M. L., Larsson, E., et al. (2012) · Cancer Discovery · doi:10.1158/2159-8290.cd-12-0095 · Cited by 17,455
Gefitinib or Carboplatin–Paclitaxel in Pulmonary Adenocarcinoma
Mok, T. S., Wu, Y.-L., Thongprasert, S., Yang, C.-H., Chu, D.-T., Saijo, N., Sunpaweravong, P., Han, B., Margono, B., Ichinose, Y., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0810699 · Cited by 8,261
Network biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903
Improved survival with vemurafenib in melanoma with BRAF V600E mutation
Chapman, P. B., Hauschild, A., Robert, C., Haanen, J. B., Ascierto, P., Larkin, J., Dummer, R., Garbe, C., Testori, A., Maio, M., et al. (2011) · New England Journal of Medicine · doi:10.1056/nejmoa1103782 · Cited by 7,720
Self-citationSingle Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche
Sato, T., Vries, R. G., Snippert, H. J., van de Wetering, M., Barker, N., Stange, D. E., van Es, J. H., Abo, A., Kujala, P., Peters, P. J., et al. (2009) · Nature · doi:10.1038/nature07935 · Cited by 7,204
Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., et al. (2006) · Science · doi:10.1126/science.1132939 · Cited by 5,257
The future of immune checkpoint therapy
Sharma, P., & Allison, J. P. (2015) · Science · doi:10.1126/science.aaa8172 · Cited by 4,878
Cancer drug resistance: an evolving paradigm
Holohan, C., Van Schaeybroeck, S., Longley, D. B., & Johnston, P. G. (2013) · Nature Reviews Cancer · doi:10.1038/nrc3599 · Cited by 4,681
Anaplastic Lymphoma Kinase Inhibition in Non–Small-Cell Lung Cancer
Kwak, E. L., Bang, Y., Camidge, D. R., Shaw, A. T., Solomon, B., Maki, R. G., Ou, S. I., Dezube, B. J., Janne, P. A., Costa, D. B., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1006448 · Cited by 4,477
Sato, T., Stange, D. E., Ferrante, M., Vries, R. G. J., van Es, J. H., van den Brink, S., van Houdt, W. J., Pronk, A., van Gorp, J., Siersema, P. D., et al. (2011) · Gastroenterology · doi:10.1053/j.gastro.2011.07.050 · Cited by 3,753
Flagged first, then by citations
Cited by
Sorted by citations
Items 301–400 of 571
Xande, J. G., Dias, A. P., Tamura, R. E., Cruz, M. C., Brito, B., Ferreira, R. A., Strauss, B. E., & Costanzi-Strauss, E. (2019) · Gene Therapy · doi:10.1038/s41434-019-0096-1 · Cited by 14
Shimada, K., Muhlich, J. L., & Mitchison, T. J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.13.874776 · Cited by 14
Precision head and neck surgery: robotics and surgical vision technology
Holsinger, F. C., Birkeland, A. C., & Topf, M. C. (2021) · Current Opinion in Otolaryngology & Head & Neck Surgery · doi:10.1097/moo.0000000000000706 · Cited by 13
Big data in breast cancer: Towards precision treatment
Zhang, H., Hussin, H., Hoh, C.-C., Cheong, S.-H., Lee, W.-K., & Yahaya, B. H. (2024) · Digital Health · doi:10.1177/20552076241293695 · Cited by 13
Conditional cell reprogramming for modeling host‐virus interactions and human viral diseases
Liu, X., & Mondal, A. M. (2020) · Journal of Medical Virology · doi:10.1002/jmv.26093 · Cited by 13
Microfluidic-based dynamic BH3 profiling predicts anticancer treatment efficacy
Manzano-Muñoz, A., Yeste, J., Ortega, M. A., Martín, F., López, A., Rosell, J., Castro, S., Serrano, C., Samitier, J., Ramón‐Azcón, J., et al. (2022) · npj Precision Oncology · doi:10.1038/s41698-022-00333-0 · Cited by 14
A TOP2A‑derived cancer panel drives cancer progression in papillary renal cell carcinoma
Ye, M., He, Z., Dai, W., Li, Z., Chen, X., & Liu, J. (2018) · Oncology Letters · doi:10.3892/ol.2018.9179 · Cited by 13
Hribar, K. C., Wheeler, C. J., Bazarov, A. V., Varshneya, K., Yamada, R., Buckley, P., & Patil, C. G. (2019) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-18-0359 · Cited by 13
Chen, J., Liu, J., & Calhoun, V. D. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2913145 · Cited by 13
Towards the Delivery of Precision Veterinary Cancer Medicine
Katogiritis, A., & Khanna, C. (2019) · Veterinary Clinics of North America Small Animal Practice · doi:10.1016/j.cvsm.2019.04.011 · Cited by 13
Qiu, T., Li, W., Zhang, F., Wang, B., & Ying, J. (2019) · Cancer Biology & Therapy · doi:10.1080/15384047.2019.1674070 · Cited by 13
Huang, Y., Xu, J., Li, K., Wang, J., Dai, Y., & Kang, Y. (2021) · Cancer Management and Research · doi:10.2147/cmar.s276799 · Cited by 13
Autonomous All-in-One Patch for Closed-Loop Diagnosis–Therapy of Melanoma
Guo, L., Zhang, F., Wang, L., Zhou, G., Zheng, Y., Ren, L., Wang, Z., Yin, J., Wu, W., & Yuan, M. (2025) · ACS Sensors · doi:10.1021/acssensors.5c00791 · Cited by 12
Is Personalized Medicine Here?
Carneiro, B. A., Costa, R. L. B., Taxter, T., Chandra, S., Chae, Y. K., Cristofanilli, M., & Giles, F. J. (2016) · PubMed · Cited by 12
Unveiling Pharmacogenomics Insights into Circular RNAs: Toward Precision Medicine in Cancer Therapy
Alqahtani, S., Alqahtani, T., Venkatesan, K., Sivadasan, D., Ahmed, R., Elfadil, H., Paulsamy, P., & Periannan, K. (2025) · Biomolecules · doi:10.3390/biom15040535 · Cited by 12
Mangalam, M. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103272 · Cited by 12
64Cu/177Lu-DOTA-diZD, a Small-Molecule-Based Theranostic Pair for Triple-Negative Breast Cancer
Huang, Y., Yang, Z., Li, F., Zhao, H., Li, C., Yu, N., Hamilton, D. J., & Li, Z. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c01957 · Cited by 12
Detergents: Friends not foes for high‐performance membrane proteomics toward precision medicine
Zhang, X. (2016) · PROTEOMICS · doi:10.1002/pmic.201600209 · Cited by 14
Gál, B., Veselý, M., Čolláková, J., Nekulová, M., Jůzová, V., Chmelík, R., & Veselý, P. (2017) · PLoS ONE · doi:10.1371/journal.pone.0183399 · Cited by 12
Precision medicine for multiple sclerosis promotes preventative medicine
Hansen, M. R., & Okuda, D. T. (2018) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13846 · Cited by 12
Ge, C., Luo, L., Zhang, J., Meng, X., & Chen, Y. (2021) · BioMed Research International · doi:10.1155/2021/4312850 · Cited by 12
Natural bioactive molecules and chemotherapeutics synergism for enhanced cancer therapy
Hashemi, M., Heshmatzad, K., Shahsavan, G., Tavakolpour, V., Esfahani, S. K., Karimi, P., Beikzadeh, N., Mashhadikhan, S., Nasirzade, S., Farahani, A. V., et al. (2026) · Nutrition & Metabolism · doi:10.1186/s12986-026-01094-4 · Cited by 11
Bai, Y., Lü, Z., Zhang, C., Guo, M., Xia, L., Tang, Z., Liu, Y., & Deng, S. (2023) · Oncogene · doi:10.1038/s41388-023-02866-5 · Cited by 12
Delineating functional and molecular impact of ex vivo sample handling in precision medicine
Struyf, N., Österroos, A., Vesterlund, M., Arnroth, C., James, T., Sunandar, S., Mermelekas, G., Bohlin, A., Levedahl, K. H., Bengtzén, S., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00528-7 · Cited by 11
Song, F., Yi, X., Zheng, X., Zhang, Z., Zhao, L., Shen, Y., Ye, Z., Liu, T., Liu, X., Xu, T., et al. (2024) · Drug Resistance Updates · doi:10.1016/j.drup.2024.101162 · Cited by 11
Kaur, J., Rahat, B., Thakur, S., & Kaur, J. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809411-2.00015-5 · Cited by 14
Artificial Intelligence Approaches in Drug Discovery: Towards the Laboratory of the Future
Frusciante, L., Visibelli, A., Geminiani, M., Santucci, A., & Spiga, O. (2022) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026622666221006140825 · Cited by 11
Ishimaru, S., Shimoi, T., Sunami, K., Nakajima, M., Ando, Y., Okita, N. T., Nakamura, K., Shibata, T., Fujiwara, Y., & Yamamoto, N. (2023) · International Journal of Clinical Oncology · doi:10.1007/s10147-023-02439-2 · Cited by 11
Pharmacometabolomics in Drug Disposition, Toxicity, and Precision Medicine
Trevor, G. R., Lim, Y. J., & Urquhart, B. L. (2024) · Drug Metabolism and Disposition · doi:10.1124/dmd.123.001074 · Cited by 11
Lee, H.-Y., Chen, Y., Li, C., Li, W., Hsu, Y.-L., Yeh, H., Ke, H., Huang, C., Li, C., Wu, W., et al. (2018) · International Journal of Medical Sciences · doi:10.7150/ijms.29560 · Cited by 11
Liu, X., Meng, C., Ji, G., Liu, J., Zhu, P. W., Qian, J., Zhu, S., Zhang, Y., & Ling, Y. (2021) · Materials Chemistry Frontiers · doi:10.1039/d1qm00411e · Cited by 11
Functional Precision Oncology: The Next Frontier to Improve Glioblastoma Outcome?
Panovska, D., & Smet, F. D. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23158637 · Cited by 11
Grundy, M. A., Jones, T., Elmi, L., Hall, M., Graham, A., Russell, N. H., & Pallis, M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196805 · Cited by 11
Yang, M., Fang, X., Li, J., Xu, D., Xiao, Q., Yu, S., Hu, H., Weng, S., Ding, K., & Yuan, Y. (2018) · Cancer Biology & Therapy · doi:10.1080/15384047.2018.1529120 · Cited by 11
Prognostic significance of LAPTM4B and p27kip1 expression in triple-negative breast cancer
Li, X., Song, C., Wang, K., Li, N., Sun, S., Li, N., Zhao, Z., & Li, M. (2019) · Cancer Biomarkers · doi:10.3233/cbm-182094 · Cited by 11
Panoptic View of Prognostic Models for Personalized Breast Cancer Management
Saini, G., Mittal, K., Rida, P. C. G., Janssen, E. A. M., Gogineni, K., & Aneja, R. (2019) · Cancers · doi:10.3390/cancers11091325 · Cited by 12
Lin, Y., Zhong, W., Lin, Q., Ye, Y., Li, S., Chen, H., Liu, H., Xu, L., Zhuang, W., Chen, S., et al. (2023) · PubMed Central · Cited by 10
Rodríguez, J. V., Piñol, M., Sorolla, M. A., Parisi, E., Sorolla, A., Santacana, M., Ruiz, M., Parra, G., Aracil, M. B. B., Iglesias, M., et al. (2025) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2024-010024 · Cited by 10
Weng, W., Han, J. C., Xie, H.-X., & Pan, T.-M. (2016) · Journal of The Electrochemical Society · doi:10.1149/2.1221614jes · Cited by 10
A personalized approach to acute myeloid leukemia therapy: current options
Illangeswaran, R. S. S., Das, S., Paul, D. Z., Mathews, V., & Balasubramanian, P. (2019) · Pharmacogenomics and Personalized Medicine · doi:10.2147/pgpm.s168267 · Cited by 10
Constitutive BAK/MCL1 complexes predict paclitaxel and S63845 sensitivity of ovarian cancer
Liu, D., Hou, X., Wu, W., Zanfagnin, V., Li, Y., Correia, C., Zhao, Z., Zhao, C., Liu, Z., Zhang, T., et al. (2021) · Cell Death and Disease · doi:10.1038/s41419-021-04073-0 · Cited by 10
Zhang, Y., Huo, J., Yu, S., Feng, W., Tuersun, A., Chen, F., Lv, Z., Liu, W., Zhao, J., Xu, Z., et al. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.115585 · Cited by 10
Novel biomolecules in targeted cancer therapy: a new approach towards precision medicine
Kannampuzha, S., Murali, R., Gopalakrishnan, A. V., Mukherjee, A. G., Wanjari, U. R., Namachivayam, A., George, A., Dey, A., & Vellingiri, B. (2023) · Medical Oncology · doi:10.1007/s12032-023-02168-6 · Cited by 11
The LMIT: Light-mediated minimally-invasive theranostics in oncology
Fan, Y., Liu, S., Gao, E., Guo, R., Dong, G., Li, Y., Gao, T., Tang, X., & Liao, H. (2023) · Theranostics · doi:10.7150/thno.87783 · Cited by 10
Nanotechnology in Hematology: Enhancing Therapeutic Efficacy With Nanoparticles
Fard, N. T., Khademi, M., Salahi‐Niri, A., & Esmaeili, S. (2025) · Health Science Reports · doi:10.1002/hsr2.70647 · Cited by 10
Functional diagnostics using fresh uncultured lung tumor cells to guide personalized treatments
Talwelkar, S. S., Mäyränpää, M. I., Søraas, L., Potdar, S., Bao, J., Hemmes, A., Linnavirta, N., Lømo, J., Räsänen, J. V., Knuuttila, A., et al. (2021) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100373 · Cited by 10
DNA‐Scaffolded Disulfide Redox Network for Programming Drug‐Delivery Kinetics
Ji, W., Li, X., Xiao, M., Sun, Y., Lai, W., Zhang, H., Pei, H., & Li, L. (2021) · Chemistry - A European Journal · doi:10.1002/chem.202100149 · Cited by 10
Friedman, A. A., Xia, Y. F., Trippa, L., Le, L. P., Igras, V., Frederick, D. T., Wargo, J. A., Tanabe, K. K., Lawrence, D. P., Neuberg, D. S., et al. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-16-3029 · Cited by 11
Self-citationApproaches to identify kinase dependencies in cancer signalling networks
Dermit, M., Dokal, A. D., & Cutillas, P. R. (2017) · FEBS Letters · doi:10.1002/1873-3468.12748 · Cited by 10
Comprehensive evaluation of computational cell-type quantification methods for immuno-oncologyPreprint
Sturm, G., Finotello, F., Petitprez, F., Zhang, J. D., Baumbach, J., Fridman, W. H., List, M., & Aneichyk, T. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/463828 · Cited by 10
Computational Methods to Investigate the Impact of miRNAs on Pathways
Alaimo, S., Micale, G., Ferlita, A. L., Ferro, A., & Pulvirenti, A. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9207-2_11 · Cited by 9
Mattei, P. (2020) · Israel Journal of Health Policy Research · doi:10.1186/s13584-020-0361-1 · Cited by 9
Zhang, X., Fan, X., Sun, C., Wang, L., Miao, Y., Wang, L., Yang, P., Xu, Y., Ren, X., Wu, X., et al. (2022) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.306 · Cited by 9
Nrf2, A Target for Precision Oncology in Cancer Prognosis and Treatment
Ngo, H. K. C., Le, H., & Surh, Y. (2023) · Journal of Cancer Prevention · doi:10.15430/jcp.2023.28.4.131 · Cited by 9
Research on Pharmaceutical Product Life Cycle Patterns for Sustainable Growth
Teramae, F., Makino, T., Sengoku, S., Lim, Y., Natori, T., & Kodama, K. (2020) · Sustainability · doi:10.3390/su12218938 · Cited by 9
Kasi, A., Al‐Jumayli, M., Park, R., Baranda, J. C., & Sun, W. (2020) · Journal of Pancreatic Cancer · doi:10.1089/pancan.2020.0010 · Cited by 9
Precision Medicine in Myeloma: Challenges in Defining an Actionable Approach
González‐Calle, V., Keane, N., Braggio, E., & Fonseca, R. (2017) · Clinical Lymphoma Myeloma & Leukemia · doi:10.1016/j.clml.2017.06.021 · Cited by 9
Kutikhin, A. G., Sinitsky, M. Y., Yuzhalin, A. E., & Velikanova, E. A. (2018) · Microarrays · doi:10.3390/ht7010005 · Cited by 10
Screening Dys-Methylation Genes and Rules for Cancer Diagnosis by Using the Pan-Cancer Study
Zhang, Y., Zeng, T., Pan, X., Guo, W., Gan, Z., Zhang, Y., Huang, T., & Cai, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2961402 · Cited by 9
Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithmPreprint
Ahn, W., Gu, H., Shen, Y. I., Haines, N., Hahn, H. A., Teater, J. E., Myung, J. I., & Pitt, M. A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/567412 · Cited by 9
Translational Opportunities for Microfluidic Technologies to Enable Precision Epigenomics
Xu, Y. F., Doonan, S. R., Ördög, T., & Bailey, R. C. (2020) · Analytical Chemistry · doi:10.1021/acs.analchem.0c01288 · Cited by 10
Genetic Testing and its Role in Precision Medicine: Understand Disease Risk and Tailoring Treatments
Islam, Z., Zainullah, Khan, A., Aslam, M., Khan, A. U., & Rehman, M. U. (2025) · Indus journal of bioscience research. · doi:10.70749/ijbr.v3i1.515 · Cited by 8
Trigos, A., Goudey, B., Bedő, J., Conway, T., Faux, N. G., & Wyres, K. L. (2021) · mSphere · doi:10.1128/msphere.00245-21 · Cited by 8
Novel Therapeutics for Advanced Differentiated Thyroid Cancer
Lieberman, L., & Worden, F. P. (2022) · Endocrinology and Metabolism Clinics of North America · doi:10.1016/j.ecl.2021.11.019 · Cited by 8
Kimmerling, R. J., Stevens, M. M., Olçum, S., Minnah, A., Vacha, M., LaBella, R., Ferri, M., Wasserman, S. C., Fujii, J., Shaheen, Z., et al. (2022) · Communications Biology · doi:10.1038/s42003-022-04270-3 · Cited by 8
Evaluation of the prognostic significances of γ‑secretase genes in pancreatic cancer
Jeon, Y., Ha, M., Kim, S. H., Kim, M. K., Lee, C., Oh, C., Han, M., Oh, S., & Kim, Y. H. (2019) · Oncology Letters · doi:10.3892/ol.2019.10113 · Cited by 8
The role of PET in imaging of the tumour microenvironment and response to immunotherapy
Lilburn, D. M. L., & Groves, A. M. (2021) · Clinical Radiology · doi:10.1016/j.crad.2021.08.004 · Cited by 8
Faris, J. G., Orbidan, D., Wells, C., Petersen, B. K., & Sprenger, K. G. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1029167 · Cited by 8
Doroshow, D. B., & Doroshow, J. H. (2019) · The Cancer Journal · doi:10.1097/ppo.0000000000000386 · Cited by 9
In Silico Cell-Type Deconvolution Methods in Cancer Immunotherapy
Sturm, G., Finotello, F., & List, M. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0327-7_15 · Cited by 9
Liao, C., Yang, L., Lee, L., Lin, C., Wang, H., Ng, S., Yen, T., Fan, W., & Hsieh, C. (2021) · Oral Oncology · doi:10.1016/j.oraloncology.2021.105547 · Cited by 8
Integrating functional genomics to accelerate mechanistic personalized medicinePreprint
Tyner, J. W. (2017) · Molecular Case Studies · doi:10.1101/mcs.a001370 · Cited by 10
Ziogas, D. E., Kyrochristos, I. D., & Roukos, D. H. (2017) · Expert Review of Medical Devices · doi:10.1080/17434440.2017.1419059 · Cited by 8
Chan, S. P. Y., Rashid, M. B. M. A., Lim, J. J., Goh, J. J. N., Wong, W. Y., Hooi, L., Ismail, N. N., Luo, B., Chen, B. J., Noor, N. F. B. M., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00851-7 · Cited by 7
Gold, M., Pachmann, K., Kiani, A., & Schobert, R. (2021) · Molecular and Clinical Oncology · doi:10.3892/mco.2021.2363 · Cited by 7
Global Genetics Research in Prostate Cancer: A Text Mining and Computational Network Theory Approach
Azam, M. F., Musa, A., Dehmer, M., Yli-Harja, O. P., & Emmert‐Streib, F. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00070 · Cited by 7
Patel, K. R., Brahmbhatt, J., Pandya, P. A., Bhadresha, K. P., Daraji, D. G., Patel, H. D., Rawal, R., Baran, S. K., & Jayanthi, S. (2021) · ChemistrySelect · doi:10.1002/slct.202100025 · Cited by 7
Accelerating the Development and Validation of New Value-Based Diagnostics by Leveraging Biobanks
Schneider, D. J., Riegman, P. H. J., Cronin, M., Negrouk, A., Moch, H., Balling, R., Penault-Llorca, F. M., Zatloukal, K., & Horgan, D. (2016) · Public Health Genomics · doi:10.1159/000446534 · Cited by 7
Lim, K., Langley, E. J., Gao, F., Luo, J. R., Li, L., Meyer, G. E., Kim, P., Singh, S., Kushnir, V. M., Early, D. S., et al. (2017) · Oncotarget · doi:10.18632/oncotarget.15653 · Cited by 7
Epilepsy: Advances in genetics and pathophysiology
Mantegazza, M. (2017) · Neuroscience Letters · doi:10.1016/j.neulet.2017.10.026 · Cited by 7
Beaumont, K. G., Hamou, W., Bozinovic, N., Silvers, T. R., Shah, H., Dave, A., Allette, K., Strahl, M., Wang, Y., Arib, H., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/457010 · Cited by 7
Advancing Preclinical Biology for Ewing Sarcoma: An International Effort
Cruz, F. S. D., Stewart, E. A., Surdez, D., Daley, J. D., Soragni, A., Tomazou, E. M., Alvarez-Perez, J., Feinberg, T. Y., Amatruda, J. F., Ganapathi, S. S., et al. (2025) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-25-0428 · Cited by 6
Patient-derived melanoma models
Karras, F., & Kunz, M. (2024) · Pathology - Research and Practice · doi:10.1016/j.prp.2024.155231 · Cited by 6
Louandre, C., Donnadieu, J., Lachaier, E., Page, C., Chauffert, B., & Galmiche, A. (2016) · International Journal of Oncology · doi:10.3892/ijo.2016.3345 · Cited by 6
Progress towards precision functional genomics in cancer
Johannessen, C. M., & Boehm, J. S. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.02.002 · Cited by 6
Calderon, S., Moya, D., Cruz, J. C., & Valverde, J. M. (2016) · IEEE Central America and Panama Convention (CONCAPAN) · doi:10.1109/concapan.2016.7942344 · Cited by 6
Next-generation Sequencing-based genomic profiling: Fostering innovation in cancer care?
Fernandes, G. D. S., Marques, D. F., Girardi, D. M., Braghiroli, M. I., Coudry, R. A., Meireles, S. I., Katz, A., & Hoff, P. M. (2017) · Clinics · doi:10.6061/clinics/2017(10)01 · Cited by 6
Pharmacological approaches to understanding protein kinase signaling networks
Stephenson, E. H., & Higgins, J. M. G. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1310135 · Cited by 6
A SNaPshot of potentially personalized care: Molecular diagnostics in gynecologic cancer
Penson, R. T., Sales, E., Sullivan, L., Borger, D. R., Krasner, C. N., Goodman, A., del Carmen, M. G., Growdon, W. B., Schorge, J. O., Boruta, D. M., et al. (2016) · Gynecologic Oncology · doi:10.1016/j.ygyno.2016.02.032 · Cited by 6
A microchip for integrated single-cell genotoxicity assay
Dong, H., Sun, H. R., & Zheng, J. (2016) · Talanta · doi:10.1016/j.talanta.2016.09.040 · Cited by 6
Stulpinas, A., Imbrasaitė, A., Krestnikova, N., & Kalvelytė, A. V. (2020) · IntechOpen eBooks · doi:10.5772/intechopen.89395 · Cited by 7
Conditionally Reprogrammed Cells and Robotic High-Throughput Screening for Precision Cancer Therapy
Alkhilaiwi, F. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.761986 · Cited by 6
A Novel High-Throughput Multispectral Cell Segmentation Algorithm
Golbstein, J., Tocker, Y., Sharivkin, R., Tarcic, G., & Vidne, M. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-60964-5_66 · Cited by 6
Jaiswal, A., Yadav, B., Wennerberg, K., & Aittokallio, T. A. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8891-4_12 · Cited by 6
Evolution of oncosurgical management of pancreatic cancer
Klaiber, U., Roth, S., Hackert, T., & Neoptolemos, J. P. (2019) · European surgery. Supplement/European surgery · doi:10.1007/s10353-019-0587-8 · Cited by 6
Tian, J., Lian, J., Xia, Y., Ma, L., Zhang, M., Zhu, X., Wang, G., Zhong, Y., & Zeng, C. (2025) · Renal Failure · doi:10.1080/0886022x.2025.2590283 · Cited by 5
Patient-Derived Xenograft Models in Cancer Research: Methodology, Applications, and Future Prospects
Aslani, S., & Saad, M. I. (2024) · Methods in molecular biology · doi:10.1007/978-1-0716-3858-3_2 · Cited by 5
Saeed, D., Xing, H., AlBadani, B., Li, F., Al-Sabri, R., Abdullah, M., & Rehman, A. (2025) · BMC Bioinformatics · doi:10.1186/s12859-024-05987-0 · Cited by 5
Morfouace, M., Hewitt, S. M., Salgado, R., Hartmann, K., Litière, S., Tejpar, S., Golfinopoulos, V., Lively, T., Thurin, M., Conley, B. A., et al. (2018) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2018.01.003 · Cited by 5
Yan, D., Zhang, H., Xu, X., Ren, C., Han, C., & Li, Z. (2022) · Pharmacological Research · doi:10.1016/j.phrs.2022.106241 · Cited by 5
Metadata
- Authors
- Friedman, A. A., Letai, A., Fisher, D. E., & Flaherty, K. T.
- Affiliations
- Harvard University (US)
- Corresponding author
- Friedman, Adam A.
- Venue
- Nature Reviews Cancer
- Published
- 2015-11-05
- Volume
- 15
- Issue
- 12
- Pages
- 747-756
- ISSN
- 1474-175X (ISSN-L)
- 1474-1768
- PMC
- PMC4970460
- OpenAlex
- W2176372762
- Cited by
- 570
- References
- 108
- Citations by year
- 2026: 19 · 2025: 43 · 2024: 26 · 2023: 42 · 2022: 41 · 2021: 67 · 2020: 93 · 2019: 63 · 2018: 59 · 2017: 73 · 2016: 43
- FWCI
- 5.69
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature reviews. Cancer (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Cancer Institute (UM1CA186709, P01CA066996)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Health Sciences › Medicine › Oncology › Cancer Cells and Metastasis
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Treatments and Mutations
- Keywords
- precision oncology, functional diagnosis, chemosensitivity testing, genotype-phenotype correlation, next-generation sequencing, immune profiling, drug repurposing
- MeSH terms
- Humans, Neoplasms, Genomics, Molecular Diagnostic Techniques, Precision Medicine, High-Throughput Nucleotide Sequencing
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