The DOI matches its Crossref record; 172 of the 172 references it cites were found in a registry or an index.
Combinatorial drug therapy for cancer in the post-genomic era
Abstract
Over the past decade, whole genome sequencing and other 'omics' technologies have defined pathogenic driver mutations to which tumor cells are addicted. Such addictions, synthetic lethalities and other tumor vulnerabilities have yielded novel targets for a new generation of cancer drugs to treat discrete, genetically defined patient subgroups. This personalized cancer medicine strategy could eventually replace the conventional one-size-fits-all cytotoxic chemotherapy approach. However, the extraordinary intratumor genetic heterogeneity in cancers revealed by deep sequencing explains why de novo and acquired resistance arise with molecularly targeted drugs and cytotoxic chemotherapy, limiting their utility. One solution to the enduring challenge of polygenic cancer drug resistance is rational combinatorial targeted therapy.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“Combination therapies using two or more therapeutic agents can be superior to single-agent therapies in the treatment of cancers (1–3).”
From Statistical Assessment of Drug Synergy from In Vivo Combination Studies Using Mouse Tumor Models (2023) · cites this paper as [1]
In the citing paper
“combinations of inhibitors targeting multiple effector and/or feedback pathways have shown impressive activity in KRAS mutant model systems”
From A Landscape of Therapeutic Cooperativity in KRAS Mutant Cancers Reveals Principles for Controlling Tumor Evolution (2017) · cites this paper as Al-Lazikani et al., 2012
In the citing paper
“The activity of single agent therapies targeting individual RAS effector pathways is limited”
From A Landscape of Therapeutic Cooperativity in KRAS Mutant Cancers Reveals Principles for Controlling Tumor Evolution (2017) · cites this paper as Al-Lazikani et al., 2012
In the citing paper
“To minimize the emergence of resistant clones and to achieve maximal therapeutic response, combinations of different classes of therapeutic agents123 are commonly used.”
From MDM2, MDMX and p53 in oncogenesis and cancer therapy (2013) · cites this paper as [123]
Citations
Cites
Hallmarks of Cancer: The Next Generation
Hanahan, D., & Weinberg, R. A. (2011) · Cell · doi:10.1016/j.cell.2011.02.013 · Cited by 66,590
Hanahan, D., & Weinberg, R. A. (2000) · Cell · doi:10.1016/s0092-8674(00)81683-9 · Cited by 29,219
Improved Survival with Ipilimumab in Patients with Metastatic Melanoma
Hodi, F. S., O'Day, S. J., McDermott, D. F., Weber, R. W., Sosman, J. A., Haanen, J. B., Gonzalez, R., Robert, C., Schadendorf, D., Hassel, J. C., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1003466 · Cited by 15,155
Slamon, D. J., Leyland-Jones, B., Shak, S., Fuchs, H., Paton, V., Bajamonde, A., Fleming, T., Eiermann, W., Wolter, J., Pegram, M., et al. (2001) · New England Journal of Medicine · doi:10.1056/nejm200103153441101 · Cited by 11,500
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
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950
Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1113205 · Cited by 7,774
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
Chou, T.-C., & Talalay, P. (1984) · Advances in Enzyme Regulation · doi:10.1016/0065-2571(84)90007-4 · Cited by 7,178
Coiffier, B., Lepage, E., Brière, J., Herbrecht, R., Tilly, H., Bouabdallah, R., Morel, P., Van Den Neste, E., Salles, G., Gaulard, P., et al. (2002) · New England Journal of Medicine · doi:10.1056/nejmoa011795 · Cited by 5,574
Flagged first, then by citations
Research works printed but not linked: 1
- Goldie, J.H. & Coldman, A.J. A mathematic model for relating the drug sensitivity of tumors to their spontaneous mutation rate. Cancer Treat. Rep. 63, 1727–1733 (1979).
Cited by
Sorted by citations
Borst, O., Walker, B., Münzer, P., Russo, A., Schmid, E., Faggio, C., Bigalke, B., Laufer, S., Gawaz, M., & Lang, F. J. W. (2013) · Cellular Physiology and Biochemistry · doi:10.1159/000350110 · Cited by 1,867
MDM2, MDMX and p53 in oncogenesis and cancer therapy
Wade, M., Li, Y.-C., & Wahl, G. M. (2013) · Nature Reviews Cancer · doi:10.1038/nrc3430 · Cited by 1,279
Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset
Seashore-Ludlow, B., Rees, M. G., Cheah, J. H., Cokol, M., Price, E. V., Coletti, M. E., Jones, V., Bodycombe, N. E., Soule, C. K., Gould, J., et al. (2015) · Cancer Discovery · doi:10.1158/2159-8290.cd-15-0235 · Cited by 813
“Combination treatments may mitigate the development of the drug resistance often observed with single-treatment therapies (42), yet principled methods to identify effective combinations have yet to be defined.”
Preclinical Mouse Cancer Models: A Maze of Opportunities and Challenges
Day, C.-P., Merlino, G., & Van Dyke, T. (2015) · Cell · doi:10.1016/j.cell.2015.08.068 · Cited by 676
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
Preuer, K., Lewis, R. P. I., Hochreiter, S., Bender, A., Bulusu, K. C., & Klambauer, G. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx806 · Cited by 668
Metadata
- Authors
- Al-Lazikani, B., Banerji, U., & Workman, P.
- Affiliations
- Royal Marsden NHS Foundation Trust (GB)
- Institute of Cancer Research (GB)
- Corresponding author
- Banerji, Udai
- Workman, Paul
- Venue
- Nature Biotechnology
- Published
- 2012-07
- Volume
- 30
- Issue
- 7
- Pages
- 679-692
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W2061061337
- Cited by
- 1,070
- References
- 172
- Citations by year
- 2026: 63 · 2025: 80 · 2024: 71 · 2023: 73 · 2022: 87 · 2021: 73 · 2020: 80 · 2019: 91 · 2018: 81 · 2017: 76 · 2016: 83 · 2015: 90 · 2014: 69 · 2013: 46 · 2012: 6
- FWCI
- 5.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research
- Cancer Research UK (C309/8992, 11566, C309/A8274)
- National Institute for Health and Care Research
- National Institutes of Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Treatments and Mutations
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Keywords
- combination therapy, targeted cancer therapy, driver mutations, synthetic lethality, tumor vulnerability, precision oncology, acquired drug resistance, de novo resistance, polygenic resistance, cytotoxic chemotherapy
- MeSH terms
- Antineoplastic Combined Chemotherapy Protocols, Clinical Trials as Topic, Combined Modality Therapy, Humans, Neoplasms, Drug Design, Genome, Human, Genetic Heterogeneity, Metabolic Networks and Pathways, Precision Medicine, Molecular Targeted Therapy, 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
