The DOI matches its Crossref record, with a small difference in title or year; 10 of the 10 references it cites were found in a registry or an index.
Pharmacogenomic agreement between two cancer cell line data sets
Open accessAbstract
Large cancer cell line collections broadly capture the genomic diversity of human cancers and provide valuable insight into anti-cancer drug response. Here we show substantial agreement and biological consilience between drug sensitivity measurements and their associated genomic predictors from two publicly available large-scale pharmacogenomics resources: The Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer databases.
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?
In the citing paper
“However, large-scale drug sensitivity studies using the MEK and ERK inhibitors did not reveal the same degree of selectivity in KRAS mutant cells (69, 70) (Figure 4).”
From KRAS mutation in pancreatic cancer (2021) · cites this paper as [70]
In the citing paper
“Notably, the majority of KRAS-mutant CRC cell lines exhibit elevated ABCB1 expression (39)”
From The drug efflux pump MDR1 promotes intrinsic and acquired resistance to PROTACs in cancer cells (2022) · cites this paper as [39]
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
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
A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes
Neve, R. M., Chin, K., Fridlyand, J., Yeh, J., Baehner, F. L., Fevr, T., Clark, L., Bayani, N., Coppé, J., Tong, F. K., et al. (2006) · Cancer Cell · doi:10.1016/j.ccr.2006.10.008 · Cited by 3,224
Systematic identification of genomic markers of drug sensitivity in cancer cells
Garnett, M. J., Edelman, E. J., Heidorn, S. J., Greenman, C. D., Dastur, A., Lau, K. W., Greninger, P., Thompson, I. R., Luo, X., Soares, J., et al. (2012) · Nature · doi:10.1038/nature11005 · Cited by 2,456
Garraway, L. A., Widlund, H. R., Rubin, M. A., Getz, G., Berger, A. J., Ramaswamy, S., Beroukhim, R., Milner, D. A., Granter, S. R., Du, J., et al. (2005) · Nature · doi:10.1038/nature03664 · Cited by 1,548
Self-citationBRAF mutation predicts sensitivity to MEK inhibition
Solit, D. B., Garraway, L. A., Pratilas, C. A., Sawai, A., Getz, G. A., Basso, A., Ye, Q., Lobo, J. M., She, Y., Osman, I., et al. (2005) · Nature · doi:10.1038/nature04304 · Cited by 1,326
Cell line-based platforms to evaluate the therapeutic efficacy of candidate anticancer agents
Sharma, S. V., Haber, D. A., & Settleman, J. (2010) · Nature reviews. Cancer · doi:10.1038/nrc2820 · Cited by 600
Inconsistency in large pharmacogenomic studies
Haibe-Kains, B., El-Hachem, N., Birkbak, N. J., Jin, A. C., Beck, A. H., Aerts, H. J. W. L., & Quackenbush, J. (2013) · Nature · doi:10.1038/nature12831 · Cited by 499
Predicting drug susceptibility of non–small cell lung cancers based on genetic lesions
Sos, M. L., Michel, K., Zander, T., Weiss, J. M., Frommolt, P., Peifer, M., Li, D., Ullrich, R. T., Koker, M., Fischer, F., et al. (2009) · Journal of Clinical Investigation · doi:10.1172/jci37127 · Cited by 258
Advances in the preclinical testing of cancer therapeutic hypotheses
Caponigro, G., & Sellers, W. R. (2011) · Nature Reviews Drug Discovery · doi:10.1038/nrd3385 · Cited by 165
Comparison and validation of genomic predictors for anticancer drug sensitivity
Papillon‐Cavanagh, S., Jay, N. D., El-Hachem, N., Olsen, C., Bontempi, G., Aerts, H. J. W. L., Quackenbush, J. F., & Haibe‐Kains, B. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001442 · Cited by 65
Flagged first, then by citations
Cited by
Sorted by citations
Items 401–420 of 420
Vural, S., Palmisano, A., Reinhold, W. C., Pommier, Y., Teicher, B. A., & Krushkal, J. (2021) · Figshare · doi:10.6084/m9.figshare.14176021
Yogodzinski, C., Arab, A., Pritchard, J. R., Goodarzi, H., & Gilbert, L. A. (2021) · Figshare · doi:10.6084/m9.figshare.16829968
Vural, S., Palmisano, A., Reinhold, W. C., Pommier, Y., Teicher, B. A., & Krushkal, J. (2021) · Figshare · doi:10.6084/m9.figshare.14176015
Additional file 1 of Prevalence, causes and impact of TP53-loss phenocopying events in human tumors
Fito-Lopez, B., Salvadores, M., Alvarez, M.-M., & Supek, F. (2024) · Figshare · doi:10.6084/m9.figshare.26584566
Xu, J.-Y., Tian, M.-Q., Yang, R., Li, Z.-X., Lin, Z. Y., Wang, Y., Chu, Y.-H., Sun, W.-N., & Wang, Y. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27020713
Kye, Y.-H., Moon, S., Cha, H.-R., Kim, T., Eom, J. Y., Myung, J., & Kong, G. (2026) · British Journal of Cancer · doi:10.1038/s41416-026-03347-8
ATM functions as a rheostat of metabolic stress in small-cell lung cancerPreprint
Halder, D., Sen, U., Jethalia, V., Chakraborty, S., Elliott, A., Ventura, K., VanderWalde, A., Halmos, B., Borghaei, H., Thin, T. H., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.13.711672
Multi-modal contrastive learning based on molecular and textual data for drug response prediction
Duan, M., Li, X., Hou, X., Qu, Y., Cui, H. N., & Zhang, Y. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.108882
Weighted transfer learning with fused regularization for high-dimensional regression
Ni, P., & Hu, Q. (2026) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2026.2640209
Machine learning approaches for drug virtual screening
Playe, B. (2019) · theses.fr (ABES) · doi:10.70675/8bd92cdfzf773z462dzba58za5132a1d4e92 · Cited by 1
Caractérisation de nouvelles dépendances oncogéniques dans les hémopathies myéloïdes
Roux, B. (2022) · theses.fr (ABES) · doi:10.70675/d76b6764z8851z423dza26fz9f68e953227e
Network-based machine learning methods to model variability across individuals
Yousefi, B. (2023) · theses.fr (ABES) · doi:10.70675/b7843ad7z3ae0z43caz9e47zf95945cd3b83
KMT2D depletion promotes KRAS-induced pancreatic carcinogenesis independent of TP53
Tolosa, E. J., Arul, G. L. R., Sigafoos, A. N., Ferrero, V., Pease, D. R., Almada, L. L., Vera, R. E., Ruiz, N. M. P., Hogenson, T. L., LaRue-Nolan, K. C., et al. (2026) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2026.113203 · Cited by 1
Wangermez, C., Carlier, F. M., Lecocq, M., Pilette, C., & Smet, C. D. (2026) · Discover Oncology · doi:10.1007/s12672-026-05380-8
Regulation of TIPIN and replication fork dynamics by USP37
Stromberg, B. R., Scarberry, L., Cho, I. S., & Summers, M. K. (2026) · Molecular Biology of the Cell · doi:10.1091/mbc.e25-08-0391
Zhang, J., Ning, J., Fang, S., Han, Y., Li, M., Chen, W., Dong, T., Bai, Q., Jia, M., Hao, P., et al. (2026) · Genes & Diseases · doi:10.1016/j.gendis.2026.102515
Large language models in personalized oncology
Solovev, I. A., & Golubev, D. (2026) · Patient-Oriented Medicine and Pharmacy · doi:10.37489/2949-1924-0148
Metadata
- Authors
- The Cancer Cell Line Encyclopedia Consortium, & The Genomics of Drug Sensitivity in Cancer Consortium
- Venue
- Nature
- Published
- 2015-11-16
- Volume
- 528
- Issue
- 7580
- Pages
- 84-87
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC6343827
- OpenAlex
- W2318806991
- Cited by
- 418
- References
- 10
- Citations by year
- 2026: 17 · 2025: 26 · 2024: 33 · 2023: 37 · 2022: 49 · 2021: 47 · 2020: 38 · 2019: 78 · 2018: 36 · 2017: 37 · 2016: 19
- FWCI
- 31.30
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- Funders
- Wellcome Trust (086357, 102696STRATTON, 102696)
- Cancer Research UK (A16629)
- National Institutes of Health (5u54hg006097-03)
- National Human Genome Research Institute (1U54HG006097-01, U54-HG006097)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- cancer cell lines, drug sensitivity, pharmacogenomics, GDSC, data concordance, anticancer drug response, drug library
- MeSH terms
- Humans, Neoplasms, Pharmacogenetics, Reproducibility of Results, Databases, Factual, Inhibitory Concentration 50, Cell Line, Tumor, Datasets as Topic
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
