A systematic study on drug-response associated genes using baseline gene expressions of the cancer cell line encyclopedia
Open accessAbstract
We have studied drug-response associated (DRA) gene expressions by applying a systems biology framework to the Cancer Cell Line Encyclopedia data. More than 4,000 genes are inferred to be DRA for at least one drug, while the number of DRA genes for each drug varies dramatically from almost 0 to 1,226. Functional enrichment analysis shows that the DRA genes are significantly enriched in genes associated with cell cycle and plasma membrane. Moreover, there might be two patterns of DRA genes between genders. There are significantly shared DRA genes between male and female for most drugs, while very little DRA genes tend to be shared between the two genders for a few drugs targeting sex-specific cancers (e.g., PD-0332991 for breast cancer and ovarian cancer). Our analyses also show substantial difference for DRA genes between young and old samples, suggesting the necessity of considering the age effects for personalized medicine in cancers. Lastly, differential module and key driver analyses confirm cell cycle related modules as top differential ones for drug sensitivity. The analyses also reveal the role of TSPO, TP53, and many other immune or cell cycle related genes as important key drivers for DRA network modules. These key drivers provide new drug targets to improve the sensitivity of cancer therapy.
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?
The metrics are used frequently in evaluating performance.
“The metrics are used in evaluating the performance frequently [64,65,66,67,68,69,70,71,72].”
From An Efficient Classifier for Alzheimer’s Disease Genes Identification (2018) · cites this paper as [71]
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
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890 · see citing works
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252 · see citing works
Hierarchical Grouping to Optimize an Objective Function
Ward, J. H. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500845 · Cited by 21,341 · see citing works
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 · see citing works
KEGG as a reference resource for gene and protein annotation
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., & Tanabe, M. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1070 · Cited by 7,387 · see citing works
Druker, B. J., Talpaz, M., Resta, D. J., Peng, B., Buchdunger, E., Ford, J., Lydon, N., Kantarjian, H. M., Capdeville, R., Ohno-Jones, S., et al. (2001) · New England Journal of Medicine · doi:10.1056/nejm200104053441401 · Cited by 5,237 · see citing works
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062 · see citing works
Self-citationCancer 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 · see citing works
Gene Ontology Consortium: going forward
The Gene Ontology Consortium (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1179 · Cited by 3,492 · see citing works
AN EXACT TEST FOR POPULATION DIFFERENTIATION
Raymond, M., & Rousset, F. (1995) · Evolution · doi:10.1111/j.1558-5646.1995.tb04456.x · Cited by 2,795 · see citing works
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 · see citing works
TP53 mutations in human cancers: origins, consequences, and clinical use.
Olivier, M., Hollstein, M., & Hainaut, P. (2010) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a001008 · Cited by 2,199 · see citing works
Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis
Leek, J. T., & Storey, J. D. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030161 · Cited by 1,930 · see citing works
Mangoni, A. A., & Jackson, S. H. (2003) · British Journal of Clinical Pharmacology · doi:10.1046/j.1365-2125.2003.02007.x · Cited by 1,887 · see citing works
Integrated Systems Approach Identifies Genetic Nodes and Networks in Late-Onset Alzheimer’s Disease
Zhang, B., Gaiteri, C., Bodea, L.-G., Wang, Z., McElwee, J., Podtelezhnikov, A. A., Zhang, C., Xie, T., Tran, L., Dobrin, R., et al. (2013) · Cell · doi:10.1016/j.cell.2013.03.030 · Cited by 1,657 · see citing works
MicroRNAs and other non-coding RNAs as targets for anticancer drug development
Ling, H. Y., Fabbri, M., & Călin, G. A. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd4140 · Cited by 1,465 · see citing works
p53- and Drug-Induced Apoptotic Responses Mediated by BH3-Only Proteins Puma and Noxa
Villunger, A., Michalak, E. M., Coultas, L., Müllauer, F., Böck, G., Ausserlechner, M. J., Adams, J. M., & Strasser, A. (2003) · Science · doi:10.1126/science.1090072 · Cited by 1,327 · see citing works
BRAF 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 1,317 · see citing works
An Exact Test for Population Differentiation
Raymond, M., & Rousset, F. (1995) · Evolution · doi:10.2307/2410454 · Cited by 1,257 · see citing works
Drug Metabolism and Variability among Patients in Drug Response
Wilkinson, G. R. (2005) · New England Journal of Medicine · doi:10.1056/nejmra032424 · Cited by 1,185 · see citing works
MicroRNAs en route to the clinic: progress in validating and targeting microRNAs for cancer therapy
Kasinski, A. L., & Slack, F. J. (2011) · Nature reviews. Cancer · doi:10.1038/nrc3166 · Cited by 934 · see citing works
Apoptosis induced by anticancer drugs
Hickman, J. A. (1992) · Cancer and Metastasis Reviews · doi:10.1007/bf00048059 · Cited by 860 · see citing works
Reddy, R. K., Mao, C., Baumeister, P., Austin, R. C., Kaufman, R. J., & Lee, A. S. (2003) · Journal of Biological Chemistry · doi:10.1074/jbc.m212328200 · Cited by 695 · see citing works
Chemosensitivity prediction by transcriptional profiling
Staunton, J. E., Slonim, D. K., Coller, H. A., Tamayo, P., Angelo, M., Park, J., Scherf, U., Lee, J. K., Reinhold, W. C., Weinstein, J. N., et al. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.191368598 · Cited by 684 · see citing works
The human multidrug resistance-associated protein MRP is a plasma membrane drug-efflux pump.
Zaman, G. J., Flens, M. J., van Leusden, M. R., de Haas, M., MÜLDER, H. S., Lankelma, J., Pinedo, H. M., Scheper, R. J., Baas, F., & Broxterman, H. J. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.19.8822 · Cited by 679 · see citing works
Greenblatt (1982) · doi:10.1056/nejm198205063061804 · Cited by 584 · see citing works
Predicting drug sensitivity and resistance
Szakács, G., Annereau, J., Lababidi, S., Shankavaram, U. T., Arciello, A., Bussey, K. J., Reinhold, W. C., Guo, Y., Kruh, G. D., Reimers, M., et al. (2004) · Cancer Cell · doi:10.1016/j.ccr.2004.06.026 · Cited by 533 · see citing works
Novel mechanism of lapatinib resistance in HER2-positive breast tumor cells: activation of AXL
Liu, L., Greger, J., Shi, H., Liu, Y., Greshock, J., Annan, R., Halsey, W., Sathe, G. M., Martin, A.-M., & Gilmer, T. M. (2009) · Cancer Research · doi:10.1158/0008-5472.can-08-4490 · Cited by 495 · see citing works
Pharmacogenetics, Pharmacogenomics, and Individualized Medicine
Ma, Q., & Lu, A. Y. H. (2011) · Pharmacological Reviews · doi:10.1124/pr.110.003533 · Cited by 466 · see citing works
Anticancer drug targets: cell cycle and checkpoint control
Shapiro, G. I., & Harper, J. W. (1999) · Journal of Clinical Investigation · doi:10.1172/jci9054 · Cited by 464 · see citing works
Cell cycle-mediated drug resistance: an emerging concept in cancer therapy.
Shah, M. A., & Schwartz, G. K. (2001) · PubMed · Cited by 437 · see citing works
Cell biological mechanisms of multidrug resistance in tumors.
Simon, S. M., & Schindler, M. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.9.3497 · Cited by 370 · see citing works
Sex Differences in Drug Disposition
Soldin, O. P., Chung, S. H., & Mattison, D. R. (2011) · BioMed Research International · doi:10.1155/2011/187103 · Cited by 341 · see citing works
Bidirectional crosstalk between endoplasmic reticulum stress and mTOR signaling
Appenzeller‐Herzog, C., & Hall, M. N. (2012) · Trends in Cell Biology · doi:10.1016/j.tcb.2012.02.006 · Cited by 316 · see citing works
Mitosis-targeted anti-cancer therapies: where they stand
Chan, K.-S., Koh, C.-G., & Li, H. (2012) · Cell Death and Disease · doi:10.1038/cddis.2012.148 · Cited by 311 · see citing works
Burris, H. A. (2004) · The Oncologist · doi:10.1634/theoncologist.9-suppl_3-10 · Cited by 296 · see citing works
Changes in Endoplasmic Reticulum Luminal Environment Affect Cell Sensitivity to Apoptosis
Nakamura, K., Bossy‐Wetzel, E., Burns, K., Fadel, M. P., Lozyk, M. D., Goping, I. S., Opas, M., Bleackley, R. C., Green, D. R., & Michalak, M. (2000) · The Journal of Cell Biology · doi:10.1083/jcb.150.4.731 · Cited by 286 · see citing works
Wang, Y.-C., Morrison, G., Gillihan, R., Guo, J., Ward, R., Fu, X., Botero, M. F., Healy, N. A., Hilsenbeck, S. G., Phillips, G. D. L., et al. (2011) · Breast Cancer Research · doi:10.1186/bcr3067 · Cited by 280 · see citing works
Veenman, L., Papadopoulos, V., & Gavish, M. (2007) · Current Pharmaceutical Design · doi:10.2174/138161207781368710 · Cited by 249 · see citing works
Molecular mechanisms of acquired resistance to tyrosine kinase targeted therapy
Sierra, J. R., Cepero, V., & Giordano, S. (2010) · Molecular Cancer · doi:10.1186/1476-4598-9-75 · Cited by 230 · see citing works
Yang, J., Huang, T., Petralia, F., Long, Q., Zhang, B., Argmann, C., Zhao, Y. B., Mobbs, C. V., Schadt, E. E., Zhu, J., et al. (2015) · Scientific Reports · doi:10.1038/srep15145 · Cited by 198 · see citing works
Self-citationWang, E., Zaman, N., Mcgee, S., et al. (2014) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2014.04.002 · Cited by 194 · see citing works
Regulation of Postnatal Lung Development and Homeostasis by Estrogen Receptor β
Patrone, C., Cassel, T. N., Pettersson, K., et al. (2003) · Molecular and Cellular Biology · doi:10.1128/mcb.23.23.8542-8552.2003 · Cited by 188 · see citing works
Horwitz, E. P., Stein, I., Andreozzi, M., Németh, J., Shoham, A., Pappo, O., Schweitzer, N., Tornillo, L., Kanarek, N., Quagliata, L., et al. (2014) · Cancer Discovery · doi:10.1158/2159-8290.cd-13-0782 · Cited by 185 · see citing works
Janvilisri, T., Venter, H., Shahi, S., Reuter, G., Balakrishnan, L., & van Veen, H. W. (2003) · Journal of Biological Chemistry · doi:10.1074/jbc.m301358200 · Cited by 158 · see citing works
Human DNAJ in cancer and stem cells
Sterrenberg, J. N., Blatch, G. L., & Edkins, A. L. (2011) · Cancer Letters · doi:10.1016/j.canlet.2011.08.019 · Cited by 116 · see citing works
A Genomic Strategy to Elucidate Modules of Oncogenic Pathway Signaling Networks
Chang, J. T., Carvalho, C. M., Mori, S., Bild, A. H., Gatza, M. L., Wang, Q., Lucas, J. E., Potti, A., Febbo, P. G., West, M., et al. (2009) · Molecular Cell · doi:10.1016/j.molcel.2009.02.030 · Cited by 107 · see citing works
Sarkar, S., Mazumdar, A., Dash, R., et al. (2010) · Cancer Biology & Therapy · doi:10.4161/cbt.9.8.11103 · Cited by 97 · see citing works
Shridhar, V., Bible, K. C., Staub, J. K., et al. (2001) · PubMed · Cited by 92 · see citing works
Melanoma Survival Disadvantage in Young, Non-Hispanic White Males Compared With Females
Gamba, C. S., Clarke, C. A., Keegan, T. H. M., Tao, L., & Swetter, S. M. (2013) · JAMA Dermatology · doi:10.1001/jamadermatol.2013.4408 · Cited by 87 · see citing works
RAF265 Inhibits the Growth of Advanced Human Melanoma Tumors
Su, Y., Vilgelm, A. E., Kelley, M. C., Hawkins, O. E., Liu, Y., Boyd, K. L., Kantrow, S. M., Splittgerber, R. C., Short, S. P., Sobolik, T., et al. (2012) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-11-1122 · Cited by 62 · see citing works
Matsumori, Y., Yano, S., Goto, H., et al. (2006) · Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics · doi:10.3727/000000006783981260 · Cited by 52 · see citing works
Age-Dependent Morphine Tolerance Development in the Rat
Wang, Y., Mitchell, J., Moriyama, K., Kim, K.-J., Sharma, M. L., Xie, G.-X., & Pierce, P. A. (2005) · Anesthesia & Analgesia · doi:10.1213/01.ane.0000152192.23851.40 · Cited by 52 · see citing works
Network Topology Reveals Key Cardiovascular Disease Genes
Sarajlic, A., Janjic, V., Stojković, N., Radak, D., & Przulj, N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071537 · Cited by 49 · see citing works
Iiizumi, M., Arakawa, H., Mori, T., Ando, A., & Nakamura, Y. (2002) · PubMed · Cited by 48 · see citing works
Personal genomes, quantitative dynamic omics and personalized medicine
Mias, G. I., & Snyder, M. P. (2013) · Quantitative Biology · doi:10.1007/s40484-013-0005-3 · Cited by 27 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Wang Y. et al. Sex differences in drug disposition. Anesthesia and Analgesia 100(6), 1733– 1739 (2005). 15920206
- Reddy R. K. et al. Systematic and integrative analysis of large gene lists using david bioinformatics resources. J Biol Chem. 278(23), 20915– 24 (2003).12665508
Cited by
Sorted by citations
An Efficient Classifier for Alzheimer’s Disease Genes Identification
Xu, L., Liang, G., Liao, C., Chen, G.-D., & Chang, C.-C. (2018) · Molecules · doi:10.3390/molecules23123140 · Cited by 107 · see citing works
- Authors
- Liu, X., Yang, J., Zhang, Y., Fang, Y., Wang, F., Wang, J., Zheng, X., & Yang, J.
- Affiliations
- Shanghai Normal University (CN)
- National University of Singapore (SG)
- Hebei University of Science and Technology (CN)
- Corresponding author
- Yang, Jiasheng
- Zheng, Xiaoqi
- Yang, Jialiang
- Venue
- Scientific Reports
- Published
- 2016-03-10
- Volume
- 6
- Issue
- 1
- Pages
- 22811-22811
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4785360
- OpenAlex
- W2296251405
- Cited by
- 63
- References
- 56
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 2 · 2023: 4 · 2022: 14 · 2021: 12 · 2020: 4 · 2019: 9 · 2018: 5 · 2017: 4 · 2016: 3
- FWCI
- 6.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (11201306, 61572327, 11171088, 13YZ065)
- Shanghai Municipal Education Commission
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- Cancer Cell Line Encyclopedia, drug sensitivity, gene expression, cell cycle genes, sex differences, age effects, personalized medicine, network modules, TP53, TSPO, therapeutic target
- MeSH terms
- Age Factors, Antineoplastic Agents, Cell Cycle, Female, Gene Expression Regulation, Humans, Male, Neoplasms, Sex Factors, Drug Resistance, Neoplasm, Cell Line, Tumor, Systems Biology, Gene Regulatory Networks, Biomarkers, Pharmacological
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
