The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
CMScaller: an R package for consensus molecular subtyping of colorectal cancer pre-clinical models
Open accessAbstract
Colorectal cancers (CRCs) can be divided into four gene expression-based biologically distinct consensus molecular subtypes (CMS). This classification provides a potential framework for stratified treatment, but to identify novel CMS-drug associations, translation of the subtypes to pre-clinical models is essential. The currently available classifier is dependent on gene expression signals from the immune and stromal compartments of tumors and fails to identify the poor-prognostic CMS4-mesenchymal group in immortalized cell lines, patient-derived organoids and xenografts. To address this, we present a novel CMS classifier based on a filtered set of cancer cell-intrinsic, subtype-enriched gene expression markers. This new classifier, referred to as CMScaller, recapitulated the subtypes in both in vitro and in vivo models (551 in total). Importantly, by analyzing public drug response data from patient-derived xenografts and cell lines, we show that the subtypes are predictive of response to standard CRC drugs. CMScaller is available as an R package.
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458
Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012
Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D. M., Forman, D., & Bray, F. (2015) · International Journal of Cancer · doi:10.1002/ijc.29210 · Cited by 28,766
GSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440
The Molecular Signatures Database Hallmark Gene Set Collection
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.004 · Cited by 13,807
NCBI GEO: archive for functional genomics data sets—update
Barrett, T., Wilhite, S. E., Ledoux, P., Evangelista, C., Kim, I. F., Tomashevsky, M., Marshall, K. A., Phillippy, K. H., Sherman, P. M., Holko, M., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1193 · Cited by 10,418
Comprehensive molecular characterization of human colon and rectal cancer
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11252 · Cited by 8,499
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
Cited by
Exploring immunotherapy in colorectal cancer
Weng, J., Li, S., Zhu, Z., Liu, Q., Zhang, R., Yang, Y., & Li, X. (2022) · Journal of Hematology & Oncology · doi:10.1186/s13045-022-01294-4 · Cited by 355
Inflammatory fibroblasts mediate resistance to neoadjuvant therapy in rectal cancer
Nicolas, A. M., Pesic, M., Engel, E., Ziegler, P. K., Diefenhardt, M., Kennel, K. B., Buettner, F., Conche, C., Petrocelli, V., Elwakeel, E., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.01.004 · Cited by 349
Chen, B., Scurrah, C. R., McKinley, E. T., Simmons, A. J., Ramirez-Solano, M. A., Zhu, X., Markham, N. O., Heiser, C. N., Vega, P. N., Rolong, A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.031 · Cited by 325
Woolston, A., Khan, K., Spain, G., Barber, L. J., Griffiths, B., Gonzalez-Exposito, R., Hornsteiner, L., Punta, M., Patil, Y., Newey, A., et al. (2019) · Cancer Cell · doi:10.1016/j.ccell.2019.05.013 · Cited by 254
Valdeolivas, A., Amberg, B., Giroud, N., Richardson, M., Gálvez, E. J. C., Badillo, S., Julien-Laferrière, A., Túrós, D., Voith von Voithenberg, L., Wells, I., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-023-00488-4 · Cited by 130
Metadata
- Authors
- Eide, P. W., Bruun, J., Lothe, R. A., & Sveen, A.
- Venue
- Scientific Reports
- Published
- 2017-11-30
- Volume
- 7
- Issue
- 1
- Pages
- 16618-16618
- ISSN
- 2045-2322 (ISSN-L)
- OpenAlex
- W2770459634
- Cited by
- 437
- References
- 61
- FWCI
- 4.74
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
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- Eide, P. W., Bruun, J., Lothe, R. A., & Sveen, A. (2017). CMScaller: an R package for consensus molecular subtyping of colorectal cancer pre-clinical models. Scientific Reports. https://doi.org/10.1038/s41598-017-16747-x
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