The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
The Proteomic Landscape of Triple-Negative Breast Cancer
Open accessAbstract
Triple-negative breast cancer is a heterogeneous disease characterized by poor clinical outcomes and a shortage of targeted treatment options. To discover molecular features of triple-negative breast cancer, we performed quantitative proteomics analysis of twenty human-derived breast cell lines and four primary breast tumors to a depth of more than 12,000 distinct proteins. We used this data to identify breast cancer subtypes at the protein level and demonstrate the precise quantification of biomarkers, signaling proteins, and biological pathways by mass spectrometry. We integrated proteomics data with exome sequence resources to identify genomic aberrations that affect protein expression. We performed a high-throughput drug screen to identify protein markers of drug sensitivity and understand the mechanisms of drug resistance. The genome and proteome provide complementary information that, when combined, yield a powerful engine for therapeutic discovery. This resource is available to the cancer research community to catalyze further analysis and investigation.
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.
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
Comprehensive molecular portraits of human breast tumours
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11412 · Cited by 12,129
Microenvironmental regulation of tumor progression and metastasis
Quail, D. F., & Joyce, J. A. (2013) · Nature Medicine · doi:10.1038/nm.3394 · Cited by 7,994
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
Vogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S., Diaz, L. A., & Kinzler, K. W. (2013) · Science · doi:10.1126/science.1235122 · Cited by 7,863
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566 · cited 2×
“Protein quantifications were calculated using the iBAQ approach (Schwanhäusser et al., 2011).”
MethodThe genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups
METABRIC Group, Curtis, C., Shah, S. P., Chin, S.-F., Turashvili, G., Rueda, O. M., Dunning, M. J., Speed, D., Lynch, A. G., Samarajiwa, S., et al. (2012) · Nature · doi:10.1038/nature10983 · Cited by 6,097
A census of human cancer genes
Futreal, P. A., Coin, L., Marshall, M., Down, T., Hubbard, T., Wooster, R., Rahman, N., & Stratton, M. R. (2004) · Nature Reviews Cancer · doi:10.1038/nrc1299 · Cited by 3,184
A draft map of the human proteome
Kim, M.-S., Pinto, S. M., Getnet, D., Nirujogi, R. S., Manda, S. S., Chaerkady, R., Madugundu, A. K., Kelkar, D. S., Isserlin, R., Jain, S., et al. (2014) · Nature · doi:10.1038/nature13302 · Cited by 2,198
Ras, PI(3)K and mTOR signalling controls tumour cell growth
Shaw, R. J., & Cantley, L. C. (2006) · Nature · doi:10.1038/nature04869 · Cited by 2,019
Mass-spectrometry-based draft of the human proteome
Wilhelm, M., Schlegl, J., Hahne, H., Gholami, A. M., Lieberenz, M., Savitski, M. M., Ziegler, E., Butzmann, L., Gessulat, S., Marx, H., et al. (2014) · Nature · doi:10.1038/nature13319 · Cited by 1,820
Correlation of mRNA and protein in complex biological samples
Maier, T., Güell, M., & Serrano, L. (2009) · FEBS Letters · doi:10.1016/j.febslet.2009.10.036 · Cited by 1,802
Proteogenomic characterization of human colon and rectal cancer
CPTAC, T. N., Zhang, B., Wang, J., Wang, X., Zhu, J., Liu, Q., Shi, Z., Chambers, M. C., Zimmerman, L. J., Shaddox, K. F., et al. (2014) · Nature · doi:10.1038/nature13438 · Cited by 1,462
Carracedo, A., Ma, L., Teruya-Feldstein, J., Rojo, F., Salmena, L., Alimonti, A., Egia, A., Sasaki, A. T., Thomas, G., Kozma, S. C., et al. (2008) · Journal of Clinical Investigation · doi:10.1172/jci34739 · Cited by 1,381
Deep proteome and transcriptome mapping of a human cancer cell line
Nagaraj, N., Wisniewski, J. R., Geiger, T., Cox, J., Kircher, M., Kelso, J., Pääbo, S., & Mann, M. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.81 · Cited by 979
The quantitative proteome of a human cell line
Beck, M., Schmidt, A., Malmstroem, J., Claassen, M., Ori, A., Szymborska, A., Herzog, F., Rinner, O., Ellenberg, J., & Aebersold, R. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.82 · Cited by 816
Geiger, T., Wehner, A., Schaab, C., Cox, J., & Mann, M. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.014050 · Cited by 797
Tian, Q., Price, N. D., & Hood, L. (2012) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2011.02498.x · Cited by 224
Vidal, M., Chan, D. W., Gerstein, M., Mann, M., Omenn, G. S., Tagle, D., Sechi, S., & Participants, W. (2012) · Clinical Proteomics · doi:10.1186/1559-0275-9-6 · Cited by 41
Cited by
The ectonucleotidases CD 39 and CD 73: Novel checkpoint inhibitor targets
Allard, B., Longhi, M. S., Robson, S. C., & Stagg, J. (2017) · Immunological Reviews · doi:10.1111/imr.12528 · Cited by 927
The adenosine pathway in immuno-oncology
Allard, B., Allard, D., Buisseret, L., & Stagg, J. (2020) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-020-0382-2 · Cited by 586
An Optimized Shotgun Strategy for the Rapid Generation of Comprehensive Human Proteomes
Bekker-Jensen, D. B., Kelstrup, C. D., Batth, T. S., Larsen, S. C., Haldrup, C., Bramsen, J. B., Sørensen, K. D., Høyer, S., Ørntoft, T. F., Andersen, C. L., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2017.05.009 · Cited by 490
Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics
Ding, L., Bailey, M. H., Porta-Pardo, E., Thorsson, V., Colaprico, A., Bertrand, D., Gibbs, D. L., Weerasinghe, A., Huang, K.-L., Tokheim, C., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.033 · Cited by 399
Self-citationImmunosuppressive Activities of Adenosine in Cancer
Allard, B., Beavis, P. A., Darcy, P. K., & Stagg, J. (2016) · Current Opinion in Pharmacology · doi:10.1016/j.coph.2016.04.001 · Cited by 292
Metadata
- Authors
- Lawrence, R. T., Perez, E. M., Hernández, D., Miller, C. P., Haas, K. M., Irie, H. Y., Lee, S.-I., Blau, C. A., & Villén, J.
- Venue
- Cell Reports
- Publisher
- Elsevier BV
- Published
- 2015-04
- Volume
- 11
- Issue
- 4
- Pages
- 630-644
- ISSN
- 2211-1247 (ISSN-L)
- OpenAlex
- W1996822667
- Cited by
- 224
- References
- 18
- FWCI
- 12.59
- Type
- journal article
- Language
- en
- Open access
- gold
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
