The DOI matches its Crossref record; 88 of the 88 references it cites were found in a registry or an index.
Molecular biology in breast cancer: Intrinsic subtypes and signaling pathways
Abstract
The last decade has brought a breakthrough in the knowledge of the biology of breast cancer. The technological development, and in particular the high throughput technologies, have allowed researchers to inquire more deeply into the nature of the disease through the comparative study of large numbers of samples. The classification of breast cancer by traditional parameters has been joined by rankings based on gene expression. Among the most popular platforms are MammaPrint®, Oncotype DX® the wound-response model, the rate of two genes model, the genomic grade index and the intrinsic subtype model. The latter one provides the amplest biological information and allows for the classification of breast cancer into six intrinsic subtypes: luminal A, luminal B, HER2-enriched, basal-like, normal breast and claudin-low. These new classifications are not yet fully applicable to clinical practice not only because they have not been standardized, but also because they entail a substantial economic outlay. Nevertheless, they have provided valuable information on tumor biology that has led to a better understanding of the signaling pathways governing the processes of formation, maintenance and expansion of the tumors. Researchers now know more about the HER2, estrogen receptor, IGF1R, PI3K/AKT, mTOR, AMPK and angiogenesis pathways which has allowed for the development of new targeted therapeutics now being tested in ongoing clinical trials. In general, one can say that the last decade has changed the way researchers understand, classify and study breast cancer, and it has reshaped the way doctors diagnose and treat this disease. In addition, it has undoubtedly changed the search for alternative therapies by integrating molecular studies and the selection of study populations based on their molecular markers into clinical trials. The present review summarizes the advances that have allowed researchers to both better classify the disease, as well as explore some of the most important signaling pathways.
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
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
Molecular portraits of human breast tumours
Perou, C. M., Sørlie, T., Eisen, M. B., van de Rijn, M., Jeffrey, S. S., Rees, C. A., Pollack, J. R., Ross, D. T., Johnsen, H., Akslen, L. A., et al. (2000) · Nature · doi:10.1038/35021093 · Cited by 16,261
Slamon, D. J., Clark, G. M., Wong, S. G., Levin, W. J., Ullrich, A., & McGuire, W. L. (1987) · Science · doi:10.1126/science.3798106 · Cited by 11,760
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
Lynch, T. J., Bell, D. W., Sordella, R., Gurubhagavatula, S., Okimoto, R. A., Brannigan, B. W., Harris, P. L., Haserlat, S. M., Supko, J. G., Haluska, F. G., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa040938 · Cited by 11,486
Sørlie, T., Perou, C. M., Tibshirani, R., Aas, T., Geisler, S., Johnsen, H., Hastie, T., Eisen, M. B., van de Rijn, M., Jeffrey, S. S., et al. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.191367098 · Cited by 10,938
Angiogenesis in cancer and other diseases
Carmeliet, P., & Jain, R. K. (2000) · Nature · doi:10.1038/35025220 · Cited by 9,070
Bang, Y.-J., Van Cutsem, E., Feyereislova, A., Chung, H. C., Shen, L., Sawaki, A., Lordick, F., Ohtsu, A., Omuro, Y., Satoh, T., et al. (2010) · The Lancet · doi:10.1016/s0140-6736(10)61121-x · Cited by 7,302
A Gene-Expression Signature as a Predictor of Survival in Breast Cancer
van de Vijver, M. J., He, Y. D., van 't Veer, L. J., Dai, H., Hart, A. A. M., Voskuil, D. W., Schreiber, G. J., Peterse, J. L., Roberts, C., Marton, M. J., et al. (2002) · New England Journal of Medicine · doi:10.1056/nejmoa021967 · Cited by 6,488
A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer
Paik, S., Shak, S., Tang, G., Kim, C., Baker, J., Cronin, M., Baehner, F. L., Walker, M. G., Watson, D., Park, T., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa041588 · Cited by 6,277
Cited by
Georgoudaki, A.-M., Prokopec, K. E., Boura, V. F., Hellqvist, E., Sohn, S., Östling, J., Dahan, R., Harris, R. A., Rantalainen, M., Klevebring, D., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.04.084 · Cited by 609
Metastatic breast cancer: the potential of miRNA for diagnosis and treatment monitoring
McGuire, A., Brown, J. A. L., & Kerin, M. J. (2015) · Cancer and Metastasis Reviews · doi:10.1007/s10555-015-9551-7 · Cited by 329
Improved breast cancer histological grading using deep learning
Wang, Y., Acs, B., Robertson, S., Liu, B., Solorzano, L., Wählby, C., Hartman, J., & Rantalainen, M. (2022) · Annals of Oncology · doi:10.1016/j.annonc.2021.09.007 · Cited by 211
Metadata
- Authors
- Eroles, P., Bosch, A., Alejandro Pérez-Fidalgo, J., & Lluch, A.
- Venue
- Cancer Treatment Reviews
- Publisher
- Elsevier BV
- Published
- 2012-10
- Volume
- 38
- Issue
- 6
- Pages
- 698-707
- ISSN
- 0305-7372 (ISSN-L)
- OpenAlex
- W2158794266
- Cited by
- 624
- References
- 88
- FWCI
- 9.03
- Type
- journal article
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Molecular biology in breast cancer: Intrinsic subtypes and signaling pathways. https://app.cochat.ai/papers/10.1016/j.ctrv.2011.11.005
- The paper
- Eroles, P., Bosch, A., Alejandro Pérez-Fidalgo, J., & Lluch, A. (2012). Molecular biology in breast cancer: Intrinsic subtypes and signaling pathways. Cancer Treatment Reviews. https://doi.org/10.1016/j.ctrv.2011.11.005
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.
