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Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes
Open accessAbstract
Purpose: To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression–based “intrinsic” subtypes luminal A, luminal B, HER2-enriched, and basal-like. Methods: A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen. Results: The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%. Conclusion: Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy.
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von der Lippe Gythfeldt, H., Lien, T., Tekpli, X., Silwal‐Pandit, L., Borgen, E., Garred, Ø., Skjerven, H., Schlichting, E., Lundgren, S., Wist, E., et al. (2020) · International Journal of Cancer · doi:10.1002/ijc.33108 · Cited by 20
Omene, C., Ma, L., Moore, J., Ouyang, H., Illa-Bochaca, I., Chou, W., Patel, M. S., Sebastiano, C., Demaria, S., Mao, J.-H., et al. (2020) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-19-0253 · Cited by 16
Proteome-wide copy-number estimation from transcriptomics
Sweatt, A. J., Griffiths, C. D., Groves, S. M., Paudel, B. B., Wang, L., Kashatus, D. F., & Janes, K. A. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00064-3 · Cited by 5
Metadata
- Authors
- Parker, J. S., Mullins, M., Cheang, M. C. U., Leung, S., Voduc, D., Vickery, T., Davies, S., Fauron, C., He, X., Hu, Z., Quackenbush, J. F., Stijleman, I. J., Palazzo, J., Marron, J. S., Nobel, A. B., Mardis, E., Nielsen, T. O., Ellis, M. J., Perou, C. M., & Bernard, P. S.
- Venue
- Journal of Clinical Oncology
- Published
- 2009-03-10
- Volume
- 27
- Issue
- 8
- Pages
- 1160-1167
- ISSN
- 0732-183X (ISSN-L)
- 1527-7755
- OpenAlex
- W2132619562
- Cited by
- 4,721
- References
- 2
- FWCI
- 45.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
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 ›
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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.
