In:
Molecular Cancer Research, American Association for Cancer Research (AACR), Vol. 11, No. 10_Supplement ( 2013-10-01), p. A022-A022
Abstract:
Introduction: Estrogen Receptor (ER) positive Breast Cancers account for approximately 70% of all breast cancers and have a better prognosis than ER- breast cancer. These patients are amenable to endocrine treatment, including tamoxifen, which eliminates recurrence in a large group of patients, but approximately 30% will relapse within 15 years of diagnosis. The most important predictor of recurrence in ER+ breast cancer is lymph node (LN) status. Patients with LN metastases (LN+) have increased risk of systemic recurrence, compared to ER+ patients without LN metastases (LN-). However, it is difficult for clinicians to determine appropriate treatment for ER+ LN+ breast cancer, so this group is generally treated aggressively. Several commercially available molecular signatures have been developed to predict outcome of early stage breast cancers, but none have been exclusively designed for ER+ breast cancer patients, inclusive of lymph node status. Methods: Here, three publicly available datasets (Gene Expression Omnibus, NCBI), consisting of gene expression profiles from primary ER+ breast cancer tumours were used to develop prognostic gene signatures. Patients from these cohorts were treated exclusively with tamoxifen for 5 years and were followed for at least 10 years past diagnosis. Gene expression significantly related to high risk of distant metastasis free survival (DMFS) of patients from our training cohort, at 10 years, was examined using the Prediction Analysis of Microarray (PAM, Stanford) and used to comprise our novel molecular signatures. Three independent signatures were developed using cohorts of patients with LN- disease exclusively, LN+ disease exclusively, or combined lymph node status. The performance of these signatures was evaluated using an independent cohort of patients with either LN- or LN+ disease. We also examined biologically relevant pathways, using Gene Set Enrichment Analysis (GSEA, Broad Institute), to examine whether the heterogeneous nature of ER+ breast cancers can be related to phenotype or outcome. Results: Gene expression and DMFS data from LN-, LN+, or combined patient samples were evaluated to identify sets of genes that predict patient outcome. The LN- signature could accurately predict DMFS of LN- patients from independent cohorts, but was unable to assign LN+ patients to low and high risk of DMFS groups. Similarly, the LN+ signature could accurately predict outcome of LN+ patients, but not LN- patients. Conversely, the combined signature was able to predict DMFS of all patients, regardless of LN status. We further evaluated gene set enrichment and found differences in gene sets associated with LN- and LN+ disease and with different outcomes. Conclusions: This research demonstrates the importance of considering the lymph node status of patients with both developing and employing prognostic gene signatures to predict outcome of early stage ER+ breast cancer patients. Also, it appears that the development of a signature using an exclusive population (i.e. LN-) of patients is not optimal to predict outcome in patients with different pathological parameters. In the future, using a combined gene signature may help direct treatment decisions for patients with early stage ER+ breast cancer. Further, understanding the biological heterogeneity of this disease, through GSEA, may lead to discovery of appropriate therapeutic targets for patients. Citation Format: Jessica G. Cockburn, Robin M. Hallett, John A. Hassell, Anita Bane. The use of LN status on developing prognostic gene signatures for ER+ breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research: Genetics, Biology, and Clinical Applications; Oct 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2013;11(10 Suppl):Abstract nr A022.
Type of Medium:
Online Resource
ISSN:
1541-7786
,
1557-3125
DOI:
10.1158/1557-3125.ADVBC-A022
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2013
detail.hit.zdb_id:
2097884-4
SSG:
12
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