Abstract A022: The use of LN status on developing prognostic gene signatures for ER+ breast cancer
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".