Abstract 5289: Identification of basal-like subtype-specific MicroRNAs in lymph node negative breast cancer .
Bibliographic record
Abstract
Abstract Background: Breast cancer is a heterogeneous disease both clinically and biologically. With the recent recognition of the many different molecular subtypes, a desire to more specifically categorize tumors to allow tailoring of treatment to individual patients has developed. One particularly aggressive and treatment-resistant group is the “basal-like” subtype. However, the molecular events driving this subtype of breast cancer are largely unknown. Here, we propose to examine whether MicroRNAs specifically expressed in basal-like breast cancers contribute to their development and progression. Materials and Methods: Fifty-four formalin fixed paraffin embedded primary tumour samples from lymph-node negative breast cancer patients who were participants in a phase III randomized trial of tamoxifen (Tam) +/- whole breast radiation were collected. Intrinsic molecular subtyping was determined using semi-quantitative analysis of ER, PR, Ki-67, HER2, EGFR and cytokeratin (CK) 5/6 on tissue microarrays (TMAs) constructed from the tumor blocks. Patients were classified into the following categories: luminal A, luminal B, luminal-HER2, HER2 enriched or basal-like phenotypes. The median follow-up for this cohort was 10 years. The expression of 365 miRNAs was measured in these samples using the qRT-PCR based Taqman low density array human miRNA panel (Applied Biosystems). Data were validated in cell line models and further analyzed using level 3 TCGA breast cancer data. Results: Twenty-three miRNAs were identified at a significantly different expressed level in basal-like breast cancers (n = 6) compared with other subtypes (n = 48). This differential expression was validated in publically available RNA-Seq data generated by the TCGA for 20 of the 23 miRNAs. Eight of these twenty miRNAs were found to correlate with corresponding copy-number alterations at their genomic loci using the data generated by TCGA. Further analysis is currently underway to determine the potential contribution of methylation to the expression differences observed. The putative target genes and pathways involved will also be analyzed. Conclusions: MiRNAs are known to play numerous roles in cancer development and progression. In the current study we report several miRNAs with distinct expression patterns in basal-like breast cancers. Further characterization of the phenotypic impact of this deregulation may lead to a better understanding of this aggressive subtype and provide valuable insight needed to develop novel targeted therapies to treat these tumours. Citation Format: Wei Shi, Jeff Bruce, Rui Yan, Fei-Fei Liu. Identification of basal-like subtype-specific MicroRNAs in lymph node negative breast cancer . [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5289. doi:10.1158/1538-7445.AM2013-5289
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".