Abstract 1395: Identification of genes and pathways related to lymphovascular invasion in axillary-node negative breast cancer: A bioinformatic analysis of gene expression data
Bibliographic record
Abstract
Abstract Breast Cancer patients whose disease has not spread to the lymph nodes (axillary node-negative breast cancer, ANNBC) generally have a good prognosis; however, 20-30% will experience a recurrence. We previously reported that the presence of Lymphovascular invasion (LVI) is an independent predictor of recurrence in ANNBC. Given that breast cancers generally metastasize through LVI, identification of genes with altered expression when comparing LVI+ from LVI- tumors is relevant from both the fundamental and the clinical perspective. To discover genes and pathways involved in LVI and recurrence in ANNBC we performed differential gene expression microarray analysis of LVI+ (n = 37) and LVI- (n=68) ANNBC tumors and assessed pathway enrichment using Ingenuity IPA. We also analysed differences within the LVI+ group designed to compare genes for their association with early recurrence (n = 14; ≤ 4 years) versus free of recurrence for at least 10 years (n = 23). The biofunctions predicted to be activated that distinguish LVI+ from LVI- tumors are largely related to cell movement and invasion. Those enriched in the subset of patients with early recurrence were mostly related to cell fate. In addition, we observed that inflammation related pathways are enriched in the LVI+ subgroup that undergoes early recurrence. TGF-β1 was predicted to be an upstream regulator in the LVI+ vs LVI- dataset and also highly ranked in the early recurrence vs. late recurrence analysis. Our bioinformatics analyses are consistent with a migratory phenotype for cancer cells associated with LVI. The results suggest important pathways as well as upstream regulators associated with LVI and recurrence that are involved in invasion and cell fate biofunctions. The findings are currently being evaluated through validation and functional studies. Note: This abstract was not presented at the meeting. Citation Format: Mathieu Blais, Dushanthi Pinnaduwage, Sheley Bull, Irene L. Andrulis. Identification of genes and pathways related to lymphovascular invasion in axillary-node negative breast cancer: A bioinformatic analysis of gene expression data. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1395. doi:10.1158/1538-7445.AM2014-1395
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".