Abstract 1949: The identification of potentially prognostic microRNAs in human nasopharyngeal carcinoma.
Bibliographic record
Abstract
Abstract Purpose/Objective: The majority of nasopharyngeal carcinoma (NPC) patients undergo curative radiation therapy (RT), with concurrent chemotherapy (CT) in cases of locally advanced disease. Current treatments achieve a modest 5-year overall survival rate of ∼70%, underscoring the need for a better understanding of NPC biology in order to develop novel tools to target this disease. In recent years, it has become apparent that micro-RNAs (miRNAs) play important roles in many, if not all human malignancies. Furthermore, miRNAs have been identified whose expression is capable of predicting patient outcome in a variety of human cancers; hence, we proceed to address the primary hypothesis that there is a miRNA expression signature capable of predicting outcome for NPC patients. Materials/Methods: The expression level of 734 unique human and viral miRNAs in 121 fully clinically-annotated NPC samples plus 10 normal nasopharyngeal epithelial tissues from healthy individuals was analyzed using the nCounter human microRNA panel from Nanostring®. These data were analyzed in relation to patient clinical and outcomes data (median follow-up time was 6.9 years), in order to identify associations between individual and combinations of miRNAs with clinical parameters. Results: Global miRNA expression analyses identified 49 miRNAs which were differentially-expressed between NPC and normal nasopharyngeal epithelial tissues. Using a 2-fold cut-off and Bonferroni correction for multiple comparisons, 39 miRNAs were significantly over-expressed and 10 were significantly under-expressed (miR-145, -216a, -100, -423, -424, -99b, -125a, -125b, -30c, -200a). Additionally, the expression level of 21 miRNAs, appeared to be strongly associate with clinical outcome. Specifically, there were two under-expressed miRNAs which were able to classify patients into low, medium, or high risk groups for distant relapse, wherein their 5-year distant relapse rates were: 0%, 10%, and 36%, respectively; p=0.0006). Conclusions: These preliminary data suggest a potential miRNA signature set which can predict for distant relapse, the major cause of death in NPC given the excellent loco-regional control rates with the use of IMRT. Our next step will be to measure the expression level of these 21 candidate miRNAs in an independent validation set of 131 NPC patients to confirm whether any of these miRNAs indeed have prognostic value in NPC. Biological insights will also be acquired by evaluating these miRNAs in pre-clinical NPC models. Prognostic signatures acquired could be used to categorize risk groups, and also facilitate the discovery and development of novel molecularly-targeted therapies which could improve outcome for future NPC patients. Citation Format: Jeff Bruce, Angela Hui, Daryl Waggot, Bayardo Perez-Ordonez, Ilan Weinreb, Wei Xu, Fei-Fei Liu. The identification of potentially prognostic microRNAs in human nasopharyngeal carcinoma. [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 1949. doi:10.1158/1538-7445.AM2013-1949
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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 | 0.001 | 0.001 |
| 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".