Abstract 3161: Identification of HPV/p16-associated microRNAs in primary oropharyngeal carcinoma
Bibliographic record
Abstract
Abstract Introduction: Deregulation of micro-RNAs (miR) is associated with oncogenesis of various malignancies, suggesting that miR expression profiling have the potential to unravel the complex biology of human tumours. It is now established that human papillomavirus (HPV)-associated oropharyngeal carcinoma (OPC) is a completely distinct entity, with a significantly more favourable outcome, compared to HPV-negative OPCs. The mechanisms underlying these differences currently remain incompletely understood; we therefore postulated that miR profilings might offer additional biological insights. Hence, the objectives of this study were to determine whether: a) there were miRs which associate with HPV (or p16) status; and b) there might be any association with clinical outcome, independent of HPV (or p16) status. Experimental Procedures: Archival formalin fixed and paraffin embedded (FFPE) diagnostic biopsy specimens were collected from 88 non-metastatic OPC samples (p16+ve:58; p16-ve:34), from patients who were treated for cure, as per institutional guidelines. Macrodissection was performed to ensure that each specimen contained >70% tumor cells. Seven FFPE specimens derived from histologically normal tonsils were used as controls. RNA was isolated using a commercially available extraction kit designed specifically for FFPE samples (Ambion). Expression levels of 365 miRs plus 3 endogenous controls were simultaneously measured by quantitative RT-PCR using the Human TaqMan Low Density Array (Applied Biosystem). Results: Four patient samples were excluded due to poor RNA quality. Overall, 224 miRs were expressed in >80% of both tumors and normal samples. Slightly more than half (128/224; 57%) of miRs were significantly differentially expressed between tumor and normal tissues (p<0.05); the majority of which (120/128; 94%) were up-regulated in tumor samples. The top 5 most significantly up-regulated miRs were miR-21, let-7g, miR-25, let-7f and miR-130b. It was noted that many of the deregulated miRs were located at known fragile sites of the chromosome. The top 5 most significantly p16-associated miRs included up-regulated miR-20b, miR-9, miR-9*, as well as down-regulated miR-193b and miR-23a. Further analyses identified three potential miR sets that associated with overall survival (miR-107, miR-151, miR-492; p=0.0002), disease free survival (miR-107, miR-151, miR-182, miR-20b, miR-361; p=0.0001), and distant metastasis (miR-324-5p, miR-151, miR-152, miR-361, miR492; p=0.0087), which retained significance even after adjusting for p16 status. Conclusions: We have identified a panel of p16/HPV-associated miRs in OPC, which appear to associate with clinical outcome, independent of p16 status. These data require further validation, but further evaluations of these miRs might offer biological insights into the mechanisms underlying the differences between HPV-positive vs. HPV-negative OPC. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3161. doi:1538-7445.AM2012-3161
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".