Abstract PO-011: Analysis of nuclear receptor expression in head and neck cancer
Bibliographic record
Abstract
Abstract Background: The nuclear receptor (NR) superfamily contains a wide array of transcription factors responsible for cellular metabolism, inflammation, differentiation, and hormonal functions. For 45 years these receptors have been of interest in head and neck squamous cell carcinoma (HNSCC) treatment and prevention, especially retinoic acid receptor (RAR), peroxisome proliferator-activated receptor (PPAR), and vitamin D receptor (VDR) families. In the current study we examined expression patterns of these receptors in The Cancer Genome Atlas (TCGA). Methods: TCGA RNA-Seq data within the cBioportal platform comparing HNSCC samples (n=515 patients/samples) to normal tissue (n=82 patients/samples) was interrogated for significant differences in nuclear receptor expression. Genes with a z-score relative to normal samples (log RNA-Seq V2 RSEM normalized) greater than +2 or less than -2 in ≥50% of cases were considered significant. Unpaired, two-tailed t-tests were used to analyze comparisons in nuclear receptor genes’ z-scores across multiple clinical and pathologic parameters, p<0.05 was deemed significant. Statistical stringency for differential gene expression conformed to cBioportal and the University of Alabama at Birmingham’s Cancer Data Analysis Portal (UALCAN). Results: Of the 46 NR genes and 19 NR cofactors examined, ninety nine percent of tumor samples in the TCGA had some form of NR gene ‘alteration’ compared to normal tissue. These alterations predominantly encompass expression up- or down-regulation. NR genes Peroxisome Proliferator Activated Receptor Gamma (PPARγ) and Retinoic Acid Receptor Related Orphan Receptor C (RORC) (both in the thyroid hormone receptor-like family), and the NR cofactor, Nuclear Receptor Coactivator 1 (NCOA1), were differentially expressed and downregulated in tumors compared to normal tissue. Differences in specific NR expression varied significantly with HPV status (30 NRs), organ site (oral cavity vs hypopharynx & larynx; 20 NRs), tumor size (T1/2 vs. T3/4; 21 NRs), gender (male vs. female; 7 NRs); tumor staging (stage I/II vs. stage III/IV; 6 NRs); lymph node status (N0 vs. N+; 5 NRs), smoking history (smokers vs. non-smokers; 3 NRs), and patient race (White vs. Black/African American; 2 NRs). Conclusions: In prior studies of nuclear receptors in aerodigestive cancer, the retinoid receptor axis was downregulated, and a subsequent goal was stimulation and restoration of activity with natural or synthetic retinoids as a rationale for treatment. Presently, we have discovered significant decreases in PPARγ expression and are targeting this receptor with thiazolidinediones in a series of preclinical and human trials in a similar rationale to promote restoration of normal signaling and events like maturation. Here, we observed significant differences in a variety of parameters of several receptors associated with risk factors, stage, race, and gender that are observed and may become the focus of interest in future targeting efforts. Citation Format: Lindsey Mortensen, Beverly R. Wuertz, Frank G. Ondrey. Analysis of nuclear receptor expression in head and neck cancer [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-011.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".