Abstract 4985: M4OC-Prevent: Clinical evaluation of metformin for oral cancer precision prevention
Bibliographic record
Abstract
Abstract Despite encouraging recent results from novel treatment options, such as immunotherapy, for head and neck squamous cell carcinoma (HNSCC), limited progress has been made in improving outcomes for most patients. Prevention and early detection are key to improving the prognosis of HNSCC. We have previously shown that persistent activation of the PI3K/mTOR signaling circuitry is the most frequent dysregulated signaling mechanism in HNSCC, and that PI3K/mTOR inhibition exerts potent antitumor activity in a large series of genetically-defined and chemically-induced HNSCC models. However, potential immunosuppression and other dose-dependent side effects raise concerns regarding long term use of PI3K/mTOR inhibitors as chemopreventive agents. The repurposed drug metformin, used for frontline treatment of type 2 diabetes, decreases mTOR signaling in HPV- and HPV+ experimental HNSCC models and displays potent chemopreventive activity in mouse oral-premalignancy models. Furthermore, two recent large retrospective case-control cohort studies involving more than 300,000 diabetic patients demonstrated a decreased HNSCC risk in patients using metformin. Therefore, we conducted a single-arm, open label phase IIa clinical trial (NCT02581137) in individuals with oral premalignant lesions (OPL; oral leukoplakia or erythroplakia) to explore the potential of metformin for HNSCC prevention. Subjects were eligible if they had an OPL at any site with dysplasia or hyperplasia not associated with mechanical factors, and were otherwise healthy, without diabetes. They underwent pre- and post-treatment clinical exam of the oral cavity with lesion measurement and biopsy, along with saliva and blood collection. Eligible participants received metformin for 12 weeks (500 mg per day for the first week, 1,000 mg per day for the second week, and then 2,000 mg per day for the remaining treatment period). 26 participants were accrued and 22 were assessable for response. The toxicity profile was consistent with the known side effect profile of metformin. The clinical response rate was 18%, lower than the 30% spontaneous regression rate documented in several published long term oral leukoplakia trials. The clinical progression rate was also 18%. The significance of the clinical response assessment was limited by the short duration of treatment (only 12 weeks). Of interest, however, the histologic response rate was 59%, including 13% complete responses, and the histologic progression rate was 18%. Most of the responses consisted of a one grade improvement in histology. Circulating and tissue biomarker analyses, the latter focused on the impact of metformin on the AMPK-mTOR signaling network, are being analyzed. The goal of our studies is to provide a mechanistic framework for the selection of patients who may benefit from metformin for precision prevention of oral cancer. Citation Format: J Silvio Gutkind, Frank G. Ondrey, Denise Laronde, Miriam Rosin, Alfredo A. Molinolo, Charles Coffey, Beverly R. Wuertz, Leigha D. Rock, Huyen Huynh, H-H. Sherry Chow, Valerie D. Butler, Scott M. Lippman, Eva Szabo. M4OC-Prevent: Clinical evaluation of metformin for oral cancer precision prevention [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4985.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".