Abstract 1242: Ornithine decarboxylase as a therapeutic target in endometrial cancer
Bibliographic record
Abstract
Abstract Endometrial cancer is the 4th most common cancer and the 6th deadliest cancer in US women. The American Cancer Society estimates there will be 60,050 new endometrial cancers in 2016, an increase of more than 10% from the previous year highlighting the need for more effective treatments and prevention. Ornithine Decarboxylase (ODC) a key enzyme in polyamine synthesis is often overexpressed in cancers and contributes to cell proliferation and tumor growth. Therefore, ODC and the polyamine pathway are considered rational targets for cancer treatment or prevention. We noted ubiquitous expression of ODC1 in our previously published endometrial cancer gene array data and confirmed this in the cancer genome atlas (TCGA), finding expression in all four molecular sub-types with highest expression in copy number high cancers which have the worst clinical outcomes. Therefore, we explored the association of ODC1 gene expression with clinical outcomes of overall survival (OS) and recurrence in the TCGA cohort and noted that elevated ODC1 was significantly related to OS (Wald test p=0.001) and recurrence (p=0.01). Importantly, we confirmed these observations using QRT-PCR in a validation cohort of 60 endometrial cancers and found that endometrial cancers with elevated ODC1 had significantly shorter recurrence-free intervals (p=5.59x10-5) and elevated hazard ratio=3.72. Similar to TCGA data we also noted a strong trend to worse OS ( p=0.00014) with elevated hazard ratio 3.81. Numerous studies including clinical trials have examined the chemopreventive and anti-tumor effects of difluoromethylornithine (DFMO), a specific inhibitor of ODC. We found that DFMO treatment significantly reduced cell proliferation, cell viability, and colony formation in human cell line models derived from undifferentiated, endometrioid, serous, MMT and clear cell endometrial cancers. In contrast, immortalized uterine endometrial epithelial cells (EM E6/E7 TERT1) were less sensitive to DFMO. To confirm the significant effects of DFMO in vitro we performed an in vivo study with human endometrial cancer (ACI-98) tumor-bearing athymic nude mice. Xenografted mice were either treated with 2% (w/v) DFMO supplied in drinking water or water only (n=10/group). DFMO significantly reduced the tumor burden in mice compared to controls (p=0.0023). ODC-regulated polyamines (putrescine [Put] and spermidine [Spd]) known activators of cell proliferation were strongly decreased in response to DFMO, in both tumor tissue ([Put] (p=0.0006), [Spd] (p<0.0001)) and blood plasma ([Put] (p<0.0001), [Spd] (p=0.0049)) of treated mice. Results of these studies indicate that some endometrial cancers appear particularly sensitive to DFMO. Our findings indicate that the polyamine pathway in endometrial cancers in general and specifically those most clinically relevant endometrial cancers could be targeted for effective treatment, chemoprevention or chemoprevention of recurrence. Citation Format: Hong Im Kim, Chad R. Schultz, Andrea L. Buras, Elizabeth Friedman, Alyssa M. Fedorko, Leigh G. Seamon, Gadisetti Chandramouli, André S. Bachmann, John I. Risinger. Ornithine decarboxylase as a therapeutic target in endometrial cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1242. doi:10.1158/1538-7445.AM2017-1242
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".