Abstract 2592: Therapeutic testing of a novel inhibitor GAP-107B8 on ovarian cancer cells
Bibliographic record
Abstract
Abstract Background: Ovarian cancer is the most fatal gynaecologic disease in the western world. In 2010 in the United States, an estimated 21,880 women will develop ovarian cancer and an estimated 13,850 women will succumb to this disease. Current treatments are limited to surgery and chemotherapy, but the disease often recurs highlighting the need for novel cancer therapeutics. We are currently evaluating the efficacy of a novel therapeutic, GAP-107B8 (PharmaGap Inc, Ottawa) using in vitro and in vivo models. GAP-107B8 was designed as a protein kinase C (PKC) inhibitor. The PKC family of serine/threonine kinases are involved in cellular proliferation, differentiation, apoptosis and cell polarity. One PKC isoform, PKC iota, has recently been identified as a human oncogene and has been shown to be overexpressed in epithelial ovarian cancers and is thus a potential therapeutic target for ovarian cancer. Objectives: 1) To test the novel inhibitor GAP-107B8 on ovarian cancer cell lines to determine its effects on cell proliferation, cell cycle progression and apoptosis; and 2) To determine the therapeutic potential of GAP-107B8 in xenograft models of two different ovarian cancer cell lines. Methods: Three ovarian cancer cell lines were treated with three different concentrations of GAP-107B8 and screened using high throughput assays to measure the proliferation of cells in adherent cultures. Apoptosis was measured by TUNEL staining, PARP cleavage and cell cycle analysis. Tumor burden was assessed in mice receiving subcutaneous implants of two ovarian cancer cell lines, followed by daily intra-tumoral injections of GAP-107B8. Results: GAP-107B8 caused a significant reduction in cell proliferation in 3 ovarian cancer cell lines tested (86% to 95%; p<0.001), including a cell line resistant to standard chemotherapy. In vivo, intra-tumoral treatment with GAP-107B8 resulted in a 45% reduction in average tumor size in the A2780cp-derived tumours (n=6/group, p<0.01) and 75% in the HEY-derived tumors (n=3/group, p<0.001). Cell killing may be mediated by apoptosis based on the observation of TUNEL staining 30 hours after treatment of cells in vitro with GAP-107B8. Apoptosis was confirmed by flow cytometry and PARP cleavage. GAP-107B8 also inhibited progression of cells through the cell cycle by blocking or delaying progression of cells through G2/M into the G1 phase. Conclusion: The novel inhibitor GAP-107B8 displays good efficacy in vitro in suppressing the proliferation of ovarian cancer cell lines. Cytotoxicity may be manifested in perturbations of the cell cyle and induction of apoptosis. Finally, GAP-107B8 showed therapeutic efficacy in ovarian cancer xenograft models. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2592. doi:10.1158/1538-7445.AM2011-2592
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".