Abstract POSTER-THER-1415: Translational study of DPX-survivac peptide vaccine and metronomic cyclophosphamide using a novel HLA-A2/ survivin expressing ovarian tumor model to mimic phase I clinical data
Bibliographic record
Abstract
Abstract Survivin, a member of the inhibitors of the apoptosis protein family, is involved in critical pathways of cancer cell growth and survival. Survivin overexpression is detected in 90% of epithelial ovarian cancers. DPX-Survivac is a peptide vaccine containing five survivin HLA class I peptides (A1, A2, A3, A24 and B7) and a universal HLA class II helper peptide derived from tetanus toxin (A16L) formulated in the novel adjuvanting vaccine platform DepoVaxTM. In a phase I clinical study DPX-Survivac was combined with a continuous (metronomic) low dose of cyclophosphamide (mCPA) to treat patients with advanced or recurrent ovarian cancer. In this study, we found mCPA significantly enhanced the immune responses induced by DPX-Survivac. In order to investigate this effect and to understand the mechanisms involved, we developed an ovarian tumor model in HLA-A2 transgenic mice. Vaccination of HLA-A2 transgenic mice generates an immune response towards the A2-restricted survivin peptide in DPX-Survivac. Mouse Ovarian Surface Epithelial (MOSE) cell lines are “natural” ovarian cancer models established by spontaneous in vitro transformation of primary ovarian surface epithelial cells. MOSE cell lines were confirmed to express HLA-A2 and overexpress survivin, and formed subcutaneous and intraperitoneal ovarian tumors when implanted into syngeneic recipients. HLA-A2 transgenic mice bearing MOSE tumors were treated with either DPX-Survivac, mCPA (administrated at 20 mg/kg/day in drinking water) or a combination of mCPA and DPX-Survivac. We found that combining DPX-Survivac with mCPA provided most effective control of tumor growth. Ex vivo IFN-γ ELISPOT revealed that mCPA increases immunogenicity of the DPX-Survivac in mice bearing MOSE tumors. Analysis of the blood and tumor microenvironment by RT-qPCR showed that mice treated with the combination therapy had an altered immune gene signature with increased cytotoxic T lymphocyte markers compared to mice treated with DPX-Survivac or metronomic cyclophosphamide alone. Our data indicates that the combination treatment enhances the infiltration of CD8+ T cells into tumors, enhancing tumor specific immune responses. In summary, we have established a new model of epithelial ovarian cancer in HLA-A2 transgenic mice. This model was used to investigate the immunogenicity and efficacy of DPX-Survivac, an HLA-restricted peptide vaccine with proven immunogenicity in human clinical trials, in combination with the immune modulator mCPA. This model can be used to explore mechanisms of establishment and progression of ovarian cancer as well as for evaluation of new treatment strategies including new immune modulator/ vaccine combinations. Citation Format: Olga Hrytsenko, Genevieve M. Weir, Mohan Karkada, Neil L. Berinstein, Marianne M. Stanford, Marc Mansour. Translational study of DPX-survivac peptide vaccine and metronomic cyclophosphamide using a novel HLA-A2/ survivin expressing ovarian tumor model to mimic phase I clinical data [abstract]. In: Proceedings of the 10th Biennial Ovarian Cancer Research Symposium; Sep 8-9, 2014; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(16 Suppl):Abstract nr POSTER-THER-1415.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".