Abstract 1761: Combination of a T cell activating immunotherapy with immune modulators alters the tumor microenvironment and promotes more effective tumor control in preclinical models
Bibliographic record
Abstract
Abstract Combinations of immune therapies for cancer treatment will likely improve clinical responses, and a treatment that stimulates a robust T cell response may be a key component of therapy in patients with poorly infiltrated tumors. DPX-Survivac is a T cell activating therapy targeting survivin, formulated in DepoVax™ (DPX), an oil based delivery platform. In clinical studies, the MHC class I peptide antigens in DPX-Survivac induced strong and sustained T cell responses when used in combination with metronomic cyclophosphamide (mCPA) in ovarian cancer patients. Epacadostat is an indoleamine-2,3-dioxygenase 1 (IDO1) inhibitor which has shown to reduce immune suppression in tumors and has demonstrated encouraging results in clinical trials. Using preclinical mouse tumor models, we evaluated the combination of these three immune therapies. C57Bl/6 mice were implanted subcutaneously with murine pancreatic adenocarcinoma (Panc02) cells. Groups of mice were vaccinated with DPX-Survivac vaccine (containing murine H2D peptides) by subcutaneous injection and treated with mCPA (20 mg/kg/day, PO) and epacadostat (6 mg/day, PO). The combination of the three treatments provided a significant delay in tumor progression, and improvement in survival over untreated animals. Similar findings were also observed in the HPV16 E7 expressing C3 tumor model, using an HPV16 minimal peptide epitope (HPV16 E749-57) formulated in DPX. In this model, mice were terminated at defined endpoints to evaluate systemic immune responses in the spleen by IFN-γ ELISPOT and profile tumor infiltration by flow cytometric analysis. Although antigen-specific immune responses in the spleen were not increased by the triple combination in comparison to the DPX-based vaccine, there was a significant impact on several immune subtypes found in the tumor. Notably, antigen-specific CD8+ T cells (as detected by dextramer analysis) were increased and regulatory CD4+CD25+FoxP3+ T cells (Tregs) were decreased. In comparison, the Treg population in the spleen was highest in the triple therapy group (p=0.0046 compared to DPX/mCPA alone). This may indicate a selective exclusion of Tregs from the tumor microenvironment is induced by epacadostat, which can facilitate the anti-tumor immune response mediated by CD8+ T cells induced by DPX. Other immune modulating therapies, such as anti-PD-L1, may further enhance the tumor control induced by this treatment. The combination of DPX-based, T cell activating therapy with epacadostat, a drug that reduced tumor immune suppression is a rational, synergistic combination that is currently being evaluated in advanced ovarian cancer patients in the DeCidE1 clinical trial (NCT0278520). Citation Format: Alecia MacKay, Genevieve Weir, Holly Koblish, Ava Vila- Leahey, Valarmathy Kaliaperumal, Cynthia Tram, Peggy Scherle, Marianne Stanford. Combination of a T cell activating immunotherapy with immune modulators alters the tumor microenvironment and promotes more effective tumor control in preclinical models [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 1761.
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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 | 0.000 |
| 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) | 0.004 | 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".