Abstract B113: Translational studies demonstrate that treatment with anti-PD-1 in unresponsive tumors can be improved by enhancing T cell activation in the tumor microenvironment with vaccine based immune therapy
Bibliographic record
Abstract
Abstract Checkpoint blockade using monoclonal antibodies has been a significant breakthrough for patients that are non-responsive to conventional therapy. Anti-PD-1 therapy can reactivate tumor infiltrating leukocytes (TILs) that were rendered exhausted due to suppression mediated by PD-L1-upregulation on tumor or antigen presenting cells. However if there are no pre-existing TILs, anti-PD-1 therapy is believed to be less effective. DPX-Survivac is a potent, peptide-based T cell activation therapy that contains multiple HLA class 1 restricted antigens from the oncoprotein survivin and is formulated in the DepoVaxTM platform, a unique lipid-in-oil formulation. In Phase 1/1b clinical trials, DPX-Survivac in combination with metronomic cyclophosphamide (mCPA; 50 mg BID) could enhance systemic immune responses detected by IFN-γ ELISPOT and tetramer analysis of PBMCs. We evaluated if this combination could potentially facilitate treatment of anti-PD-1 non-responsive tumors in a HPV-expressing murine tumor model (C3). Untreated C3 tumors had low expression of PD-L1 in vivo and low TILs. Anti-PD-1 treatment alone provided no protection from tumor growth. Treatment of mice bearing advanced tumors with DPX peptide vaccine and mCPA (20 mg/kg PO) increased the levels of antigen-specific CD8a+ T cells within the tumor microenvironment, detected using flow cytometry. We also detected increased expression of PD-1 on the T cells and PD-L1 on the tumor cells, suggesting that the tumor microenvironment (TME) was mediating immune suppression through increased PD-1:PD-L1 signaling. Treatment of tumor bearing mice with vaccine, mCPA and anti-PD-1 resulted in better tumor control of established tumors. Analysis of tumor infiltrating leukocytes by flow cytometry demonstrated that anti-PD-1 treatment did not further enhance tumor infiltration with antigen-specific CD8+ T cells induced by the vaccine/ mCPA treatment. However, RT-qPCR analysis of the tumor detected an increase in expression of cytotoxic T cell gene signatures within the tumor in combination with anti-PD-1 treatment. Clonal analysis was performed of the total TCRβ sequences using gDNA extracted from the tumors. Vaccine and mCPA treatment resulted in selective expansion of clones, as the top 10 clones accounted for 35% of the total TCRβ sequences; tri-therapy including anti-PD-1 significantly enhanced the expansion of T cells within the TME so that the top 10 clones accounted for 46% of the total TCRβ sequences (p<0.05). We conclude that anti-PD-1 therapy can enhance the efficacy of vaccine immunotherapy by promoting the activity and expansion of antigen-specific T cells within the TME. This preclinical analysis was supported by analysis of tumor tissue and TIL populations from an ongoing Phase 2 clinical trial of DPX-Survivac with mCPA in DLBCL patients. This analysis demonstrated that patients that responded to DPX-Survivac treatment also increased PD-1 expression within the tumor after treatment. There were also patients that demonstrated high PD-1 levels prior to vaccination, and this likely hampered their ability to respond to DPX-Survivac treatment. This suggests that anti-PD-1 in combination with a T cell activation therapy may act synergistically in this patient population. The data also provides support for effective T cell activation therapies as rational combination with checkpoint inhibitors in upcoming clinical development. Citation Format: Genevieve Weir, Olga Hrytsenko, Richard van der Jagt, Matthew Cheung, Rena Buckstein, Tara Quinton, Lisa MacDonald, Rita Nigam, Marc Mansour, Neil L. Berinstein, Marianne Stanford. Translational studies demonstrate that treatment with anti-PD-1 in unresponsive tumors can be improved by enhancing T cell activation in the tumor microenvironment with vaccine based immune therapy [abstract]. In: Proceedings of the Second CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; 2016 Sept 25-28; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(11 Suppl):Abstract nr B113.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".