Combination of a T cell activating therapy and anti-phosphatidylserine enhances anti-tumour immune responses in a HPV16 E7-expressing C3 tumour model
Bibliographic record
Abstract
T cells and drives antigen-specific cytotoxic T cells into tumours. Cancer cells upregulate phosphatidylserine (PS) on the cell surface as a mechanism to induce an immunosuppressive microenvironment. Development of anti-PS targeting antibodies have highlighted the ability of a PS-blockade to enhance tumour control by T cells by releasing immunosuppression. Here, C57BL/6 mice were implanted with HPV16 E7 target-expressing C3 tumours and subjected to low dose intermittent cyclophosphamide (CPA) in combination with DPX-R9F treatment targeting an E7 antigen with and without anti-PS and/or anti-PD-1 targeting antibodies. Immune responses were assessed via IFN-γ ELISPOT assay and the tumour microenvironment was further analyzed using RT-qPCR. We show that the combination of DPX-R9F and PS-targeting antibodies with and without anti-PD-1 demonstrated increased efficacy compared to untreated controls. All treatments containing DPX-R9F led to T cell activation as assessed by IFN-γ ELISPOT. Furthermore, DPX-R9F/anti-PS treatment significantly elevated cytotoxic T cells, macrophages and dendritic cells based on RT-qPCR analysis. Overall, our data indicates that anti-tumour responses are driven through a variety of immune cells within this model and highlights the need to investigate combination therapies which increase tumour immune infiltration, such as anti-phosphotidylserine.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".