Genetically Modified DCs Engineered To Express PSA and/or PSMA Can Induce a Potent Immune Response Against Prostate Cancer Cells.
Bibliographic record
Abstract
Abstract Prostate cancer (Pca) is the most frequently diagnosed cancer in American men, with an estimated 230,110 cases expected in 2004. Despite various treatment strategies for patients including androgen ablation, radical prostatectomy, radiotherapy, and chemotherapy, the incidence of recurrence remains high and there is limited impact on survival, specially for metastatic disease. Our strategy involves the use of genetically-modified dendritic cells (DCs) to induce an immune response. We have previously demonstrated in a murine model that mature DCs engineered to express prostate tumor-associated antigens (TAAs) can stimulate immune system to specifically target TAA-expressing tumor cells. In view of the heterogenous nature of Pca, we hypothesized that stimulating the immune system against two antigens simultaneously may augment the anti-tumor activity. We generated murine DCs from whole bone marrow from mice by culturing them in granulocytemonocyte colony stimulating factor (GM-CSF) and IL-4 (20ng/ml each) and later with TNF-α. During the DC development, they were transduced with a concentrated oncoretrovirus that engineers the coexpression of prostate specific antigen (PSA) and CD25 (a cell surface marker for tranduced cells) (DC-PSA) or solely the expression of prostate specific membrane antigen (DC-PSMA). Transductions of DCs resulted in 30–60% expression of the either CD25 or PSMA as checked by flowcytometry. These DCs displayed high expression of DC markers like CD11c, CD80, CD86, CD40 and MHC class II molecules. There was no change in their allostimulatory capacity as checked by mixed lymphocyte reaction. Later, mice were injected either with DC non-transduced(NT), DC-PSA, with DC-PSMA. After two immunizations at different time points, the splenocytes were collected from all the groups one week after the last immunization. These splenocytes were stimulated to become effectors and were subsequently analysed to check for IFN-γ secretion, IL-10 secretion and cytolytic assays, using the targets as syngeneic murine prostate tumor cells, RM1 engineered to express PSA and PSMA. The effectors showed high IFN-γ and high cytolytic activity low IL-10 secretion as compared to controls. Our next step will be to test the increase of the levels of IFN-γ secretion and cytolytic activity in the mice immunized with DC-PSA and DC-PSMA both as compared to DC-PSA alone and DC-PSMA alone. To show clinically feasibility of our approach, we extended our work to human cells. HuDCs were generated using human CD34+ hematopoietic cells by culturing them in GM-CSF, SCF, Flt3L and TNF-α for 12 days. During DC production, they were transduced to express PSA or PSMA using a concentrated oncoretrovirus. They were checked for DC markers and the expression of the respective TAAs i.e PSA (CD25) or PSMA. Later, these cells were co-cultured with autologous T-cells. When these immunized T cells were used as effectors against the HLA-matched prostate cancer cell lines expressing PSA and PSMA, they showed high IFN-γ secretion and Low IL-10 secretion as compared controls. Thus, we have found that human DCs can be used to sensitize T cells to show antitumor responses and we are going to test in murine model the augmentation of such antitumour response by using multiple antigen immunotherapy approach.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".