Cancer treatment by photodynamic therapy combined with adoptive immunotherapy using genetically altered natural killer cell line
Bibliographic record
Abstract
Adoptive immunotherapy mediated by human natural killer (NK) cell line genetically altered to produce interleukin-2 (NK92MI) was evaluated as adjuvant to photodynamic therapy (PDT) of subcutaneous tumors. The combined effect of these two modalities was first examined with SiHa tumors (human cervical squamous cell carcinoma) growing in NOD-scid mice. The most effective protocol for NK92MI cell transfer in conjunction with PDT mediated by photosensitizer mTHPC was the injection of 5 x 10(7) cells (peritumoral or intravenous) given immediately after PDT, which produced a marked improvement in the therapeutic outcome compared with the effect of PDT alone. The same protocol was tested with HT-29 tumor model (human colorectal adenocarcinoma) xenografted in NOD-scid mice. The results demonstrate that the adoptive immunotherapy with NK92MI cells (which when used alone were not effective in controlling tumor growth) significantly improved the cures of PDT-treated HT-29 tumors, whereas such benefit was not observed with the parental cell line NK92 (not producing interleukin-2). Flow cytometry-based analysis revealed a higher percentage of p.t. injected NK92MI cells in PDT-treated than in non-treated HT-29 tumors. Further investigation showed that the NK92MI cell-based adoptive immunotherapy is also a highly effective adjuvant for PDT treatment of murine EMT6 tumors growing in immunocompetent syngeneic BALB/c mice. This result diminishes the concern that adoptively transferred NK92MI cells may be rendered ineffective by an allogenic reaction of the host. The findings of this study suggest that advanced protocols of NK cell-based adoptive immunotherapy can be developed as efficient adjuvants to PDT used for the treatment of solid malignant tumors.
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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".