Evaluating the Combination of Radioimmunotherapy and Immunotherapy in a Melanoma Mouse Model
Bibliographic record
Abstract
Immunotherapy has changed the oncology landscape during the last decade and become standard of care for several cancers. The combinations of immunotherapy with other treatment modalities are also being investigated. One of the challenges to investigate such combinations is to identify suitable mouse models for the pre-clinical experiments. In the past, we and other researchers showed that murine B16-F10 melanoma in C57Bl6 mice is refractory to treatment with immune checkpoint inhibitors. In this work we studied the suitability of an alternative syngeneic model, Cloudman S91 murine melanoma in DBA/2 mouse (DBA/2NCrl), to study the combination of immunotherapy targeting PD-1 and radioimmunotherapy targeting melanin. DBA/2 male and female mice were injected subcutaneously with 3–6 million Cloudman S91 cells. When the tumors reached ~150 mm3 volume, the animals were treated intraperitoneally with PBS (sham), h8C3 unlabeled (cold) antibody to melanin, immunotherapy with anti-PD-1 antibody, radioimmunotherapy with 213Bismuth (213Bi)-labeled h8C3 antibody, or several combinations of immunotherapy and radioimmunotherapy. Treatments with immunotherapy alone produced very modest effect on the tumor size, while combination therapy resulted in significant slowing down of the tumor growth, increased animal survival, and no decrease in animal body weight. We conclude that Cloudman S91 murine melanoma in DBA/2 mouse is a suitable model to evaluate combination of immunotherapy of melanoma with tangentially targeted treatments.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".