Fractionated but Not Single-Dose Radiotherapy Induces an Immune-Mediated Abscopal Effect when Combined with Anti–CTLA-4 Antibody
Bibliographic record
Abstract
PURPOSE: This study tested the hypothesis that the type of dose fractionation regimen determines the ability of radiotherapy to synergize with anti-CTLA-4 antibody. EXPERIMENTAL DESIGN: TSA mouse breast carcinoma cells were injected s.c. into syngeneic mice at two separate sites, defined as a "primary" site that was irradiated and a "secondary" site outside the radiotherapy field. When both tumors were palpable, mice were randomly assigned to eight groups receiving no radiotherapy or three distinct regimens of radiotherapy (20 Gy x 1, 8 Gy x 3, or 6 Gy x 5 fractions in consecutive days) in combination or not with 9H10 monoclonal antibody against CTLA-4. Mice were followed for tumor growth/regression. Similar experiments were conducted in the MCA38 mouse colon carcinoma model. RESULTS: In either of the two models tested, treatment with 9H10 alone had no detectable effect. Each of the radiotherapy regimens caused comparable growth delay of the primary tumors but had no effect on the secondary tumors outside the radiation field. Conversely, the combination of 9H10 and either fractionated radiotherapy regimens achieved enhanced tumor response at the primary site (P < 0.0001). Moreover, an abscopal effect, defined as a significant growth inhibition of the tumor outside the field, occurred only in mice treated with the combination of 9H10 and fractionated radiotherapy (P < 0.01). The frequency of CD8+ T cells showing tumor-specific IFN-gamma production was proportional to the inhibition of the secondary tumor. CONCLUSIONS: Fractionated but not single-dose radiotherapy induces an abscopal effect when in combination with anti-CTLA-4 antibody in two preclinical carcinoma models.
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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) | 0.001 | 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) | 0.002 | 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".