225Actinium-armed antibody targeting CCR8+ regulatory T cells synergizes with immunotherapy to promote tumor rejection in syngeneic colorectal cancer models
Bibliographic record
Abstract
Background Colorectal cancer (CRC) remains a formidable threat to health worldwide. Immunotherapy with immune checkpoint inhibitors results in only a minority of CRC patients experiencing long-term progression-free survival, at the expense of significant autoimmune toxicity. Development of new therapeutics to “wake up” the immune system to fight CRC is necessary. Here we investigated for the first time radioimmunotherapy (RIT) directed towards CCR8, a marker of tumor-infiltrating immunosuppressive T-regulatory cells (ti-Tregs) as a method to recover anti-tumor immunity followed by immunotherapy in CRC models. Methods 225Actinium (225Ac)-labeled anti-CCR8 antibody and anti-CTLA-4 immunotherapy were used to assess their potential synergistic effects in syngeneic murine CRC models CT26 and MC38. The safety of all treatments was assessed through complete blood counts and blood chemistry. 225Ac-anti-CCR8 RIT-treated tumors were analyzed immunohistochemically for FoxP3 and CCR8 expression while mechanistic studies of tumor-infiltrating lymphocytes were done by flow cytometry. Results 225Ac-anti-CCR8 RIT alone demonstrated effectiveness in CRC models but dramatic anti-tumor response was observed when it was combined with anti-CTLA-4 immunotherapy. Immunotherapy alone failed to control tumor growth. Tumor immunohistochemistry post 225Ac-anti-CCR8 RIT showed ablation of CCR8+ ti-Tregs while flow cytometry analysis revealed CCR8-specific increased influx of effector CD8+ T cells, M1 macrophages and NK cells in comparison with 225Ac-control antibody. Conclusions These data demonstrate a synergistic effect of anti-aCCR8 RIT with immunotherapy through enhancement of adaptive and innate anti-tumor responses. Further investigation of anti-CCR8 RIT as a potential cancer-agnostic agent and its combinations with other immunotherapy agents such as anti-PD-1, LAG3 or TIGIT is warranted.
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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 | 0.000 | 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".