Abstract LB155: Combination of IGF-1R targeted alpha therapy with checkpoint inhibitors results in synergistic efficacy in a syngeneic colorectal tumor model
Bibliographic record
Abstract
Abstract Objective: In the last decade, immunotherapy has revolutionized cancer care and became a mainstream therapy along with surgery, chemotherapy and radiation. Unfortunately, only a limited number of cancers exhibit intrinsic sensitivity to immunotherapies and the overall response rate is only 20-25%. Therefore, there is a strong impetus to identify treatments which can sensitize patients to immunotherapies. Fusion utilizes targeted alpha therapy (TAT) which enables delivery of alpha particle emitting isotopes (actinium 225) to the targeted tumor cells. The rationale for the combination of TAT and immunotherapy stems from known immune stimulating properties of radiation, leading to release of tumor-associated antigens, maturation of antigen-presenting cells (APCs) and in turn activation and proliferation of CD8+ T cells. Therefore, we have hypothesized that combination of TAT with immunotherapy will lead to a robust therapeutic effect resulting in a synergistic response as compared to monotherapy alone. Methods: The syngeneic CT26 colon cancer model was used to evaluate therapeutic efficacy of combination treatment with actinium 225-radiolabelled IGF-1R antibody (mAb), FPI-1792, and checkpoint inhibitors. FPI-1792 was used as a surrogate therapeutic that cross-reacts with mouse IGF1R. Mice with subcutaneous tumors (tumor volume ~175 mm3) were treated with either vehicle, an anti-CTLA-4 mAb (5 mg/kg) or anti-PD-1 mAb alone (5 mg/kg), FPI-1792 alone (375 kBq/kg) or the respective combinations. CD8+ T cells infiltration was evaluated via flow cytometry. Results: While only transient tumor growth suppression was detected in animals treated with either checkpoint inhibitors or FPI-1792 alone, complete tumor regression was observed in 13 out of 15 mice treated with combination therapies, indicating development of potent synergy in the combination groups. To investigate whether animals with regressing tumors will be able to reject a secondary tumor, all surviving animals were re-challenged with CT26 cells on the contralateral flank. Rejection of the secondary tumors was detected in 87% of mice previously treated with either FPI-1792 alone or a combination therapy, while tumors grew in all control animals. To gain a better understanding of the mechanism responsible for tumor regression, tumors were collected 14 days post re-challenge and analyzed via flow cytometry. An increased frequency of CD8+ T cells was observed in mice treated with combination therapy as compared to untreated animals (5-20% vs 1-2%, respectively). Importantly, high frequency of AH1 antigen-specific T cells was detected using a tetramer staining - 30-70% in the treated animals as compared to 2-3% in the control mice. Conclusions: The combination of targeted alpha therapy with checkpoint inhibitors led to tumor regression in a CT26 syngeneic model. Moreover, mice re-challenged with the same tumor on the contralateral flank were protected due to development of a strong immune response. Finally, increased frequency of antigen specific CD8+ T cells in re-challenged tumors suggests that combined treatment can break T cell tolerance and elicit a strong CD8+ T cell mediated immune response, culminating in tumor rejection. Citation Format: Sonal Patel, Natalie Grinshtein, Ryan Simms, Meiduo Hu, John Valliant, Eric Burak. Combination of IGF-1R targeted alpha therapy with checkpoint inhibitors results in synergistic efficacy in a syngeneic colorectal tumor model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr LB155.
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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".