Abstract 560: Development of a novel class of traps that potently block transforming growth factor-beta (TGF-beta) thereby counteracting TGF-beta mediated immunosuppression and promoting T-cell infiltration into tumors
Bibliographic record
Abstract
Abstract Introduction: Elevated TGF-β ligand markedly augments cancer progression primarily by suppressing the immune system in the tumor microenvironment, in particular by suppressing T-cell recruitment and/or activation. We developed a novel class of decoy receptor traps to potently block TGF- β and induce T-cell infiltration into tumors. This promotes the “T-cell-inflamed” tumor state, which is expected to render tumors sensitive to immune checkpoint inhibitors and other immunotherapeutics. Experimental Procedures: We have computationally designed a class of avidity-enhanced receptor-ectodomain-based traps which bind and neutralize TGF-β. Several trap formats have been produced and tested, with each format exhibiting varying characteristics, including differing circulating half-lives and in vitro blocking potencies (from nM to pM). Representative therapeutic candidates from the different trap formats were evaluated for efficacy in in vivo studies using the syngeneic 4T1 triple negative breast cancer (TNBC) tumor model. Additionally, ex vivo studies were performed on CD4+ and CD8+ T-cells harvested from the draining lymph nodes of treated animals. Results: In efficacy studies using the syngeneic 4T1 TNBC model, novel TGF-β traps were shown to promote significant T-cell infiltration into tumors. This infiltration resulted in reduced primary tumor growth as well as significant reductions in metastatic lesions. Additionally, ex vivo studies revealed that trap treatment decreased T-cell apoptosis, promoted T-cell proliferation in response to tumor cell lysates in the presence of dendritic cells, as well as increased the capacity of T-cells to specifically lyse 4T1 tumor cells. Conclusion: Novel computationally-designed TGF-β traps are capable of promoting the “T-cell-inflamed” tumor state. Combination studies in which this novel class of anti-TGF-β immunotherapy is combined with immune checkpoint inhibitors are ongoing. Citation Format: Maureen D. O’Connor-McCourt, Anne E.G. Lenferink, John Zwaagstra, Traian Sulea, Jason Baardsnes, Catherine Collins, Christiane Cantin, Yves Durocher, Renu Singh, James Koropatnick. Development of a novel class of traps that potently block transforming growth factor-beta (TGF-beta) thereby counteracting TGF-beta mediated immunosuppression and promoting T-cell infiltration into tumors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 560.
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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 | 0.000 |
| 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".