Abstract 4688: AVID200: a novel computationally-designed TGF beta trap promoting anti-tumor T cell activity
Bibliographic record
Abstract
Abstract AVID200 is a computationally-designed, avidity-enhanced, receptor ectodomain-based trap that binds and neutralizes TGF-beta 1 and 3 with low pM potency. Of the three TGF-beta isoforms, blockade of TGF-beta 2 has been shown to promote cancer metastasis. Therefore, AVID200 is designed to inhibit TGF-beta 1 and 3 only. TGF-beta is a strongly immunosuppressive molecule that, when expressed by cancers, mediates escape from immune surveillance. Increased TGF-beta ligand promotes cancer progression by suppressing anti-tumor immunity, predominantly by suppressing recruitment and activation of anti-tumor T cells. Consequently, TGF-beta is an important therapeutic target in cancer.AVID200 immunotherapy blocks the capacity of TGF-beta 1 and 3 ligands to interact with their receptors. That blockage induces T cell infiltration into tumors, thereby promoting a "T cell-inflamed" tumor state. Multiple trap formats with differing in vitro blocking potency, biophysical traits, and circulating half-lives in rodents have been designed, synthesized, and tested. On the basis of favorable characteristics, the AVID200 molecule was selected for in vivo assessment of efficacy in inhibiting growth of syngeneic 4T1 triple negative breast cancer (TNBC) homografts in immunocompetent host mice. In addition, CD4+ and CD8+ T cells isolated from draining lymph nodes of 4T1 tumor-bearing mice, treated and untreated with AVID200 and other candidate TGF-beta traps, were assessed for activities important in anti-tumor immune activity. We report that AVID200 immunotherapy decreased T-cell apoptosis, stimulated T-cell proliferation in response to tumor cell lysate-loaded dendritic cells, and enhanced the capacity of T-cells to specifically recognize and kill 4T1 tumor cells in ex vivo 2D cultures. The effect of combining AVID200 with immune checkpoint inhibitors in the treatment of syngeneic homografts in immune-competent mice is being assessed. AVID200 is a promising new immunotherapy to selectively inhibit the TGF-beta 1 and 3 isoforms, thereby enhancing desirable anti-tumor immunity while avoiding the tumor-promoting effects resulting from TGF-beta 2 neutralization. Citation Format: Maureen D. O'Connor-McCourt, Anne E. Lenferink, John Zwaagstra, Traian Sulea, Catherine Collins, Renu Singh, Yves Durocher, Christiane Cantin, James Koropatnick. AVID200: a novel computationally-designed TGF beta trap promoting anti-tumor T cell activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4688. doi:10.1158/1538-7445.AM2017-4688
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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".