Control of Established Colon Cancer Xenografts Using a Novel Humanized Single Chain Antibody-Streptococcal Superantigen Fusion Protein Targeting the 5T4 Oncofetal Antigen
Bibliographic record
Abstract
Superantigens (SAgs) are microbial toxins that cross-link T cell receptors with major histocompatibility class II (MHC-II) molecules leading to the activation of large numbers of T cells. Herein, we describe the development and preclinical testing of a novel tumor-targeted SAg (TTS) therapeutic built using the streptococcal pyrogenic exotoxin C (SpeC) SAg and targeting cancer cells expressing the 5T4 tumor-associated antigen (TAA). To inhibit potentially harmful widespread immune cell activation, a SpeC mutation within the high-affinity MHC-II binding interface was generated (SpeCD203A) that demonstrated a pronounced reduction in mitogenic activity, yet this mutant could still induce immune cell-mediated cancer cell death in vitro. To target 5T4+ cancer cells, we engineered a humanized single chain variable fragment (scFv) antibody to recognize 5T4 (scFv5T4). Specific targeting of scFv5T4 was verified. SpeCD203A fused to scFv5T4 maintained the ability to activate and induce immune cell-mediated cytotoxicity of colorectal cancer cells. Using a xenograft model of established human colon cancer, we demonstrated that the SpeC-based TTS was able to control the growth and spread of large tumors in vivo. This required both TAA targeting by scFv5T4 and functional SAg activity. These studies lay the foundation for the development of streptococcal SAgs as 'next-generation' TTSs for cancer immunotherapy.
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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) | 0.001 | 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".