Enhancing the pro-inflammatory anti-cancer T cell response via biomanufactured, secretome-based, immunotherapeutics
Bibliographic record
Abstract
T lymphocytes play a critical role in the pro-inflammatory anti-cancer response; hence, significant pharmacologic efforts have been made to enhance the endogenous T cell response. Unfortunately, significant toxicity arises consequent to pan T cell activation. In contrast, the less robust T cell alloresponse has also demonstrated an anti-cancer effect, but poses an inherent risk of GvHD. To overcome the GvHD risk, an acellular pro-inflammatory agent (IA1) has been biomanufactured from the secretome of the allorecognition response. To assess IA1’s immunomodulatory activity, T cell proliferation and differentiation were determined in vitro . The pro-inflammatory properties of the IA1 therapeutic were mediated by the miRNA-enriched fractions. Moreover, cross-species efficacy was observed consequent to the evolutionary conservation of miRNA. IA1 exerted no toxicity to resting PBMC but induced significant proliferation of resting CD3 + (CD4 + and CD8 + ) T cells and skewed the response towards a pro-inflammatory state ( i.e. , increased Teff:Treg ratio). Crucially, IA1-activated PBMC demonstrated a potent inhibition of cancer cell (HeLa and SH-4 melanoma) proliferation relative to the resting PBMC. The anti-proliferation effect of IA1-activated PBMC was noted within ˜12 h versus 4–5 days for resting cells. A second biomanufactured therapeutic (IA2; produced using HeLa cells) surprisingly demonstrated direct toxicity to cancer cells but was less effective than IA1 in inducing a cell-mediated response. This study demonstrates that miRNA-enriched therapeutics can be biomanufactured from the secretome and can induce a potent pro-inflammatory, anti-cancer, effect on resting lymphocytes.
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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".