Remolding the tumor microenvironment by bacteria augments adoptive T cell therapy in advanced-stage solid tumors
Bibliographic record
Abstract
The intricate tumor microenvironment presents formidable obstacles to the efficacy of adoptive T cell therapy in the management of solid tumors by limiting the infiltration and inducing exhaustion of the transferred T cells. Here, we developed a bacterial-based adjuvant approach that augments the efficacy of adoptive T-cell therapy for solid tumor treatment. Our study reveals that intratumor injection of E. coli MG1655 normalizes tumor vasculatures and reprograms tumor-associated macrophages into M1 phenotype that produce abundant CCL5, together facilitating tumor infiltration of adoptively transferred T cells. The depletion of tumor-associated macrophages or CCL5 neutralization in vivo leads to the significantly decreased solid tumor infiltration of adoptive T cells in the presence of bacteriotherapy. This combinatorial therapy, consisting of E. coli adjuvant and adoptive T-cell therapy, effectively eradicates early-stage melanoma and inhibits the progression of pancreatic tumors. Notably, this dual strategy also strengthened the distal tumor control capabilities of adoptive T-cell therapy through the induction of in situ tumor vaccination. This dual therapeutic approach involving bacterial therapy targeting the interior of solid tumors and adoptive T-cell therapy attacking the tumor periphery exhibits potent therapeutic efficacy in achieving the eradication of advanced-stage tumors, including melanoma and hepatocellular carcinoma, by converging attacks from both inside and outside the tumor tissues.
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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".