Abstract IA04: Reprogramming the tumor–immune microenvironment: From stromal circuits to engineered microbial immunotherapies
Bibliographic record
Abstract
Abstract The gut and intratumoral microbiota are increasingly recognized as modulators of host immunity and cancer immunotherapy outcomes. Harnessing this potential, we have developed a platform for engineering probiotic bacteria into programmable immunotherapeutic agents capable of reshaping the tumor microenvironment. Using E. coli Nissle 1917, we engineered microbial therapies that locally deliver checkpoint inhibitors, cytokines, and chemokines within tumors – achieving potent immune activation without systemic toxicity. Building on this approach, we recently engineered this system to serve as a personalized neoantigen immunotherapy platform, delivering tumor-specific neoantigens directly into host cells. This strategy drives robust neoantigen-specific CD4+ and CD8+ T cell responses, reconditions the tumor microenvironment, and induces durable tumor regression in preclinical models. These studies demonstrate the potential of microbial vectors as versatile and modular platforms for enhancing immunologic fitness and therapeutic precision in solid tumors. In parallel, we are uncovering fundamental mechanisms of immune regulation within the tumor stroma, focusing on non-immune components that shape immunologic fitness. Through single-cell and spatial transcriptomics, we identified a previously unrecognized population of immunomodulatory cancer-associated fibroblasts (imCAFs) that act as key orchestrators of local immunosuppression, functioning through a chemokine signaling axis to recruit and support hyper-suppressive regulatory T cells (Tregs) at the tumor border. Genetic perturbation of this stromal–immune circuit reprograms the tumor microenvironment toward cytotoxic T cell activation and tumor control. Underscoring the clinical relevance of these findings – and highlighting a potential avenue for therapeutic intervention by targeting the stromal compartment – we also observe analogous imCAF–Treg spatial organization in human NSCLC samples, with increased expression of imCAF markers in lung tumors being associated with reduced cytotoxicity scores and decreased progression-free survival. Together, these complementary efforts highlight how microbial engineering and stromal immunobiology can be integrated to uncover new therapeutic strategies and mechanistic insights. By leveraging synthetic biology, spatial omics, and immunoengineering, this work aims to define and modulate the determinants of immunologic fitness in solid tumors, with the ultimate goal of developing precise, effective, and durable cancer immunotherapies. Citation Format: Nicholas Arpaia. Reprogramming the tumor–immune microenvironment: From stromal circuits to engineered microbial immunotherapies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr IA04.
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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 | 0.001 | 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".