Abstract P014: Driving immune-dependent metabolic vulnerabilities in the breast tumor microenvironment
Bibliographic record
Abstract
Abstract The tumor microenvironment (TME) is a complex arms race composed of host stroma and rapidly adapting cancer cells. This symbiosis is further complicated when dissecting the influence of inflammation, due to its multifaceted function within the TME as a driver of both pro- and anti-tumor responses. Comprising a major component of the TME, targeting or exploiting tumor-associated inflammation has long been sought for therapeutic purposes. However, the optimal amount and composition of inflammation for such purposes remains elusive. We have recently identified an interferon (IFN)-dependent transcriptional response in breast cancer cells that renders a unique sensitivity to oxidative stress produced by the biguanide-class complex I inhibitor, phenformin. Using syngeneic murine models representing luminal B (PyMT) and basal (4T1) breast cancer, we identified a modest sensitivity to phenformin administration in vivo. However, phenformin effectiveness is dramatically enhanced when used in combination with the toll-like receptor 3 (TLR3) agonist, polyinosinic:polycytidylic acid (poly (I:C)). Critically, the effectiveness of this combination treatment was lost when performed in immune-deficient mice (SCID-beige), indicating that an immune component was essential. These findings highlight a novel role for inflammation and the immune system to promote sensitivity to oxidative stress in the TME. By identifying cell types responsible for cultivating a microenvironment conducive to biguanide sensitivity in breast cancer, we can identify a novel immune-biomarker of complex I sensitivity. Therefore, using high-parameter flow cytometry, we are analyzing systemic and tumor-infiltrating leukocyte diversity and enumeration in both our genetically and phenotypically distinct breast cancer models. Combination-therapy-induced immune populations will be further characterized in their molecular and cellular response to TLR agonism and complex I inhibition. Functional responses such as inflammatory-mediator production, immunomodulatory activity, and direct and indirect tumor cytotoxicity will be assessed, ex vivo. Furthermore, monoclonal antibody-mediated depletion of candidate cell types will be used to validate their requirement and contribution to the observed synergy to the combination therapy. Examples of tumoricidal synergy between biguanides and the inflammatory TLR agonists have not yet been described, establishing these findings as novel additions to the field of tumor biology. Furthermore, identifying how sensitivity to oxidative stress can become situationally immune-dependent can greatly advance our understanding of how inflammation and metabolism intersect within the TME. Citation Format: John Heath, Stephanie Totten, Young Kyuen Im, Valerie Sabourin, Kathryn Hunt, Josie Ursini-Siegel. Driving immune-dependent metabolic vulnerabilities in the breast tumor microenvironment [abstract]. In: Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; 2021 Oct 5-6. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(1 Suppl):Abstract nr P014.
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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 | 0.001 |
| 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".