Exploiting tumor inflammation to increase the therapeutic impact of biguanides in oncology
Bibliographic record
Abstract
Breast cancer is the most common cancer among Canadian women; 1 in 8 women are expected to develop breast cancer in their lifetime. Breast cancer is grouped into four distinct types, which informs treatment strategies and patient outcomes. Indeed, some of these breast tumor types are more likely to develop therapeutic resistance, leading to relapse for these women. Moreover, regardless of tumor type, treatments are rarely curative for women who are diagnosed with breast cancers at an advanced stage, including metastatic disease. For these reasons, identifying therapies that target essential vulnerabilities is necessary for improving survival outcomes of women with such hard-to-treat cancers. Biguanides, including metformin and phenformin, suppress mitochondrial ATP production by inhibiting complex I of the electron transport chain and are typically used for the treatment of Type II diabetes. The repurposing of biguanides as anticancer agents has gained interest, but clinical trials examining the ability of metformin to improve survival in women with breast cancer have been disappointing. Even so, we have shown that when phenformin is used in combination with an inflammatory mediator, poly IC, to treat models of breast cancer in mice, we increase the cytotoxicity of phenformin by inducing an increase in oxidative stress. I hypothesize that this increase in sensitivity to biguanides is due to an increase in cytotoxic neutrophils, elicited by the combination therapy. To this end, a monoclonal antibody was administered to deplete neutrophils from two models of breast cancer in mice, representative of Luminal B and Triple Negative breast cancers. These mice were then administered phenformin and poly IC and tumor volume was measured over time and compared to a control. Furthermore, the elicited neutrophil population was functionally characterized through in vivo and in vitro assays looking at metastatic potential, maturation, cytotoxicity, and reactive oxygen species (ROS) production. Neutrophils were required for the anti-neoplastic efficacy of the combination therapy consisting of phenformin and poly IC. The neutrophils were found to be cytotoxic in vitro and capable of killing cancer cells in vivo
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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".