Fatty acid transport protein inhibition sensitizes breast and ovarian cancers to oncolytic virus therapy via lipid modulation of the tumor microenvironment
Bibliographic record
Abstract
Introduction: Adipocytes in the tumour microenvironment are highly dynamic cells that have an established role in tumour progression, but their impact on anti-cancer therapy resistance is becoming increasingly difficult to overlook. Methods: We investigated the role of adipose tissue and adipocytes in response to oncolytic virus (OV) therapy in adipose-rich tumours such as breast and ovarian neoplasms. Results: We show that secreted products in adipocyte-conditioned medium significantly impairs productive virus infection and OV-driven cell death. This effect was not due to the direct neutralization of virions or inhibition of OV entry into host cells. Instead, further investigation of adipocyte secreted factors demonstrated that adipocyte-mediated OV resistance is primarily a lipid-driven phenomenon. When lipid moieties are depleted from the adipocyte-conditioned medium, cancer cells are re-sensitized to OV-mediated destruction. We further demonstrated that blocking fatty acid uptake by cancer cells, in a combinatorial strategy with virotherapy, has clinical translational potential to overcome adipocyte-mediated OV resistance. Discussion: Our findings indicate that while adipocyte secreted factors can impede OV infection, the impairment of OV treatment efficacy can be overcome by modulating lipid flux in the tumour milieu.
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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".