Bibliographic record
Abstract
Background: Obesity is associated with a greater risk of developing cancer and a higher mortality rate, which can in part be attributed to the influence of the adipose tissue microenvironment (ATME). White adipose tissue (WAT) stores energy but accumulates in obesity to cause chronic inflammation. On the other hand, brown adipose tissue (BAT) produces heat from chemical energy through thermogenesis. Although BAT is scarce in adults, WAT can develop BAT-like characteristics through a process called “browning” upon exposure to thermogenic stimuli. Although BAT and WAT are known to influence metabolite availability and nutrient composition of tissues, little is known about how the obese ATME can influence metabolite availability within the tumor microenvironment. Our preliminary data showed that obesity (i.e. elevated WAT) promotes tumor growth, while thermogenesis (i.e. WAT browning) blunts tumor progression in lean and obese mice using a melanoma model. Therefore, the objective of this project was to identify how the obese ATME promotes cancer progression in additional cancer models, and whether thermogenesis can be used to offset obesity-driven cancer. Methods: Female C57BL6 mice were placed on a high-fat diet or low-fat diet for 15 weeks and injected with tumor cells in the flank or mammary fat pads. Thermogenesis was induced with a β3-adrenoceptor agonist (CL-316,243) or cold exposure. Transgenic female MMTV-PyMT mice were housed at three temperatures (6.5°C, 22°C, 26.5°C; collaboration with the Kazak lab) to simulate cold, room temperature, and thermoneutral conditions, respectively. Tumor interstitial fluid (TIF) from lean and obese mice was analyzed by liquid chromatography–mass spectrometry to study changes in metabolite availability within the tumor microenvironment. Results: Consistent with previous data in breast cancer (E0771, PyMT) and melanoma (B16F10) models, obesity was sufficient to accelerate tumor growth in the MC-38 (colorectal cancer) and Pan02 (pancreatic ductal adenocarcinoma) models. However, unlike the melanoma model, treatment with CL-316,243 had no effect on tumor growth. Cold exposure also had no effect on tumor growth in the MMTV-PyMT (breast cancer) model. To further explore how obesity contributes to accelerated tumor growth, a protocol for isolating and extracting TIF was optimized (collaboration with the McGill Metabolomics Core). Trends in TIF metabolite availability were found and differed from the content of blood plasma between lean and obese mice. Conclusions: This research may provide a better understanding of the underlying mechanisms that contribute to obesity-driven cancer, and suggests that changes in metabolite availability may in part underlie differences in tumor progression between lean and obese preclinical models
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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".