The role of mitochondrial metabolism and PGC-1α on breast cancer bioenergetics and metastasis
Bibliographic record
Abstract
Breast cancer is the most common cancer affecting women today. Although many advancements in treatment have been made, breast cancer is still the second leading cause of cancer-related deaths in Canadian women- demonstrating the need for a greater understanding of this complex disease. The re-wiring of cellular metabolism represents an important alteration in cancer cells that is needed to support their exponential growth. In addition, each step of cancer progression to metastatic disease presents novel bioenergetic and biosynthetic challenges, requiring metabolic adaptations within the cancer cells. Despite increasing knowledge about cancer progression, the characterization of such metabolic changes is incomplete. Classically, the Warburg effect states that cancer cells adopt an extremely glycolytic phenotype, taking up large amounts of glucose for rapid ATP production. Over the years this has been attributed to dysfunctional mitochondria in cancer cells. More recently however it is understood that mitochondria play vital roles in cancer progression and support various cellular metabolic rearrangements in cancer cells. In this work, we show a novel methodology to study isolated functional mitochondria from cancer cells in order to obtain insights into their intrinsic properties- to ultimately understand their contribution to cancer progression. Using this approach, we investigate and characterize the mode of action of metformin, the most commonly prescribed drug for the treatment of type II diabetes, which is being evaluated for use in cancer treatment. We show that metformin directly targets mitochondria and deregulates cellular bioenergetics and metabolism in cancer cells. Lastly, we investigate PGC-1, a central regulator of cellular and mitochondrial metabolism, and its role in breast cancer progression, metastasis and response to bioenergetic drugs. Overall, we elucidate the role of mitochondrial metabolism on cancer cell progression and metastasis as well as shed light on the effects of metformin on these processes.
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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".