The DRP1 receptor FIS1 is critical to the expansion of triple-negative breast cancer tumor-initiating cells
Bibliographic record
Abstract
PURPOSE: To investigate whether individually targeting the outer mitochondrial membrane fission receptors FIS1 and MFF rather than the universally essential fission GTPase DRP1 is sufficient to suppress tumor initiating cells (TICs) without causing general mitochondrial dysfunction. METHODS: FIS1 or MFF were silenced or knocked out in triple-negative breast cancer (TNBC) cells to investigate their essentiality for maintaining TICs in cell culture and xenograft models. We further investigate the impact of FIS1 deficiency on several functional properties of mitochondria including morphology, membrane potential and ROS production. RESULTS: We demonstrate that FIS1 absence consistently suppressed TIC populations in cultured TNBC cells, and reduced tumor initiating activity in TNBC xenografts. Remarkably, we found that this phenotypic effect occurred in the absence of significant changes in ROS production, mitochondrial membrane potential and oxidative phosphorylation complex abundance even though FIS1-deficient TICs harbored a more reticular mitochondrial network. Finally, our in silico analyses established that all four DRP1 receptors (FIS1, MFF, MID49 and MID51) are ubiquitously expressed in healthy human tissues, and FIS1 is the most highly expressed DRP1 receptor in mammary gland. CONCLUSION: Our data collectively suggest that FIS1 targeting should allow for the suppression of TICs in TNBC tumors without compromising mitochondrial functionality or causing major, systemic toxicity. We believe our findings have the potential to facilitate the development of TIC suppressing therapies for TNBC patients, which is of considerable clinical relevance given that this malignancy has very limited targeted treatment options and is associated with a high mortality rate. CLINICAL TRIAL NUMBER: Not applicable.
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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) | 0.002 | 0.001 |
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".