Energetic regulation of coordinated leader–follower dynamics during collective invasion of breast cancer cells
Bibliographic record
Abstract
The ability of primary tumor cells to invade into adjacent tissues, followed by the formation of local or distant metastasis, is a lethal hallmark of cancer. Recently, locomoting clusters of tumor cells have been identified in numerous cancers and associated with increased invasiveness and metastatic potential. However, how the collective behaviors of cancer cells are coordinated and their contribution to cancer invasion remain unclear. Here we show that collective invasion of breast cancer cells is regulated by the energetic statuses of leader and follower cells. Using a combination of in vitro spheroid and ex vivo organoid invasion models, we found that cancer cells dynamically rearrange leader and follower positions during collective invasion. Cancer cells invade cooperatively in denser collagen matrices by accelerating leader-follower switching thus decreasing leader cell lifetime. Leader cells exhibit higher glucose uptake than follower cells. Moreover, their energy levels, as revealed by the intracellular ATP/ADP ratio, must exceed a threshold to invade. Forward invasion of the leader cell gradually depletes its available energy, eventually leading to leader-follower transition. Our computational model based on intracellular energy homeostasis successfully recapitulated the dependence of leader cell lifetime on collagen density. Experiments further supported model predictions that decreasing the cellular energy level by glucose starvation decreases leader cell lifetime whereas increasing the cellular energy level by AMP-activated kinase (AMPK) activation does the opposite. These findings highlight coordinated invasion and its metabolic regulation as potential therapeutic targets of cancer.
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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".