Targeting the dependence on PIK3C3-mTORC1 signaling in dormancy-prone breast cancer cells blunts metastasis initiation
Bibliographic record
Abstract
Halting breast cancer metastatic relapses following primary tumor removal and the clinical dormant phase, remains challenging, due to a lack of specific vulnerabilities to target during dormancy. To address this, we conducted genome-wide CRISPR screens on two breast cancer cell lines with distinct dormancy properties: 4T1 (short-term dormancy) and 4T07 (prolonged dormancy). We discovered that loss of class-III PI3K, Pik3c3, revealed a unique vulnerability in 4T07 cells. Surprisingly, dormancy-prone 4T07 cells exhibited higher mTORC1 activity than 4T1 cells, due to lysosome-dependent signaling occurring at the cell periphery. Pharmacological inhibition of Pik3c3 counteracted this phenotype in 4T07 cells, and selectively reduced metastasis burden only in the 4T07 dormancy-prone model. This mechanism was also detected in human breast cancer cell lines in addition to a breast cancer patient-derived xenograft supporting that it may be relevant in humans. Our findings suggest dormant cancer cell-initiated metastasis may be prevented in patients carrying tumor cells that display PIK3C3-peripheral lysosomal signaling to mTORC1. Statement of Significance: We reveal that dormancy-prone breast cancer cells depend on the class III PI3K to mediate a constant peripheral lysosomal positioning and mTORC1 hyperactivity. Targeting this pathway might blunt breast cancer metastasis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".