Targeting the Dependence on PIK3C3-mTORC1 Signaling in Dormancy-Prone Breast Cancer Cells Blunts Metastasis Initiation
Bibliographic record
Abstract
Halting breast cancer metastatic relapse following primary tumor removal remains challenging due to a lack of specific vulnerabilities to target during the clinical dormancy phase. To identify such vulnerabilities, we conducted genome-wide CRISPR screens on two breast cancer cell lines with distinct dormancy properties: 4T1 (short-term dormancy) and 4T07 (prolonged dormancy). The dormancy-prone 4T07 cells displayed a unique dependency on class III PI3K (PIK3C3). Unexpectedly, 4T07 cells exhibited higher mechanistic target of rapamycin complex 1 (mTORC1) activity than 4T1 cells due to lysosome-dependent signaling occurring at the cell periphery. Pharmacologic inhibition of PIK3C3 suppressed this phenotype in the 4T1-4T07 models as well as in human breast cancer cell lines and a breast cancer patient-derived xenograft. Furthermore, inhibiting PIK3C3 selectively reduced metastasis burden in the 4T07 model and eliminated dormant cells in a HER2-dependent murine breast cancer dormancy model. These findings suggest that PIK3C3-peripheral lysosomal signaling to mTORC1 may represent a targetable axis for preventing dormant cancer cell-initiated metastasis in patients with breast cancer. SIGNIFICANCE: Dormancy-prone breast cancer cells depend on the class III PI3K to mediate peripheral lysosomal positioning and mTORC1 hyperactivity, which can be targeted to 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".