Rab11-FIP1C Is a Critical Negative Regulator in ErbB2-Mediated Mammary Tumor Progression
Bibliographic record
Abstract
Rab coupling protein (FIP1C), an effector of the Rab11 GTPases, including Rab25, is amplified and overexpressed in 10% to 25% of primary breast cancers and correlates with poor clinical outcome. Rab25 is also frequently silenced in triple-negative breast cancer, suggesting its ability to function as either an oncogene or a tumor suppressor, depending on the breast cancer subtype. However, the pathobiologic role of FIP family members, such as FIP1C, in a tumor-specific setting remains elusive. In this study, we used ErbB2 mouse models of human breast cancer to investigate FIP1C function in tumorigenesis. Doxycycline-induced expression of FIP1C in the MMTV-ErbB2 mouse model resulted in delayed mammary tumor progression. Conversely, targeted deletion of FIP1C in the mammary epithelium of an ErbB2 model coexpressing Cre recombinase led to accelerated tumor onset. Genetic and biochemical characterization of these FIP1C-proficient and -deficient tumor models revealed that FIP1C regulated E-cadherin (CDH1) trafficking and ZONAB (YBX3) function in Cdk4-mediated cell-cycle progression. Furthermore, we demonstrate that FIP1C promoted lysosomal degradation of ErbB2. Consistent with our findings in the mouse, the expression of FIP1C was inversely correlated with ErbB2 levels in breast cancer patients. Taken together, our findings indicate that FIP1C acts as a tumor suppressor in the context of ErbB2-positive breast cancer and may be therapeutically exploited as an alternative strategy for targeting aberrant ErbB2 expression. Cancer Res; 76(9); 2662-74. ©2016 AACR.
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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) | 0.001 | 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".