Multi-omics analysis of TNBC organoids identifies phosphorylation of the membrane trafficking machinery as key event associated with FER-mediated invasion
Bibliographic record
Abstract
Abstract Triple Negative Breast Cancer (TNBC) is characterised by unfavourable outcome due to the combination of its metastatic propensity, chemo-refractory behaviour and the lack of effective targeted interventions. Expression of the feline sarcoma-related (FER) kinase constitutes an independent prognostic factor that correlates with poor patient survival. FER promotes invasive behaviour in TNBC cells by regulating endosomal sorting and recycling (ESR) of adhesion proteins. Yet, the ESR machinery supporting invasion in TNBC, particularly within 3D environments, remains poorly understood. Here, we used FER-expressing TNBC patient-derived xenograft organoids (PDXOs) and MDA-MB-231 cells to identify the membrane trafficking machinery promoting invasion. Using a combination of proteomics, phospho-proteomics, and single cell RNA-sequencing, we show that the invasion of FER-expressing PDXO cells in collagen-I is mainly associated with the differential phosphorylation of membrane trafficking regulators, including SEC16A and a marked increase in Rab4-positive tubules. SEC16A depletion impairs cell invasion and reduces the number of focal adhesions and Rab4-positive tubules. Importantly, FER regulates SEC16A levels and localization, specifically in TNBC. Altogether, we identified SEC16A as a key player in FER-driven TNBC invasion, highlighting the membrane trafficking machinery as a promising target for the development of new therapeutic strategies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".