SPSB1 Promotes Breast Cancer Recurrence by Potentiating c-MET Signaling
Bibliographic record
Abstract
UNLABELLED: Breast cancer mortality is principally due to tumor recurrence; however, the molecular mechanisms underlying this process are poorly understood. We now demonstrate that the suppressor of cytokine signaling protein SPSB1 is spontaneously upregulated during mammary tumor recurrence and is both necessary and sufficient to promote tumor recurrence in genetically engineered mouse models. The recurrence-promoting effects of SPSB1 result from its ability to protect cells from apoptosis induced by HER2/neu pathway inhibition or chemotherapy. This, in turn, is attributable to SPSB1 potentiation of c-MET signaling, such that preexisting SPSB1-overexpressing tumor cells are selected for following HER2/neu downregulation. Consistent with this, SPSB1 expression is positively correlated with c-MET activity in human breast cancers and with an increased risk of relapse in patients with breast cancer in a manner that is dependent upon c-MET activity. Our findings define a novel pathway that contributes to breast cancer recurrence and provide the first evidence implicating SPSB proteins in cancer. SIGNIFICANCE: The principal cause of death from breast cancer is recurrence. This study identifies SPSB1 as a critical mediator of breast cancer recurrence, suggests activation of the SPSB1-c-MET pathway as an important mechanism of therapeutic resistance in breast cancers, and emphasizes that pharmacologic targets for recurrence may be unique to this stage of tumor progression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".