Inhibition of relaxin autocrine signaling confers therapeutic vulnerability in ovarian cancer
Bibliographic record
Abstract
Ovarian cancer (OC) is the most deadly gynecological malignancy, with unmet clinical need for new therapeutic approaches. The relaxin peptide is a pleiotropic hormone with reproductive functions in the ovary. Relaxin induces cell growth in several types of cancer, but the role of relaxin in OC is poorly understood. Here, using cell lines and xenograft models, we demonstrate that relaxin and its associated GPCR RXFP1 form an autocrine signaling loop essential for OC in vivo tumorigenesis, cell proliferation, and viability. We determined that relaxin signaling activates expression of prooncogenic pathways, including RHO, MAPK, Wnt, and Notch. We found that relaxin is detectable in patient-derived OC tumors, ascites, and serum. Further, inflammatory cytokines IL-6 and TNF-α activated transcription of relaxin via recruitment of STAT3 and NF-κB to the proximal promoter, initiating an autocrine feedback loop that potentiated expression. Inhibition of RXFP1 or relaxin increased cisplatin sensitivity of OC cell lines and abrogated in vivo tumor formation. Finally, we demonstrate that a relaxin-neutralizing antibody reduced OC cell viability and sensitized cells to cisplatin. Collectively, these data identify the relaxin/RXFP1 autocrine loop as a therapeutic vulnerability in OC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".