PTHrP Drives Pancreatic Cancer Growth and Metastasis and Reveals a New Therapeutic Vulnerability
Bibliographic record
Abstract
Abstract Pancreatic cancer metastasis is a leading cause of cancer-related deaths, yet very little is understood regarding the underlying biology. As a result, targeted therapies to inhibit metastasis are lacking. Here, we report that the parathyroid hormone–related protein (PTHrP encoded by PTHLH) is frequently amplified as part of the KRAS amplicon in patients with pancreatic cancer. PTHrP upregulation drives the growth of both primary and metastatic tumors in mice and is highly enriched in pancreatic ductal adenocarcinoma metastases. Loss of PTHrP—either genetically or pharmacologically—dramatically reduces tumor burden, eliminates metastasis, and enhances overall survival. These effects are mediated in part through a reduction in epithelial-to-mesenchymal transition, which reduces the ability of tumor cells to initiate metastatic cascade. Spp1, which encodes osteopontin, is revealed to be a downstream effector of PTHrP. Our results establish a new paradigm in pancreatic cancer whereby PTHrP is a driver of disease progression and emerges as a novel therapeutic vulnerability. Significance: Pancreatic cancer often presents with metastases, yet no strategies exist to pharmacologically inhibit this process. Herein, we establish the oncogenic and prometastatic roles of PTHLH, a novel amplified gene in pancreatic ductal adenocarcinoma. We demonstrate that blocking PTHrP activity reduces primary tumor growth, prevents metastasis, and prolongs survival in mice. This article is highlighted in the In This Issue feature, p. 1601
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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) | 0.001 | 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".