Abstract 1995: PTHrP drives tumor initiation signaling pathways in the PyMT model of breast cancer progression
Bibliographic record
Abstract
Abstract Parathyroid hormone-related peptide (PTHrP) was first discovered in cancer patients as the primary cause of malignancy associated hypercalcemia (MAH) & is overexpressed in most human breast tumors. We previously showed that PTHrP ablation, in the MMTV-PyMT murine model of breast cancer progression can dramatically prolong tumor latency, slows tumor growth & metastases. Here we examined the signaling pathways under the control of PTHrP in the pre-neoplastic stage of tumor development (hyperplasia) by generating MMTV-PyMT model in which the mammary epithelium, expressing membrane-targeted GFP, is distinguished from the membrane-targeted red fluorescent backlight of stromal & nonepithelial-derived mammary tissues. We constructed Pthrpflox/flox; Cre+ mT/mG tumor mice (PTHrP conditional KO) & Pthrpwt/wt; Cre+ mT/mG tumor mice (WT control). Next, we applied FACS to enrich the GFP+ mammary epithelial cells isolated from PTHrP KO & control tissues for subsequent RNAseq analyses. We then examined differentially expressed genes (DEGs) by comparing purified cell populations from PTHrP KO & control tissues. We identified 939 DEGs (p value <0.01) & used DAVID bioinformatics resources to systematically analyze the KEGG pathways. Among the most significant pathways, extracellular matrix (ECM), focal adhesion,PI3K-Akt, RAS and WNT pathways were up-regulated in WT control cells compared to KO cells. In summary, PTHrP controls critical signaling pathways involved in breast cancer initiation at the pre-neoplastic stage & suggests that PTHrP ablation is a promising therapeutic strategy in breast cancer. Citation Format: Rui Zhang, Jiarong Li, Dunarel Badescu, Andrew Karaplis, Jiannis Ragoussis, Richard Kremer. PTHrP drives tumor initiation signaling pathways in the PyMT model of breast cancer progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1995.
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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 | 0.001 | 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 | 0.001 |
| 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".