Abstract B002: Tumor-secreted PTHrP facilitates pancreatic cancer cachexia by regulation of <i>de novo</i> lipogenesis pathway
Bibliographic record
Abstract
Abstract Pancreatic cancer is associated with one of the highest and most severe forms of cancer- associated cachexia amongst solid tumors. In an effort to further our understanding of the molecular etiology of pancreatic cancer-associated cachexia, we have developed mouse models to study adipose tissue wasting during Pancreatic Ductal Adenocarcinoma (PDAC) progression. In these pursuits, we have identified a pro-cachectic factor, PTHrP, that is produced and secreted by PDAC tumor cells and directly signals to adipocytes to facilitate wasting. Genetic deletion and pharmacological inhibition of PTHrP significantly extended the survival of tumor-bearing animals and dramatically reduced cachectic phenotypes in these animals, manifesting as decreased adipose and muscle tissue wasting. Mechanistic studies have shown crosstalk between tumor-cell derived PTHrP and adipose tissue PTH1R that drives the cachectic phenotype. Finally, bulk RNA sequencing analysis in PTHrP-driven cachectic models point towards a role for reduced de novo lipogenesis and adipogenesis in cachectic adipose depots, suggesting potential rewiring of the metabolic profile of white adipose tissue in tumor-bearing mice with higher PTHrP expression. Citation Format: Nikita Bhalerao, Jessica Peura, Yamini Ogoti, Calvin Johnson, Qingbo Chen, Ekaterina Korobkina, Faith Keller, Maximillan Wengyn, Robert Norgard, Richard Kremmer, Emma Watson, Marcus Ruscetti, David Guertin, Jason R Pitarresi. Tumor-secreted PTHrP facilitates pancreatic cancer cachexia by regulation of de novo lipogenesis pathway [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B002.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".