Abstract B39: PTP1B deficiency potentiate prostate cancer invasiveness by sensitizing Pten-null tumors to high-fat diet
Bibliographic record
Abstract
Abstract Specific diets can affect the risk and progression of prostate cancer (PCa). However, the interplay between diet and genetic alterations remains ill defined. Here we show that progression of PCa that is driven by Pten loss is mostly unresponsive to a high fat diet; however, in the absence of protein tyrosine phosphatase Ptpn1 (which encodes PTP1B) mice that are fed a high fat diet develop a highly invasive disease that is characterized by increased cell proliferation and Akt activation. Together with the finding that prostate-specific PTP1B overexpression does not initiate PCa by itself, we conclude that PTP1B act as an environment-dependent tumor suppressor in the context of Pten-null prostate tumors. PTP1B is a validated therapeutic target at the crossroad of metabolism (diabetes, obesity) and cancer (breast), and is currently being investigated in clinical trials. Due to PTP1B's nutrient sensing capabilities, we suggest that a careful monitoring of the balance between improving metabolic syndrome and promoting oncogenic effects under particular diets be pursued when using PTP1B-targeted therapeutics. Citation Format: David P. Labbé, Noriko Uetani, Valérie Vinette, Isabelle Aubry, Eva Migon, Jacinthe Sirois, Jody J. Haigh, Laurent Lessard, Louis R. Bégin, Lloyd C. Trotman, Marilène Paquet, Michel L. Tremblay. PTP1B deficiency potentiate prostate cancer invasiveness by sensitizing Pten-null tumors to high-fat diet. [abstract]. In: Proceedings of the AACR Special Conference: Metabolism and Cancer; Jun 7-10, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(1_Suppl):Abstract nr B39.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 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".