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Abstract B055: Hyperinsulinemia acts via acinar cell insulin receptors to drive obesity-associated pancreatic cancer initiation

2024· article· en· W4390915013 on OpenAlexaff
Anni Zhang, Yi Han Xia, Jeffrey S.H. Lin, Ken Chu, Wei Chuan K. Wang, Titine J.J. Ruiter, Jenny C.C. Yang, Nan Chen, Justin Chhuor, Shilpa Patil, Haoning Howard Cen, Elizabeth J. Rideout, Vincent R. Richard, David F. Schaeffer, René P. Zahedi, Christoph H. Borchers, James D. Johnson, Janel L. Kopp

Bibliographic record

VenueCancer Research · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsUniversity of ManitobaMcGill UniversityUniversity of British Columbia
Fundersnot available
KeywordsHyperinsulinemiaEndocrinologyInternal medicineInsulinPancreatic cancerBiologyInsulin receptorPancreasCancerCancer researchPancreatic Intraepithelial NeoplasiaMedicineInsulin resistance

Abstract

fetched live from OpenAlex

Abstract The rising incidence of pancreatic cancer is largely driven by increased prevalence of obesity and type 2 diabetes (T2D). Hyperinsulinemia is a cardinal feature of obesity and T2D, and is associated with increased cancer incidence and mortality. Whether insulin alone plays a causal role in increasing cancer risk by directly affecting the tumor cell of origin remains unclear. Our previous studies demonstrated that genetically reducing production of insulin suppressed formation of pancreatic intraepithelial neoplasia (PanIN) pre-cancerous lesions in mice with mutant Kras. However, consistent with a role for insulin in modulating the immune system, we saw changes in fibrosis, as well as changes in the transcriptomes of immune cells in the pancreas in mice with reduced insulin. Thus, it remained unclear whether hyperinsulinemia exerted its effects directly on the cells that give rise to PanINs or indirectly on the tumor microenvironment. Here, we tested whether insulin receptor (Insr) in KrasG12D-expressing pancreatic acinar cells was necessary for the effects of diet-induced hyperinsulinemia on pancreatic cancer development. Loss of Insr in KrasG12D-expressing acinar cells did not halt the development of hyperinsulinemia or weight gain associated with high fat diet consumption in mice. However, solely reducing Insr expression in KrasG12D-expressing acinar cells significantly reduced formation of PanIN and tumors. Mechanistically, proteomic analyses suggested that insulin and Insr coordinately regulate the production of digestive enzymes, the main function of acinar cells, by modulating the activity of the spliceosome, ribosome, and secretory machinery. Consistent with this adding insulin to TGF-alpha treated wild-type acini ex vivo increased their conversion into ductal structures and this was blocked by treatment with trypsin inhibitor. Additionally, loss of Insr also reduced the formation of duct-like structures in acinar explants after TGF-alpha stimulation and made them insensitive to insulin. Collectively, these data demonstrated that insulin action on the insulin receptor in acinar cells promotes conditions that cooperate with Kras signaling to increase the risk of developing pancreatic cancer especially in the context of hyperinsulinemia and obesity. Citation Format: Anni M.Y. Zhang, Yi Han Xia, Jeffrey S.H. Lin, Ken H. Chu, Wei Chuan K. Wang, Titine J.J. Ruiter, Jenny C.C. Yang, Nan Chen, Justin Chhuor, Shilpa Patil, Haoning H. Cen, Elizabeth J. Rideout, Vincent R. Richard, David F. Schaeffer, Rene P. Zahedi, Christoph H. Borchers, James D. Johnson, Janel L. Kopp. Hyperinsulinemia acts via acinar cell insulin receptors to drive obesity-associated pancreatic cancer initiation [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr B055.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.002

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.

Opus teacher head0.032
GPT teacher head0.350
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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