Integrated Systems-Analysis of the Human and Murine Pancreatic Cancer Glycomes Reveal a Tumor Promoting Role for ST6GAL1
Bibliographic record
Abstract
ABSTRACT Pancreatic ductal adenocarcinoma (PDA) is the 3 rd leading cause of cancer-death in the U.S.. Glycans, such as CA-19-9, are biomarkers of PDA and are emerging as important modulators of cancer phenotypes. Herein, we utilized a systems-based approach integrating glycomic analysis of human PDA and the well-established KC mouse model, with transcriptomic data to identify and probe the functional significance of aberrant glycosylation in pancreatic cancer. We observed both common and distinct patterns of glycosylation in pancreatic cancer across species. Common alterations included increased levels of α-2,3- and α-2,6-sialic acids, bisecting GlcNAc and poly-LacNAc. However, core fucose, which was increased in human PDAC, was not seen in the mouse, indicating that not all human glycomic changes can be modeled in the KC mouse. In silico a nalysis of bulk and single cell sequencing data identified ST6GAL1, which underlies α-2,6-sialic acid, as overexpressed in human PDA, concordant with histological data. Enzymes levels correlated with the stage of clinical disease. To test whether ST6GAL1 promotes pancreatic cancer we created a novel mouse in which a pancreas-specific genetic deletion of this enzyme overlays the KC mouse model. Analysis of our new model showed delayed cancer formation and a significant reduction in fibrosis. Our results highlight the importance of a strategic systems-approach to identifying glycans whose functions can be modeled in mouse, a crucial step in the development of therapeutics targeting glycosylation in pancreatic cancer. SIGNIFICANCE Pancreatic ductal adenocarcinoma (PDA) is the 3 rd leading cause of cancer-death in the U.S.. Glycosylation is emerging as an important modulator of cancer phenotype. Herein we use a systems-approach integrating glycomics of human PDA and a well-established PDA mouse model with transcriptomic data to identify ST6GAL1, the enzyme underlying α-2,6-sialic acid, as a potential cancer promoter. A pancreatic specific ST6GAL1 knockout in the KC mouse showed delayed cancer formation and a reduction in fibrosis. Our results highlight the importance of a strategic systems-approach to identifying glycans whose functions can be modeled in mouse, a crucial step in the development of therapeutics targeting glycosylation in pancreatic cancer.
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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 | 0.001 |
| 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) | 0.001 | 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".