GPLD1+ cancer stem cells contribute to chemotherapy resistance and tumour relapse in intestinal cancer
Bibliographic record
Abstract
Cancer stem cells (CSCs) play a central role in cancer progression, therapy resistance, and disease recurrence. With the use of a quadruple-mutant mouse intestinal cancer organoid model and single-cell RNA-sequencing analysis, we have now identified glycosylphosphatidylinositol-specific phospholipase D1 (GPLD1), an enzyme that catalyzes the cleavage of glycosylphosphatidylinositol (GPI) anchors of membrane proteins, as a marker of slowly cycling CSCs. Ablation of Gpld1+ cells in combination with 5-fluorouracil treatment greatly attenuated cell viability in and regrowth of the intestinal cancer organoids. In addition, we identified serine protease 8 (PRSS8) as a key substrate of GPLD1 in human colorectal cancer cells. GPLD1 cleaves the GPI anchor of PRSS8 and thereby mediates release of the protease from the plasma membrane, resulting in the activation of Wnt signalling and promotion of the epithelial-mesenchymal transition (EMT) in the cancer cells. Pharmacological inhibition of GPLD1 suppressed Wnt signalling activity and EMT in association with upregulation of the amount of functional PRSS8 at the plasma membrane. Our findings suggest that targeting of GPLD1 in colorectal cancer might contribute to a new therapeutic strategy that is based on suppression of Wnt signalling and EMT-related cancer progression driven by CSCs.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".