CP-03. EXTRACELLULAR MATRIX REMODELING REVEALS NON-CELL AUTONOMOUS TUMOR FORMATION MECHANISM IN ADAMANTINOMATOUS CRANIOPHARYNGIOMAS
Bibliographic record
Abstract
Abstract Adamantinomatous Craniopharyngioma (ACP) is a rare pituitary tumor of the sellar region, with a bimodal presentation pattern in children and middle-aged adults. ACPs are primarily driven by a mutation in CTNNB1 that prevents beta-catenin from degrading properly, resulting in accumulation in the nuclei of epithelial whorl-like structures sometimes referred to as cluster cells. Clinical management is challenging due to the location of the tumor, such that complete surgical resection is often not possible so radiation therapy, which can lead to high morbidity, is also used. Greater insight into these tumors can help develop less damaging treatment strategies. In this analysis we have employed single-cell RNA sequencing technology to examine seven pediatric human ACPs (28594 cells). Using consensus non-negative matrix factorization, and traditional marker genes, we have partitioned our dataset into several cell types including cluster cells, palisading epithelial cells, microglia, T-cells, B-cells, plasma cells, and fibroblasts. Though cluster cells themselves do not actively proliferate, we believe the complex inter-cellular signaling is responsible for tumor formation. We propose a model in which malignant fibroblasts form from the existing epithelium and remodel the extracellular matrix (ECM) creating a stiffened tumor microenvironment. To support this claim we show activation markers suggesting myofibroblast differentiation, the expression of EMT signatures indicating a transformation from epithelial to fibroblast, and RNA velocity analysis that demonstrates lineage tracing. We believe that the remodeling of the ECM is a wound healing response influenced by TGF-beta, SPP1, and other signaling pathways. Our analysis of ACPs reveals the role of the tumor microenvironment in the formation of these tumors.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
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".