Investigating the Maintenance of Desmoid Tumors beyond Beta-catenin
Bibliographic record
Abstract
Despite substantial advances in the genomic characterization of neoplasia, effective therapy of many diseases remains elusive. Beyond genomic alterations, several signalling processes maintain the neoplastic phenotype including active protein kinases that increase proliferation, gene expression programs that maintain a neoplastic identity, and stroma-derived factors that influence tumor cell behavior. Determination of the signalling pathways involved in disease maintenance have allowed for the identification of effective therapeutic strategies in some common diseases. However, the treatment of rare diseases has seen little development because of lack of patient material and poor characterization of such material. Desmoid tumors are rare soft-tissue neoplasms with unpredictable clinical behavior that consist of mesenchymal fibroblast-like cells initiated by mutations stabilizing beta-catenin. Here we hypothesized that additional pathways are dysregulated in desmoid tumors and identifying these, and their regulators, will identify putative therapeutic targets. We integrated information from compound library screens of protein kinase inhibitors, gene expression datasets to establish a predictive molecular signature, as well as clonal approaches to identify factors contributing to the neoplastic phenotype. We found that PDGFRB signalling, microRNA-29 and glucocorticoids, and stroma-derived factors and STAT6 signalling, are all factors regulating cellular proliferation in desmoid tumors. We also describe clonal expansion techniques and identify surface markers of mutant and non-mutant cells to address intratumor heterogeneity. These findings improve our current understanding of the signalling processes that maintain desmoid tumor growth and identify several promising targets for therapy. Moreover, the methods we describe here will improve the characterization of existing and future samples and help explain the unpredictability that has been a feature of this disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".