Abstract IA005: On the use of stem cells to generate sarcomas in vitro models
Bibliographic record
Abstract
Abstract Sarcomas are rare cancers arising in soft tissue and bone. They are difficult to diagnose and treat, with 38-48% mortality for this often young age group. It is now generally accepted that molecular changes in cancer open avenues for personalized medicine. However, to achieve this for sarcoma there are several challenges, as representative model systems are scarce. Moreover, sarcomas are heterogeneous and >70 sarcoma types are recognized. A simple dichotomy based on molecular pathways of simple versus complex genomics is often used as a conceptual framework. My group has used mesenchymal stem cells to generate a model for complex genome sarcomas such as osteosarcoma. Murine and canine mesenchymal stem cells (MSCs) can be used to model spontaneous malignant transformation towards sarcomas with complex genomics. These MSCs have an abnormal karyotype, many structural variants and point mutations at whole genome sequencing analysis, and form sarcomas (osteosarcoma, undifferentiated pleomorphic and spindle cell sarcoma) after injection into mice. Cross-species analysis reveals that p53 loss is an early event in sarcomagenesis, and MSCs with a knockout in Trp53 transform earlier compared to wild-type MSCs. In addition, loss of p16Ink4a is shown to be a driver of osteosarcomagenesis: the majority of spontaneously transformed murine MSCs has alterations in p15Ink4b, p16Ink4a, or p19Arf and knockout of these genes in MSCs results in accelerated transformation. Of interest, osteosarcoma cells with defective p16INK4A are sensitive to the CDK4/CDK6 inhibitor Palbociclib, suggesting that a subset of osteosarcoma patients with intact Rb, but defective p16 or overexpression of CDK4 and/or CDK6 (in total 20-23%) might benefit from CDK4/CDK6 inhibition. Bone and soft tissue tumors with a simple genome often carry chromosomal translocations. We have generated a model for pseudomyogenic hemangioendothelioma (PHE), a rare locally aggressive vascular tumor. PHE has a tumor-specific t(7;19)(q22;q13) translocation resulting in a SERPINE1-FOSB fusion. We used CRISPR/Cas9 to generate this fusion in induced pluripotent stem cells (hiPSCs), which were subsequently differentiated into ECs (hiPSC-ECs) to address this. Comparison of parental with PHE hiPSC-ECs shows invasive growth in mice after transplantation, and transcriptome analysis identifies PI3K-Akt and MAPK signaling pathways as possible therapeutic targets. The modified hiPSC-ECs thus recapitulate functional features of PHE and demonstrate how these stem cell based translocation models can be used to understand tumorigenic mechanisms and identify therapeutic targets. Citation Format: Judith V.M.G. Boveé. On the use of stem cells to generate sarcomas in vitro models [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr IA005.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".