Abstract 5129: Model osteosarcoma by Li-Fraumeni syndrome patient-specific induced pluripotent stem cells
Bibliographic record
Abstract
Abstract Li-Fraumeni syndrome (LFS) is a genetically inherited autosomal dominant cancer syndrome characterized by multiple tumors within an individual, early tumor onset and multiple affected family members. Germline mutations in the p53 tumor suppressor gene are responsible for LFS. Although there has been extensive research on cancer cell lines and even mouse models of LFS to study the role(s) of p53, these model systems do not fully recapitulate the range of human tumors or their properties. Therefore, while p53 is a promising target to treat tumors, the lack of appropriate models limits the development of reliable therapeutics. In vitro modeling of human disease has recently become feasible with the adoption of induced pluripotent stem cell (iPSC) technology. Here, we established patient-derived iPSCs from a LFS family and investigated the role of mutant p53 in the development of osteosarcoma. The osteoblasts, differentiated from LFS iPSC-derived mesenchymal stem cells, recapitulate osteosarcoma features including defective osteoblastic differentiation and tumorigenic ability, suggesting that our established LFS disease model is a “disease in a dish” platform for elucidating p53 mutant-mediated disease pathogenesis. The gene expression patterns of LFS osteoblasts are similar to those of tumor samples obtained from osteosarcoma patients and these tumorigenic features strongly correlate with shorter tumor recurrence times and poorer patient survival rates. Importantly, osteosarcoma is characterized by numerous chromosomal alterations and rearrangements. The high levels of genomic instability present in both osteosarcoma and in osteosarcoma cell lines make analyses of the initial steps of tumor development particularly challenging; however, we found that LFS-derived osteoblasts are free of cytogenetic rearrangements, which provides particular value to the cancer community because they permit the study of early oncogenic mechanisms prior to the accumulation of secondary genomic alterations. Furthermore, the global transcriptome by mRNA-seq to reveal that LFS OBs exhibit impaired expression of the imprinted gene H19 during osteogenesis. Our functional studies implicate the essential H19 gene in normal osteogenesis and inhibition of tumorigenesis. In order to decipher the underlying mechanisms by which H19 mediates osteogenesis and tumor suppression, we characterized and analyzed the human imprinted gene network (IGN) and revealed the unidentified role of p53 in regulating the IGN culminating in osteogenic differentiation defects and tumorigenesis. In summary, these findings demonstrate the feasibility of studying inherited human cancer syndromes with iPSCs and also provide molecular insights into the role of the IGN in p53 mutation-mediated tumorigenesis. Citation Format: Dung-Fang Lee, Jie Su, Huen Suk Kim, Betty Chang, Ruiying Zhao, Dmitri Papatsenko, Ye Yuan, Julian Gingold, Weiya Xia, Henia Darr, Christoph Schaniel, Razmik Mirzayans, Mien-Chie Hung, Ihor R. Lemischka. Model osteosarcoma by Li-Fraumeni syndrome patient-specific induced pluripotent stem cells. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 5129. doi:10.1158/1538-7445.AM2015-5129
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".