Abstract PR010: Development of a pre-clinical metastatic model of human sarcoma to identify therapeutic targets
Bibliographic record
Abstract
Abstract Targeted therapies have led to significant advances in the treatment of multiple tumor types resulting in more effective and often, less toxic therapeutic options. In sarcomas, the development of targeted therapies has been met limited success. There are more than 70 sarcoma subtypes that vary in histology, clinical course and patient demographics. Despite these clear differences, clinically, sarcomas are treated similarly with variable efficacy. Patients with localized disease are treated with surgery, radiation, and often chemotherapy. Even with this aggressive multimodality treatment, 35% of patients will develop incurable metastatic disease. This highlights the need for additional therapies targeting micrometastatic disease or cells within the primary tumor with a high propensity for metastasis. Understanding the pathways driving the formation of sarcoma metastasis would allow for the development of new therapies. Targeted treatments are limited due to the heterogeneity of the disease and the paucity of pre-clinical models that accurately reflect the human disease. We have focused on creating in vivo models of sarcoma development and metastasis that can be used as the basis of further studies and to test potential therapeutic targets. Mesenchymal stem cells (MSCs) are the presumed cell of origin for sarcomas and therefore, the starting cell for our investigations. We hypothesized that by recreating key genetic events in human MSCs, we could generate sarcomas in vivo that are reflective of the human disease. RB1 and P53, tumor suppressors that are often mutated or functionally inactive in sarcomas were first targeted using CRISPR-Cas9 technology in MSCs that have been immortalized by human telomerase (hTERT). Genes that are overexpressed or amplified in The Cancer Genome Atlas (TCGA) data were identified and a library of potential oncogenes was generated. This library was then added to RB1-/-P53+/- cells through lentiviral transduction. Targeting of key tumor suppressors and adding oncogenic drivers resulted in the formation of high-grade human sarcomas subcutaneously. We then sought to investigate the ability of these cells to metastasize. Injection of cells intramuscularly (into the thigh) in immunocompromised mice resulted in the formation of spontaneous lung metastasis without clear evidence of disease in other organs. This pattern clinically reflects that of human disease. Comparing oncogenic genes from metastatic outgrowths to primary tumors identified KLF4, DDIT3, JUN, and KRAS as being enriched in metastatic cells. This system allows for reproducible and robust genetic manipulation of tumor cells to characterize key drivers of metastatic growth. This will result in the identification and validation of new therapeutic targets to treat or prevent metastatic disease in our patients. Citation Format: Janai R. Carr-Ascher. Development of a pre-clinical metastatic model of human sarcoma to identify therapeutic targets [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 PR010.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".