Abstract A45: Targeted drug therapies for osteosarcoma
Bibliographic record
Abstract
Abstract Osteosarcoma (OS) is a highly aggressive cancer that has had no new treatments options in over 30 years. OS is a heterogeneous disease that is characterized by widespread and recurrent somatic copy-number alterations (SCNAs) with relatively few recurrent point mutations. Our laboratory has recently published that SCNAs contain key oncogenic drivers that can be used to identify patient-specific candidates for targeted therapies (Sayles, Breese, et al., Cancer Discovery Jan. 2019). Using patient-derived tumor xenografts (PDX), we demonstrated that targeting of patient-specific oncogenes within SCNAs leads to significant decrease in tumor burden. However, no single-agent therapy was able to regress tumors completely, suggesting that combination therapies would be required for disease management. In order to assess the applicability of our PDX models to the patient tumor and the stability of the oncogenes within SCNAs, we performed whole-genome sequencing (WGS). We observed that SCNAs are highly stable across samples from the same patient in addition to multiple PDX passages and PDX-derived cell lines, highlighting the equivalence between the PDX models and their derived cell lines to the human disease. This allows us to use the PDX cell lines as a surrogate for the identification of combination drug therapies that may be of benefit in OS. Currently, we have generated 6 PDX cell lines that encompass the most common SCNAs observed in patients, including MYC, CCNE1, and CDK4 gain and alterations in the PI3K/PTEN pathway. We have performed a single-agent drug screen with 38 agents and have identified several efficacious compounds, including HDAC inhibitors and cell cycle and Wee1 inhibitors. We also observed a differential response between PDX cell lines to various chemotherapeutics, including gemcitabine, paclitaxel, and SN-38. We are currently testing combination drug therapies in vitro and will validate using our PDX xenografts in vivo. While these experiments are still ongoing, we can report that MYC-driven OS PDX xenografts show a striking resensitization to cisplatin after AURKB inhibitor (barasertib) pretreatment. We have 2 aggressive PDX models with high MYC amplification generated from metastatic lesions. These PDX models are resistant to cisplatin, which is part of the standard of care. We treated these PDX with barasertib and then with cisplatin for two cycles. This resulted in a decrease in tumor volume for the combination therapy compared to vehicle or either single agent alone. Additionally, we observed retention of the platinum adduct 24 hrs after cisplatin dosing only when tumors were pretreated with barasertib. Further work is needed to assess the mechanism of this synergy and whether it can be of use in other SCNA-driven OS and to identify other possible combination therapies that could be of importance in this disease. Citation Format: Leanne C. Sayles, Marcus R. Breese, Henry Martell, Alex G. Lee, Stanley Leung, Avanthi T. Shah, E. Alejandro Sweet-Cordero. Targeted drug therapies for osteosarcoma [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A45.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".