Abstract IA19: Genomic analysis of osteosarcoma: Insights into tumor evolution and therapy response
Bibliographic record
Abstract
Abstract While significant progress has been made in most other cancers to advance targeted therapy, there has been essentially no change in our approach to treatment of osteosarcoma in over 30 years. Osteosarcomas exhibit an extremely complex genome characterized by multiple copy number changes and structural alterations. Using patient-derived xenografts and primary tumors, we are employing whole-genome sequencing (WGS) and RNAseq to characterize the evolution of this disease with a goal of understanding how evolutionary constraints can identify targetable vulnerabilities. We have identified a number of potential subtypes of osteosarcoma based on copy number alterations and defined how these copy number alterations can be exploited for therapeutic benefit using a large and clinically annotated patient-derived xenograft (PDX) collection. A key current effort is to identify novel genome-informed combination therapies for this disease. To facilitate this work, we have developed a panel of cell lines derived from PDX models, which have also be characterized with regards to their genomic characteristics. We have used these cell lines to screen for response to a wide variety of drugs with the goal of matching response to specific genomic characteristics. Lastly, we are using PDX models to study the process of metastatic progression and have identified a possible role for the enzyme ENPP1 in osteosarcoma metastasis. Citation Format: E. Alejandro Sweet-Cordero. Genomic analysis of osteosarcoma: Insights into tumor evolution and therapy response [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 IA19.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".