Abstract A77: Targeting lymphocyte exclusion in metastatic osteosarcoma
Bibliographic record
Abstract
Abstract Osteosarcoma (OS) is the most common bone tumor in pediatric and adolescent/young adult patients. Over the past three decades, significant improvements in the survival rates or therapeutic approaches for these patients have not been made, especially in the context of metastatic disease. While immune checkpoint blockade has revolutionized the therapeutic landscape in various adult malignancies, its impact in OS has been largely underwhelming. Currently, it is unknown whether the lack of therapeutic benefit of immune checkpoint inhibition observed in patients with OS is truly due to treatment inefficacy rather than a limited understanding of the tumor microenvironment that supports this aggressive disease. To address this knowledge gap, we have performed targeted gene expression profiling of metastatic and nonmetastatic osteosarcoma specimens. Our data demonstrate that T cells are largely excluded from the metastatic specimens and that this exclusion significantly correlates with markers of vascular instability. In a pathologic setting, such as that of cancer, VEGF and ANG2 signaling promote vascular instability, which limits leukocyte extravasation and subsequent tumor infiltration. Our data suggest that vascular destabilization mediated by VEGF/ANG2 signaling impedes T-cell infiltration specifically in metastatic OS and identify these molecules as potential targets for therapeutic intervention. Citation Format: Laurie Sorenson, Troy A. McEachron. Targeting lymphocyte exclusion in metastatic 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 A77.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".