Abstract IA019: How to target lung metastasis: dynamic changes in chromatin accessibility during osteosarcoma lung metastasis expose vulnerabilities
Bibliographic record
Abstract
Abstract The metastatic cascade describes the series of steps required for a cancer cell to successfully spread from its primary tumor to a secondary organ. Despite knowing that metastasis is a fluid process, almost all studies to date have relied on end-point assays. As a result, little is known about the continuum of molecular events required for successful growth within the secondary organ. By profiling accessible chromatin in osteosarcoma cells grown in vitro and isolated from an early and late timepoint during lung metastasis, we identify temporally distinct epigenomic reprogramming events that occur as tumors form and grow within the metastatic microenvironment. Furthermore, these data pinpoint key upstream transcription factors responsible for the dynamic chromatin changes. Through paired in vivo and in vitro CRISPR drop-out screens, we show a subset of these putative pro-metastatic transcription factors are essential for lung metastasis, but dispensable for in vitro growth. Targeting the same factors with chemical probes in vitro and ex vivo demonstrated similar context-dependent essentiality. Altogether, our study demonstrates the epigenomes of metastasizing cancer cells are tightly regulated in a dynamic and context-specific manner, and the transcription factors controlling this process may serve as novel vulnerabilities. Citation Format: Peter C. Scacheri. How to target lung metastasis: dynamic changes in chromatin accessibility during osteosarcoma lung metastasis expose vulnerabilities [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 IA019.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".