Abstract IA015: ASPSCR1-TFE3 reprograms transcription by organizing enhancer loops
Bibliographic record
Abstract
Abstract Aberrant transcription in alveolar soft part sarcoma and some Xp11-rearranged renal cell carcinomas is orchestrated by the fusion oncoprotein, ASPSCR1-TFE3, expressed from a t(X;17) chromosomal translocation-mediated fusion gene. Here, proteomic analysis of proteins co-immunoprecipitated with ASPSCR1-TFE3 from human cell lines and mouse genetically-induced tumors revealed strong enrichment of a AAA+ ATPase with known segregase function. Native gel and electron microscopy found that ASPSCR1-TFE3 associates multi-valently with segregase hexamers. Forward and reverse genetic experiments with ASPSCR1-TFE3 and the segregase demonstrated that they function co-dependently for cancer cell proliferation. The presence, hexamer assembly, and enzymatic function of the segregase enabled the transcriptional impact of ASPSCR1-TFE3. The two proteins co-distributed across chromatin genome-wide, associated with enhancers, indicated by flanking H3K27ac enrichment in human cell lines, human tumors, and genetically engineered mouse tumors. These assembled into higher-order chromatin conformation structures demonstrated by HiChIP and downregulated by loss of ASPSCR1-TFE3 or the segregase. Thus, a segregase was found to assemble chromatin into three-dimensional structures as a co-factor of oncogenic transcriptional regulation. Citation Format: Kevin B. Jones. ASPSCR1-TFE3 reprograms transcription by organizing enhancer loops [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 IA015.
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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 | 0.002 |
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".