Abstract 4412: Defining chromatin alterations in liver cancer at single cell resolution
Bibliographic record
Abstract
Abstract Hepatocellular carcinoma (HCC) is the most common liver cancer and arises from the malignant transformation of hepatocytes. HCC tumors often express genes critical for hepatocyte development but the regulatory mechanisms driving aberrant expression remain unclear. Recent evidence indicates that epigenetic alterations contribute to HCC progression with nearly 50% of cases associated with mutations in chromatin modifiers. Thus, it is imperative to understand how chromatin is altered in HCC and identify the DNA regulatory sequences associated with HCC tumorigenesis. Our study aims to map the chromatin landscape and transcriptome in HCC tumors using a multiomic approach at single cell resolution. Single-cell ATAC- and RNA-seq will be conducted in HCC tumors, matched controls and normal livers to identify regulatory sequences and their target genes critical for HCC tumor progression. We will characterize these regulatory sequences in the context of liver development using genetic manipulation and epigenomic datasets generated from human pluripotent stem cells differentiated to hepatic lineages. Our goal is to elucidate how epigenetic alterations in HCC drive cancer progression and how they relate to a dedifferentiated state. Citation Format: Miguel Ramirez, Tabea Stephan, Daivd Schaeffer, Pamela Hoodless. Defining chromatin alterations in liver cancer at single cell resolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4412.
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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 | 0.001 | 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 | 0.001 |
| 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".