Abstract 5899: Comparative analysis of transcription factor activities derived from low-pass whole genome sequencing of cell-free DNA and from ATAC-sequencing of tumors
Bibliographic record
Abstract
Abstract Background: cell-free DNA (cfDNA) in plasma primarily originates from hematopoietic cells in healthy individuals, but in cancer patients, it includes circulating tumor DNA from dead tumor cells. cfDNA mutation analysis is already used in clinical practice for biomarker research and treatment decisions. Recent studies have shown that cfDNA fragmentation patterns reflect tumor tissue chromatin status. Inferring transcription factor (TF) activity from cfDNA TF binding site (TFBS) coverage patterns offer a minimally invasive assay to understand cancer biology with limited sequencing depth. However, the accuracy of TF activity inference has only been examined for a few well-known TFs such as AR and ESR1. We conducted a comparative analysis of 377 TF activities between cfDNA whole genome sequence (WGS) and tumor Assay for Transposase-Accessible Chromatin using sequencing (ATAC-sequencing). Methods: Two liver cancer cell lines, HepG2 with a gain-of-function CTNNB1 mutation and HuH7 with wild-type CTNNB1, were used to generate xenograft models. ATAC-sequencing/RNA-sequencing and WGS were performed on the tumors and pooled plasma-derived cfDNA from each model. TCF/LEF family TF activities, downstream targets of CTNNB1, were compared between models with different CTNNB1 mutation statuses in both tumor ATAC-sequencing and cfDNA WGS. For 377 TFs with at least 10, 000 TFBS, the correlation of TF activities between tumor ATAC-sequencing and cfDNA WGS was examined in each model. Furthermore, tumor model-specific TFs were identified based on ATAC-sequencing, followed by comparison of cfDNA TFBS coverage of these TFs between the two models. Results: In pilot analysis with TCF/LEF family TFs, both tumor ATAC- sequencing and cfDNA WGS from HepG2 xenograft model showed higher TCF7 and TCF7L2 activities compared to HuH7. In an expanded analysis of 377 TFs, we found a significant and strong correlation between tumor and cfDNA TF activities (Spearman’s rank correlation coefficients for HepG2 and HuH7: -0.90 and -0.86, respectively). For tumor model-specific TFs, “HepG2-HIGH” and “HuH7-HIGH” which are composed of 29 and 18 TFs with the highest variance between models, cfDNA TFBS coverage of these two groups of TFs also showed significant differences between tumors (p<0.05). Conclusion: Our results indicate that cfDNA can accurately estimate the activity of over 300 TFs in tumor. To assess the potential utility of cfDNA TFBS analysis in clinical samples, further studies are warranted. Citation Format: Ryuji Tamaki, Koji Sagane, Shuyu Dan Li, Taisuke Hoshi. Comparative analysis of transcription factor activities derived from low-pass whole genome sequencing of cell-free DNA and from ATAC-sequencing of tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5899.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".