Abstract 3243: Fragmentomic features of plasma cell-free DNA in patients with osteosarcoma
Bibliographic record
Abstract
The rapid advancement of liquid biopsy technologies represents a major breakthrough in cancer management, allowing for sensitive detection of tumor-derived molecules and providing real-time insights into disease progression. Osteosarcoma (OS), the most common primary bone malignancy, currently lacks robust biomarkers for therapy response. We hypothesize that a multi-omic and multi-modal approach to identifying OS-specific markers in circulating tumor DNA (ctDNA) will facilitate precise patient stratification and improve treatment outcomes. It has been demonstrated by others that plasma ctDNA in OS patients carry specific genomic markers, such as point mutations and copy number alterations. However, the fragmentomic features of cell-free DNA (e.g., fragment length and nucleotide composition) remain poorly characterized in OS patients. To provide the rationale for studying fragmentomic features of plasma DNA in OS, we sought to demonstrate the feasibility of detecting distinct fragmentomic patterns in the publicly available whole genome sequencing data from cfDNA of 3 OS patients (NCBI SRA accession number PRJNA917431) together with our previously published shallow whole genome sequencing data from cell-free DNA of 7 patients with leiomyosarcoma, 12 patients with leiomyoma, 3 patients with dedifferentiated liposarcoma and 3 patients with well-differentiated liposarcoma (PMIDs: 29463554, 32232185, 34986184). We determined the first 4 nucleotide sequence (i.e., a 4-mer motif) on each 5′ fragment end of plasma DNA molecules. We calculated the frequency of each of the 256 possible motifs and normalized it to the total number of reads in each specimen. We applied multiclass differential analysis of the frequency of these motifs in 5 different types of tumors, and identified 64 motifs significantly enriched in OS (mean frequency difference between groups > 2.5-fold, false discovery rate < 0.05). In OS cell-free DNA, we observed decrease of motifs associated with DNASE1L3 nuclease (e.g., CCCA, CCAG, CCTG) and increase of motifs such as TAAA, AAAA, and TTTT. In the next steps, we will examine the expression of different DNA nucleases in patient tissue specimens by immunohistochemistry and will analyze plasma DNA from a new cohort of 50 OS patients to validate our preliminary findings. We will investigate whether these fragmentomic patterns of cell-free DNA have the predictive and prognostic value in OS patients. Our preliminary results warrant further studies of the frequency and diversity of the 4-mer motifs and expression of different DNA nucleases in OS. Citation Format: Anastasia E. Mackeracher, Joanna Przybyl. Fragmentomic features of plasma cell-free DNA in patients with osteosarcoma [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 3243.
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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 | 0.001 |
| 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 | 0.000 |
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".