Abstract 3678: Liquid biopsy fragmentomics approach for the diagnosis of uterine tumors
Bibliographic record
Abstract
Abstract Background: Leiomyomas (LM), also known as fibroids, are common benign tumors of the smooth muscle of the uterus that can cause pain, infertility, and abnormal menstrual bleeding. Diagnosing LM is challenging using clinical indications alone, as they share symptoms with leiomyosarcoma (LMS), a rare, aggressive uterine malignancy with a ∼50% disease-specific survival. Pre-operative distinction between LM and LMS is difficult because these tumors are rarely biopsied before surgery. We hypothesize that a non-invasive circulating tumor DNA (ctDNA) test based on LMS- and LM-specific molecular markers will provide accurate pre-operative diagnosis and guide appropriate surgical treatment. Methods: We previously analyzed point mutations and copy number alterations (CNAs) in ctDNA using deep targeted sequencing and shallow whole-genome sequencing (WGS) from plasma specimens of 7 LMS and 12 LM patients (PMIDs: 29463554, 32232185). To build on this, we performed a new analysis of the first 4 nucleotide sequences (4-mer motifs) on the 5’ end of the cell-free DNA fragments using shallow WGS data. The frequency of 256 possible 4-mer motifs was calculated using R programming and normalized to total reads in each specimen. Two-class differential analysis identified motifs enriched in LM and LMS (false discovery rate < 0.05). Results: Our previous studies demonstrated the feasibility of ctDNA detection in LMS and LM patients. In LMS patients, ctDNA was detected in 6 of 7, with >98% specificity; in LM patients, ctDNA was detected in 6 of 12, with 76% specificity. To increase ctDNA detection sensitivity, we incorporated fragmentomics as a new ctDNA marker. Our new analysis identified 66 significantly enriched and 21 significantly decreased motifs in cell-free DNA from LMS patients compared to LM patients. Motifs associated with DNASE1L3 nuclease activity (e.g., CCCA) were significantly decreased in LM compared to LMS patients. Conclusion: We identified new tumor-specific fragmentomic markers in cell-free DNA from LMS and LM patients. Combining detection of point mutations, CNAs, and fragmentomic patterns could enable a highly sensitive ctDNA assay for accurate pre-operative distinction between LM and LMS. Next, we will validate DNASE1L3 and other nucleases in tissue microarrays of 100+ LM and 200+ LMS specimens by immunohistochemistry, and validate distinct fragmentomic patterns using expanded plasma collections. This research addresses an unmet clinical need for accurate pre-operative diagnosis of uterine tumors. Citation Format: Meagan S. Cobb, Philippe Jolivet, Kristen N. Ganjoo, Deirdre A. Lum, Matt van de Rijn, Joanna Przybyl. Liquid biopsy fragmentomics approach for the diagnosis of uterine 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 3678.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".