Abstract 3896: Multi-omic and multi-region profiling of uterine leiomyoma reveals intra- and inter-tumor heterogeneity
Bibliographic record
Abstract
Abstract Objective: Molecular heterogeneity has been well-documented in malignant tumors but has yet to be thoroughly studied in benign non-metastasizing tumors. Uterine leiomyomas (LM) (fibroids) are benign tumors originating from the myometrium, commonly affecting women of reproductive age. Approximately half of patients present with multiple tumors, termed multifocal LM. LM are thought to be monoclonal proliferations based on their pattern of X chromosome inactivation. Mutations of the MED12 gene are the genetic hallmark of LM. Treatment of LM includes surgery and hormone therapy. It is unknown whether multifocal LM exhibit inter-tumor heterogeneity, contributing to variable response to hormone therapy. Anecdotal reports suggest that LM may occasionally transform into malignant metastasizing tumor called leiomyosarcoma (LMS). Better understanding of intra-tumor heterogeneity of LM may identify cell clones that may contribute to oncogenic transformation. In this study, we sought to perform a comprehensive multi-omic investigation of the possible intra- and inter-tumor heterogeneity of uterine LM. Methods: We performed multi-omic analysis of 62 specimens from 21 LM patients (median of 3 tumors per patient, range: 2-8). We analyzed multiple regions of a single tumor, and multiple concurrent LM from patients with multifocal disease. We profiled DNA methylation (EPIC microarrays), point mutations (whole exome sequencing) and gene expression (whole transcriptome RNA-seq) in these specimens. Results: Multi-omic profiling showed a remarkable intra- and inter-tumor heterogeneity of genomic, epigenomic and transcriptomic patterns. Through reconstruction of phylogenetic trees based on single nucleotide variants, we identified novel clonal and subclonal somatic mutations in LM. We detected for the first time different MED12 mutations in co-existing nodules in the same patient. DNA methylation and transcriptomic profiles appeared to have a similar degree of variability within individual tumors and between different tumors from the same patient. We also observed a significant enrichment of different molecular pathways between distinct regions of the same tumor. Conclusion: Molecular heterogeneity is well-established in malignant tumors, and we report it here for the first time in histologically bland lesions like LM. Our study reveals that in multifocal LM, distinct tumors can acquire unique molecular alterations, meaning that single-tumor analysis may not capture the full spectrum of molecular changes. This highlights the importance of considering molecular heterogeneity in diagnosis and evaluation of response to hormonal treatment in LM. We also demonstrate for the first time the presence of intra-tumor heterogeneity of LM. It remains to be further investigated whether clonal evolution may contribute to occasional oncogenic transformation of benign LM into malignant LMS. Citation Format: Chelsea De Bellis, Sujay Vennam, Christopher Eeles, Philippe Jolivet, Deirdre Lum, Benjamin Haibe-Kains, Matt van de Rijn, Joanna Przybyl. Multi-omic and multi-region profiling of uterine leiomyoma reveals intra- and inter-tumor heterogeneity [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 3896.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".