Abstract B004: Precision medicine opportunities: uniformity across stromal components of ovarian cancer histotype contrasts heterogeneous disease
Bibliographic record
Abstract
Abstract Introduction: Clinical and molecular heterogeneity are hallmarks that distinguish each of the five major ovarian carcinoma histotypes. High-grade serous ovarian carcinomas (HGSC) are by far the most prominent type (70%) and thought to arise from the fallopian tube and appear driven by DNA damage phenotypes with high levels of copy number change. The second and third most common types are clear cell (CCOC) and endometrioid (ENOC) (20-25%), both of which are known to originate from endometriosis, with emerging evidence of molecular substructure in each type. Using an-omics approaches we regulatory elements and resulting expression signatures across tumor-epithelium and stromal compartments of these three most common histologies. Methods: We performed compartment-specific laser-capture microdissection and conducted methylation and gene expression sequencing on 75 fresh frozen primary tumor samples from each of CCOC, HGSC, ENOC (20 per subtype). Immunohistochemistry was used to determine the state of common molecular markers (e.g., WT1, p53, HNF1B, ER, PR). Results Preliminary RNA-Seq data complements previous findings in the bulk tissue analysis: WT1 is highly overexpressed in HGSC, HFN1B is overexpressed in CCOC and high ESR1-levels and low HAVCR1 expression can be found comparing ENOC with CCOC tissue. In the stroma differential gene expression overall had smaller logFC, however, CLCN2 was low in CCOC and HGSC compared to ENOC and MYH2/14 was significantly higher in both endometriosis-associated types. Estimations of immune cell infiltration (CIBERSORT) showed a gradual immune response between compartments and histotypes, with highest involvement seen in (stromal) HGSC. Beside multiple collagen genes, THBS1 has been highly expressed in stroma across all histotypes which can be targeted with approved drugs. Curiously, the epigenetic analysis showed that there is little variation in the stroma across these histotype, with similar to what was seen in the transcriptome. Conclusion: The tumor epithelial compartments of HGSC, ENOC, and CCOC showed unique regulatory signatures, consistent with previous bulk analysis. In contrast we observed relatively little heterogeneity in the stromal compartments, suggesting the tumor associated fibroblast component in particular may play a passive role in tumor progression and may contribute less to observed regulatory signatures. Nonetheless, this uniformity in gene expression may be exploited with pan-ovarian carcinoma treatment strategies. Citation Format: Karolin Heinze, Ian Beddows, Bianca Ribeiro de Souza, Hui Shen, Martin Koebel, Michael S. Anglesio. Precision medicine opportunities: uniformity across stromal components of ovarian cancer histotype contrasts heterogeneous disease [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B004.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".