Abstract 4765: The role of estrogen receptor signalling in serous ovarian cancer.
Bibliographic record
Abstract
Abstract Epithelial ovarian cancer is a heterogeneous group of diseases with multiple histotypes which can be broadly divided into a dualistic model based on morphological, molecular genetic, and clinical features. The most common and aggressive of all EOC, accounting for 90% of deaths, is High Grade Serous Carcinoma (HGSC). It is characterized by a genetically unstable, rapidly growing phenotype, is diagnosed at advanced stage with poorly defined cancer precursors compared to the more indolent Low Grade Serous Carcinoma (LGSC). Reproductive hormone receptor status (estrogen/ progesterone receptor) may be an important indicator of response to anti-hormonal therapy in ovarian cancer. There have been few reports detailing the expression of ER in HGSC, LGSC and the normal fallopian tube epithelium, the likely cell of origin of both histotypes. We hypothesize that some ER mediated signaling is maintained despite the loss of progesterone receptor in HGSC compared to the LGSC. Methodology: Snap-frozen tissues (43 HGSC and 18 LGSC) were selected from the UHN Biobank. We used our previously published gene expression profiles to generate a candidate gene list, which was chosen based on the presence of known estrogen responsive elements. They were validated by qPCR and immunohistochemistry on HGSC and LGSC tissue microarrays. Human fallopian tube epithelial cell lines were treated with estradiol at 50nm to determine ‘normal’ ER response. An ER positive cancer cell line (SKOV3) was used to determine the cellular response to candidate genes. IHC was scored using automated image analysis. Statistical analysis was performed using ANOVA and Fisher's Exact Test. Results: 35/43 (81%) of HGSC were ER+/PR- whilst 9/27 of LGSC were ER+/PR- and 18/27 ER+/PR+. Gene expression analysis of ER+ HGSC and ER- HGSC revealed 881 genes with more than a 2-FC in gene expression including 202 genes with known or putative estrogen responsive elements. They are involved in immune response, locomotion, metanephrous development and cellular adhesion. We compared genes that were differentially expressed between normal fallopian tube cells obtained during pre- or post-ovulation (93 genes). 42 ‘FTE-normal’ genes were differentially expressed between ER- versus ER+ HGSC. We selected 5 genes to validate by qPCR and IHC, based on gene ontology and known associations to cancer pathways. Conclusions: These results show that HGSC is predominantly an ER+/ PR- cancer (80%) while LGSC is predominantly an ER positive/ PR positive cancer (66%). Our data also indicate that some ER signalling is maintained in the absence of the progesterone receptor and ER+ HGSC are transcriptionally different from ER- HGSC although, there is no distinct clinical benefit between these two groups. ER is still able to transcriptionally activate a subset of known ER target genes and this information should provide further insight into the use of hormone receptors as indicators of anti-hormone therapy in ovarian cancer. Citation Format: Sophia Hl George, Anca Milea, Ramlogan Sowamber, Danielle Toccalino, Patricia A. Shaw. The role of estrogen receptor signalling in serous ovarian cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4765. doi:10.1158/1538-7445.AM2013-4765
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".