Abstract 1454: Estrogen receptor signaling in FTE of BRCA mutation carriers
Bibliographic record
Abstract
Abstract Genetic and reproductive factors predicate epidemiological risk factors underlying epithelial ovarian cancer. The fallopian tube epithelia (FTE), the presumptive etiological site of high-grade serous ovarian cancer (HGSC), is a hormonal responsive tissue. Estrogen is known to promote cell proliferation and its metabolism produces reactive oxygen species that damage DNA and promote tumorigenesis. Estrogen receptor (ER) is rarely mutated, amplified or deleted in HGSC, yet only 10% of patients respond to anti-estrogen treatment, suggesting that intrinsic variables to the ER pathway contribute to this clinical outcome. TP53 mutations occur in almost 100% of HGSCs, indicating that mutated p53 supports a model as an early event in the pathogenesis of HGSC. We hypothesized that in the presence of dysfunctional p53, subsequent promiscuous binding of ER will yield aberrant signaling, ultimately significantly contributing to cellular transformation. Mutant p53 and ER co-localize in FTE cells, suggesting potential synergy. We established cell lines with p53 mutations and treated them with estradiol, an estrogen analog, to observe any changes in response. The genome binding sites of ER-regulated transcription factors were then identified and mapped by whole genome chromatin immunoprecipitation-deep sequencing (ChIP-Seq). The data generated will facilitate the development of gene signatures that will predict response to anti-estrogen therapy in serous ovarian cancer patients, and contribute to the discovery of biomarkers to more accurately identify patients who will benefit from hormonal therapies. Citation Format: Leah V. Dodds, Omar L. Nelson, Ramlogan Sowamber, Andres Rodrigues, Victoria de Castro, Wendell Henry, Guillermo Morales, Brian Slomovitz, Patricia Shaw, Sophia H. George. Estrogen receptor signaling in FTE of BRCA mutation carriers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1454. doi:10.1158/1538-7445.AM2017-1454
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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 | 0.000 |
| 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) | 0.008 | 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".