Abstract A27: Transcriptome analyses of human ampulla and fimbriae highlight similarities and differences.
Bibliographic record
Abstract
Abstract Background: Recently described precursors of high-grade serous carcinoma (HGSC), the p53 signature, a latent precursor, and Serous Tubal Intraepithelial Carcinoma (STIC), a pre-malignant precursor, occur most frequently at the distal and fimbriated end of the fallopian tube (FTE). In 3 previous reports, we have demonstrated that the FTE of BRCA1 mutation carriers, at genetic risk of HGSC, have altered signaling pathways compared to controls. Ovarian production of ROS is released after the LH surge to induce ovulation. Reactive oxygen species (ROS) have been implicated in serous carcinogenesis. The objective of this study is to compare the transcriptome profiles of normal fimbria (high-risk epithelia prone to transformation) FTE and normal ampulla (low-risk epithelia) FTE which may lead to understanding the distal end of the fallopian tube as the preferential anatomic location of the fallopian to tube for cellular transformation. Method: Snap-frozen matched fimbria and ampulla tissues were controlled for age and ovarian cycle status. Cases included 12 luteal phase and 12 follicular phase women at no known risk for ovarian cancer. Laser capture microscopy was used to microdissect FTE cells, using 7-10 sections per case. Total RNA was isolated, RNA extracted and cDNA amplified. The expression profiles were generated using Affymetrix Human Genome HTA-2.0 Array. Bayes moderated t-statistics implemented in the R/Bioconductor package LIMMA to compare mRNA expression values for probes of samples based on their ‘phase’ and ‘anatomy’, respectively. t-statistics was generated from the aforementioned respective analyses, and ranked based Wilcoxon tests on gene sets from the Reactome, GO and Cytoband databases. Results: The cases clustered predominantly by ovarian cycle status rather than by their differences in anatomical origin or their matched pair. There were 75 unique probe IDs that were significantly different (p<0.05) between ampulla and fimbria which were independent of ovarian cycle status, – SALL1, ME1 – higher in the ampulla; GSTA1, GSTA2, PDK4, SERPINA3 -higher in the fimbria – genes involved in metabolic pathways) were observed between the ampulla and fimbria FTE. This gene signature when applied to transcriptome analyes of normal FTE and serous carcinoma, segregate the samples by cancer versus normal. Most of the genes were down-regulated in cancer. Conclusions: The epithelia of the anatomically high-risk fallopian tube – the fimbria, show few differences in gene expression profiles compared to the lower risk portion – the ampulla. Expression differences predominantly are in response to the hormonal milieu, post-ovulation with an antioxidant program. The increased anatomic risk of the fimbria is likely due to effects of the microenvironment, such as repeated exposure to follicular fluid at ovulation (higher ROS) and antioxidant genes and their interaction with DNA repair genes (like TP53), rather than intrinsic differences of the FTE in the two sites Citation Format: Sophia George, Anca Milea, Sowamber Ramlogan, Michael Considine, Leslie Cope, Noor Salman, Patricia Shaw. Transcriptome analyses of human ampulla and fimbriae highlight similarities and differences. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: Exploiting Vulnerabilities; Oct 17-20, 2015; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(2 Suppl):Abstract nr A27.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".