Characterization of low grade serous carcinoma of the ovary and its precursorss
Bibliographic record
Abstract
Background: Low Grade Serous Carcinoma (LGSC) is a slow growing ovarian neoplasm affecting women in their 30s and 40s and is highly resistant to current chemotherapeutic regimens. Recent genomic hybridization and molecular biology studies have suggested that Ovarian Low Malignant Potential Tumor (LMP) may be a precursor lesion to LGSC as part of the type I pathway of ovarian carcinogenesis (Singer et al. Am Jo Path. 2002. 160;4:1223-1228). In addition, the significance of micropapillary features within LMP is being extensively studied as these features may indicate a more aggressive tumor behavior. (Seidman et al. Hum. Path. 1:539, 2000, Staebler et al., Hum. Path. 22:47, 2002). Hypothesis: We propose to study the expression profiles of LMP, LMP with micropapillary features (LMP-MP) and LGSC. We hypothesize that given its increased aggressive tumor behaviour, LMP-MP may exhibit a genetic profile that is similar to LGSC and not LMP and may represent an intermediate lesion in the malignant transformation of LMP into LGSC. Genes that are differentially expressed between the tumors will be studied in an attempt to identify those involved in carcinogenesis. Materials & Methods: Snap-frozen tissue samples from primary ovarian tumors, diagnosed as LMP (n=19), LMP-MP (n=8), or LGSC (n=12) were selected from the Toronto Ovarian Tissue Bank. Laser Capture Microdissection was used to separately isolate epithelial cells from tumor specimens. RNA was extracted, amplified, reverse transcribed to cDNA and hybridized to Affymetrix U133 Plus 2 arrays. Following normalization, the expression data was analyzed by Significance Analysis of Microarrays (SAM) as an initial step. Further analysis using Array Assist and Binary Tree Structured Vector Quantization are underway. Integration of the list of differentially expressed genes with a database of known and predicted protein-protein interactions will be used to select functionally related genes for validation and functional studies. Validation will include both real-time quantitative PCR and immunohistochemistry using tissue microarrays. We will then use in vitro models to initially determine the impact of these key genes on proliferation, cell motility, and/or invasion. For those genes we suspect as playing a key role in malignant transformation, we will attempt to alter expression in cultured ovarian epithelial cells and determine the impact on colony forming ability in soft agar and/or anchorage independent growth. Preliminary Results: A total of 40 tumor samples have been profiled. Preliminary SAM analysis has indicated 134 probe sets (representing genes) as differentially expressed between LMP and LGSC, 47 genes between LMP and LMP-MP, and 180 genes between LMP and LMP-MP+LGSC. No differential gene expression was detected between LMP-MP and LGSC at a false discovery rate below 89%. Significance: These initial findings appear to support our hypothesis that LMP-MP is genetically similar to LGSC. Further analysis of the individual tumors’ genetic profiles is underway, which may allow us to identify specific markers to refine their pathological diagnosis. In addition, we will attempt to identify genes that are differentially expressed between the non-invasive disease (LMP) and the invasive tumors (LMP-MP and LGSC). We propose that these genes will likely be involved in malignant transformation. We will use a bioinformatics approach and integrate these selected proteins with the list of 1732 potential secreted proteins identified by Welsh et al (Proc Natl Acad Sci U.S.A 100:3410, 2003) to highlight potential serum markers for early stage disease. Furthermore, we may be able to identify markers that would allow us to predict which low malignant potential tumors are more likely to progress to an aggressive neoplasm, which would allow clinicians to tailor medical and surgical treatments according to the patient’s individual risk of progressing to a frank malignancy. Lastly, we hope to identify potential molecular therapeutic targets that may lead to the development of novel treatments for LGSC, which would improve patient management and overall survival.
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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) | 0.001 | 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".