Abstract 3037: Integrated Genomic, MicroRNA (miRNA) and Proteomic Profiling of Ovarian Carcinoma for Biomarker Discovery
Bibliographic record
Abstract
Abstract Ovarian cancer (OCa) is the fifth leading cause of cancer-related deaths in North American women and the first due to a gynecologic malignancy. The long-term effectiveness of standard therapy is generally poor and is accompanied by serious side effects. Thus there is a need for developing markers not only for diagnosis and prognosis, but also for predicting therapeutic response. Tumour progression and resistance to therapy is a consequence of the complexity of DNA, RNA and proteins. The search for effective and specific biomarkers should integrate aspects of all these factors. We have previously demonstrated that KLK6 is a promising biomarker for OCa and its observed over-expression is linked to copy-number gains of the 19q13.3/4 locus. Here, we demonstrate by multi-colour FISH analyses that the KLK locus in 81 OCas is subject to high-level of genomic instability (p<0.001); and such instability is significantly co-related to grade (p<0.001). KLK6-specific immunohistochemistry (IHC) showed no strong corelation with KLK6 copy-number, suggesting that other mechanisms, together with copy-number, drive its over-expression. Because 19q contains the highest number of annotated microRNAs (miRNAs) and copy-number instability may affect expression of these miRNAs, we investigated the role of miRNAs in OCa, not only for regulating KLK6, but as biomarkers for OCa. miRNA profiling of OCa cell lines and primary tumours by RT2-PCR showed the differential expression of miRNAs, consistent with other published studies in OCa. Since miRNAs can potentially affect the protein expression of hundreds of genes, the identification of such differentially expressed proteins not only provides putative biomarkers, but may also elucidate pathways for therapeutic intervention. Using Stable Isotope Labelling with Amino Acids in Cell Culture (SILAC) coupled to mass spectrometry for the OVCAR-3 cell line, cultures were labeled separately in light-Arg/Lys and heavy-Arg/Lys. In this control experiment, over 2,800 proteins were identified with 2,465 quantified. Over 94% of these quantified proteins showed a heavy:light ratio between 0.8 and 1.2, making this a robust system for quantitatively distinguishing differentially expressed proteins in the presence of miRNA precursors or inhibitors. Our profiling, as well as others, has shown the loss of expression of let-7 family members and hsa-125a-5p in OCas, and both miRNAs show decreased expression in OVCAR-3. Interestingly, these miRNAs are also predicted to target KLK6. Thus, we hypothesize that these miRNAs will not only affect the expression of KLK6, but also the expression of other target genes. In the future, using SILAC, we will identify the differentially expressed proteins affected upon re-introduction of these miRNAs through the differential labeling of miRNA-transfected OVCAR-3 vs. non-transfected OVCAR-3; thus revealing novel biomarkers for OCa. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3037.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".