Abstract 1168: Integrated genomic, microRNA (miRNA) and proteomic profiling by stable isotope labeling with amino acids in cell culture (SILAC) 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 for gynecologic malignancies. The long-term effectiveness of standard therapy is poor. Thus, there is a need for developing markers for diagnosis, prognosis, and for predicting therapeutic response. Transformation, malignancy and therapy resistance are consequences of changes at the DNA, RNA and protein levels, requiring an integrative approach for biomarker discovery. We, and others, have previously demonstrated that elevated protein levels of Kallikrein 6 (KLK6) in OCa are clinically relevant; and that copy-number gains of the KLK locus (19q13.3/13.4) is associated with elevated levels of KLKs, increasing grade and genomic instability. Recent work has indicated KLK6 protein expression is also regulated by microRNAs (miRNAs). Our profiling of OCa cell lines and primary tumours showed the differential expression of miRNAs, consistent with other published studies. Moreover, miRNAs predicted to target KLK6 were shown to be decreased in a KLK6-overexpressing OCa cell line (OVCAR-3), in comparison to a KLK6-non-expressing cell line (TOV21G) or to miRNAs derived from normal ovarian tissue. Among these are members of the hsa-let-7 family of miRNAs, found also to be in regions of copy-number loss in OVCAR-3. Since miRNAs can affect the protein expression of many genes, the identification of differentially expressed proteins, in addition to KLK6 not only suggests putative biomarkers, but may help elucidate pathways for therapeutic interventions. To identify differentially expressed proteins upon the transient transfection of hsa-let-7 family members into the OVCAR-3 cell line, we utilized Stable Isotope Labelling with Amino Acids in Cell Culture (SILAC) coupled to mass spectrometry. OVCAR-3 cultures were labeled separately in light-Arg/Lys and heavy-Arg/Lys isotopes, such that “light” and “heavy” peptides of the same proteins will generate spectra that are different due to a mass shift, thus enabling relative quantification. In control experiments, equal Light/OVCAR3 and Heavy/OVCAR3 total protein mixtures were profiled with 2,800 proteins identified, and 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 distinguishing differentially expressed proteins. Light/OVCAR-3 was transfected with hsa-let-7a or hsa-let-7e while heavy/OVCAR-3 was transfected with a scrambled miRNA control. KLK6-specific ELISA confirmed its decrease by 50% in transfected cultures over controls. Preliminary SILAC profiling has identified a number of differentially expressed proteins upon transfection with the miRNAs, which will be discussed in the context of OCa pathogenesis and implications for novel biomarker panels and proteomic signatures. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1168. doi:10.1158/1538-7445.AM2011-1168
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".