Abstract 3155: Identification of biomarkers of chemoresistance in serous epithelial ovarian cancer by integrative molecular profiling
Bibliographic record
Abstract
Abstract Development of primary resistance to carboplatin and paclitaxel pose a major challenge in the management of ovarian cancer. To identify the molecular mechanisms underlying this process, we used microarrays to profile the 1) copy number alteration and SNP, 2) mRNA, 3) miRNA and 4) methylation signatures in 11 chemoresistant and 13 sensitive tumour samples, as defined by the RECIST criteria. The profiles are analyzed by Bayes statistics, based on R/Bioconductor packages, and the relevant pathways determined using the Ingenuity Pathway Analysis. The data from each array platforms are integrated using bioinformatic analytical and visual tools developed in house to not only decipher the most critical biological pathways, but also to identify the molecular mechanisms driving the pathways. Analysis to date identified the metabolic network involving HNRNPC, JAK1, Erbb2, ARF1, among others, which converge to deregulate the PI3K pathway. Interestingly, this is consistent with PTEN loss which is frequently observed in serous low grade tumours. The complexity of this pathway is reflected by its implication in multiple biological functions including growth promoting pathways, proliferation, differentiation, anti-apoptosis, tumorigenesis and angiogenesis. The integrated analysis will dissect and elucidate the roles that the CNA, SNP, methylation and miRNA play in the deregulation of this and other pathways involved in primary chemoresistance. Our research findings will yield diagnostic and prognostic biomarkers that will lead to development of specific treatment regimens for the improved control of serous epithelial ovarian cancer. 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 3155. doi:10.1158/1538-7445.AM2011-3155
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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) | 0.001 | 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".