Abstract 3645: Integrative molecular profiling in serous epithelial ovarian cancer for identification of biomarkers of chemoresistance
Bibliographic record
Abstract
Abstract Ovarian cancer is the fifth leading cause of death due to gynecological cancers in women in the western world. Development of primary resistance to carboplatin and paclitaxel poses a major challenge in the management of serous epithelial ovarian cancer. To identify the molecular mechanisms underlying the development of instrinsic resistance upon exposure to standard first-line therapy for ovarian cancer, we used microarrays to profile the 1) copy number alteration and SNP, 2) mRNA, 3) miRNA and 4) methylation signatures in a cohort comprising 11 chemoresistant and 14 chemosensitive tumour samples. Copy number analysis showed significant copy number alterations in the chemoresistant group (gains on chromosomal regions, 4q, 6q, 8p, 8q, 19q, 7q and 22q; losses on 8p and 10q) compared to the sensitive group. Gene expression data analysis using R/bioconductor revealed a set of 248 discriminating genes in the two cohorts. Pathway analysis of these genes using Ingenuity Pathway Analysis revealed enrichment in genes primarily involved in epithelial to mesenchymal transition and PI3 Kinase pathway. Additionally, genes related to the pro-inflammatory cytokine pathways, as well as drug transport demonstrated significant differential expression between the two groups. Ongoing concurrent comparative analyses of miRNA profiles within this cohort have also identified several differentially expressed transcripts including mir-34b, mir-155, mir-214, mir-200c and mir-143. Some of these miRNAs have been earlier reported to be associated with tumour progression. Further integrated analysis will elucidate the synergistic roles that the genetic and epigenetic alterations in the deregulation of these 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 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3645. doi:1538-7445.AM2012-3645
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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".