Abstract 3004: Biomarker identification through integrative bioinformatics analysis of serous epithelial ovarian cancer tumor samples
Bibliographic record
Abstract
Abstract Ovarian cancer is the leading cause of death from gynecological malignancies and the fifth major cancer in women in the world. Once diagnosed, ovarian cancer is usually treated by cytoreductive surgery followed by platinum and taxane-based chemotherapeutic drugs. However, resistance to chemotherapy is a major impediment in management of serous epithelial ovarian cancer (SEOC). We hypothesize that a multifaceted view of the alterations taking place at multiple cellular levels using molecular profiling technologies will offer insight into the mechanisms which play key roles in drug resistant ovarian carcinomas. Also, the application of appropriate bioinformatic and statistical data processing and analysis is of utmost importance in identification of key drug resistance pathways. Current study is performed on 25 high-grade serous epithelial ovarian tumor tissue samples from patients that demonstrated favorable, or unfavorable response to chemotherapy treatment. Four different microarray platforms were used for molecular profiling of the full sample cohort at different molecular levels, namely: Single Nucleotide Polymorphisms (SNP), mRNA expression, miRNA expression and promotor tiling arrays (methylation)., Integrative and systematic analyses using up-to-date statistical approaches, such as empirical Bayes, AUC, SAM, permuted t-test and lassoed PCA, among others, have been employed on these large datasets obtained through the various high-throughput platforms. Preliminary mRNA expression analysis identified an enrichment of upregulated genes involved in cellular growth and proliferation, cellular development as well as differential gene expression changes in the TGFB1, TNF, PI3K, IFNG networks, between the chemotherapy responsive and unresponsive groups. The major molecular and cellular functions associated were cell-to-cell signaling, molecular transport and cellular movement. Differences were also seen in the, CTNNB1, LH and FSH networks as analysed by Ingenuity Pathway Analysis. Additionally, genes involved in activation of NFκB pathway showed differential expression in the two groups. Furthermore, our ongoing development of a streamlined database in which the multiple data types obtained from our statistical analyses are stored, will allow for localized or genome wide querying across the multiple levels of biological data. These approaches and software will potentially elucidate the synergistic roles that the various biological levels play in the deregulation of pathways involved in primary chemoresistance. Our research findings will lead to the determination of putative candidates for diagnostic and prognostic biomarkers that can be targeted for development of treatment regimens for the treatment of SEOC. 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 3004. doi:1538-7445.AM2012-3004
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".