Abstract A53: Biomarkers of chemotherapy resistance in serous epithelial ovarian cancer identified by integrative genomic and transcriptomic analysis
Bibliographic record
Abstract
Abstract Resistance to platinum-based chemotherapy remains a major impediment in the treatment of serous epithelial ovarian cancer. The objective of this study was to use gene expression and copy number profiling to delineate major deregulated pathways and biomarker networks associated with the development of intrinsic chemotherapy resistance with exposure to standard first-line therapy for ovarian cancer. The study cohort comprised 28 high grade serous ovarian cancer patients divided into two groups based on their varying sensitivity to first-line chemotherapy using progression free survival (PFS) as a surrogate of response. Twelve patient tumors demonstrating relative resistance to platinum based chemotherapy corresponding to shorter PFS (less than 6 months) were compared to 16 tumors from platinum-sensitive patients (PFS more than 18months). Molecular profiling was performed using Affymetrix high-resolution microarray platforms to permit global comparisons of gene expression levels and copy number profiles between tumors from the resistant group with the sensitive group. Microarray data analysis using statistical methods revealed a set of 204 discriminating genes of which expression levels may be influencing differential chemotherapy response between the two groups. Pathway analysis of the differentiating genes showed IGF1 network to be significantly altered between the two groups in addition to PI3K, NFkB, distinguishing the chemotherapy resistant with the sensitive group. Copy number analysis showed differences in the chromosomal regions, 4q31.22, 5q13.2, 9p24.3, 2p23.2, 16q21, 6q14.1, 7p22.3, 12p13 and Xq. Integrative copy number and gene expression profiling will delineate the drivers of chemotherapy resistance in patients undergoing standard platinum-based treatment of ovarian cancer. Future studies to validate these markers are necessary to apply this knowledge to biomarker-based clinical trials. Citation Format: Madhuri Koti, Robert J. Gooding, Paulo Nuin, Alexandria Haslehurst, Colleen Crane, Johanne Weberpals, Timothy Chids, Peter Bryson, Moyez Dharsee, Kenneth R. Evans, Harriet E. Feilotter, Paul C. Park, Jeremy A. Squire. Biomarkers of chemotherapy resistance in serous epithelial ovarian cancer identified by integrative genomic and transcriptomic analysis. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research: From Concept to Clinic; Sep 18-21, 2013; Miami, FL. Philadelphia (PA): AACR; Clin Cancer Res 2013;19(19 Suppl):Abstract nr A53.
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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".