Abstract 1106: The tumor immune microenvironment modulates response to chemotherapy in high-grade serous epithelial ovarian cancer
Bibliographic record
Abstract
Abstract Resistance to platinum-based chemotherapy remains a major impediment in the treatment of serous epithelial ovarian cancer. Recent studies have drawn attention to the role of the tumor-microenvironment in mediating chemotherapy resistance, in addition to established mechanisms of tumor progression. This project is investigating the role of the tumor inflammatory microenvironment in facilitating intrinsic chemotherapy resistance using a cohort of 36 high-grade serous epithelial ovarian cancer patient tumor samples. Total RNA from forty-eight, HGSEOC tumor tissue samples was subjected to gene expression profiling using NanoString technology. This type of bar-coded expression profiling has proven to be equally efficient to study gene expression as quantitative real time PCR technique. The gene panel consisted of 184 human genes that are differentially expressed in human inflammation and 6 internal reference genes. The cohort comprised 18 sensitive (PFS >18months) and 18 resistant (PFS<6 months) samples. A multiplex cytokine assay using the bioplex system was also performed on serum samples from a subset of this cohort. The results showed significant differences in expression of some inflammatory cytokines in the serum that were concurrent with the observed gene expression differences in the tumor samples. The results are indicative of a cooperative role of a specific inflammatory gene signature within the tumor microenvironment that may augment pathways leading to differential drug response. These studies need further independent validations to derive non-invasive blood-based biomarkers for use in predicting chemotherapy resistance in serous epithelial ovarian cancer. Citation Format: Madhuri Koti, Andrew Edwards, Jeremy A. Squire. The tumor immune microenvironment modulates response to chemotherapy in high-grade serous epithelial ovarian cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1106. doi:10.1158/1538-7445.AM2014-1106
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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 | 0.000 | 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) | 0.004 | 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".