Abstract TMEM-035: GLYCOMARKERS FOR PREDICTING PLATINUM–DRUG RESPONSE IN OVARIAN CANCER
Bibliographic record
Abstract
Abstract Platinum-based drugs (pt-drugs) continue to be the mainstay of first-line therapy for a wide range of cancers including ovarian carcinoma. Despite common applications, resistance to pt-drugs is an ongoing dilemma in cancer treatments, because tumor cells have different molecular characteristics that affect their responses to drugs. A molecular test that can accurately identify pt-drug resistance would provide invaluable guidance for chemotherapy strategy and reduce the random use of ineffective drugs. This would be a significant advancement in treatment management. Our studies address this unmet need in cancer care by introducing an innovative method to predict pt-drug resistance, before administrating the drug. Our method is based on a new concept linking glycan cell surface expression with drug reactivity. We have identified a glycan structure, namely Glycomarker 1, whose expression level on the cancer cell surface is associated with a response to first-line chemotherapy. A patent for this discovery has been issued by the US patent office in 2009(1). Flow cytometry with a series of fluorescent lectins was initially used to profile the cell surface glycans on three isogenic pairs of ovarian carcinoma cell lines, each pair consisting of parental chemosensitive cell lines, 2008, A2780, IGROV-1, and their corresponding resistant cell lines 2008/C13*5.25, A2780/CP and IGROV-1/CP, respectively. The cell surface glycan comparison revealed that the expression levels of Glycomarker 1 on resistant phenotypes were at least ten times lower than that of sensitive parental cells, on all three pairs of cell lines. Further experiments by various methods of fluorescent confocal microscopy (with fluorescently labeled cisplatin), colony forming assay, glycan modifications, and mass spectrometry confirmed the association of Glycomarker 1 with drug uptake by ovarian carcinoma cell lines(1). Lectin histochemistry (LHC) was adopted as a clinical method to test the Glycomarker 1 on human ovarian tissue samples. The LHC was optimized and validated for reproducibility, specificity, and sensitivity by colorimetric and fluorescent staining systems. The feasibility of LHC as a clinical test was evaluated on a training panel of 64 ovarian tissue sections. In retrospective studies, LHC correctly predicted drug-response in 22 out of 27 (81.4%) cancer specimens from patients with a known history of response to first-line chemotherapy. In the course of testing the clinical samples for Glycomarker 1, we identified another glycan structure, namely Glycomarker 2, whose expression pattern was strongly similar to that of the Glycomarker 1, suggesting the association of two glycan motifs with drug response. A patent for this discovery is pending in the US, Canada and European patent office(2). The ultimate objective of this work is to translate the assays to clinical tests used to predict platinum response in ovarian cancer. This is a novel direct predictive method, truly different from existing procedures, which will provide molecular information for treatment strategy. Thus far, our experiments have focused on ovarian cancer. However, evidence indicates that this method may also apply to other cancers that are treated with pt-drugs. 1) US patent #7585503 2) PCT patent # 20150024409, 01/22/2015 pending in the US, Canada & Europe. Citation Format: Nahid Razi, Afshin Bahador, Nathalie Scholler and Nissi Varki. GLYCOMARKERS FOR PREDICTING PLATINUM–DRUG RESPONSE IN OVARIAN CANCER [abstract]. In: Proceedings of the 11th Biennial Ovarian Cancer Research Symposium; Sep 12-13, 2016; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(11 Suppl):Abstract nr TMEM-035.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".