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Record W2051571422 · doi:10.1158/1538-7445.am2011-3155

Abstract 3155: Identification of biomarkers of chemoresistance in serous epithelial ovarian cancer by integrative molecular profiling

2011· article· en· W2051571422 on OpenAlexaff
Madhuri Koti, Ricardo Vidal, Alexandria Haslehurst, Paulo Nuin, Johanne I. Weberpals, Timothy Childs, Peter Bryson, Harriet Feilotter, Jeremy A. Squire, Paul C. Park

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsQueen's University
Fundersnot available
KeywordsBiologySerous fluidOvarian cancerCancer researchPTENPI3K/AKT/mTOR pathwayComputational biologymicroRNAPDGFRACancerBioinformaticsSignal transductionGeneticsStromal cell

Abstract

fetched live from OpenAlex

Abstract Development of primary resistance to carboplatin and paclitaxel pose a major challenge in the management of ovarian cancer. To identify the molecular mechanisms underlying this process, we used microarrays to profile the 1) copy number alteration and SNP, 2) mRNA, 3) miRNA and 4) methylation signatures in 11 chemoresistant and 13 sensitive tumour samples, as defined by the RECIST criteria. The profiles are analyzed by Bayes statistics, based on R/Bioconductor packages, and the relevant pathways determined using the Ingenuity Pathway Analysis. The data from each array platforms are integrated using bioinformatic analytical and visual tools developed in house to not only decipher the most critical biological pathways, but also to identify the molecular mechanisms driving the pathways. Analysis to date identified the metabolic network involving HNRNPC, JAK1, Erbb2, ARF1, among others, which converge to deregulate the PI3K pathway. Interestingly, this is consistent with PTEN loss which is frequently observed in serous low grade tumours. The complexity of this pathway is reflected by its implication in multiple biological functions including growth promoting pathways, proliferation, differentiation, anti-apoptosis, tumorigenesis and angiogenesis. The integrated analysis will dissect and elucidate the roles that the CNA, SNP, methylation and miRNA play in the deregulation of this 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 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3155. doi:10.1158/1538-7445.AM2011-3155

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.036
GPT teacher head0.360
Teacher spread0.324 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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