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Record W2323991864 · doi:10.1158/1538-7445.am2011-2223

Abstract 2223: ELISA coupled anion exchange methodology for separation of KLK6 glycoprotein subpopulations in biological fluids

2011· article· en· W2323991864 on OpenAlexaff
Uroš Kuzmanov, Christopher R. Smith, Antoninus Soosaipillai, Eleftherios P. Diamandis

Bibliographic record

VenueCancer Research · 2011
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsMount Sinai HospitalUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsOvarian cancerChemistryGlycoproteinGlycosylationAscitesOvaryCancerChromatographyBiochemistryBiologyEndocrinologyInternal medicineMedicine

Abstract

fetched live from OpenAlex

Abstract Ovarian cancer is the leading cause of death among all gynecological disorders. Elevated levels of Kallikrein 6 (KLK6), a secreted trypsin-like N-glycosyalted protease, have been shown to have unfavourable prognostic value in ovarian cancer patients. It has been revealed to be aberrantly up regulated at both transcriptional and translational levels in ovarian cancer. Aberrant glycosylation, or more specifically, increased sialylation of proteins has been observed in the cancer of the ovary. KLK6 isolated from ascites fluid of ovarian cancer patients has been shown to have enrichment of terminal alpha 2-6 linked sialic acid at its single N-glycosylation site when compared to protein isolated from cerebrospinal fluid (CSF), which is believed to be the major source of KLK6 present in circulation. However, due to the relatively low levels of KLK6 in serum (ng/ml range) neither mass spectrometry nor lectin based characterization of this protein found in serum has been achieved. Here, we report a novel HPLC anion exchange method, coupled to a KLK6 specific ELISA, capable of differentiating KLK6 glycoform subgroups in biological fluids, including serum, at physiologically relevant levels. In brief, biological fluids were injected onto a MonoQ bead matrix (GE Healthcare), bound proteins were eluted over a salt gradient and the resulting fractions were analyzed for KLK6 content resulting in a chromatographic elution profile containing four distinct peaks. Utilizing this assay, the KLK6 elution profile and distribution across peaks of a small set (n=8) of ovarian cancer patient matched serum and ascites fluid samples was found to be different than the profile of serum and CSF of normal individuals (n=10). Utilizing tandem mass spectrometry (MS/MS), recombinant KLK6 (rKLK6) purified from an immortalized human cell line was characterized and found to have a highly heterogeneous KLK6 population, encompassing the majority of glycoforms previously shown to be present in the protein from CSF and ovarian cancer ascites. This protein was subjected to the developed assay and was found to contain all of the four diagnostic KLK6 peaks present in the previously assayed biological fluids. Due to the available high quantity of protein from this source (mg levels) we were able to analyze the rKLK6 glycoform composition of each peak utilizing lectin affinity and MS/MS based glycopeptide quantification by single reaction monitoring. The combined results showed a significant increase in terminal alpha 2-6 linked sialic acid on the N-glycans found on KLK6 from ovarian cancer serum and ascites, as opposed to CSF and serum of normal individuals. Therefore, further development and application of this methodology might lead to improvement of KLK6 as a serum ovarian cancer biomarker. 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 2223. doi:10.1158/1538-7445.AM2011-2223

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.618
GPT teacher head0.553
Teacher spread0.065 · 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
GenreMethods

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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