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Abstract 1375: Multiplex gene expression using the HyCEAD assay in CTCs isolated with the Parsortix™ system

2019· article· en· W4254205573 on OpenAlexaff
David Englert, Mariya D. Kolesnikova, Nishat Zaman, Arianna Hustler, Gabrielle Wishart, Daniel J. O’Shannessy

Bibliographic record

VenueClinical Research (Excluding Clinical Trials) · 2019
Typearticle
Languageen
FieldMedicine
TopicMedical Imaging Techniques and Applications
Canadian institutionsAptose Biosciences (Canada)
Fundersnot available
KeywordsMultiplexGeneComputational biologyBiologyComputer scienceGenetics

Abstract

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Background: Discrimination between benign and malignant pelvic masses by gene expression profiling of circulating tumor cells (CTCs) may provide information to assist in treatment decisions and improve outcomes. CTCs can be isolated and harvested from blood based on cell size and deformability using the Parsortix™ system. Because the number of CTCs in the blood of pelvic mass patients suspected of having ovarian malignancies is likely very low, gene expression profiling of these CTCs requires a highly sensitive detection system that is tolerant of the presence of normal nucleated blood cells.Objective: To assess the suitability of the HyCEAD assay for gene expression profiling of cells in Parsortix harvests. HyCEAD (Hybrid Capture Enrichment Amplification and Detection) is a fast and simple method for the simultaneous analysis of 100 or more mRNA species captured from cell lysates. The products of the multiplex amplification with HyCEAD can be quantified by hybridization on a flow-through chip.Methods: Multiple HyCEAD primer/probe sets were designed for 125 relevant genes, and purified total RNA or lysates from Parsortix harvests were processed with the HyCEAD assay. Primer/probe sets were screened using total RNA from cell lines and ovarian cancer tissues. Genes with significant expression in white blood cells were culled from the gene set. A set of non-human poly-adenylated mRNA molecules (separately quantified using digital droplet PCR) were used as spike-in standards to assess absolute sensitivity. CaOV3 cells spiked into blood from normal healthy volunteers (NHVs) were used as a model system for sensitivity analyses.Results: Many genes were identified that yielded substantially greater signal intensities in harvests from HNV blood spiked with CaOV3 cells relative to the unspiked blood samples. The expression levels could be quantified over a signal intensity range of 2.5 log10 units. Small numbers of non-human standard molecules could be detected; stochastic detection failures were observed when five or fewer molecules of the standards were assayed (dropout level). CVs in repeatability experiments were typically less than 20% with numbers of standard molecules greater than the dropout level. CVs in repeatability measurements of human genes related to ovarian cancer in spiked Parsortix harvests averaged about 25%. CVs were greater between assays of Parsortix harvests of different HNVs, reflecting in part biological variability and variation of the number of cells actually spiked into the blood and the number of cells recovered in the individual harvests.Conclusion: The HyCEAD assay provides adequate sensitivity to simultaneously profile expression levels of more than 100 genes in small numbers of CTCs directly from the lysates of Parsortix harvests. Expression profiling is limited to genes expressed at much higher levels in CTCs than in normal nucleated blood cells.Citation Format: David F. Englert, Mariya Kolesnikova, Nishat Zaman, Arianna Hustler, Gabrielle Wishart, Daniel J. O'Shannessy. Multiplex gene expression using the HyCEAD assay in CTCs isolated with the Parsortix™ system [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1375.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.136
metaresearch head score (Gemma)0.073
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1360.073
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0000.000

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 head0.708
GPT teacher head0.639
Teacher spread0.069 · 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; both teacher heads agree on what is shown here.

Study designObservational
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
Published2019
Admission routes1
Has abstractyes

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