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Record W2593355480 · doi:10.1016/j.celrep.2017.02.028

Interrogation of Functional Cell-Surface Markers Identifies CD151 Dependency in High-Grade Serous Ovarian Cancer

2017· article· en· W2593355480 on OpenAlexafffund
Mauricio Medrano, Laudine Communal, Kevin R. Brown, Marcin Iwanicki, Josée Normand, Joshua Paterson, Fabrice Sircoulomb, Paul M. Krzyzanowski, Marián Novak, Sasha A. Doodnauth, Fernando Suárez, Jane Cullis, Rima Al‐awar, Benjamin G. Neel, John D. McPherson, Ronny Drapkin, Laurie Ailles, Anne‐Marie Mes‐Masson, Robert Rottapel

Bibliographic record

VenueCell Reports · 2017
Typearticle
Languageen
FieldMedicine
TopicCell Adhesion Molecules Research
Canadian institutionsSt. Michael's HospitalUniversité de MontréalPrincess Margaret Cancer CentreOntario Institute for Cancer ResearchUniversity Health NetworkUniversity of TorontoCentre Hospitalier de l’Université de Montréal
FundersFonds de Recherche du Québec - SantéTerry Fox Research InstituteOvarian Cancer CanadaOntario Institute for Cancer ResearchCancer Research UKMcGill UniversityOvarian Cancer Research Fund
KeywordsOvarian cancerTetraspaninBiologySerous fluidCancer researchSerous ovarian cancerRNA interferenceGeneCancerCellComputational biologyGeneticsRNA

Abstract

fetched live from OpenAlex

The degree of genetic aberrations characteristic of high-grade serous ovarian cancer (HGSC) makes identification of the molecular features that drive tumor progression difficult. Here, we perform genome-wide RNAi screens and comprehensive expression analysis of cell-surface markers in a panel of HGSC cell lines to identify genes that are critical to their survival. We report that the tetraspanin CD151 contributes to survival of a subset of HGSC cell lines associated with a ZEB transcriptional program and supports the growth of HGSC tumors. Moreover, we show that high CD151 expression is prognostic of poor clinical outcome. This study reveals cell-surface vulnerabilities associated with HGSC, provides a framework for identifying therapeutic targets, and reports a role for CD151 in HGSC.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.027
GPT teacher head0.307
Teacher spread0.279 · 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 teacher head, not a consensus.

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

Citations56
Published2017
Admission routes2
Has abstractyes

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