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Record W3014855304 · doi:10.1534/g3.120.401128

A Genome-Wide Screen in Mice To Identify Cell-Extrinsic Regulators of Pulmonary Metastatic Colonisation

2020· article· en· W3014855304 on OpenAlexfundno aff
Louise van der Weyden, Agnieszka Świątkowska, Vivek Iyer, Anneliese O. Speak, David J. Adams

Bibliographic record

VenueG3 Genes Genomes Genetics · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPI3K/AKT/mTOR signaling in cancer
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchWellcome Trust
KeywordsBiologyCancer researchMetastasisMutantGeneCancerGenetics

Abstract

fetched live from OpenAlex

Abstract Metastatic colonization, whereby a disseminated tumor cell is able to survive and proliferate at a secondary site, involves both tumor cell-intrinsic and -extrinsic factors. To identify tumor cell-extrinsic (microenvironmental) factors that regulate the ability of metastatic tumor cells to effectively colonize a tissue, we performed a genome-wide screen utilizing the experimental metastasis assay on mutant mice. Mutant and wildtype (control) mice were tail vein-dosed with murine metastatic melanoma B16-F10 cells and 10 days later the number of pulmonary metastatic colonies were counted. Of the 1,300 genes/genetic locations (1,344 alleles) assessed in the screen 34 genes were determined to significantly regulate pulmonary metastatic colonization (15 increased and 19 decreased; P < 0.005 and genotype effect <-55 or >+55). While several of these genes have known roles in immune system regulation (Bach2, Cyba, Cybb, Cybc1, Id2, Igh-6, Irf1, Irf7, Ncf1, Ncf2, Ncf4 and Pik3cg) most are involved in a disparate range of biological processes, ranging from ubiquitination (Herc1) to diphthamide synthesis (Dph6) to Rho GTPase-activation (Arhgap30 and Fgd4), with no previous reports of a role in the regulation of metastasis. Thus, we have identified numerous novel regulators of pulmonary metastatic colonization, which may represent potential therapeutic targets.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.135
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.277
Teacher spread0.254 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

Explore more

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