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Record W4283793980 · doi:10.3389/fcell.2022.938685

Asymmetric Cell Division and Tumor Heterogeneity

2022· review· en· W4283793980 on OpenAlexaff
Zizhu Li, Ying Yi Zhang, Haomiao Zhang, Jiaxuan Yang, Yongze Chen, Hezhe Lu

Bibliographic record

VenueFrontiers in Cell and Developmental Biology · 2022
Typereview
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersChinese Academy of Sciences
KeywordsCell divisionCancer stem cellBiologyAsymmetric cell divisionCarcinogenesisMitosisTumor progressionStem cellCancerCellCancer cellCancer researchTumor heterogeneityPopulationCell biologyGeneticsMedicine

Abstract

fetched live from OpenAlex

Asymmetric cell division (ACD) gives rise to two daughter cells with different fates after mitosis and is a fundamental process for generating cell diversity and for the maintenance of the stem cell population. The cancer stem cell (CSC) theory suggests that CSCs with dysregulated self-renewal and asymmetric cell division serve as a source of intra-tumoral heterogeneity. This heterogeneity complicates the diagnosis and treatment of cancer patients, because CSCs can give rise to aggressive clones that are metastatic and insensitive to multiple drugs, or to dormant tumor cells that are difficult to detect. Here, we review the regulatory mechanisms and biological significance of asymmetric division in tumor cells, with a focus on ACD-induced tumor heterogeneity in early tumorigenesis and cancer progression. We will also discuss how dissecting the relationship between ACD and cancer may help us find new approaches for combatting this heterogeneity.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.034
GPT teacher head0.295
Teacher spread0.260 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations57
Published2022
Admission routes1
Has abstractyes

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