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Abstract PO-069: Characterizing cell-to-cell heterogeneity and chromosome instability induced by <i>USP22</i> deficiency in colorectal cancer

2020· article· en· W3095277969 on OpenAlexaff
Lucile M. Jeusset, Zelda Lichtensztejn, Kirk J. McManus

Bibliographic record

VenueCancer Research · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsUniversity of ManitobaResearch Institute in Oncology and HematologyCancerCare Manitoba
Fundersnot available
KeywordsChromosome instabilityBiologyCancer researchCancerMitosisGene silencingCell cycleMolecular biologyGeneticsChromosomeGene

Abstract

fetched live from OpenAlex

Abstract Chromosome instability (CIN) is an aberrant phenotype characterized by an increased rate of gains and losses of chromosomes. CIN is observed in most cancer types, including 85% of colorectal cancer (CRC) cases. Although CIN is a driver of tumor heterogeneity and associated with drug resistance and poor patient prognosis, the genetic defects underlying CIN remain poorly understood. Ubiquitin Specific Peptidase 22 (USP22) is a deubiquitinating enzyme that targets mono-ubiquitin on lysine 120 of histone H2B (H2Bub1). H2Bub1 is a dynamic post-translational modification that modulates chromatin compaction. Importantly, homozygous or heterozygous USP22 deletion is observed in 45% of CRC cases, suggesting this may be a pathogenic event promoting CRC progression. Accordingly, we sought to characterize the impact reduced USP22 expression has on CIN. To reduce USP22 expression in karyotypically stable CRC (HCT116) and immortalized fibroblast (hTERT) cell lines, we employed transient siRNA silencing and stable CRISPR-Cas9 knockout (KO) models. Single-cell quantitative image-based analyses were utilized to monitor CIN-associated phenotypes, including changes in nuclear areas, micronucleus formation and chromosome numbers. In HCT116, USP22 silencing induced CIN phenotypes and an increase in H2Bub1 abundance in mitotic chromosomes. Subsequent mechanistic studies determined that this is accompanied by an increase in the recruitment of the spindle assembly checkpoint kinase BUB1 at metaphase centromeres and an increased frequency of lagging chromosomes in anaphase. Collectively, these data suggest that reduced USP22 expression alters H2Bub1 regulation in mitosis, which impairs mitotic fidelity, thereby inducing CIN. In addition, to assess the relevance of reduced USP22 expression in a distinct cellular context, the impact of USP22 silencing was assessed in hTERT, revealing similar CIN phenotypes, including changes in nuclear area and chromosome numbers. To assess the long-term impact USP22 deletions have on CIN, novel homozygous and heterozygous USP22-KO HCT116 clones were generated and monitored for three months, which revealed dynamic CIN phenotypes and ongoing changes in chromosome complements relative to controls. Collectively, these data identify USP22 as a novel CIN gene and indicate that USP22 deletions in cancer may drive intra-tumor genetic heterogeneity and thereby promote oncogenesis. Citation Format: Lucile M. Jeusset, Zelda D. Lichtensztejn, Kirk J. McManus. Characterizing cell-to-cell heterogeneity and chromosome instability induced by USP22 deficiency in colorectal cancer [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-069.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.000

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 head0.056
GPT teacher head0.340
Teacher spread0.284 · 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
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
Published2020
Admission routes1
Has abstractyes

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