MétaCan
Menu
Back to cohort
Record W2404961651 · doi:10.18632/oncoscience.134

BCC or not: Sufu keeps it in check

2015· article· en· W2404961651 on OpenAlexaff
Wen-Chi Yin, Zhu Juan Li, Chi‐chung Hui

Bibliographic record

VenueOncoscience · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHedgehog Signaling Pathway Studies
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsHedgehogPTCH1Hedgehog signaling pathwayVismodegibBiologyCarcinogenesisCell biologyCancer researchTranscription factorSmoothenedGeneticsGeneSignal transduction

Abstract

fetched live from OpenAlex

Basal cell carcinoma (BCC), driven by aberrantly activated HEDGEHOG (HH) pathway, is the most common human malignancy. Current FDA-approved targeted therapy uses Vismodegib to inhibit SMO, a membrane component of the HH pathway. Despite initial impressive tumor regression, the positive clinical response is short-lived in some BCC patients as acquired SMO mutations confer secondary resistance[1]. Clearly, a deeper understanding of the molecular events underlying BCC tumorigenesis is required to devise effective treatments. The activity of SMO is repressed by the HH receptor PTCH1. Upon HH binding, SMO promotes dissociation of GLI transcription factors from the key negative intracellular regulator SUFU, thereby allowing expression of HH target genes[2]. Mutations in PTCH1, SMO, and SUFU, believed to unleash GLI activity, are frequently found in BCC. SUFU, like PTCH1, is a major negative regulator of the HH pathway. We have previously shown that loss of Sufu in mouse keratinocytes promotes Gli2 nuclear localization due to lack of cytoplasmic sequestration, and consequently leads to elevated target gene expression[3]. Surprisingly, unlike Ptch1, inactivation of Sufu alone in the mouse skin does not cause BCC. To identify the key oncogenic events in BCC formation, we performed microarray coupled with Gene Set Enrichment Analysis on Ptch1 and Sufu mutants[4]. The comparative analysis revealed that loss of Ptch1 in keratinocytes led to significant enrichment of gene sets involved in TGF-β signaling and extracellular matrix remodelling, consistent with the tumorigenic phenotype. In contrast, the majority of gene sets uniquely enriched in Sufu knockout keratinocytes are involved in cell cycle control, suggesting a novel role of Sufu in cell cycle regulation. Intriguingly, unlike Ptch1 knockout skin, which showed elevated number of mitotic cells, Sufu knockout skin exhibited normal mitotic count. Furthermore, while DNA damage was found in both mutants, Sufu knockout cells displayed DNA damage-induced G2/M checkpoint cell cycle arrest. These results indicate that Ptch1 knockout cells are able to override the checkpoint and continue proliferation with the unstable genome while Sufu knockouts halt, a key feature likely contributing to their differential cancer phenotypes. Arrest at G2 is typically coupled with accumulation of p53, which activates p21 and 14-3-3σ to sequester mitosis-promoting complex Cyclin-B1/CDK1. Strikingly, p53 protein and p21 transcripts remained low in Sufu mutants despite the arrest. These findings suggest that while both loss of Sufu and Ptch1 result in increased entry into cell cycle and impairment in p53 response to cell cycle-driven DNA damage, Sufu itself may be a positive regulator of cell cycle progression independent of the p53 checkpoint. Upregulation of the major HH pathway effector, Gli2, is a hallmark of BCC and is observed in Ptch1 mouse models. Consistent with our finding that loss of Ptch1 leads to genome instability and evasion of cell cycle checkpoints, Pantazi et al.[5] recently demonstrated that overexpression of GLI2 activator (GLI2sN) in human keratinocytes is sufficient to induce chromosomal aberrations. They also found that GLI2sN overexpression results in suppression of cell cycle regulators p21 and 14-3-3σ, and induction of anti-apoptotic mechanisms. These lines of evidence suggest that GLI2 is likely the major mediator of the malignant transformation induced by the loss of PTCH1 during BCC tumorigenesis. In vitro studies demonstrated that HH signaling can positively regulate cell cycle by promoting the expression of cell cycle regulators (D-type cyclins) and preventing the accumulation of p53. These are consistent with the active mitosis and evasion of cell cycle arrest observed in Ptch1 knockout cells. Our findings suggest that Sufu may also regulate cell cycle. However, it remains unclear why and how the loss of this negative HH pathway regulator causes cell cycle arrest. One possible mechanism is through DNA damage response, which involves the ATM/ATR, CHK1/CHK2, and CDC25C axis to inactivate the Cyclin-B1/CDK1 complex, leading to G2 arrest. Figure 1 Inactivation of Ptch1 and Sufu lead to distinct cellular events in keratinocytes Whether Sufu's cell cycle function is Gli-dependent is also unknown. Although ectopic HH target gene expression was found in both Sufu and Ptch1 mutants, Gli2 protein is significantly reduced in Sufu mutants compared to wildtype, with exclusive nuclear localization. It is possible that a certain threshold of Gli2 activity is required for evasion of cell cycle arrest and tumor surveillance, and that BCC tumorigenesis is stunted in Sufu mutants since the threshold is not achieved. Double knockout of Sufu and Ptch1 may help determine whether Sufu is required for the rapid cell cycle progression induced by loss of Ptch1. In addition, with the recent advances in BioID mass spectrometry[6], identification of Sufu's interactome in keratinocytes may give mechanistic insights into Sufu's involvement in cell cycle regulation. In conclusion, this comparative study of Ptch1 and Sufu mutant mice advanced our understanding of BCC tumorigenesis. Further investigations elucidating the role of Sufu in the cell cycle are warranted for the reason that if Sufu can also function as a positive regulator of the HH pathway, it may represent a potential target for therapeutic intervention of BCC.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.404
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.117
GPT teacher head0.347
Teacher spread0.229 · 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.

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

Citations4
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueOncoscienceSame topicHedgehog Signaling Pathway StudiesFrench-language works237,207