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Record W2331153369 · doi:10.1158/1538-7445.am2013-5050

Abstract 5050: KCNJ2 constitutes a marker and therapeutic target of high-risk medulloblastomas.

2013· article· en· W2331153369 on OpenAlexaff
Francesca Valdora, Florian Freier, Livia Garzia, Vijay Ramaswamy, Claudia Seyler, Thomas Hielscher, Nathan Brady, Paul A. Northcott, Marcel Kool, David Jones, Hendrik Witt, Gian Paolo Tonini, Wolfram Scheurlen, Hugo A. Katus, Andreas E. Kulozik, Edgar Zitron, Andrey Korshunov, Peter Lichter, Michael D. Taylor, Stefan M. Pfister, Marc Remke

Bibliographic record

VenueCancer Research · 2013
Typearticle
Languageen
FieldMedicine
TopicGlioma Diagnosis and Treatment
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedulloblastomaGene knockdownWnt signaling pathwayCancer researchBiologyInternal medicineOncologyMedicineCell cultureGeneticsSignal transduction

Abstract

fetched live from OpenAlex

Abstract Medulloblastoma comprises the most common malignant brain tumor in children. Non-WNT/SHH tumors define the most refractory medulloblastoma subgroups. Interestingly, 17q gain, the most common genetic aberration in medulloblastoma, comprises a cytogenetic hallmark of these molecular high-risk tumors detected in group 3 (62%), and group 4 (73%). The majority of recurrent tumors harbor 17q gain in the corresponding primary. Virtually all of these tumors develop resistance to current treatment protocols at relapse. The lack of a common molecular target hampers the development of urgently needed novel treatment strategies. Through mRNA expression profiling of 64 primary tumor samples, we identified potassium inwardly-rectifying channel J2 (KCNJ2) as one of the most upregulated genes on chromosome 17q in tumors with 17q gain. High KCNJ2 transcript levels were significantly associated with non-WNT/non-SHH grouping, anaplastic histology, metastatic dissemination, and poor clinical outcome. KCNJ2 protein expression was analyzed by immunohistochemistry in a large cohort of patients (n=199), and high protein expression levels were found to be strongly correlated with 17q gain, metastatic dissemination, and inferior prognosis (p<0.0001). To functionally validate the potential role of KCNJ2 in medulloblastoma biology, we performed knockdown experiments by small interfering RNA-mediated silencing in two well-characterized medulloblastoma cell lines. Transient knockdown of KCNJ2 resulted in a reduced proliferation rate and induction of apoptosis. Furthermore, treatment of the medulloblastoma cell lines and medulloblastoma stem cells with amiodarone and gambogic acid, two inhibitors of this class of Kir channels, phenocopied these effects in a time- and dose-dependent manner. Whole cell patch clamp results revealed a nearly complete current blockade upon inhibitor treatment. Subsequently, we showed that pharmacological inhibition of KCNJ2 and knockdown KCNJ2 significantly reduced tumor growth and resulted in prolonged survival in an orthotopic medulloblastoma mouse model. In summary, our data suggest that pharmacological inhibition of KCNJ2 may constitute a new therapeutic option for patients with high-risk medulloblastomas. Citation Format: Francesca Valdora, Florian Freier, Livia Garzia, Vijay Ramaswamy, Claudia Seyler, Thomas Hielscher, Nathan Brady, Paul A. Northcott, Marcel Kool, David TW Jones, Hendrik Witt, Gian Paolo Tonini, Wolfram Scheurlen, Hugo A. Katus, Andreas E. Kulozik, Edgar Zitron, Andrey Korshunov, Peter Lichter, Michael D. Taylor, Stefan M. Pfister, Marc Remke. KCNJ2 constitutes a marker and therapeutic target of high-risk medulloblastomas. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5050. doi:10.1158/1538-7445.AM2013-5050

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 head0.042
GPT teacher head0.360
Teacher spread0.318 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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