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Record W2137644376 · doi:10.1158/0008-5472.can-10-1108

Colorectal Tumors Are Effectively Eradicated by Combined Inhibition of β-Catenin, KRAS, and the Oncogenic Transcription Factor ITF2

2010· article· en· W2137644376 on OpenAlexaff
Luca Mologni, Hafedh Dekhil, Monica Ceccon, Stefania Purgante, Cathy Lan, Loredana Cleris, Vera Magistroni, Franca Formelli, Carlo Gambacorti‐Passerini

Bibliographic record

VenueCancer Research · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicWnt/β-catenin signaling in development and cancer
Canadian institutionsMcGill University
FundersAssociazione Italiana per la Ricerca sul Cancro
KeywordsKRASSmall hairpin RNACancer researchOncogeneGene silencingApoptosisBlockadeIn vivoColorectal cancerBiologyCateninMedicineCell cycleCancerGene knockdownInternal medicineWnt signaling pathwayReceptorSignal transductionCell biologyGeneGenetics

Abstract

fetched live from OpenAlex

Colorectal carcinomas (CRC) harbor well-defined genetic abnormalities, including aberrant activation of β-catenin (β-cat) and KRAS, but independent targeting of these molecules seems to have limited therapeutic effect. In this study, we report therapeutic effects of combined targeting of different oncogenes in CRC. Inducible short hairpin RNA (shRNA)-mediated silencing of β-cat, ITF2, or KRAS decreased proliferation by 88%, 72%, and 45%, respectively, with no significant apoptosis in any case. In contrast, combined blockade of β-cat and ITF2 inhibited proliferation by 99% with massive apoptosis. Similar effects occurred after combined shRNA against β-cat and KRAS. In vivo, single oncogene blockade inhibited the growth of established tumors by up to 30%, whereas dual β-cat and ITF2 targeting caused 93% inhibition. Similar tumor growth suppression was achieved by double β-cat/KRAS shRNA in vivo. Our findings illustrate an effective therapeutic principle in CRC based on a combination targeting strategy that includes the ITF2 oncogene, which represents a novel therapeutic target.

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)0.000
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 score0.008
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.305
Teacher spread0.286 · 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

Citations55
Published2010
Admission routes1
Has abstractyes

Explore more

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