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Record W2169212902 · doi:10.4161/cbt.19600

Sensitization of (colon) cancer cells to death receptor related therapies

2012· review· en· W2169212902 on OpenAlexaff
Alexander Pintzas, Boris Zhivotovsky, Paul Workman, Paul A. Clarke, Spiros Linardopoulos, Jean‐Claude Martinou, Juan Carlos Lacal, Sylvie Robine, George Nasioulas, Ladislav Anděra

Bibliographic record

VenueCancer Biology & Therapy · 2012
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer therapeutics and mechanisms
Canadian institutionsInstitute of Cancer Research
FundersCancer Research UK
KeywordsSensitizationProgrammed cell deathColorectal cancerApoptosisCancer researchCancerMedicinePharmacologyPharmacogenomicsCancer cellBioinformaticsBiologyImmunologyInternal medicineGenetics

Abstract

fetched live from OpenAlex

The objective of the ONCODEATH consortium [EU Research Consortium "ONCODEATH" (2006-2010)] was to achieve sensitization of solid tumor cells to death receptor related therapies using rational mechanism-based drug combinations of targeted therapies. In this collaborative effort, during a period of 42 mo, cell and animal model systems of defined oncogenes were generated. Exploitation of generated knowledge and tools enabled the consortium to achieve the following research objectives: (1) elucidation of tumor components which confer sensitivity or resistance to TRAIL-induced cell death; (2) providing detailed knowledge on how small molecule Hsp90, Aurora, Choline kinase, BRAF inhibitors, DNA damaging agents, HDAC and DNMT inhibitors affect the intrinsic apoptotic amplification and execution machineries; (3) optimization of combined action of TRAIL with these therapeutics for optimum effects with minimum concentrations and toxicity in vivo. These findings provide mechanistic basis for a pharmacogenomic approach, which could be exploited further therapeutically, in order to reach novel personalized therapies for cancer patients.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.946
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.068
GPT teacher head0.366
Teacher spread0.298 · 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.

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

Citations4
Published2012
Admission routes1
Has abstractyes

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