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Record W2077269148 · doi:10.1093/neuonc/nou206.72

COMBINED DRUG SCREENING AND PHOSPHOPROTEOMICS IDENTIFIES CANDIDATE BRAIN TUMOR THERAPEUTICS AND NOVEL TARGETS IN PRIMARY HUMAN BRAIN TUMOR-INITIATING CELLS

2014· article· en· W2077269148 on OpenAlexaffabout
David R. Kaplan, Natalie Grinshtein, Constanza Rioseco, A. Luchman, Alessandro Datti, Ahmed Aman, David T. Uehling, Michaël Prakesch, Jeffrey L. Wrana, G. Cairncross, Yingqiang Shen, Steven A. Jones, Maurizio Marra, Donna L. Senger, S. Robbins, Rima Al‐awar, Michael F. Moran, Samuel Weiss

Bibliographic record

VenueNeuro-Oncology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsPhosphoproteomicsCytotoxicityBiologyComputational biologyDrug discoveryHigh-content screeningDrugCyclin-dependent kinasePharmacologyBioinformaticsCancer researchKinaseCellGeneticsIn vitroCell cycleProtein kinase A

Abstract

fetched live from OpenAlex

BACKGROUND: “Therapeutic Targeting of Glioblastoma” is a new pan-Canadian research team of the Terry Fox Research Institute and the Canadian Stem Cell Network funded to discover efficacious therapeutics for GBM. The team's goals are also to discover novel signaling pathways regulating GBM cell survival and genetic alterations that mediate drug resistance. As a platform, we use our collection of over 100 primary GBM tumor-initiating lines (BTIC) that are subjected to drug screening by over 1400 compounds, and to genetic and phosphoproteomic analysis. METHODS: We performed drug screening of 21 BTIC lines that represent the molecular heterogeneity of GBM patients, with a library of 110 clinically-relevant kinase inhibitors at three concentrations. We have also completed a phosphotyrosine characterization of 14 BTIC lines using phosphoproteomics. RESULTS: Multiple compounds that exhibit nanomolar cytotoxicity towards all of the lines were prioritized based upon their potency, novelty for GBM, ability to accumulate to relevant concentrations in brain, clinical status and cytotoxicity towards normal cells. Of the several hit compounds that fulfilled all of these criteria, the CDK inhibitor dinaciclib was chosen as our lead compound. We are currently investigating its mechanism of action using phosphoproteomics and RNAi as well as testing its efficacy in vivo in an orthotopic xenograft model as a single agent and in combination with TMZ. Furthermore, we have identified several compounds that exhibit selective cytotoxicity towards only a specific set of BTIC lines, suggesting that genetic differences between the lines account for differential sensitivities. The sensitivity of BTICs to one such compound, EMD-1214063, a selective MET inhibitor, may correlate with Met amplification status. Finally, phosphoproteomics results show activation of RTKs (EGFR, FGFR, PDGFRA, Eph receptor family) and non-receptor kinases (FYN, FAK1, GSK3b, MAPK, CDKs, DYRK) in the majority of BTIC lines, and of adaptor/scaffold proteins and proteins important in adhesion and migration. We are now (1) determining whether drugs that inhibit the activity of identified phosphoproteins suppress BTIC survival in culture and in orthotopic models, (2) validating the importance of those phosphoproteins in GBM survival, and (3) asking whether the prioritized drug hits from our drug screens suppress the phosphorylation of the identified phosphoproteins. CONCLUSIONS: We anticipate that our drug screening approach will generate several clinical candidates that will enable us to proceed to the clinic in the next several years. We also believe that our phosphoproteomics results will lead to identification of novel therapeutic targets for GBM. SECONDARY CATEGORY: Preclinical Experimental Therapeutics.

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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.253
Teacher spread0.242 · 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
Published2014
Admission routes2
Has abstractyes

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