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Record W2506773131 · doi:10.1158/1538-7445.am2016-3794

Abstract 3794: Novel therapeutic targets in head and neck cancer

2016· article· en· W2506773131 on OpenAlexaff
Maria Kondratyev, Aleksandra Pesic, Stephano Marastoni, Troy Ketela, Jason Moffat, Carl Virtanen, Azin Sayad, Mikhail Bashkurov, Alessandro Dati, Laurie Ailles, Reidar Grénman, Marianne Koritzinsky, Bradly G. Wouters

Bibliographic record

VenueCancer Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer-related molecular mechanisms research
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalOntario Institute for Cancer ResearchOakville-Trafalgar Memorial Hospital
Fundersnot available
KeywordsCancer researchGene knockdownNotch signaling pathwayMetastasisCancerPrimary tumorMedicineBiologySignal transductionGeneInternal medicineGenetics

Abstract

fetched live from OpenAlex

Abstract HNSCC is 6th most common malignancy in the world. Despite advances in diagnosis and treatment, the survival rates remain low due to frequent recurrences the biology of which remains unclear. Using functional genomic technologies we identified new therapeutic targets for metastatic disease in HNSCC. Whole genome shRNA screens were conducted in matched sets of cell lines derived from primary tumors and respective metastatic sites, identifying genes essential for cell survival only following metastasis. To test if knockdown of selected targets inhibits metastasis in a therapeutic setting, we established orthotropic model of HNSCC that metastasize to lymph nodes in the mouse. Components of Notch pathway were identified as essential for survival of cells derived from metastatic sites. Whole exome sequencing identified a novel mutation in one of the EGF domains of Notch3 that was acquired only in the metastatic line. Mutations in EGF domains have been reported to influence interaction with specific ligands, dictating which ligand can activate Notch signaling. Our data indicate that metastatic, but not primary tumor cells, undergo apoptosis upon knockdown of Notch3 and that a distinct set of target genes is induced upon interaction between Notch3 and Jag2 ligand. Furthermore, our results indicate that suppression of Notch3 improves survival in mice bearing orthotropic tumors derived from the metastatic HNSCC lines. Our data demonstrate that metastatic cells from head and neck tumors acquire dependency on Notch3 signaling. Novel treatments targeting components of this pathway may prove effective in targeting metastatic cells alone or in combination with conventional therapies. Citation Format: Maria Kondratyev, Aleksandra Pesic, Stephano Marastoni, Troy Ketela, Jason Moffat, Carl Virtanen, Azin Sayad, Mikhail Bashkurov, Alessandro Dati, Laurie Ailles, Reidar Grenman, Marianne Koritzinsky, Brad Wouters. Novel therapeutic targets in head and neck cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3794.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.004

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 head0.059
GPT teacher head0.401
Teacher spread0.342 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

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