Abstract 3020: Novel therapeutic targets in head and neck cancer
Bibliographic record
Abstract
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. Utilizing CRISPR methodology, we established that “fixing” the mutation results in reversal of metastatic phenotype of the cells, making them Notch independent similar to their primary tumor counterparts. Mutations in EGF domains have been reported to influence interaction with specific ligands, dictating which ligand can activate Notch signaling. Our data indicate 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. Overall, 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, Troy Ketela, Azin Sayad, Stephano Marastoni, Carl Virtanen, Laurie Ailles, Soroush Samadian, Mikhail Bashkurov, Marianne Koritzinsky, Brad Wouters. Novel therapeutic targets in head and neck cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3020. doi:10.1158/1538-7445.AM2017-3020
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".