Bibliographic record
Abstract
Our laboratory has previously shown that some gefitinib-insensitive head and neck squamous cell carcinoma (HNSCC) cell lines exhibit dominant autocrine fibroblast growth factor receptor (FGFR) signaling. Herein, we deployed a whole-genome loss-of-function screen to identify genes whose knockdown potentiated the inhibitory effect of the FGFR inhibitor, AZ8010, in HNSCC cell lines. Three HNSCC cell lines expressing a genome-wide small hairpin RNA (shRNA) library were treated with AZ8010 and the abundance of shRNA sequences was assessed by deep sequencing. Under-represented shRNAs in treated cells are expected to target genes important for survival with AZ8010 treatment. Synthetic lethal hits were validated with specific inhibitors and independent shRNAs. We found that multiple alternate receptors provided protection from FGFR inhibition, including receptor tyrosine kinases (RTKs), v-erb-b2 erythroblastic leukemia viral oncogene homolog 2 (ERBB2), and hepatocyte growth factor receptor (MET). We showed that specific knockdown of either ERBB2 or MET in combination with FGFR inhibition led to increased inhibition of growth relative to FGFR tyrosine kinase inhibitor (TKI) treatment alone. These results were confirmed using specific small molecule inhibitors of either ERBB family members or MET. Moreover, the triple combination of FGFR, MET, and ERBB family inhibitors showed the largest inhibition of growth and induction of apoptosis compared with the double combinations. These results reveal a role for alternate RTKs in maintaining progrowth and survival signaling in HNSCC cells in the setting of FGFR inhibition. Thus, improved therapies for HNSCC patients could involve rationally designed combinations of TKIs targeting FGFR, ERBB family members, and MET.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.524 | 0.306 |
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".