Public-private partnerships, part 1: the next hospital wave
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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 it