Overview of current and future biologically based targeted therapies in head and neck squamous cell carcinoma
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
Recent advances in genomics, proteomics, bioinformatics and systems biology have unraveled the complex aberrant signaling networks in cancer. The knowledge accrued has dramatically increased the opportunities for discovery of novel molecular targets for drug development. Major emphasis is being laid on designing new therapeutic strategies targeting multiple signaling pathways for more effective disease management. However, the translation of in vitro findings to patient management often poses major challenges that limit their clinical efficacy. Here we will discuss how understanding the dysregulated signaling networks can explain the pitfalls in translating the laboratory findings from the bench-to-bedside and suggest novel approaches to overcome these problems using head and neck cancer as a prototype. The five year survival rates of HNSCC patients (about 50% at 5 years) have not improved significantly despite advancements in multimodality therapy including surgery, radiation and chemotherapy. Molecular targeted therapies with inhibitors of EGFR and VEGF either alone, or in combination with conventional treatments have shown limited improved efficacy. The key deregulated signaling pathways in head and neck squamous cell carcinoma (HNSCC) include EGFR, Ras, TGFbeta, NFkappaB, Stat, Wnt/beta-catenin and PI3-K/AKT/mTOR. The aberrant activities of these interrelated signaling pathways contribute to HNSCC development. In depth understanding of the cross-talks between these pathways and networks will form the basis of developing novel strategies for targeting multiple molecular components for more effective prevention and treatment of HNSCC.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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