Head and neck cancer: from anatomy to biology
Bibliographic record
Abstract
The 20th century saw great advances in anatomy-based (surgery and radiotherapy) and chemotherapy approaches for treating head and neck squamous cell carcinoma (HNSCC) and improving quality of life (QoL). However, despite these advances, the survival rate in HNSCC remains at ∼50%. Front-line treatments often cause severe toxicity and debilitating long-term impacts on QoL. In recent decades, dramatic advances have been made in our knowledge of fundamental tumor biology and signaling pathways that contribute to oncogenesis and cancer progression. These insights are presenting unprecedented opportunities to develop more effective and less toxic treatments that are specific to particular molecular targets. This review discusses some of the major, potentially targetable, molecular pathways associated with head and neck carcinogenesis. We present the general mechanism underlying the functional components for each signaling pathway, discuss how these components are aberrantly regulated in HNSCC and describe their potential as therapeutic targets. We have restricted our discussion to "drug-able targets" such as oncogenes including those associated with HPV, tumor hypoxia and microRNAs and present these changes in the context of HNSCC patient care. The specific targeting of these pathways to achieve cancer control/remission and reduce toxicity is now challenging conventional treatment paradigms in HNSCC. This new "biologic era" is transforming our ability to target causal pathways and improve survival outcomes in HNSCC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".