Innovation and Advances in Precision Medicine in Head and Neck Cancer
Bibliographic record
Abstract
Abstract The clinical utility of precision medicine through molecular characterization of tumors has been demonstrated in some malignancies, especially in cases where oncogenic driver alterations are identified. Next generation sequencing data from thousands of patients with head and neck cancers have provided vast amounts of information about the genomic landscape of this disease. Thus far, only a limited number of genomic alterations have been druggable, such asNTRKgene rearrangements in salivary gland cancers (mainly mammary analogue secretory carcinoma),NOTCHmutations in adenoid cystic cancers,HRASmutations in head and neck squamous cell cancers, and even a smaller number of these have reached regulatory approval status. In order to expand the scope of precision medicine in head and neck cancer, additional evaluation beyond genomics is necessary. For instance, there is increasing interest to perform transcriptomic profiling for target identification. Another advance is in the area of functional testing such as small interfering RNA and drug libraries on patient derived cell cultures. Liquid biopsies to detect specific tumor clones or subclones, or viral sequences such as HPV, are of great interest to enable non-invasive tracking of response or resistance to treatment. In addition, precision immuno-oncology is a tangible goal, with a growing body of knowledge on the interactions between the host immunity, the tumor and its microenvironment. Immuno-oncology combinations that are tailored to immunophenotypes of the host-tumor-microenvironment triad, personalized cancer vaccines, and adoptive cell therapies, among others, are in active development. Many therapeutic possibilities and opportunities lie ahead that ultimately will increase the reality of precision medicine in head and neck cancer.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".