Bibliographic record
Abstract
Abstract Precision oncology based on specific genomic alterations has become a standard in the management of some tumours. Different studies (NCI-MATCH, TAPUR, DRUP, or MyPathway) have used a broader approach, testing multiple genes at the same time and assigning targeted therapies in basket trials. These studies showed some encouraging results supporting the potential use of next-generation sequencing in precision oncology. However, the application in head and neck cancer (except for salivary gland tumours) is still limited. The field is moving quickly into other approaches such as whole genome and transcriptome analysis. Transcriptome analyses have successfully helped in treatment decisions in some patients within prospective studies (WINTHER, POG). In head and neck cancer, gene expression signatures evaluating hypoxia or predicting anti-epidermal growth factor receptor (EGFR) therapy response, have been explored. As immunotherapy has become a standard in the treatment of squamous head and neck carcinoma, ribonucleic acid (RNA)-based immune signatures have been proposed to select those patients with higher probabilities of response. All these analyses are based on bulk transcriptomics. Nevertheless, understanding the role of each cell and its spatial configuration could be useful in precision oncology. In this sense, single-cell RNA sequencing could provide a better understanding of the different cell populations and their activities and potentially predict treatment response. Other immune-related biomarkers such as the T-cell receptor characteristics could provide an improved insight into the resistance to immunotherapy in patients with head and neck cancer. Non-coding genome analysis may contribute to a better understanding of the mechanisms of resistance or sensitivity to drugs and aid in discovering new therapeutic targets. However, most of the current precision medicine approaches rely on tumour tissue. This is a limitation for a broader applicability due to tumor heterogeneity and the need for tumour biopsies. Easy-to-access biomarkers such as circulating tumour deoxyribonucleic acid (ctDNA) could overcome both limitations as well as provide a more dynamic overview of the disease. Further understanding of molecular biology and tumour microenvironment including multi-institution collaborative initiatives and prospective studies is needed to translate these findings into our clinical practice.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".