Immunotherapy of head and neck cancer: Emerging clinical trials from a National Cancer Institute Head and Neck Cancer Steering Committee Planning Meeting
Bibliographic record
Abstract
Recent advances have permitted successful therapeutic targeting of the immune system in head and neck squamous cell carcinoma (HNSCC). These new immunotherapeutic targets and agents are being rapidly adopted by the oncologic community and hold considerable promise. The National Cancer Institute sponsored a Clinical Trials Planning Meeting to address the issue of how to further investigate the use of immunotherapy in patients with HNSCC. The goals of the meeting were to consider phase 2 or 3 trial designs primarily in 3 different patient populations: those with previously untreated, human papillomavirus-initiated oropharyngeal cancers; those with previously untreated, human papillomavirus-negative HNSCC; and those with recurrent/metastatic HNSCC. In addition, a separate committee was formed to develop integrative biomarkers for the clinical trials. The meeting started with an overview of key immune components and principles related to HNSCC, including immunosurveillance and immune escape. Four clinical trial concepts were developed at the meeting integrating different immunotherapies with existing standards of care. These designs were presented for implementation by the head and neck committees of the National Cancer Institute-funded National Clinical Trials Network. This article summarizes the proceedings of this Clinical Trials Planning Meeting, the purpose of which was to facilitate the rigorous development and design of randomized phase 2 and 3 immunotherapeutic trials in patients with HNSCC. Although reviews usually are published immediately after the meeting is held, this report is unique because there are now tangible clinical trial designs that have been funded and put into practice and the studies are being activated to accrual. Cancer 2017;123:1259-1271. © 2016 American Cancer Society.
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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 | 0.088 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".