New Drugs for Recurrent or Metastatic Nasopharyngeal Cancer
Bibliographic record
Abstract
Abstract Chemotherapy has been the backbone for the treatment of recurrent or metastatic nasopharyngeal carcinoma (RMNPC), which remains an incurable disease. Currently the most active area of therapeutic investigations in RMNPC is in immunotherapy, especially after the results of five anti-programmed death-1 (anti-PD-1) antibodies, i.e. pembrolizumab, nivolumab, camrelizumab, toripalimab and tislelizumab, have demonstrated monotherapy objective response rates of 21%–43%. Combinations using anti-PD1/L1 antibodies as backbone to evaluate their additivity or synergy with cytotoxic chemotherapy, molecularly targeted agents, or other immuno-oncology compounds are actively being developed. Besides immune checkpoint blockade, additional ways to modulate the host immune system, such as Epstein-Barr virus (EBV)-directed vaccination against viral antigens (such as EBNA1, LMP1, LMP2) with dendritic cells or peptides, adoptive cell transfer of autologous or HLA-matched allogeneic EBV-specific cytotoxic T lymphocytes, CAR or TCR T-cell therapy, personalized cancer vaccines and oncolytic viruses are being explored. Finally, novel molecularly targeted agents that have entered human testing in RMNPC include apatinib and anlotinib (antiangiogenic agents), MAK683 (an embryonic ectoderm development or EED protein inhibitor), among others. This review provides an update of ongoing clinical trials evaluating these new compounds in RMNPC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".