Translational genomics of nasopharyngeal cancer
Bibliographic record
Abstract
Nasopharyngeal carcinoma (NPC), also named the Cantonese cancer, is a unique cancer with strong etiological association with infection of the Epstein-Barr virus (EBV). With particularly high prevalence in Southeast Asia, the involvement of EBV and genetic aberrations contributive to NPC tumorigenesis have remained unclear for decades. Recently, genomic analysis of NPC has defined it as a genetically homogeneous cancer, driven largely by NF-κB signaling caused by either somatic aberrations of NF-κB negative regulators or by overexpression of the latent membrane protein 1 (LMP1), an EBV viral oncoprotein. This represents a landmark finding of the NPC genome. Exome and RNA sequencing data from new EBV-positive NPC models also highlight the importance of PI3K pathway aberrations in NPC. We also realize for the first time that NPC mutational burden, mutational signatures, MAPK/PI3K aberrations, and MHC Class I gene aberrations, are prognostic for patient outcome. Together, these multiple genomic discoveries begin to shape the focus of NPC therapy development. Given the challenge of NF-κB targeting in human cancers, more innovative drug discovery approaches should be explored to target the unique atypical NF-κB activation feature of NPC. Our next decade of NPC research should focus on further identification of the -omic landscapes of recurrent and metastatic NPC, development of gene-based precision medicines, as well as large-scale drug screening with the newly developed and well-characterized EBV-positive NPC models. Focused preclinical and clinical investigations on these major directions may identify new and effective targeting strategies to further improve survival of NPC patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".