Report of the First International Symposium on NUT Carcinoma
Bibliographic record
Abstract
NUT carcinoma is a rare, aggressive cancer defined by rearrangements of the NUTM1 gene. No routinely effective treatments of NUT carcinoma exist, despite harboring a targetable oncoprotein, most commonly BRD4-NUT. The vast majority of cases are fatal. Poor awareness of the disease is a major obstacle to progress in the treatment of NUT carcinoma. While the incidence likely exceeds that of Ewing sarcoma, and BRD4-NUT heralded the bromodomain and extra-terminal domain (BET) inhibitor class of selective epigenetic modulators, NUT carcinoma is incorrectly perceived as "impossibly rare," and therefore receives comparatively little private or governmental funding or prioritization by pharma. To raise awareness, propagate scientific knowledge, and initiate a consensus on standard and targeted treatment of NUT carcinoma, we held the First International Symposium on NUT Carcinoma on March 3, 2021. This virtual event had more than eighty attendees from the Americas, Europe, Asia, and Australia. Patients with NUT carcinoma and family members were represented and shared perspectives. Broadly, the four areas discussed by experts in the field included (1) the biology of NUT carcinoma; (2) standard approaches to the treatment of NUT carcinoma; (3) results of clinical trials using BET inhibitors; and (4) future directions, including novel BET bromodomain inhibitors, combinatorial approaches, and immunotherapy. It was concluded that standard chemotherapeutic approaches and first-generation BET bromodomain inhibitors, the latter complicated by a narrow therapeutic window, are only modestly effective in a minority of cases. Nonetheless, emerging second-generation targeted inhibitors, novel rational synergistic combinations, and the incorporation of immuno-oncology approaches hold promise to improve the prognosis of this disease.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.013 |
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".