Abstract PL04-04: Oncohistones in cancer: How to turn a cell's symphony into non harmonic rap
Bibliographic record
Abstract
Abstract Recent studies have shown that chromatin-associated proteins and transcription factors have more somatic alterations than any other class of oncoproteins in childhood CNS tumors. Different Histone H3 genes (H3F3A, Hist3.1B) and variants (K27, G34, K36) can be affected with remarkable specific association between tumor location and type as well as the particular H3 residue or variant that is mutated. We have shown a high prevalence of H3 mutations in pediatric and young adult High Grade Astrocytoma, in sarcomas such as Giant Cell Tumors of the bone and chondroblastomas, and most recently, in Head and Neck Squamous Cell Carcinomas. These ground-breaking discoveries of oncohistones implicate a direct effect of epigenetic misregulation in oncogenesis. Here, we describe these epigenetic misfits and our knowledge of their effects, along with novel tools needed to study them. We will also discuss how we are harnessing synergies between the approaches of cancer genomics and chemical biology to help make sense of the pathogenesis of oncohistones and describe how oncohistones are promoting global redistribution of important epigenetic marks, seemingly hijacking our epigenome. Citation Format: Nada Jabado. Oncohistones in cancer: How to turn a cell's symphony into non harmonic rap [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr PL04-04.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".