Identification of driver genes in hepatocellular carcinoma by exome sequencing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
UNLABELLED: Genetic alterations in specific driver genes lead to disruption of cellular pathways and are critical events in the instigation and progression of hepatocellular carcinoma (HCC). As a prerequisite for individualized cancer treatment, we sought to characterize the landscape of recurrent somatic mutations in HCC. We performed whole-exome sequencing on 87 HCCs and matched normal adjacent tissues to an average coverage of 59×. The overall mutation rate was roughly two mutations per Mb, with a median of 45 nonsynonymous mutations that altered the amino acid sequence (range, 2-381). We found recurrent mutations in several genes with high transcript levels: TP53 (18%); CTNNB1 (10%); KEAP1 (8%); C16orf62 (8%); MLL4 (7%); and RAC2 (5%). Significantly affected gene families include the nucleotide-binding domain and leucine-rich repeat-containing family, calcium channel subunits, and histone methyltransferases. In particular, the MLL family of methyltransferases for histone H3 lysine 4 were mutated in 20% of tumors. CONCLUSION: The NFE2L2-KEAP1 and MLL pathways are recurrently mutated in multiple cohorts of HCC.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it