Abstract 4598: Identification of driver genes in hepatocellular carcinoma by exome sequencing.
Bibliographic record
Abstract
Abstract Genetic alterations in specific driver genes lead to disruption of cellular pathways and are critical events in the instigation and progression of hepatocellular carcinoma. As a prerequisite for individualized cancer treatment, we sought to characterize the landscape of recurrent somatic mutations in hepatocellular carcinoma. We performed whole exome sequencing on 87 hepatocellular carcinomas and matched normal adjacent tissues to an average coverage of 59x. The overall mutation rate was roughly 2.5 mutations per Mb, with a median of 45 non-synonymous mutations that altered the amino acid sequence (range 2 to 381). We found recurrent mutations in several genes with high transcript levels: TP53 (20%), CTNNB1 (15%), IGSF3 (9%) and KEAP1 (8%). 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. Clinical data demonstrated significantly decreased disease-free survival in tumors with p53 mutations (p=0.006) and a trend was observed towards higher rates of recurrence and shorter disease free survival among MLL mutation carriers. The NFE2L2-KEAP1 and MLL pathways are recurrently mutated in multiple cohorts of hepatocellular carcinoma. The elucidation of genetic changes in HCC will enhance our understanding of hepatocarcinogenesis and influence disease prognosis and therapy. Citation Format: Sean P. Cleary, William R. Jeck, Xiaobei Zhao, Sara R. Selitsky, Gleb L. Savich, Ting-Xu Tan, Michael C. Wu, Gad Getz, Michael S. Lawrence, Joel S. Parker, Jinyu Li, Scott Powers, Hyeja Kim, Sandra E. Fischer, Maha Guindi, Anand Ghanekar, Derek Y. Chiang. Identification of driver genes in hepatocellular carcinoma by exome sequencing. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4598. doi:10.1158/1538-7445.AM2013-4598
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 0.001 |
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".