A Survey of Molecular Heterogeneity in Hepatocellular Carcinoma
Bibliographic record
Abstract
Understanding the heterogeneity of dysregulated pathways associated with the development of hepatocellular carcinoma (HCC) may provide prognostic and therapeutic avenues for disease management. As HCC involves a complex process of genetic and epigenetic modifications, we evaluated expression of both polyadenylated transcripts and microRNAs from HCC and liver samples derived from two cohorts of patients undergoing either partial hepatic resection or liver transplantation. Copy number variants were inferred from whole genome low‐pass sequencing data, and a set of 56 cancer‐related genes were screened using an oncology panel assay. HCC was associated with marked transcriptional deregulation of hundreds of protein‐coding genes. In the partially resected livers, diminished transcriptional activity was observed in genes associated with drug catabolism and increased expression in genes related to inflammatory responses and cell proliferation. Moreover, several long noncoding RNAs and microRNAs not previously linked with HCC were found to be deregulated. In liver transplant recipients, down‐regulation of genes involved in energy production and up‐regulation of genes associated with glycolysis were detected. Numerous copy number variants events were observed, with hotspots on chromosomes 1 and 17. Amplifications were more common than deletions and spanned regions containing genes potentially involved in tumorigenesis. Colony stimulating factor 1 receptor (CSF1R), fibroblast growth factor receptor 3 (FGFR3), fms‐like tyrosine kinase 3 (FLT3), nucleolar phosphoprotein B23 (NPM1), platelet‐derived growth factor receptor alpha polypeptide (PDGFRA), phosphatase and tensin homolog (PTEN), G‐protein‐coupled receptors‐like receptor Smoothened (SMO), and tumor protein P53 (TP53) were mutated in all tumors; another 26 cancer‐related genes were mutated with variable penetrance. Conclusion: Our results underscore the marked molecular heterogeneity between HCC tumors and reinforce the notion that precision medicine approaches are needed for management of individual HCC. These data will serve as a resource to generate hypotheses for further research to improve our understanding of HCC biology. (Hepatology Communications 2018; 00:000‐000)
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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 | 0.003 | 0.002 |
| 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) | 0.001 | 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 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".