Analysis of prognostic germline polymorphisms in patients with advanced hepatocellular carcinoma
Bibliographic record
Abstract
Background: The prognosis of hepatocellular carcinoma (HCC) is influenced by both tumor and patient specific factors. Current therapies of advanced HCC target angiogenesis and immune evasion, however there are no clinically useful biomarkers to guide clinicians. Methods: Our aim in this retrospective cohort study was to validate single nucleotide polymorphisms (SNPs) prognostic of outcome in advanced HCC from the literature, and to analyze exploratory SNPs chosen from evaluation of the HCC tumor immune microenvironment. Using a database of patients with HCC treated with sorafenib, blood samples were genotyped, clinical variables were retrospectively collected, and SNPs were analyzed for association with progression-free survival (PFS) and overall survival (OS). A subsequent analysis was conducted to determine if identified SNPs were prognostic in trans arterial chemoembolization (TACE) treated patients. Results: Literature review identified 7 SNPs in vascular endothelial growth factor (VEGF), eNOS, angiopoietin 2 (ANGPT2) and vascular endothelial growth factor receptor 2 (VEGFR2), however none were externally validated in our dataset. Of the 35 exploratory immunomodulatory SNPs, the following were associated with PFS or OS: CCL2 C-C motif ligand 2 (CCL2) (rs1024611), interleukin-10 (IL-10) (rs1800896), cytotoxic T-lymphocyte antigen-4 (CTLA-4) (rs231775) and NFKB1 (rs28362491). Conclusions: SNPs identified by literature review to be prognostic in sorafenib treated patients with advanced HCC were not validated in our dataset. Our findings suggest potentially important prognostic implications of SNPs in VEGFR2, CCL2, IL-10, CTLA-4 and NFKB1 that deserve further study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".