Evolutionary dynamics of recurrent hepatocellular carcinoma under divergent immune selection pressures
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is a highly lethal, aggressive malignancy. Little is known about the evolutionary trajectories of HCC and how clinical decision-making could be informed based on biopsies of the initial tumour. Here, we report the whole-exome sequencing of a unique series of resected HCC tumours and matched recurrences. This cohort included patients who received a liver transplant and who were immunosuppressed at time of recurrence, in comparison to patients who underwent liver resection for HCC and immunocompetent at time of recurrence, therefore facilitating analyses of immune selection in driving evolutionary divergence. We find extensive evolutionary divergence between baseline and recurrent tumours, with the majority of mutations in our cohort being private, in the process informing sampling guidelines for precision oncology in this disease. We also find no evidence that immunosuppression relaxes immune selection pressures, given the absence of a genomic footprint reflecting the presentation of neoantigens or altered dynamics of genomic evolution. We attribute this to the presence of genetic lesions that confer the capabilities of immune evasion in these tumours prior to transplantation, and then validate the link between immune selection pressures and the emergence of these lesions in publicly available HCC datasets. Our findings point to HCC as a cancer with extensive evolutionary divergence over time, partly defined by an irreversible, genetically determined trajectory towards immune escape.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".