Poster Session 3: Basic Hepatobiliary Neoplasia
Bibliographic record
Abstract
BACKGROUND: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide.Over 600,000 new cases are diagnosed each year.Early stage disease can be cured with surgical resection or liver transplantation.Liver transplantation offers the best chance at a cure; however, recurrence rates are as high as 40% for those transplanted.The enumeration of circulating tumor cells (CTCs) is an independent prognostic biomarker in patients with malignancies including breast and colon cancer.CTCs in the peripheral blood of HCC patients have been correlated to tumor size, portal vein tumor thrombosis, and stage.There is no data regarding the utility of CTC identification and molecular characterization to predict patients at high risk of HCC recurrence.Copy number variations (CNVs) are a mechanism of genetic mutation through which malignancy can arise.PUR-POSE: The purpose of our study is to assess CNV profiles of CTCs and matched primary tumor samples as biomarkers for malignant potential and risk of HCC recurrence.METHODS: Serum and tumor samples were collected from 100 patients over three years.Peripheral blood samples collected before, at the time of, and at multiple points after surgery were analyzed for CTCs.CTCs isolated from the peripheral blood were identified using a high definition CTC assay and primary tumor tissues were sampled as touch preps.The genomes of the isolated CTC and touch prep single cells were amplified and sequenced to determine genome wide CNV profiles (single cell genomic analysis).RESULTS: Presently, 45 patients have undergone total hepatectomy with liver transplantation and 16 partial hepatic resections for HCC with 3 episodes of recurrence to date.One patient recurred after partial hepatectomy and two after transplant.CTC levels varied between patients and at different times in the clinical course.Over 80% of CTCs and primary tumor cells identified were successfully isolated, and genetically amplified for CNV profiling.CNV profiles of different cell populations from the same patient often had similar mutation patterns.Some of those mutations identified have been associated with more aggressive malignancy.CONCLU-SION: We successfully demonstrated the ability to perform high-content CNV analysis of single cells from primary HCC tumors and CTCs in patients with tumor recurrence following definitive surgical therapy.The initial success of this pilot study suggests that CNV analysis of CTCs may prove beneficial in predicting risk of HCC recurrence after liver transplantation or resection.A comprehensive study to further investigate is currently underway.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.263 | 0.115 |
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".