Abstract B072: Cell alterations that drive vascular invasion and dissemination in pediatric liver cancer
Bibliographic record
Abstract
Abstract Background: Hepatoblastoma (HB) and hepatocellular carcinoma (HCC) are the most common malignant hepatocellular tumors seen in children. Patients with vascular invasion and metastasis generally have poor outcomes. The aim of this study was to delineate the unique cellular changes that drive dissemination by performing comprehensive molecular and phenotypic profiling of samples representing disseminating cells, including primary circulating tumor cells (CTCs) from patients and orthotopic patient-derived xenograt (PDX) mouse models and cell lines grown in vitro in the presence of other cells that represent the tumor microenvironment (TME). Methods: With primary patient whole blood samples, we used a RosetteSep CD45 Depletion Cocktail (Stem Cell Technologies) to enrich CTCs. We validated that these cells were CTCs by staining for a panel of validated markers, including indocyanine green (ICG), Glypican-3 (GPC3), and DAPI. We then performed single cell RNA sequencing (scRNA-seq, 10x Genomics) to comprehensively analyze gene expression of these cells. To understand tumor dissemination mechanisms influenced by the TME, we grew HepT1 HB cells in the presence of supernatant from human umbilical vein endothelial cells (HUVECs). Specifically, HUVEC and HepT1 cells were cultured in separate plates in a combination of endothelial cell medium and minimal essential medium in a 1:1 ratio. The supernatant from each plate was then added to HepT1 cells, and we examined resulting transcriptomic changes with bulk RNA-seq and phenotypic alterations with proliferation (CCK8), scratch (Incucyte), and invasion (Boyden chamber) assays. We also worked to develop a pipeline to establish stable cell lines with primary patient- and murine-derived CTCs. Results: Using our ICG/GPC3/DAPI panel, we showed that the cells isolated from patient whole blood samples were CTCs. scRNA-seq analyses of these samples revealed that there was upregulation of key pathways in CTCs, including NRF2 activity, compared to low-risk primary HB tumors. Factors secreted by HUVECs influenced HepT1 cell phenotypes and gene expression. Specifically, we showed significantly higher migration and faster wound closure when HepT1 cells were grown in the presence of secreted factors from HUVECs, compared to control HepT1 cells. Conclusions: This study provides a comprehensive transcriptomic landscape of pediatric liver cancer CTCs. This work also underscores the importance of the interactions between endothelial cells and tumor cells in the initiation of metastasis. Taken together, this work builds a strong foundation for future studies elucidating the specific mechanisms of how pediatric liver tumor cells successfully disseminate with the overall goal of developing novel therapeutic regimens that target these mechanisms. Citation Format: Priyanka Rao, Andres Espinoza, Roma Patel, Mohammad J. Najaf Panah, Pavel Sumazin, Sanjeev A. Vasudevan, Sarah E. Woodfield. Cell alterations that drive vascular invasion and dissemination in pediatric liver cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B072.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".