Abstract 1580: Cancer cells promote phenotypic alterations in adjacent hepatocytes to facilitate vessel co-option in colorectal cancer liver metastasis
Bibliographic record
Abstract
Abstract Vessel co-option is associated with resistance against anti-angiogenic therapy in colorectal cancer liver metastases (CRCLM). Vessel co-opted lesions are characterized by highly motile cancer cells that move toward and along the pre-existing vessels in the surrounding non-malignant tissue and hijack them to gain access to nutrient. In order to access the sinusoidal vessels, the cancer cells must displace the hepatocytes and occupy their space. However, the mechanisms underlying this displacement are unknown. Herein, we examined the involvement of apoptosis, motility, epithelial-mesenchymal transition (EMT) and autophagy pathways in hepatocyte’s displacement by cancer cells. We demonstrated that cancer cells induce the expression of the proteins that associated with upregulation of apoptosis, motility and EMT in adjacent hepatocytes in vitro and in vivo. Accordingly, we observed upregulation of cleaved caspase-3, cleaved poly (ADP-ribose) polymerase-1 (PARP-1) and Actin Related Protein 2/3 (ARP2/3) in adjacent hepatocytes to cancer cells, while we noticed lower expression levels of E-cadherin. Importantly, Runt-related transcription factor 1 (RUNX1) knockdown in the cancer cells has impaired their function towards adjacent hepatocytes in vitro and in vivo. Altogether, our data indicating that cancer cells may exploit various mechanisms to displace the hepatocytes and access to the sinusoidal vessels to establish vessel co-option. Citation Format: Miran Rada, Anthoula Lazaris, Peter Metrakos. Cancer cells promote phenotypic alterations in adjacent hepatocytes to facilitate vessel co-option in colorectal cancer liver metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1580.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".