Co-option of Liver Vessels and Not Sprouting Angiogenesis Drives Acquired Sorafenib Resistance in Hepatocellular Carcinoma
Bibliographic record
Abstract
BACKGROUND: The anti-angiogenic Sorafenib is the only approved systemic therapy for advanced hepatocellular carcinoma (HCC). However, acquired resistance limits its efficacy. An emerging theory to explain intrinsic resistance to other anti-angiogenic drugs is 'vessel co-option,' ie, the ability of tumors to hijack the existing vasculature in organs such as the lungs or liver, thus limiting the need for sprouting angiogenesis. Vessel co-option has not been evaluated as a potential mechanism for acquired resistance to anti-angiogenic agents. METHODS: To study sorafenib resistance mechanisms, we used an orthotopic human HCC model (n = 4-11 per group), where tumor cells are tagged with a secreted protein biomarker to monitor disease burden and response to therapy. Histopathology, vessel perfusion assessed by contrast-enhanced ultrasound, and miRNA sequencing and quantitative real-time polymerase chain reaction were used to monitor changes in tumor biology. RESULTS: While sorafenib initially inhibited angiogenesis and stabilized tumor growth, no angiogenic 'rebound' effect was observed during development of resistance unless therapy was stopped. Instead, resistant tumors became more locally infiltrative, which facilitated extensive incorporation of liver parenchyma and the co-option of liver-associated vessels. Up to 75% (±10.9%) of total vessels were provided by vessel co-option in resistant tumors relative to 23.3% (±10.3%) in untreated controls. miRNA sequencing implicated pro-invasive signaling and epithelial-to-mesenchymal-like transition during resistance development while functional imaging further supported a shift from angiogenesis to vessel co-option. CONCLUSIONS: This is the first documentation of vessel co-option as a mechanism of acquired resistance to anti-angiogenic therapy and could have important implications including the potential therapeutic benefits of targeting vessel co-option in conjunction with vascular endothelial growth factor receptor signaling.
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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) | 0.001 | 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".