Abstract 1741: Inhibition of pcsk9 impairs the development of vessel co-option and potentiates anti-angiogenic therapy in colorectal cancer liver metastases
Bibliographic record
Abstract
Abstract Colorectal cancer liver metastatic (CRCLM) tumours present as two main histopathological growth patterns (HGPs) including desmoplastic HGP (DHGP) and replacement HGP (RHGP). The DHGP tumours obtain their blood supply by sprouting angiogenesis, whereas the RHGP tumours utilize an alternative vascularisation known as vessel co-option. In vessel co-option, the cancer cells hijack the mature sinusoidal vessels to obtain blood supply. Vessel co-option has been reported as an acquired mechanism of resistance to anti-angiogenic treatment in CRCLM. Herein, we show the connection between serum cholesterol concentration and vessel co-option development in CRCLM. Our clinical data suggested that the elevation of serum cholesterol levels correlates with the risk of developing vessel co-opting tumours. Moreover, inhibition of PCSK9, the key modulator of cholesterol metabolism, significantly attenuated the development of vessel co-opting CRCLM tumours and sensitized the tumours to anti-angiogenic therapy in vivo. Altogether, these data suggest the importance of cholesterol in the development of vessel co-option tumours and propose that inhibiting PCSK9 is a promising strategy to overcome resistance to anti-angiogenic therapy in CRCLM. Citation Format: Miran Rada, Lucyna Krzywon, Audrey Kapelanski-Lamoureux, Stephanie Petrillo, Andrew Reynolds, Anthoula Lazaris, Nabil Seidah, Peter Metrakos. Inhibition of pcsk9 impairs the development of vessel co-option and potentiates anti-angiogenic therapy in colorectal cancer liver metastases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1741.
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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) | 0.001 | 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) | 0.006 | 0.001 |
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".