Abstract 4601: Vitamin D supplementation attenuates resistance to anti-angiogenic therapy in colorectal cancer liver metastases
Bibliographic record
Abstract
Abstract Colorectal cancer liver metastasis (CRCLM) is one of the deadliest cancers. CRCLM tumours have two distinct histopathological growth patterns (HGPs) including desmoplastic HGP (DHGP) and replacement HGP (RHGP). The DHGP tumours are angiogenic, while their RHGP counterparts are vessel co-opting. The patients with RHGP tumours showed poor response to anti-angiogenic agents, as well as a worse prognosis. Herein, we conducted a retrospective cohort study that comprised 106 CRCLM patients to examine the effect of vitamin D supplementation on the HGPs of the tumours and the 5-year overall survival (OS). Interestingly, we found an inverse correlation between vitamin D supplementation and the presence of RHGP tumours in CRCLM patients. Additionally, the population who used vitamin D supplementation had significantly better 5-year OS. Moreover, our in vivo results suggested that vitamin D supplementation significantly improves the response of CRCLM tumours to anti-angiogenic therapy. Mechanistically, we found that vitamin D can suppress cancer cell motility, which is essential for the development of vessel co-option tumours and resistance to anti-angiogenic therapy. Collectively, this study suggests vitamin D supplementation is a promising way to attenuate resistance to anti-angiogenic therapy and improve the prognosis of CRCLM patients. Citation Format: Miran Rada, Lucyna Krzywon, Audrey Kapelanski-Lamoureux, Stephanie Petrillo, Anthoula Lazaris, Peter Metrakos. Vitamin D supplementation attenuates resistance to 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 4601.
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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) | 0.004 | 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".